A leaf is an organ of a vascular plant, as identified in botanical terms, and in particular in plant morphology. Naturally a leaf is a thin, flattened organ bear above ground and it is mainly used for photosynthesis. Recognition of plants has become an active area of research as most of the plant species are at the risk of extinction. Most of the leaves cannot be recognized easily since some are not flat (e.g. succulent leaves and conifers), some does not grow above ground (e.g. bulb scales), and some does not undergo photosynthetic function (e.g. cataphylls, spines, and cotyledons).In this paper, we mainly focused on tea leaves to identify the leaf type for improving tea leaf classification. Tea leaf images are loaded from digital cameras or scanners in the system. This proposed approach consists of three phases such as preprocessing, feature extraction and classification to process the loaded image. The tea leaf images can be identified accurately in the preprocessing phase by fuzzy denoising using Dual Tree Discrete Wavelet Transform (DT-DWT) in order to remove the noisy features and boundary enhancement to obtain the shape of leaf accurately. In the feature extraction phase, Digital Morphological Features (DMFs) are derived to improve the classification accuracy. Radial Basis Function (RBF) is used for efficient classification. The RBF is trained by 60 tea leaves to classify them into 6 types. Experimental results proved that the proposed method classifies the tea leaves with more accuracy in less time. Thus, the proposed method achieves more accuracy in retrieving the leaf type.
This document summarizes a research paper that proposes a new method for identifying tea leaf species using image analysis and clustering. The method involves three main steps: 1) Preprocessing tea leaf images through techniques like converting to grayscale, fuzzy denoising using Dual Tree Discrete Wavelet Transform, and boundary enhancement. 2) Extracting morphological and geometrical features from the preprocessed images. 3) Clustering the tea leaf images into different species using the Fuzzy C-Means algorithm based on the extracted features. The method is tested on 60 tea leaf images belonging to 6 species, and experimental results show it can accurately cluster the tea leaf images by species with high accuracy and in less time compared to other methods.
A Spectral Domain Dominant Feature Extraction Algorithm for Palm-print Recogn...CSCJournals
In this paper, a spectral feature extraction algorithm is proposed for palm-print recognition, which can efficiently capture the detail spatial variations in a palm-print image. The entire image is segmented into several spatial modules and the task of feature extraction is carried out using two dimensional Fourier transform within those spatial modules. A dominant spectral feature selection algorithm is proposed, which offers an advantage of very low feature dimension and results in a very high within-class compactness and between-class separability of the extracted features. A principal component analysis is performed to further reduce the feature dimension. From our extensive experimentations on different palm-print databases, it is found that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods.
Flower Classification Using Neural Network Based Image ProcessingIOSR Journals
Abstract: In this paper, it is proposed to have a method for classification of flowers using Artificial Neural Network (ANN) classifier. The proposed method is based on textural features such as Gray level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT). A flower image is segmented using a threshold based method. The data set has different flower images with similar appearance .The database of flower images is a mixture of images taken from World Wide Web and the images taken by us. The ANN has been trained by 50 samples to classify 5 classes of flowers and achieved classification accuracy more than 85% using GLCM features only. Keywords: Artificial Neural Network, DWT, GLCM, Segmentation.
This document summarizes an article that proposes using image processing techniques in agriculture to detect weed areas in crop fields. The researchers took images from agricultural fields and used MATLAB to implement image segmentation algorithms to identify weed areas. The article provides background on how image processing can be used for various agricultural applications like detecting diseased plants, quantifying affected areas, and determining fruit size and shape. It also reviews different existing image classification techniques used for agricultural disease detection, such as neural networks, support vector machines, and others.
This document presents a method for leaf identification using feature extraction and an artificial neural network. Leaf images are preprocessed, segmented, and features like eccentricity, aspect ratio, area, and perimeter are extracted. These features are used as inputs to train an artificial neural network classifier. The neural network is tested on leaf images and achieves 98.8% accuracy at identifying leaves using a minimum of seven input features. This approach provides an effective and computationally efficient way to identify plant leaves based on images.
dFuse: An Optimized Compression Algorithm for DICOM-Format Image ArchiveCSCJournals
Medical images are useful for knowing the details of the human body for health science or remedial reasons. DICOM is structured as a multi-part document in order to facilitate extension of these images. Additionally, DICOM defined information objects are not only for images but also for patients, studies, reports, and other data groupings. More information details in DICOM, resulted in large size, and transferring or communicating these files took lots of time. To solve this, files can be compressed and transferred. Efficient compression solutions are available and they are becoming more critical with the recent intensive growth of data and medical imaging. In order to receive the original and less sized image, we need effective compression algorithm. There are different algorithms for compression such as DCT, Haar, Daubuchies which has its roots in cosine and wavelet transforms. In this paper, we propose a new compression algorithm called “dFuse”. It uses cosine based three dimensional transform to compress the DICOM files. We use the following parameters to check the efficiency of the proposed algorithm, they are i) file size, ii) PSNR, iii) compression percentage and iv) compression ratio. From the experimental results obtained, the proposed algorithm works well for compressing medical images.
Foliage Measurement Using Image Processing TechniquesIJTET Journal
Automatic detection of fruit and leaf diseases is essential to automatically detect the symptoms of diseases as early as they appear on the growing stage. This system helps to detect the diseases on fruit during farming , right from plan and easily monitoring the diseases of grapes leaf and apple fruit. By using this system we can avoid the economical loss due to various diseases in agriculture production. K-means clustering technique is used for segmentation. The features are extracted from the segmented image and artificial neural network is used for training the image database and classified their performance to the respective disease categories. The experimental results express that what type of disease can be affected in the fruit and leaf .
A Novel Method for Detection of Architectural Distortion in MammogramIDES Editor
Among various breast abnormalities architectural
distortion is the most difficult type of tumor to detect. When
area of interest is medical image data, the major concern is to
develop methodologies which are faster in computation and
relatively noise free in processing. This paper is an extension
of our own work where we propose a hybrid methodology that
combines a Gabor filtration with directional filters over the
directional spectrum for digitized mammogram processing.
The most commendable thing in comparison to other
approaches is that complexity has been lowered as well as the
computation time has also been reduced to a large extent. On
the MIAS database we achieved a sensitivity of 89 %.
This document summarizes a research paper that proposes a new method for identifying tea leaf species using image analysis and clustering. The method involves three main steps: 1) Preprocessing tea leaf images through techniques like converting to grayscale, fuzzy denoising using Dual Tree Discrete Wavelet Transform, and boundary enhancement. 2) Extracting morphological and geometrical features from the preprocessed images. 3) Clustering the tea leaf images into different species using the Fuzzy C-Means algorithm based on the extracted features. The method is tested on 60 tea leaf images belonging to 6 species, and experimental results show it can accurately cluster the tea leaf images by species with high accuracy and in less time compared to other methods.
A Spectral Domain Dominant Feature Extraction Algorithm for Palm-print Recogn...CSCJournals
In this paper, a spectral feature extraction algorithm is proposed for palm-print recognition, which can efficiently capture the detail spatial variations in a palm-print image. The entire image is segmented into several spatial modules and the task of feature extraction is carried out using two dimensional Fourier transform within those spatial modules. A dominant spectral feature selection algorithm is proposed, which offers an advantage of very low feature dimension and results in a very high within-class compactness and between-class separability of the extracted features. A principal component analysis is performed to further reduce the feature dimension. From our extensive experimentations on different palm-print databases, it is found that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods.
Flower Classification Using Neural Network Based Image ProcessingIOSR Journals
Abstract: In this paper, it is proposed to have a method for classification of flowers using Artificial Neural Network (ANN) classifier. The proposed method is based on textural features such as Gray level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT). A flower image is segmented using a threshold based method. The data set has different flower images with similar appearance .The database of flower images is a mixture of images taken from World Wide Web and the images taken by us. The ANN has been trained by 50 samples to classify 5 classes of flowers and achieved classification accuracy more than 85% using GLCM features only. Keywords: Artificial Neural Network, DWT, GLCM, Segmentation.
This document summarizes an article that proposes using image processing techniques in agriculture to detect weed areas in crop fields. The researchers took images from agricultural fields and used MATLAB to implement image segmentation algorithms to identify weed areas. The article provides background on how image processing can be used for various agricultural applications like detecting diseased plants, quantifying affected areas, and determining fruit size and shape. It also reviews different existing image classification techniques used for agricultural disease detection, such as neural networks, support vector machines, and others.
This document presents a method for leaf identification using feature extraction and an artificial neural network. Leaf images are preprocessed, segmented, and features like eccentricity, aspect ratio, area, and perimeter are extracted. These features are used as inputs to train an artificial neural network classifier. The neural network is tested on leaf images and achieves 98.8% accuracy at identifying leaves using a minimum of seven input features. This approach provides an effective and computationally efficient way to identify plant leaves based on images.
dFuse: An Optimized Compression Algorithm for DICOM-Format Image ArchiveCSCJournals
Medical images are useful for knowing the details of the human body for health science or remedial reasons. DICOM is structured as a multi-part document in order to facilitate extension of these images. Additionally, DICOM defined information objects are not only for images but also for patients, studies, reports, and other data groupings. More information details in DICOM, resulted in large size, and transferring or communicating these files took lots of time. To solve this, files can be compressed and transferred. Efficient compression solutions are available and they are becoming more critical with the recent intensive growth of data and medical imaging. In order to receive the original and less sized image, we need effective compression algorithm. There are different algorithms for compression such as DCT, Haar, Daubuchies which has its roots in cosine and wavelet transforms. In this paper, we propose a new compression algorithm called “dFuse”. It uses cosine based three dimensional transform to compress the DICOM files. We use the following parameters to check the efficiency of the proposed algorithm, they are i) file size, ii) PSNR, iii) compression percentage and iv) compression ratio. From the experimental results obtained, the proposed algorithm works well for compressing medical images.
Foliage Measurement Using Image Processing TechniquesIJTET Journal
Automatic detection of fruit and leaf diseases is essential to automatically detect the symptoms of diseases as early as they appear on the growing stage. This system helps to detect the diseases on fruit during farming , right from plan and easily monitoring the diseases of grapes leaf and apple fruit. By using this system we can avoid the economical loss due to various diseases in agriculture production. K-means clustering technique is used for segmentation. The features are extracted from the segmented image and artificial neural network is used for training the image database and classified their performance to the respective disease categories. The experimental results express that what type of disease can be affected in the fruit and leaf .
A Novel Method for Detection of Architectural Distortion in MammogramIDES Editor
Among various breast abnormalities architectural
distortion is the most difficult type of tumor to detect. When
area of interest is medical image data, the major concern is to
develop methodologies which are faster in computation and
relatively noise free in processing. This paper is an extension
of our own work where we propose a hybrid methodology that
combines a Gabor filtration with directional filters over the
directional spectrum for digitized mammogram processing.
The most commendable thing in comparison to other
approaches is that complexity has been lowered as well as the
computation time has also been reduced to a large extent. On
the MIAS database we achieved a sensitivity of 89 %.
This document presents a study on using color texture feature analysis to detect surface defects on pomegranates. The researchers developed a method involving cropping images of pomegranates, converting them to HSI color space, generating SGDM matrices to extract 18 texture features for each image, and using support vector machines (SVM) classification to identify the best features for detecting infections. The optimal features identified were cluster shade, product moment, and mean intensity, achieving classification accuracy of 99.88%, 99.88%, and 99.81% respectively.
A STUDY ON WEED DISCRIMINATION THROUGH WAVELET TRANSFORM, TEXTURE FEATURE EXT...ijcsit
Texture based weed classification has played an important role in agricultural applications. In the recent years weed classification based on wavelet transform is an effective method. But the feature extraction is main issue for proper classification of weed species. In this paper, the issue of statistical and texture
classification based on wavelet transform has been analysed. The efficient texture feature extraction
methods are developed for weed discrimination. Three group feature vector can be constructed by the mean
and standard deviation of the wavelet statistical features (WSF), Texture feature as Contrast, Cluster
Shade, Cluster Prominence and Local Homogeneity (WCSPH) and Energy, Correlation, Cluster Shade,
Cluster Prominence and Entropy features (WECSPE) which are derived from the sub-bands of the wavelet
decomposition and are used for classification. Experimental results show that Rbio33 Wavelet with
WECSPE texture feature obtaining high degree of success rate in classification.
Orientation Spectral Resolution Coding for Pattern RecognitionIOSRjournaljce
In the approach of pattern recognition, feature descriptions are of greater importance. Features are represented in spatial domain and transformed domain. Wherein, spatial domain features are of lower representation, transformed domains are finer and more informative. In the transformed domain representation, features are represented using spectral coding using advanced transformation technique such as wavelet transformation. However, the feature extraction approach considers the band coefficients; the orientation variation is not considered. In this paper towards inherent orientation variation among each spectral band is derived, and the approach of orientation filtration is made for effective feature representation. The obtained result illustrates an improvement in the recognition accuracy, in comparison to conventional retrieval system.
MEDICAL IMAGE TEXTURE SEGMENTATION USINGRANGE FILTERcscpconf
Medical image segmentation is a frequent processing step in image understanding and computer
aided diagnosis. In this paper, we propose medical image texture segmentation using texture
filter. Three different image enhancement techniques are utilized to remove strong speckle noise as well enhance the weak boundaries of medical images. We propose to exploit the concept of range filtering to extract the texture content of medical image. Experiment is conducted on ImageCLEF2010 database. Results show the efficacy of our proposed medical image texture segmentation.
Image Mining for Flower Classification by Genetic Association Rule Mining Usi...IJAEMSJORNAL
Image mining is concerned with knowledge discovery in image databases. It is the extension of data mining algorithms to image processing domain. Image mining plays a vital role in extracting useful information from images. In computer aided plant identification and classification system the image mining will take a crucial role for the flower classification. The content image based on the low-level features such as color and textures are used to flower image classification. A flower image is segmented using a histogram threshold based method. The data set has different flower species with similar appearance (small inter class variations) across different classes and varying appearance (large intra class variations) within a class. Also the images of flowers are of different pose with cluttered background under varying lighting conditions and climatic conditions. The flower images were collected from World Wide Web in addition to the photographs taken up in a natural scene. The proposed method is based on textural features such as Gray level co-occurrence matrix (GLCM). This paper introduces multi dimensional genetic association rule mining for classification of flowers effectively. The image Data mining approach has four major steps: Preprocessing, Feature Extraction, Preparation of Transactional database and multi dimensional genetic association rule mining and classification. The purpose of our experiments is to explore the feasibility of data mining approach. Results will show that there is promise in image mining based on multi dimensional genetic association rule mining. It is well known that data mining techniques are more suitable to larger databases than the one used for these preliminary tests. Computer-aided method using association rule could assist people and improve the accuracy of flower identification. In particular, a Computer aided method based on association rules becomes more accurate with a larger dataset .Experimental results show that this new method can quickly and effectively mine potential association rules.
Evaluation of image segmentation and filtering with ann in the papaya leafijcsit
Precision agriculture is area with lack of cheap technology. The refinement of the production system brings
large advantages to the producer and the use of images makes the monitoring a more cheap methodology.
Macronutrients monitoring can to determine the health and vulnerability of the plant in specific stages. In
this paper is analyzed the method based on computational intelligence to work with image segmentation in
the identification of symptoms of plant nutrient deficiency. Artificial neural networks are evaluated for
image segmentation and filtering, several variations of parameters and insertion impulsive noise were
evaluated too. Satisfactory results are achieved with artificial neural for segmentation same with high
noise levels.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
PERFORMANCE ANALYSIS OF TEXTURE IMAGE RETRIEVAL FOR CURVELET, CONTOURLET TRAN...ijfcstjournal
This paper analyzes the performance of texture feature extraction techniques like curvelet transform, contourlet transform, and local ternary pattern (LTP) for magnetic resonance image (MRI) brain tumor retrieval using deep neural network (DNN) classification. Texture features are extracted from 1000 brain tumor MRI images using the three techniques. The features are classified using DNN and the techniques are evaluated based on performance metrics like sensitivity, specificity, accuracy, error rate, and F-measure. Experimental results show that contourlet transform provides better retrieval performance than curvelet transform and LTP according to these evaluation metrics.
HYBRID APPROACH FOR NOISE REMOVAL AND IMAGE ENHANCEMENT OF BRAIN TUMORS IN MA...acijjournal
In medical image processing, Magnetic Resonance Imaging (MRI) is one of significant diagnostic
techniques. It provides high quality of important information about the analysis of human soft tissue when
measured with CT imaging modalities; hence it is suitable for diagnosis at best. However, if it gives quality
of information, image may distorted by noise because of image acquisition device and transmission. The
noises in MR image reduces the quality of image and also damages the segmentation task which can lead
to faulty diagnosis. Noises have to reduce at the same time there is no information loss. This paper propose
a hybrid approach to enhance the brain tumor MRI images using combined features of Anisotropic
Diffusion Filter (ADF) with Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF).
ADF scheme provides a superior performance by removing noise while preserving image details and
enhancing edges. MDBUTMF helps in image denoising as well as preserving edges satisfactorily when the
noise level is high. The performance of this filter is evaluated by carrying out a qualitative comparison of
this method with other filters namely, ADF filter, Modified Decision Algorithm, Median filter, MDBUTMF.
Image restoration model with wavelet based fusionAlexander Decker
1. The document discusses various techniques for image restoration, which aims to recover a sharp original image from a degraded one using mathematical models of degradation and restoration.
2. It analyzes techniques like deconvolution using Lucy Richardson algorithm, Wiener filter, regularized filter, and blind image deconvolution on different image formats based on metrics like PSNR, MSE, and RMSE.
3. Previous studies have applied techniques like Wiener filtering, wavelet-based fusion, and iterative blind deconvolution for motion blur restoration and compared their performance.
Wavelet transformation based detection of masses in digital mammogramseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Wavelet transformation based detection of masses in digital mammogramseSAT Journals
This document describes a wavelet transformation-based method for detecting masses in digital mammograms. The method uses wavelet analysis to highlight variations in intensities that may indicate masses. It applies preprocessing techniques like median filtering to reduce noise and morphological operations to remove the pectoral muscle and suppress artifacts. Region properties and seeded region growing are then used to accurately segment abnormal masses. The combined use of wavelet transformation and region growing enables effective mass segmentation, demonstrating the effectiveness of the proposed technique. The method is tested on over 30 mammograms and shows improvements over traditional mass detection approaches.
This document discusses using smoothing filters based on rough set theory for medical image enhancement. It introduces common smoothing filters like mean, median, mode, and triangular filters. These filters can reduce noise and enhance edges in medical images. The document proposes a parallel rough set based model that implements multiple smoothing filters at once to obtain independent results and generate an enhanced mean image for improved medical image quality and complex image processing.
Identification and Classification of Leaf Diseases in Turmeric PlantsIJERA Editor
Plant disease identification is the most important sector in agriculture. Turmeric is one of the important
rhizomatous crops grown in India. The turmeric leaf is highly exposed to diseases like rhizome rot, leaf spot,
and leaf blotch. The identification of plant diseases requires close monitoring and hence this paper adopts
technologies to manage turmeric plant diseases caused by fungi to enable production of high quality crop yields.
Various image processing and machine learning techniques are used to identify and classify the diseases in
turmeric leaf. The dataset with 800 leaf images of different categories were pre-processed and segmented to
promote efficient feature extraction. Machine learning algorithms like support vector machine, decision tree and
naïve bayes were applied to train the model. The performance of the model was evaluated using 10 fold cross
validation and the results are reported.
This document summarizes a research paper on developing a real-time system for identifying crop diseases, pest damage, and nutrient deficiencies using image processing. The proposed system uses a camera to capture images of plant leaves which are then analyzed using MATLAB software. Machine learning algorithms like K-means clustering and support vector machines are used to analyze images, extract features, and classify diseases. If a disease is identified, the system will automatically sprinkle the appropriate fertilizers. The goal is to help farmers more easily and accurately monitor crop health without requiring constant supervision or expert knowledge, thereby improving yields.
Defect Fruit Image Analysis using Advanced Bacterial Foraging Optimizing Algo...IOSR Journals
This document presents a method for segmenting defect areas on fruit images using an improved bacterial foraging optimization algorithm (ABFOA). The algorithm first decomposes the input fruit image into its red, green, and blue color components. It then applies the ABFOA to each color component separately to calculate individual thresholds. The final threshold is calculated as the average of the individual thresholds. This threshold is then applied to the original image to segment the defected areas. The method is tested on images of apples with defects like scab, rot, and blotch disease. Results show the ABFOA approach more accurately segments the defect areas compared to Otsu thresholding in terms of entropy, standard deviation, and peak signal-to
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Abstract In this paper illustrates the improvement of a low cost machine vision system using webcams and image processing algorithms for defect detection and sorting of tomatoes The sorting decision was based on three features extracted by the different image processing algorithms. This methodology based on the color features, which used for detecting the BER from good tomatoes. Two methods were developed for decision based sorting. The color image threshold method with shape factor was found efficient for differentiating good and defective tomatoes. The overall accuracy of defect detection attained was 94 and 96.5% respectively. Comparison of the results is also presented in this paper. Keywords: Dither Image, Stem Image, Histogram, Tomato.
Instant fracture detection using ir-raysijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
A Survey of User Authentication Schemes for Mobile DeviceIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
An Effective Policy Anomaly Management Framework for FirewallsIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
This document presents a study on using color texture feature analysis to detect surface defects on pomegranates. The researchers developed a method involving cropping images of pomegranates, converting them to HSI color space, generating SGDM matrices to extract 18 texture features for each image, and using support vector machines (SVM) classification to identify the best features for detecting infections. The optimal features identified were cluster shade, product moment, and mean intensity, achieving classification accuracy of 99.88%, 99.88%, and 99.81% respectively.
A STUDY ON WEED DISCRIMINATION THROUGH WAVELET TRANSFORM, TEXTURE FEATURE EXT...ijcsit
Texture based weed classification has played an important role in agricultural applications. In the recent years weed classification based on wavelet transform is an effective method. But the feature extraction is main issue for proper classification of weed species. In this paper, the issue of statistical and texture
classification based on wavelet transform has been analysed. The efficient texture feature extraction
methods are developed for weed discrimination. Three group feature vector can be constructed by the mean
and standard deviation of the wavelet statistical features (WSF), Texture feature as Contrast, Cluster
Shade, Cluster Prominence and Local Homogeneity (WCSPH) and Energy, Correlation, Cluster Shade,
Cluster Prominence and Entropy features (WECSPE) which are derived from the sub-bands of the wavelet
decomposition and are used for classification. Experimental results show that Rbio33 Wavelet with
WECSPE texture feature obtaining high degree of success rate in classification.
Orientation Spectral Resolution Coding for Pattern RecognitionIOSRjournaljce
In the approach of pattern recognition, feature descriptions are of greater importance. Features are represented in spatial domain and transformed domain. Wherein, spatial domain features are of lower representation, transformed domains are finer and more informative. In the transformed domain representation, features are represented using spectral coding using advanced transformation technique such as wavelet transformation. However, the feature extraction approach considers the band coefficients; the orientation variation is not considered. In this paper towards inherent orientation variation among each spectral band is derived, and the approach of orientation filtration is made for effective feature representation. The obtained result illustrates an improvement in the recognition accuracy, in comparison to conventional retrieval system.
MEDICAL IMAGE TEXTURE SEGMENTATION USINGRANGE FILTERcscpconf
Medical image segmentation is a frequent processing step in image understanding and computer
aided diagnosis. In this paper, we propose medical image texture segmentation using texture
filter. Three different image enhancement techniques are utilized to remove strong speckle noise as well enhance the weak boundaries of medical images. We propose to exploit the concept of range filtering to extract the texture content of medical image. Experiment is conducted on ImageCLEF2010 database. Results show the efficacy of our proposed medical image texture segmentation.
Image Mining for Flower Classification by Genetic Association Rule Mining Usi...IJAEMSJORNAL
Image mining is concerned with knowledge discovery in image databases. It is the extension of data mining algorithms to image processing domain. Image mining plays a vital role in extracting useful information from images. In computer aided plant identification and classification system the image mining will take a crucial role for the flower classification. The content image based on the low-level features such as color and textures are used to flower image classification. A flower image is segmented using a histogram threshold based method. The data set has different flower species with similar appearance (small inter class variations) across different classes and varying appearance (large intra class variations) within a class. Also the images of flowers are of different pose with cluttered background under varying lighting conditions and climatic conditions. The flower images were collected from World Wide Web in addition to the photographs taken up in a natural scene. The proposed method is based on textural features such as Gray level co-occurrence matrix (GLCM). This paper introduces multi dimensional genetic association rule mining for classification of flowers effectively. The image Data mining approach has four major steps: Preprocessing, Feature Extraction, Preparation of Transactional database and multi dimensional genetic association rule mining and classification. The purpose of our experiments is to explore the feasibility of data mining approach. Results will show that there is promise in image mining based on multi dimensional genetic association rule mining. It is well known that data mining techniques are more suitable to larger databases than the one used for these preliminary tests. Computer-aided method using association rule could assist people and improve the accuracy of flower identification. In particular, a Computer aided method based on association rules becomes more accurate with a larger dataset .Experimental results show that this new method can quickly and effectively mine potential association rules.
Evaluation of image segmentation and filtering with ann in the papaya leafijcsit
Precision agriculture is area with lack of cheap technology. The refinement of the production system brings
large advantages to the producer and the use of images makes the monitoring a more cheap methodology.
Macronutrients monitoring can to determine the health and vulnerability of the plant in specific stages. In
this paper is analyzed the method based on computational intelligence to work with image segmentation in
the identification of symptoms of plant nutrient deficiency. Artificial neural networks are evaluated for
image segmentation and filtering, several variations of parameters and insertion impulsive noise were
evaluated too. Satisfactory results are achieved with artificial neural for segmentation same with high
noise levels.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
PERFORMANCE ANALYSIS OF TEXTURE IMAGE RETRIEVAL FOR CURVELET, CONTOURLET TRAN...ijfcstjournal
This paper analyzes the performance of texture feature extraction techniques like curvelet transform, contourlet transform, and local ternary pattern (LTP) for magnetic resonance image (MRI) brain tumor retrieval using deep neural network (DNN) classification. Texture features are extracted from 1000 brain tumor MRI images using the three techniques. The features are classified using DNN and the techniques are evaluated based on performance metrics like sensitivity, specificity, accuracy, error rate, and F-measure. Experimental results show that contourlet transform provides better retrieval performance than curvelet transform and LTP according to these evaluation metrics.
HYBRID APPROACH FOR NOISE REMOVAL AND IMAGE ENHANCEMENT OF BRAIN TUMORS IN MA...acijjournal
In medical image processing, Magnetic Resonance Imaging (MRI) is one of significant diagnostic
techniques. It provides high quality of important information about the analysis of human soft tissue when
measured with CT imaging modalities; hence it is suitable for diagnosis at best. However, if it gives quality
of information, image may distorted by noise because of image acquisition device and transmission. The
noises in MR image reduces the quality of image and also damages the segmentation task which can lead
to faulty diagnosis. Noises have to reduce at the same time there is no information loss. This paper propose
a hybrid approach to enhance the brain tumor MRI images using combined features of Anisotropic
Diffusion Filter (ADF) with Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF).
ADF scheme provides a superior performance by removing noise while preserving image details and
enhancing edges. MDBUTMF helps in image denoising as well as preserving edges satisfactorily when the
noise level is high. The performance of this filter is evaluated by carrying out a qualitative comparison of
this method with other filters namely, ADF filter, Modified Decision Algorithm, Median filter, MDBUTMF.
Image restoration model with wavelet based fusionAlexander Decker
1. The document discusses various techniques for image restoration, which aims to recover a sharp original image from a degraded one using mathematical models of degradation and restoration.
2. It analyzes techniques like deconvolution using Lucy Richardson algorithm, Wiener filter, regularized filter, and blind image deconvolution on different image formats based on metrics like PSNR, MSE, and RMSE.
3. Previous studies have applied techniques like Wiener filtering, wavelet-based fusion, and iterative blind deconvolution for motion blur restoration and compared their performance.
Wavelet transformation based detection of masses in digital mammogramseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Wavelet transformation based detection of masses in digital mammogramseSAT Journals
This document describes a wavelet transformation-based method for detecting masses in digital mammograms. The method uses wavelet analysis to highlight variations in intensities that may indicate masses. It applies preprocessing techniques like median filtering to reduce noise and morphological operations to remove the pectoral muscle and suppress artifacts. Region properties and seeded region growing are then used to accurately segment abnormal masses. The combined use of wavelet transformation and region growing enables effective mass segmentation, demonstrating the effectiveness of the proposed technique. The method is tested on over 30 mammograms and shows improvements over traditional mass detection approaches.
This document discusses using smoothing filters based on rough set theory for medical image enhancement. It introduces common smoothing filters like mean, median, mode, and triangular filters. These filters can reduce noise and enhance edges in medical images. The document proposes a parallel rough set based model that implements multiple smoothing filters at once to obtain independent results and generate an enhanced mean image for improved medical image quality and complex image processing.
Identification and Classification of Leaf Diseases in Turmeric PlantsIJERA Editor
Plant disease identification is the most important sector in agriculture. Turmeric is one of the important
rhizomatous crops grown in India. The turmeric leaf is highly exposed to diseases like rhizome rot, leaf spot,
and leaf blotch. The identification of plant diseases requires close monitoring and hence this paper adopts
technologies to manage turmeric plant diseases caused by fungi to enable production of high quality crop yields.
Various image processing and machine learning techniques are used to identify and classify the diseases in
turmeric leaf. The dataset with 800 leaf images of different categories were pre-processed and segmented to
promote efficient feature extraction. Machine learning algorithms like support vector machine, decision tree and
naïve bayes were applied to train the model. The performance of the model was evaluated using 10 fold cross
validation and the results are reported.
This document summarizes a research paper on developing a real-time system for identifying crop diseases, pest damage, and nutrient deficiencies using image processing. The proposed system uses a camera to capture images of plant leaves which are then analyzed using MATLAB software. Machine learning algorithms like K-means clustering and support vector machines are used to analyze images, extract features, and classify diseases. If a disease is identified, the system will automatically sprinkle the appropriate fertilizers. The goal is to help farmers more easily and accurately monitor crop health without requiring constant supervision or expert knowledge, thereby improving yields.
Defect Fruit Image Analysis using Advanced Bacterial Foraging Optimizing Algo...IOSR Journals
This document presents a method for segmenting defect areas on fruit images using an improved bacterial foraging optimization algorithm (ABFOA). The algorithm first decomposes the input fruit image into its red, green, and blue color components. It then applies the ABFOA to each color component separately to calculate individual thresholds. The final threshold is calculated as the average of the individual thresholds. This threshold is then applied to the original image to segment the defected areas. The method is tested on images of apples with defects like scab, rot, and blotch disease. Results show the ABFOA approach more accurately segments the defect areas compared to Otsu thresholding in terms of entropy, standard deviation, and peak signal-to
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Abstract In this paper illustrates the improvement of a low cost machine vision system using webcams and image processing algorithms for defect detection and sorting of tomatoes The sorting decision was based on three features extracted by the different image processing algorithms. This methodology based on the color features, which used for detecting the BER from good tomatoes. Two methods were developed for decision based sorting. The color image threshold method with shape factor was found efficient for differentiating good and defective tomatoes. The overall accuracy of defect detection attained was 94 and 96.5% respectively. Comparison of the results is also presented in this paper. Keywords: Dither Image, Stem Image, Histogram, Tomato.
Instant fracture detection using ir-raysijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
A Survey of User Authentication Schemes for Mobile DeviceIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
An Effective Policy Anomaly Management Framework for FirewallsIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Presentasi ini membahas sumber hukum Islam yaitu ijtihad. Ada dua macam ijtihad yaitu ijtihad sendiri dan ijtihad bersama. Ada beberapa syarat untuk melakukan ijtihad antara lain memiliki pengetahuan yang mendalam tentang Al-Quran, hadis, bahasa Arab, dan ilmu-ilmu lain yang relevan.
Spectroscopic and Thermal Characterization of Charge-Transfer Complexes Forme...IJMER
1) The document describes the formation and characterization of three charge-transfer complexes formed from the reaction of 2-amino-6-ethylpyridine with different π-electron acceptors.
2) Spectroscopic analysis revealed the complexes had formulas of [(2A6EPy)(TCNE)2], [(2A6EPy)2(DDQ)], and [(2A6EPy)4(TBCHD)], which were supported by elemental analysis.
3) The complexes showed intense absorption bands in the visible region associated with electronic transitions. Thermal and spectroscopic properties of the complexes were analyzed.
The document compares two hierarchical routing schemes for wireless sensor networks: EEPSC and EEEPSC. EEPSC divides the network into static clusters and uses temporary cluster heads to distribute energy load. EEEPSC extends EEPSC by also considering distance between nodes and cluster heads when selecting cluster heads, in order to reduce inter-cluster communication costs. Simulation results show that EEEPSC increases network lifetime by consuming less energy and keeping more nodes alive over time compared to EEPSC.
Taguchi Analysis of Erosion Wear Maize Husk Based Polymer CompositeIJMER
Amids the growing concern on environmental issues, science is seeking various alternatives to replace the synthetic and non degradable fibers composites with environment friendly biocomposites of comparable characteristics and performance. Visualizing the importance of polymer composites and owing to issue of ecological concerns, this experiment is an attempt to further investigate possibility of bio composites (Particularly maize husk) as an alternative of available synthetic polymer composites. Taking one leap forward the experiment also approximate qualities the effect of individual parameters on erosion by the application of Taguchi Technique. Experimental system were devised and designed to study the erosion rate of maize husk fiber Reinforced Polymer composites at various impingement angles, with profound variables such as particle velocity, fiber content, and particle size (erodent size) To cast the composite epoxy Resin LY 556 with corresponding hardener HY 551 was used. The erodent size was in range of it irregular shape. The tribological performance of sheets was investigated in respect to set of various variable parameters as suggested by L16 series of Taguchi Techniques. The morphological feature before and after the experiments were studies using SEM.
Comparing: Routing Protocols on Basis of sleep modeIJMER
The architecture of ad hoc wireless network consists of mobile nodes for communication
without the use of fixed-position routers. The communication between them takes place without
centralized control. Routing is a very crucial issue, so to deal with this routing algorithms must deliver
the packet in significant delay. There are different protocols for handling the mobile environment like
AODV, DSR and OLSR. But this paper will focus on performance of AODV and OLSR routing protocols.
The performance of these protocols is analyzed on two metrics: time and throughput
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Este documento describe los sistemas contra fuego de tableros de yeso marca USG TABLAROCA®. Los tableros son resistentes al fuego porque el núcleo de yeso es no flamable y puede resistir temperaturas altas, retrasando la propagación del fuego. Los tableros FIRECODE® tipo "X" y tipo "C" tienen pruebas UL que ofrecen resistencia al fuego de hasta 4 horas. Estos sistemas son ideales para oficinas, hospitales, cines y centros comerciales para proteger a las personas.
This document summarizes an experiment on using electrocoagulation with iron electrodes to remove mercury from wastewater. Key findings include:
1) Maximum mercury removal of 94.5% was achieved after 40 minutes of electrocoagulation at an applied potential of 9V, agitation of 400 rpm, initial pH of 4.5, and electrolyte concentration of 1.333 g/L.
2) Higher applied potentials and agitation rates decreased mercury removal efficiency due to excessive oxygen generation and unsuitable floc formation.
3) Operating costs were calculated based on energy consumption and electrode material costs. Electrocoagulation was found to be an efficient and fast method for mercury removal compared to conventional techniques.
Fast Data Collection with Interference and Life Time in Tree Based Wireless S...IJMER
This document discusses techniques for fast data collection in wireless sensor networks using a tree-based topology. It specifically focuses on minimizing the schedule length for aggregated convergecast (where data is aggregated at each hop) and raw-data convergecast (where packets are individually relayed to the sink).
It first considers time scheduling on a single channel, and then combines scheduling with transmission power control and multiple frequencies to further reduce interference and schedule length. It provides lower bounds on schedule length when interference is eliminated, and proposes algorithms that achieve these bounds.
Evaluation of different channel assignment methods, routing tree topologies, interference models, and their impact on schedule length is also presented. The key findings are that combining scheduling, power control,
This document proposes a new approach called Comprehensive Long-Run Incremental Cost (LRIC)-Voltage Network Pricing to determine network charges that account for the cost of maintaining nodal voltage levels given network contingencies. The approach links nodal power perturbations to nodal voltage degradation rates and the incremental investment costs required to support voltage levels. It employs the use of nodal voltage spare capacity to gauge the time needed to invest in reactive power compensation devices. The time to invest takes into account network nodal voltage profiles under N-1 circuit contingencies. The approach is demonstrated on the IEEE 14 bus network, showing the difference in charges when considering contingencies compared to the existing power factor penalty approach.
Integrating Environmental Accounting in Agro-Allied and Manufacturing Industr...IJMER
‘ONLY WHEN THE LAST TREE IS CUT, ONLY WHEN THE LAST RIVER IS
POLLUTED, ONLY WHEN THE LAST FISH IS CAUGHT, ONLY THEN WILL THEY REALIZE
THAT YOU CANNOT EAT MONEY’ American proverb
Due to growing awareness and concern on the impact of human activity on the ecosystem, there is an
increasing trend to judge organizations in relation to the community in which it operates. The
impact of the activities on the environment with regard to pollution of water, air, land and abuse of
natural resources are coming under scrutiny of governments, stakeholders and citizens. Education is
considered the key to effective development strategies and TVET institutions then must be the master
key that can alleviate poverty, promote peace, conserve the environment, improve the quality of life
for all and help achieve sustainable development. Unless proper accounting work is done, it cannot
be determined that both have been fulfilling their responsibilities. The aim of the study was to explore
whether distinctive processes of environmental accounting are possible in agro-allied and
manufacturing industries with a view to enhancing sustainability. To accomplish this aim, this
research explores environmental accountability practices in TVET institutions. This paper is in part
of an exploratory research project and it is limited in that it attempts to be illuminative and
theoretically driven. The paper aims to prove that environmental reporting and disclosure will
enable in agro-allied and manufacturing industries undertake a major transformation that includes
approaches that harmonize economic prosperity, environmental conservation and social well-being.
However, while strategies for achieving this goal are not widespread, a range of international
experiences is beginning to suggest ways forward. These initiatives include national TVET policy
reforms, green campus, green curriculum, green community, green research and green culture. The
paper includes suggested templates that can be useful in agro-allied and manufacturing industries
The document discusses the need to integrate disaster management into engineering curriculum. It argues that engineers will be better prepared to handle disasters if concepts of disaster management are incorporated across engineering subjects rather than as an elective. Integrating these concepts will give students a holistic understanding and help them apply theoretical knowledge to practical problems. The document recommends using problem-based and interdisciplinary learning to motivate students and help them develop skills like teamwork and communication that are important for disaster response.
Experimental studies have been conducted to understand disc brake noise and vibration. Researchers have used brake dynamometers and on-road testing to examine different parameters and operating conditions. They have measured vibration behavior using microphones and accelerometers. Studies have found that slotted discs can eliminate squeal vibration, while friction material and pad geometry changes can reduce it. Holographic interferometry has been used to view vibration modes on self-excited brakes. Further research aims to standardize noise measurement methods and remove subjective assessments to better understand noise generation and reduction.
This document discusses the challenges faced when using TCP in mobile ad hoc networks (MANETs). Some key challenges include: media access control issues like hidden terminals; power constraints of mobile nodes; frequent topology changes due to node mobility; multipath fading increasing the likelihood of path breaks; and misinterpreting packet losses as congestion rather than broken routes. TCP was designed for wired networks and assumes packet losses are always due to congestion, which does not hold in MANETs where losses can be from broken routes. Overall, TCP performs poorly in MANETs due to these challenges.
This document summarizes a study on operating a diesel engine using biodiesel from Mahua (Madhuca Indica) seeds and blends with fossil diesel. The engine tests were conducted on a single cylinder diesel engine at different brake powers up to full load using B0 (fossil diesel), B25, B50, B75 and B100 (pure Mahua biodiesel) as fuels. Emissions of CO, HC, CO2, NOx were measured. Results showed that B25 blend produced lower emissions than other blends or fossil diesel at full load. Using B25 is suggested as an alternative fuel without engine modifications. Properties of the fuels were measured and discussed.
Application of Analysis of variance and Chi- square to study diamond industryIJMER
Chi -square is a statistical test commonly used to compare observed data with data we would expect to
obtain according to a specific hypothesis. For example, if, according to Mendel's laws, you expected 10 of 20
offspring from a cross to be male and the actual observed number was 8 males, then you might want to know
about the "goodness to fit" between the observed and expected. Were the deviations (differences between
observed and expected) the result of chance, or were they due to other factors. How much deviation can occur
before you, the investigator, must conclude that something other than chance is at work, causing the observed to
differ from the expected. The chi-square test is always testing what scientists call the null hypothesis, which
states that there is no significant difference between the expected and observed result.
Automated Plant Identification with CNNIRJET Journal
This document discusses using convolutional neural networks (CNNs) for automated plant identification from images. Specifically:
- CNNs can be used to extract features from plant images and classify them to the correct species, achieving accuracies over 88%.
- Previous work has used pre-trained and custom CNN models like AlexNet along with classifiers like SVM to identify plants from leaf images.
- Deeper CNN architectures that learn features automatically perform better than shallow models relying on hand-designed features. They improve accuracy without needing feature engineering.
- The document evaluates CNN approaches on leaf image datasets, finding them effective for automated plant classification based on vein patterns.
Combination of Local Descriptors and Global Features for Leaf Recognitionsipij
Automatic leaf recognition system is a case coming to improve time-consuming and troublesome tasks which have mainly been carried out by botanists manually. This application as judged by common characteristics is popular in institutes for discovering new plant species, modernizing the management of botanical gardens and horticulture fields. In order to conduct a leaf recognition system, the features must be sufficiently distinctive to identify specific objects among many alternatives, where contain both local and global properties. So far, many researchers have represented some techniques which use local or global features only where face problems, such as many images are captured in different intensity, they are maybe sick or calamity, leaves have been damaged or cropped and so on. In this paper, a new method for leaf recognition system is proposed where both local descriptors and global features are employed, combined and finally the most discriminant features are selected by employing a linear discriminant analysis method. The experimental results show that using the feature vector containing the local features and global characteristics leads us to obtain 94.3% recognition rate.
Automatic Recognition of Medicinal Plants using Machine Learning TechniquesIRJET Journal
The document presents a method for automatic recognition of medicinal plants using machine learning techniques. Leaves from 24 medicinal plant species were collected and their features were extracted, including length, width, color, and area. A random forest classifier achieved 90.1% accuracy in identifying the plants using 10-fold cross-validation, outperforming other classifiers like k-nearest neighbor and support vector machines. The proposed method uses feature extraction and a support vector machine classifier to identify medicinal plant leaves with high accuracy.
Plant Leaf Disease Detection using Deep Learning and CNNIRJET Journal
This document proposes using convolutional neural networks and deep learning to detect plant leaf diseases. It discusses how plant diseases can impact food supply and the economy. The proposed system would use a CNN model trained on labeled images of healthy and diseased leaves to automatically detect diseases. It describes preprocessing input images, the architecture of the CNN model with convolutional, pooling and fully connected layers, and training the model on labeled image data. The system is intended to provide a low-cost and accurate way to detect leaf diseases early and help farmers address issues. The model achieved 96.4% accuracy in testing.
IRJET- Analysis of Plant Diseases using Image Processing MethodIRJET Journal
This document describes a method for detecting plant diseases using image processing techniques. The method involves capturing images of plant leaves using a digital camera, preprocessing the images by converting them to grayscale and removing noise. Edge detection algorithms like Canny and Sobel are then applied to detect edges. K-means clustering is used for image segmentation to segment unhealthy parts of leaves. The process results in an effective solution for segmenting diseased areas of leaves.
Lung Cancer Detection using Image Processing TechniquesIRJET Journal
This document presents a technique for detecting lung cancer in x-ray images using image processing. It involves enhancing images using Gabor filtering, segmenting images using marker-controlled watershed segmentation, and extracting features using binarization and masking. The key steps are collecting lung x-ray images, enhancing quality using Gabor filtering, segmenting regions of interest using watershed segmentation, extracting pixel counts and mask features, and classifying images as normal or abnormal based on these features. The goal is to enable early detection of lung cancer through automated analysis of medical images.
IRJET- Brain Tumour Detection and ART Classification Technique in MR Brai...IRJET Journal
This document describes a proposed method for detecting and classifying brain tumors in MR brain images using robust principal component analysis (RPCA) and quad tree (QT) decomposition for image fusion. The method involves fusing T1 and T2 MRI images using RPCA and QT decomposition. The fused image is then segmented using level set segmentation. Features are extracted from the segmented image using complete local binary pattern (CLBP) and pyramid histogram of oriented gradients (PHOG) approaches. The features are then classified using an adaptive resonance theory (ART) classifier to classify the brain tumor as malignant or benign. The proposed method aims to efficiently fuse multi-modal MRI images for improved brain tumor detection and classification.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Image De-noising and Enhancement for Salt and Pepper Noise using Genetic Algo...IDES Editor
Image Enhancement through De-noising is one of
the most important applications of Digital Image Processing
and is still a challenging problem. Images are often received
in defective conditions due to usage of Poor image sensors,
poor data acquisition process and transmission errors etc.,
which creates problems for the subsequent process to
understand such images. The proposed Genetic filter is capable
of removing noise while preserving the fine details, as well as
structural image content. It can be divided into: (i) de-noising
filtering, and (ii) enhancement filtering. Image Denoising
and enhancement are essential part of any image processing
system, whether the processed information is utilized for visual
interpretation or for automatic analysis. The Experimental
results performed on a set of standard test images for a wide
range of noise corruption levels shows that the proposed filter
outperforms standard procedures for salt and pepper removal
both visually and in terms of performance measures such as
PSNR.Genetic algorithms will definitely helpful in solving
various complex image processing tasks in the future.
A NOVEL DATA DICTIONARY LEARNING FOR LEAF RECOGNITIONsipij
Automatic leaf recognition via image processing has been greatly important for a number of professionals, such as botanical taxonomic, environmental protectors, and foresters. Learn an over-complete leaf dictionary is an essential step for leaf image recognition. Big leaf images dimensions and training images number is facing of fast and complete data leaves dictionary. In this work an efficient approach applies to construct over-complete data leaves dictionary to set of big images diminutions based on sparse representation. In the proposed method a new cropped-contour method has used to crop the training image. The experiments are testing using correlation between the sparse representation and data dictionary and with focus on the computing time.
Fingerprint image enhancement is the key process in IAFIS systems. In order to reduce false identification ratio and to supply good fingerprint images to IAFIS systems for exact identification, fingerprint images are generally enhanced. A filtering process tries to filter out the noise from the input image, and emphasize on low, high and directional spatial frequency components of an image. This paper presents an experimental summary of enhancing fingerprint images using Gabor filters. Frequency, width and window domain filter ranges are fixed. The orientation angle alone is modified by 0 radians, π/2, π/4 and 3π/4 radians. The experimental results show that Gabor filter enhances the fingerprint image in a better way than other filtering methods and extracts features.
Plant Monitoring using Image Processing, Raspberry PI & IOTIRJET Journal
This document describes a plant monitoring system using image processing, a Raspberry Pi, and the Internet of Things. The system uses image processing techniques like segmentation, feature extraction and classification on images of plant leaves to detect diseases. Sensors connected to an Arduino board such as a humidity sensor, gas sensors and a light sensor are used to monitor environmental conditions. The Arduino and Raspberry Pi are connected to allow the sensors data to be sent to the Raspberry Pi. The Raspberry Pi then sends notifications about the plant health and environmental conditions to smartphones. This allows remote monitoring of farm conditions.
Automatic Recognition System Using Preferential Image Segmentation For Leaf A...CSEIJJournal
This document summarizes an article about developing an automatic plant recognition system using preferential image segmentation of leaf and flower images. It begins by introducing the need for automatic plant classification systems. It then describes the general process, which includes capturing leaf images, pre-processing, feature extraction, image segmentation using preferential image segmentation, and a matching process. Preferential image segmentation is used to segment objects of interest, like leaves, from an image while ignoring background portions. The document provides details on each step and evaluates preferential image segmentation for segmenting leaves from images to aid in plant identification.
Identification and Classification of Leaf Diseases in Turmeric PlantsIJERA Editor
Plant disease identification is the most important sector in agriculture. Turmeric is one of the important
rhizomatous crops grown in India. The turmeric leaf is highly exposed to diseases like rhizome rot, leaf spot,
and leaf blotch. The identification of plant diseases requires close monitoring and hence this paper adopts
technologies to manage turmeric plant diseases caused by fungi to enable production of high quality crop yields.
Various image processing and machine learning techniques are used to identify and classify the diseases in
turmeric leaf. The dataset with 800 leaf images of different categories were pre-processed and segmented to
promote efficient feature extraction. Machine learning algorithms like support vector machine, decision tree and
naïve bayes were applied to train the model. The performance of the model was evaluated using 10 fold cross
validation and the results are reported.
IRJET- Identification of Indian Medicinal Plant by using Artificial Neural Ne...IRJET Journal
This document presents a method for identifying Indian medicinal plants using artificial neural networks. Leaf images of five medicinal plants were collected and analyzed. Features like edge detection, color histograms, and area were extracted from the images. These features were used to train an artificial neural network classifier. The neural network was able to accurately classify the plant images based on their features 75% of the time based solely on color and edge data. This demonstrates that simple image analysis of leaf features can effectively identify medicinal plants.
Plant Leaf Disease Detection and Classification Using Image ProcessingIRJET Journal
The document summarizes a research paper on detecting and classifying plant leaf diseases using image processing techniques. It begins by discussing the importance of identifying plant diseases early. It then provides an overview of traditional identification methods and their limitations. Next, it describes how image processing can be used to extract features from leaf images and classify diseases using machine learning algorithms. The paper evaluates several studies that have achieved accuracy ranging from 80-99.8% using different approaches. It also discusses challenges like variable image quality and limited datasets, and potential solutions. Finally, it presents results showing accuracy of 95-99% for different techniques depending on the dataset and diseases studied.
IDENTIFICATION AND CLASSIFICATION OF RARE MEDICINAL PLANTS USING MACHINE LEAR...IRJET Journal
This document describes a study that uses machine learning techniques to identify and classify rare medicinal plants. The researchers extracted visual features from plant leaf images like shape, color, and texture. They then applied random forest, k-nearest neighbors, and logistic regression algorithms to classify the leaves as rare medicinal or non-medicinal. Previous related works that used different machine learning approaches for medicinal plant identification are also reviewed. The goal of the project is to help identify rare medicinal plants automatically using computer vision and machine learning methods.
Texture based feature extraction and object trackingPriyanka Goswami
This document provides a project report on texture-based feature extraction and object tracking. It discusses using various texture analysis techniques like Local Binary Pattern (LBP), Local Derivative Pattern (LDP), and Local Ternary Pattern (LTP) to extract features from images for tasks like cloud tracking. It implements these techniques in MATLAB and evaluates them on standard datasets to extract features and represent images with histograms for tasks like image recognition and analysis while reducing computational requirements compared to using raw images. The techniques are then applied to track cloud motion in weather satellite images by analyzing differences in texture histograms over time.
National Flags Recognition Based on Principal Component Analysisijtsrd
Recognizing an unknown flag in a scene is challenging due to the diversity of the data and to the complexity of the identification process. And flags are associated with geographical regions, countries and nations. But flag identification of different countries is a challenging and difficult task. Recognition of an unknown flag image in a scene is challenging due to the diversity of the data and to the complexity of the identification process. The aim of the study is to propose a feature extraction based recognition system for Myanmar's national flag. Image features are acquired from the region and state of flags which are identified by using principal component analysis PCA . PCA is a statistical approach used for reducing the number of features in National flags recognition system. Soe Moe Myint | Moe Moe Myint | Aye Aye Cho "National Flags Recognition Based on Principal Component Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26775.pdfPaper URL: https://www.ijtsrd.com/other-scientific-research-area/other/26775/national-flags-recognition-based-on-principal-component-analysis/soe-moe-myint
Automatic Detection of Radius of Bone FractureIRJET Journal
This document presents a proposed algorithm for automatically detecting the radius of bone fractures in x-ray images. The algorithm involves several steps: image preprocessing using filters to reduce noise, segmentation using FCM clustering to separate bone regions, feature extraction using Hough transform to identify lines and circles, and detecting the radius of fractures based on the extracted features. The algorithm was tested on 20 x-ray images and achieved about 90% accuracy in detecting fracture radii. The proposed method provides an efficient and accurate approach for fracture detection compared to other methods. Future work may focus on enhancing the algorithm to handle multiple fractures and different image modalities like CT and MRI.
Similar to An Effective Tea Leaf Recognition Algorithm for Plant Classification Using Radial Basis Function Machine (20)
A Study on Translucent Concrete Product and Its Properties by Using Optical F...IJMER
- Translucent concrete is a concrete based material with light-transferring properties,
obtained due to embedded light optical elements like Optical fibers used in concrete. Light is conducted
through the concrete from one end to the other. This results into a certain light pattern on the other
surface, depending on the fiber structure. Optical fibers transmit light so effectively that there is
virtually no loss of light conducted through the fibers. This paper deals with the modeling of such
translucent or transparent concrete blocks and panel and their usage and also the advantages it brings
in the field. The main purpose is to use sunlight as a light source to reduce the power consumption of
illumination and to use the optical fiber to sense the stress of structures and also use this concrete as an
architectural purpose of the building
Developing Cost Effective Automation for Cotton Seed DelintingIJMER
A low cost automation system for removal of lint from cottonseed is to be designed and
developed. The setup consists of stainless steel drum with stirrer in which cottonseeds having lint is mixed
with concentrated sulphuric acid. So lint will get burn. This lint free cottonseed treated with lime water to
neutralize acidic nature. After water washing this cottonseeds are used for agriculter purpose
Study & Testing Of Bio-Composite Material Based On Munja FibreIJMER
The incorporation of natural fibres such as munja fiber composites has gained
increasing applications both in many areas of Engineering and Technology. The aim of this study is to
evaluate mechanical properties such as flexural and tensile properties of reinforced epoxy composites.
This is mainly due to their applicable benefits as they are light weight and offer low cost compared to
synthetic fibre composites. Munja fibres recently have been a substitute material in many weight-critical
applications in areas such as aerospace, automotive and other high demanding industrial sectors. In
this study, natural munja fibre composites and munja/fibreglass hybrid composites were fabricated by a
combination of hand lay-up and cold-press methods. A new variety in munja fibre is the present work
the main aim of the work is to extract the neat fibre and is characterized for its flexural characteristics.
The composites are fabricated by reinforcing untreated and treated fibre and are tested for their
mechanical, properties strictly as per ASTM procedures.
Hybrid Engine (Stirling Engine + IC Engine + Electric Motor)IJMER
Hybrid engine is a combination of Stirling engine, IC engine and Electric motor. All these 3 are
connected together to a single shaft. The power source of the Stirling engine will be a Solar Panel. The aim of
this is to run the automobile using a Hybrid engine
Fabrication & Characterization of Bio Composite Materials Based On Sunnhemp F...IJMER
This document summarizes research on the fabrication and characterization of bio-composite materials using sunnhemp fibre. The document discusses how sunnhemp fibre was used to reinforce an epoxy matrix through hand lay-up methods. Various mechanical properties of the bio-composites were tested, including tensile, flexural, and impact properties. The results of the mechanical tests on the bio-composite specimens are presented. Potential applications of the sunnhemp fibre bio-composites are also suggested, such as in fall ceilings, partitions, packaging, automotive interiors, and toys.
Geochemistry and Genesis of Kammatturu Iron Ores of Devagiri Formation, Sandu...IJMER
The Greenstone belts of Karnataka are enriched in BIFs in Dharwar craton, where Iron
formations are confined to the basin shelf, clearly separated from the deeper-water iron formation that
accumulated at the basin margin and flanking the marine basin. Geochemical data procured in terms of
major, trace and REE are plotted in various diagrams to interpret the genesis of BIFs. Al2O3, Fe2O3 (T),
TiO2, CaO, and SiO2 abundances and ratios show a wide variation. Ni, Co, Zr, Sc, V, Rb, Sr, U, Th,
ΣREE, La, Ce and Eu anomalies and their binary relationships indicate that wherever the terrigenous
component has increased, the concentration of elements of felsic such as Zr and Hf has gone up. Elevated
concentrations of Ni, Co and Sc are contributed by chlorite and other components characteristic of basic
volcanic debris. The data suggest that these formations were generated by chemical and clastic
sedimentary processes on a shallow shelf. During transgression, chemical precipitation took place at the
sediment-water interface, whereas at the time of regression. Iron ore formed with sedimentary structures
and textures in Kammatturu area, in a setting where the water column was oxygenated.
Experimental Investigation on Characteristic Study of the Carbon Steel C45 in...IJMER
In this paper, the mechanical characteristics of C45 medium carbon steel are investigated
under various working conditions. The main characteristic to be studied on this paper is impact toughness
of the material with different configurations and the experiment were carried out on charpy impact testing
equipment. This study reveals the ability of the material to absorb energy up to failure for various
specimen configurations under different heat treated conditions and the corresponding results were
compared with the analysis outcome
Non linear analysis of Robot Gun Support Structure using Equivalent Dynamic A...IJMER
Robot guns are being increasingly employed in automotive manufacturing to replace
risky jobs and also to increase productivity. Using a single robot for a single operation proves to be
expensive. Hence for cost optimization, multiple guns are mounted on a single robot and multiple
operations are performed. Robot Gun structure is an efficient way in which multiple welds can be done
simultaneously. However mounting several weld guns on a single structure induces a variety of
dynamic loads, especially during movement of the robot arm as it maneuvers to reach the weld
locations. The primary idea employed in this paper, is to model those dynamic loads as equivalent G
force loads in FEA. This approach will be on the conservative side, and will be saving time and
subsequently cost efficient. The approach of the paper is towards creating a standard operating
procedure when it comes to analysis of such structures, with emphasis on deploying various technical
aspects of FEA such as Non Linear Geometry, Multipoint Constraint Contact Algorithm, Multizone
meshing .
Static Analysis of Go-Kart Chassis by Analytical and Solid Works SimulationIJMER
This paper aims to do modelling, simulation and performing the static analysis of a go
kart chassis consisting of Circular beams. Modelling, simulations and analysis are performed using 3-D
modelling software i.e. Solid Works and ANSYS according to the rulebook provided by Indian Society of
New Era Engineers (ISNEE) for National Go Kart Championship (NGKC-14).The maximum deflection is
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In récent year various vehicle introduced in market but due to limitation in
carbon émission and BS Séries limitd speed availability vehicle in the market and causing of
environnent pollution over few year There is need to decrease dependancy on fuel vehicle.
bicycle is to be modified for optional in the future To implement new technique using change in
pedal assembly and variable speed gearbox such as planetary gear optimise speed of vehicle
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the pedal mechanism
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An Effective Tea Leaf Recognition Algorithm for Plant Classification Using Radial Basis Function Machine
1. International
OPEN ACCESS Journal
Of Modern Engineering Research (IJMER)
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss. 3 | Mar. 2014 | 35 |
An Effective Tea Leaf Recognition Algorithm for Plant
Classification Using Radial Basis FunctionMachine
Arunpriya C.1
, Antony Selvadoss Thanamani2
1
P.S.G.R Krishnammal College for Women, Coimbatore, India
2
Nallamuthu Gounder Mahalingam College, Pollachi, India
I. Introduction
Leaf is the most important organ in a plant. Plant leaf features are important for the plant species
identification.[1]Plant recognition is an essential and challenging task. Leaf recognition plays an important role
in plant classification and its key issue lies in whether the chosen features are constant and have good capability
to discriminate various kinds of leaves. The recognition procedure is very time consuming. Computer aided
plant recognition is still very challenging task in computer vision because of improper models and
inefficient representation approaches. The main aim of plant recognition is to evaluate the leaf
geometrical morphological and Fourier moment based features.
Recently, computer vision and pattern recognition techniques have been applied towards automated
process of plant recognition [2].The classification of plant leaves is a vital mechanism in botany and in tea,
cotton and other industries [3], [4]. Additionally, the morphological features of leaves are employed for plant
classification or in the early diagnosis of certain plant diseases [5].
Preprocessing is a technique in which an image includes removal of noise, edge or boundary
enhancement, automatic edge detection, automatic contrast adjustment and the segmentation. As multiple noise
damages the quality of nature of the images, improved enhancement technique is required for improving the
contrast stretch in leaf images. Image enhancement is basically improving the interpretability or discernment of
information in images for human viewers and providing enhanced input for other automated image processing
techniques [22, 23]. During this process, one or more attributes of the image are altered. Selection of attributes
and the way they are modified are specific to a given task.
Denoising has become an basic step in image analysis. Noise suppression and the preservation of actual
image discontinuity is essential in image denoising in order to detect the image details and suitably alter the
degree of noise smoothing. Fuzzy feature is used in single channel image denoising to enhance image
information. This feature space helps to distinguish between important coefficients, which depends on image
discontinuity and noisy coefficients [6]. The size of image plays an important role in order to transmit the image
in lesser time and with the allotted bandwidth. DT-DWT has been successfully used in many applications such
as image denoising, texture analysis, compression, and motion estimation.
The extraction of leaf features from a plant is a key step in the plant recognition process [7, 8]. This
feature extraction process creates a new challenge in the field of pattern recognition [9] [10]. The data
acquisition from living plant automatically by the computer has not been implemented. The main phases
Abstract: A leaf is an organ of a vascular plant, as identified in botanical terms, and in particular in plant
morphology. Naturally a leaf is a thin, flattened organ bear above ground and it is mainly used for photosynthesis.
Recognition of plants has become an active area of research as most of the plant species are at the risk of
extinction. Most of the leaves cannot be recognized easily since some are not flat (e.g. succulent leaves and
conifers), some does not grow above ground (e.g. bulb scales), and some does not undergo photosynthetic function
(e.g. cataphylls, spines, and cotyledons).In this paper, we mainly focused on tea leaves to identify the leaf type for
improving tea leaf classification. Tea leaf images are loaded from digital cameras or scanners in the system. This
proposed approach consists of three phases such as preprocessing, feature extraction and classification to process
the loaded image. The tea leaf images can be identified accurately in the preprocessing phase by fuzzy denoising
using Dual Tree Discrete Wavelet Transform (DT-DWT) in order to remove the noisy features and boundary
enhancement to obtain the shape of leaf accurately. In the feature extraction phase, Digital Morphological
Features (DMFs) are derived to improve the classification accuracy. Radial Basis Function (RBF) is used for
efficient classification. The RBF is trained by 60 tea leaves to classify them into 6 types. Experimental results
proved that the proposed method classifies the tea leaves with more accuracy in less time. Thus, the proposed
method achieves more accuracy in retrieving the leaf type.
Keywords: Leaf Recognition, Dual Tree Discrete Wavelet Transform (DT-DWT), Digital Morphological
Features (DMFs), Radial Basis Function (RBF).
2. An Effective Tea Leaf Recognition Algorithm for Plant Classification Using Radial Basis.....
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss. 3 | Mar. 2014 | 36 |
involved in this research are feature extraction and the classification. All features are extracted from digital leaf
image. Radial Basis Function(RBF) is used for efficient classification.
The paper can be organized as follows. Section II describes the related works involved regarding leaf
recognition. Section III describes about the proposed methodology. Experimental results are illustrated in
Section IV and Section V deals with the conclusion.
II. Related Works
Stephen Gang Wu et al., [11] employ Probabilistic Neural Network (PNN) with image and data
processing techniques to implement general purpose automated leaf recognition for plant classification. They
implemented a leaf recognition algorithm using easy-to-extract features and high efficient recognition algorithm.
They mainly concentrate on feature extraction and the classifier. Features are extracted from digital leaf image.
Except one feature, all features can be extracted automatically. The features are orthogonalized by Principal
Components Analysis (PCA).As to the classifier; they used PNN for its fast speed and simple structure.
Wang et al., [12] first investigated the representation efficiency of 3D DT-DWT for video and
proposed a DDWT-based scalable video coding scheme without motion estimation (DDWTVC) [13]. Better
coding efficiency in terms of PSNR and visual quality are reported compared with 3D SPIHT which also does
not use motion compensation.
Xiao Gu et al., [14] proposed a novel approach for leaf recognition by means of the result of
segmentation of leaf‟s skeleton based on the integration of Wavelet Transform (WT) and Gaussian interpolation.
And then the classifiers, a nearest neighbor classifier (1-NN), a k -nearest neighbor classifier (k-NN) and a
radial basis probabilistic neural network (RBPNN) are employed, based on Run-length Features (RF) obtained
from the skeleton to identify the leaves. Ultimately, the efficiency of this approach is illustrated by several
experiments. The results reveal that the skeleton can be effectively extracted from the entire leaf, and the
recognition rates can be significantly improved.
Jyotismita Chaki et al., [15] proposed an automated system for recognizing plant species based on leaf
images. The leaf images corresponding to three different plant types are analyzed using two different shape
modeling techniques. The first modeling is based on the Moments-Invariant (M-I) model and the second on the
Centroid Radii (C-R) model. The first four normalized central moments have been considered for the M-I model
and studied in various combinations namely in joint 2-D and 3-D feature spaces for producing optimum
results. An edge detector has been used for the C-R model to identify the boundary of the leaf shape and 36 radii
at 10 degree angular separation have been used to build the feature vector. A hybrid set of features involving
both the M-I and C-R models has been generated and explored to find whether the combination feature vector
can lead to better analysis. Neural networks are used as classifiers for discrimination.
III. Methodology
The tea leaf recognition method used in the proposed approach consists of three phases namely image
pre processing, feature extraction and classification. The steps used in the recognition of tea leaf system is
shown in the Fig.1.
3.1. Image Pre-Processing
The input tea leaf images undergo several processing steps as follows.
A. Converting RGB Image To Binary Image
The tea leaf image is obtained through scanners or digital cameras. An RGB image is firstly converted into a
grayscale image. Equation 1 is used to convert RGB value of a pixel into its grayscale value.
gray=0.2989∗R+0.85870∗G+0.1140∗B (1)
Where R, G and B corresponds to the color of the pixel, respectively.
B. Fuzzy Denoising Using Dual Tree Discrete Wavelet Transform
Here the denoising is done through Fuzzy shrinkage rule. In image denoising, where a trade-off
between noise suppression and the maintenance of actual image discontinuity must be made, solutions are
required to detect important image details and accordingly adapt the degree of noise smoothing. With respect to
this principle, use a fuzzy feature for single channel image denoising to enhance image information in wavelet
sub-bands and then using a fuzzy membership function to shrink wavelet coefficients, accordingly.
Dual Tree Discrete Wavelet Transform (DT-DWT) is used as a fuzzy denoising algorithm which
provides both shiftable sub-bands and good directional selectivity and low redundancy.
3. An Effective Tea Leaf Recognition Algorithm for Plant Classification Using Radial Basis.....
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss. 3 | Mar. 2014 | 37 |
The 2-D dual-tree discrete wavelet transform (DT-DWT) of an image is employed using two critically-
sampled separable 2-D DWT‟s in parallel .[16] The advantages of the dual-tree DWT (DT-DWT) over
separable 2D DWT is that, it can be used to employ 2D wavelet transforms which are more selective with
respect to orientation.
Fig. 1 Steps used in the recognition of tea leaf system
The real part or the imaginary part of DT-DWT [19] produces perfect reconstruction and hence it can
be employed as a stand-alone transform. Feature vector can be calculated using magnitude of subbands. The
implementation of DT-DWT is easy. An input image is decomposed by two sets of filter banks,
(𝐻0
𝑎
, 𝐻1
𝑎
) 𝑎𝑛𝑑 𝐻0
𝑏
, 𝐻1
𝑏
) separately and filtering the image both horizontally and vertically. Then eight sub bands
are obtained: LLa , HLa , LHa , HHa , LLb , HLb , LHb and HHb. Each high-pass subband from one filter bank is
combined with the corresponding subband from the other filter bank by simple linear operations: averaging or
differencing. The size of each subband is the same as that of 2D DWT at the same level. [20] But there are six
high pass subbands instead of three highpass subbands at each level. The two lowpass subbands, LLb and LLa ,
are iteratively decomposed up to a desired level within each branch.
The DT-DWT (K) can be designed in two ways to have required delays. The first is based on Farras
filters and the second employs Q-shift (quarter shift) filter design. The key issue in the design of DT-DWT (K)
is to obtain (approximate) shift invariance using any of the filter forms.[21]To use a redundant transform for
compression seems contradictory to the goal of compression which is to reduce whatever redundancy as much
as possible. However if coefficients of a redundant transform are sparse enough, compression can even benefit
from the introduced redundancy since most coefficients are nearly zero.
Processing is usually result from a modification of the spatial correlation between wavelet coefficients
(often caused by zeroing of small neighboring coefficients) or by using DWT. DWT is shift invariance and will
cause some visual artifacts in thresholding based denoising. For this reason, the fuzzy filter is used on the results
of the proposed fuzzy-shrink algorithm to reduce artifacts to some extent. First, use a window of size (2 L+1) ×
(2 K+1) centered at i, j to filter the current image pixel at position i, j . Next, the similarity of neighboring
pixels to the center pixel is calculated using the following equation.
m l, k = exp −
Ys,d i, j − Ys,d i + l, j + k
Thr
2
(2)
s l, k = exp −
l2
+ k2
N
(3)
Capture Digital Leaf Image using
digital cameras or scanners
Fuzzy denoising using Dual Tree
Discrete Wavelet Transform
Boundary enhancement
Feature Extraction
Classification using Radial Basis
Function
Display Results
4. An Effective Tea Leaf Recognition Algorithm for Plant Classification Using Radial Basis.....
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss. 3 | Mar. 2014 | 38 |
where Ys,d i, j and Ys,d i + l, j + k are central coefficient and neighbor coefficients in the wavelet sub-bands,
respectively. Thr = c × σn, 3≤c≤4, σn is estimated noise variance, and N is the number of coefficients in the
local window k∈[−K…K], and l∈[−L…L].
According the two fuzzy functions, can get adaptive weight w l, k for each neighboring coefficient:
w l, k = m l, k × s l, k (4)
Using the adaptive weightsw l, k , obtain the fuzzy feature for each coefficient in the wavelet sub-bands as
follows:
f i, j =
W(l, k) × Ys,d i + l, j + kK
K=−K
L
I=−L
W(l, k)K
K=−K
L
I=−L
(5)
After finding the fuzzy feature, will form Linguistic IF-THEN rules for shrinking wavelet coefficients as
follows:
“IF the fuzzy feature f i, j is large THEN shrinkage of wavelet coefficients Ys,d(i, j) is small”.
Finally, the output of post-processing step is determined as follows:
x i, j, c =
w l, kK
K=−K
L
I=−L × x i + 1, j + k, c
w l, kk
k=−k
L
I=−L
(6)
where x is the denoised image, which can be obtained using proposed fuzzy-shrink algorithm. After the post
processing process the enhanced leaf image is obtained as a result.
C. Boundary Enhancement
The margin of a leaf is highly focused in this pre processing step. Convolving the image with a Laplacian filter
of 3 × 3 spatial mask:
0 1 0
1 −4 1
0 1 0
(7)
Fig. 2.1 Enhanced image Fig. 2.2 Boundary Enhancement
The Fig. 2 shows the enhanced image and boundary enhancement the proposed tea leaf recognition. To
make boundary as a white curve on black background, the pixel values “1” and “0” are swapped.
3.2. Feature Extraction
The proposed approach uses common Digital Morphological Features (DMFs), so that a computer can
obtain feature values quickly and automatically The features used for extraction in the proposed method is
described as follows.
5. An Effective Tea Leaf Recognition Algorithm for Plant Classification Using Radial Basis.....
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss. 3 | Mar. 2014 | 39 |
A. Physiological Length:
The distance between the two terminals is the physiological length. It is represented as 𝐿 𝑝.The red line
in the Fig. 3 indicates the physiological length of a leaf.
Fig. 3 Physiological length of a leaf
B. Physiological Width:
Drawing a line passing through the two terminals of the main vein, infinite lines can be plotted
orthogonal to that line. The number of intersection pairs between those lines and the leaf margin is also infinite.
At the physiological width, the longest distance between points of those intersection pairs is defined. It is
represented as 𝑊𝑝. As the coordinates of pixels are discrete, two lines are considered as orthogonal if their
degree is 90◦ ± 0.5◦.The red line in the Fig. 4 indicates the physiological width of a leaf.
Fig. 4 Physiological width of a leaf
C. Aspect Ratio:
The ratio of physiological length 𝐿 𝑝 to physiological width 𝑊𝑝 is called aspect ratio and it is given by,
𝐴𝑠𝑝𝑒𝑐𝑡 𝑟𝑎𝑡𝑖𝑜 =
𝐿 𝑝
𝑊𝑝
(8)
6. An Effective Tea Leaf Recognition Algorithm for Plant Classification Using Radial Basis.....
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss. 3 | Mar. 2014 | 40 |
D. Serration Angle:
The teeth angle of a leaf can be defined using serration angle.
𝜃 = 𝑎𝑟𝑐 cos
(𝑎. 𝑏)
𝑎 𝑏
(9)
Where θ is the serration angle , a is the length of first recognizable teeth from the tip of the angle and b
is the breadth of first recognizable teeth from the tip of the angle.
Fig. 5. Serration angle obtained from the tea leaf
The serration angle obtained from the tea leaf using equation 4 is shown in the Fig. 5.
E. Segment
The segment of a leaf can be defined as the ratio of first recognizable teeth in the left side from the tip
of the angle „a‟ to the first recognizable teeth in the right side from the tip of the angle „b‟.
𝑠𝑒𝑔𝑚𝑒𝑛𝑡 =
𝑎
𝑏
(10)
F. Segment maximum width to Physiological length ratio
The leaf is divided into 10 segments as shown in the Fig. 6. Each segment width to the physiological
length ratio can be determined for all the 10 segments.
Fig. 6 Segment of a leaf
G. Tip Angle
The angle which is formed from the tip of the leaf to the first recognizable teeth on either side of the
leaf is called tip angle.
7. An Effective Tea Leaf Recognition Algorithm for Plant Classification Using Radial Basis.....
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss. 3 | Mar. 2014 | 41 |
The tip angle can be calculated using the formula,
𝜃 = 𝑎𝑟𝑐 cos
(𝑎. 𝑏)
𝑎 𝑏
(11)
Where θ is the tip angle, a and b are the first recognizable teeth from the tip of the angle on left and
right side respectively. The tip angle formed from the tea leaf is shown in the Fig. 7.
Fig. 7 Tip angle obtained from the tea leaf
3.3. Classification Using Radial Basis Function
A Radial Basis Function Neural Network (RBFNN) is a special type of neural network commonly used
for classification, regression, function approximation and data clustering problems. RBFNN uses Radial Basis
Function (RBF) as its activation function.[18] Radial Basis Function Neural Network architecture is shown in
the fig. 8.
Fig 8. Radial Basis Function Neural Network Architecture
A Radial Basis Function Neural Network has three layers such as the input layer, the hidden layer and
the output layer. The input layer in the RBF brings the coordinates of the input vector to each unit in the hidden
layer. Activation produced by each unit in the hidden layer using the Radial Basis Function. Then, each unit of
the hidden layer processes a linear combination of the activations and creates a classified output in the output
layer units. The output depends on the use of the activation function used in the hidden layer and the weights
related with the links between the hidden layer and the output layer.
Several learning algorithms available for RBF neural networks . The main applications of RBF is data
classification. Majority of the learning algorithms determines the number of units in the hidden layer, the
activation functions connected with the hidden units and the weights related with the links between the hidden
and output layers.[17] The general mathematical form of the output units in RBF network is as follows:
∑
∑
∑
IP1
IP p
OP1
OP2
OP q
Weights
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𝑓𝑗 𝑥 = 𝑤𝑖,𝑗 𝑟𝑖(𝑥)
𝑖=1
(12)
where fj is the function corresponding to the jth
output unit and it is a linear combination of radial
basis functions 𝑟1, 𝑟2, ………, 𝑟 .
In this paper, supervised learning algorithm based on gradient descent with momentum is used for
training RBF neural networks.
A. Gradient Descent with Momentum
Gradient descent with momentum is implemented to train the RBF neural network to respond to the
local gradient and to recent trends in the error surface. Momentum which acts like a low pass filter, permits the
network to ignore small features in the error surface. With momentum a network can slide through a shallow
local minimum.
Gradient descent with momentum depends on two parameters. The training parameter 𝑙𝑟 indicates the
learning rate and the training parameter 𝑚𝑐 is the momentum constant that defines the amount of momentum.
mc is set between 0 and values close to 1. The value of momentum constant 1 results in a network that is
completely insensitive to the local gradient.
GDM can be used to train RBF neural network as long as its net input, weight, and transfer functions
have derivative functions. RBF is used to calculate derivatives of performance 𝑝𝑒𝑟𝑓 with respect to the weight
and bias variables 𝑋. The variable can be adjusted based on the gradient descent with momentum,
𝑑𝑋 = 𝑚𝑐 ∗ 𝑑𝑋𝑝𝑟𝑒𝑣 + 𝑙𝑟 ∗ (1 − 𝑚𝑐)
∗ 𝑑𝑝𝑒𝑟𝑓/𝑑𝑋
(13)
Where 𝑑𝑋𝑝𝑟𝑒𝑣 is the previous change to the weight or bias. Training stops when any of these conditions occurs:
If it reaches maximum number of repetitions.
The maximum amount of time is exceeded.
Performance is minimized to the goal.
The performance gradient falls below 𝑚𝑖𝑛_𝑔𝑟𝑎𝑑.
Validation functioning has increased more than 𝑚𝑎𝑥_𝑓𝑎𝑖𝑙 times since the last time it decreased.
IV. Experimental Results
The tea leaf recognition is considered in the proposed approach. The dataset used in this approach is
UPASI dataset. The performance of the proposed approach is evaluated based on the following parameters:
Accuracy and Execution time. The different tea leaves taken in the proposed approach is shown in the Table 1.
TABLE I
DIFFERENT TYPES OF TEA LEAVES TESTED IN THE PROPOSED METHOD
Tea leaf name
Tested Samples Number of Correct
Recognition
TRF 1 58 53
UPASI - 3 61 56
UPASI - 9 57 52
UPASI - 10 55 49
UPASI -17 60 53
UPASI - 22 64 61
Table 2 shows the accuracy of the classification algorithms. The accuracy of the proposed Radial Basis
Function classification approach is compared with K- Nearest Neighbour classification approach.
TABLE 2
COMPARISON OF THE CLASSIFICATION ACCURACY
Classification Techniques Accuracy (%)
Radial Basis Function 86.2
K- Nearest Neighbour 78
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It is clearly observed from the table 2 that the proposed Radial Basis Function classification approach
outperforms the K- Nearest Neighbour classification approach. The accuracy obtained by Radial Basis Function
classification is 86.2% whereas the accuracy obtained by the K- Nearest Neighbour is 78%.
TABLE 3
COMPARISON OF THE EXECUTION TIME
Classification Techniques Time (seconds)
Radial Basis Function 2.02
K- Nearest Neighbour 3.6
Table 3 reveals the execution time of the classification algorithms. The execution time of the proposed
Radial Basis Function classification approach is compared with K- Nearest Neighbour classification approach.
It is observed from the table 3 that the proposed Radial Basis Function classification approach takes
only 2.02 seconds for execution where as the K-Nearest Neighbour classification approach takes 3.6 seconds.
V. Conclusion
A new approach of tea leaf classification based on leaf recognition is proposed in this paper. The
approach consists of three phases namely the preprocessing phase, feature extraction phase and the classification
phase. The image is preprocessed by Dual Tree Discrete Wavelet Transform (DT-DWT) using fuzzy shrinkage
rule. The features of leaf are extracted and given as an input to the classifier. Radial Basis Function Neural
Network is trained using Gradient Descent with Momentum(GDM). The computer can automatically classify 6
kinds of plants via the leaf images loaded from digital cameras or scanners. The performance of the proposed
approach is evaluated based on the accuracy and execution time. The proposed algorithm produces better
accuracy and takes very less time for execution. The perfoemance of algorithm can be further improved by
incorporating efficient kernel functions and also the performance of the classifiers can be improved.
Acknowledgement
The authors acknowledge Dr. P. Mohan Kumar (Director), Dr. R. Victor J. Ilango (Sr. Botanist) and
Dr. R. Raj Kumar (Sr. Plant Physiologist), UPASI Tea Research Foundation, Valparai, India, for providing tea
leaf samples and basic taxonomical details to carry out this research.
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