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Texture Unit based Monocular Real-world Scene Classification using SOM and KN...IDES Editor
In this paper a method is proposed to discriminate
real world scenes in to natural and manmade scenes of similar
depth. Global-roughness of a scene image varies as a function
of image-depth. Increase in image depth leads to increase in
roughness in manmade scenes; on the contrary natural scenes
exhibit smooth behavior at higher image depth. This particular
arrangement of pixels in scene structure can be well explained
by local texture information in a pixel and its neighborhood.
Our proposed method analyses local texture information of a
scene image using texture unit matrix. For final classification
we have used both supervised and unsupervised learning using
K-Nearest Neighbor classifier (KNN) and Self Organizing
Map (SOM) respectively. This technique is useful for online
classification due to very less computational complexity.
Texture Unit based Approach to Discriminate Manmade Scenes from Natural Scenesidescitation
In this paper a method is proposed to discriminate
natural and manmade scenes of similar depth. Increase in
image depth leads to increase in roughness in manmade
scenes; on the contrary natural scenes exhibit smooth behavior
at higher image depth. This particular arrangement of pixels
in scene structure can be well explained by local texture
information in a pixel and its neighborhood. Our proposed
method analyses local texture information of a scene image
using texture unit matrix. For final classification we have
used unsupervised learning using Self Organizing Map
(SOM). This technique is useful for online classification due
to very less computational complexity.
SEGMENTATION USING ‘NEW’ TEXTURE FEATUREacijjournal
Color, texture, shape and luminance are the prominent features for image segmentation. Texture is an
organized group of spatial repetitive arrangements in an image and it is a vital attribute in many image
processing and computer vision applications. The objective of this work is to segment the texture sub
images from the given arbitrary image. The main contribution of this work is to introduce “NEW” texture
feature descriptor to the image segmentation field. The NEW texture descriptor labels the neighborhood
pixels of a pixel in an image as N,W,NW,NE,WW,NN and NNE(N-North, W-West).To find the prediction
value, the gradient of the intensity functions are calculated. Eight component binary vectors are formed
and compared to prediction value. Finally end up with 256 possible vectors. Fuzzy c-means clustering is
used to segment the similar regions in textural image Extensive experimentation shows that the proposed
methodology works better for segmenting the texture images, and also segmentation performance are
evaluated.
IMAGE SEGMENTATION BY USING THRESHOLDING TECHNIQUES FOR MEDICAL IMAGEScseij
Image binarization is the process of separation of pixel values into two groups, black as background and
white as foreground. Thresholding can be categorized into global thresholding and local thresholding. This
paper describes a locally adaptive thresholding technique that removes background by using local mean
and standard deviation. Most common and simplest approach to segment an image is using thresholding.
In this work we present an efficient implementation for threshoding and give a detailed comparison of
Niblack and sauvola local thresholding algorithm. Niblack and sauvola thresholding algorithm is
implemented on medical images. The quality of segmented image is measured by statistical parameters:
Jaccard Similarity Coefficient, Peak Signal to Noise Ratio (PSNR).
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...IDES Editor
In this paper a method is proposed to discriminate
real world scenes in to natural and manmade scenes of similar
depth. Global-roughness of a scene image varies as a function
of image-depth. Increase in image depth leads to increase in
roughness in manmade scenes; on the contrary natural scenes
exhibit smooth behavior at higher image depth. This particular
arrangement of pixels in scene structure can be well explained
by local texture information in a pixel and its neighborhood.
Our proposed method analyses local texture information of a
scene image using texture unit matrix. For final classification
we have used both supervised and unsupervised learning using
K-Nearest Neighbor classifier (KNN) and Self Organizing
Map (SOM) respectively. This technique is useful for online
classification due to very less computational complexity.
Texture Unit based Approach to Discriminate Manmade Scenes from Natural Scenesidescitation
In this paper a method is proposed to discriminate
natural and manmade scenes of similar depth. Increase in
image depth leads to increase in roughness in manmade
scenes; on the contrary natural scenes exhibit smooth behavior
at higher image depth. This particular arrangement of pixels
in scene structure can be well explained by local texture
information in a pixel and its neighborhood. Our proposed
method analyses local texture information of a scene image
using texture unit matrix. For final classification we have
used unsupervised learning using Self Organizing Map
(SOM). This technique is useful for online classification due
to very less computational complexity.
SEGMENTATION USING ‘NEW’ TEXTURE FEATUREacijjournal
Color, texture, shape and luminance are the prominent features for image segmentation. Texture is an
organized group of spatial repetitive arrangements in an image and it is a vital attribute in many image
processing and computer vision applications. The objective of this work is to segment the texture sub
images from the given arbitrary image. The main contribution of this work is to introduce “NEW” texture
feature descriptor to the image segmentation field. The NEW texture descriptor labels the neighborhood
pixels of a pixel in an image as N,W,NW,NE,WW,NN and NNE(N-North, W-West).To find the prediction
value, the gradient of the intensity functions are calculated. Eight component binary vectors are formed
and compared to prediction value. Finally end up with 256 possible vectors. Fuzzy c-means clustering is
used to segment the similar regions in textural image Extensive experimentation shows that the proposed
methodology works better for segmenting the texture images, and also segmentation performance are
evaluated.
IMAGE SEGMENTATION BY USING THRESHOLDING TECHNIQUES FOR MEDICAL IMAGEScseij
Image binarization is the process of separation of pixel values into two groups, black as background and
white as foreground. Thresholding can be categorized into global thresholding and local thresholding. This
paper describes a locally adaptive thresholding technique that removes background by using local mean
and standard deviation. Most common and simplest approach to segment an image is using thresholding.
In this work we present an efficient implementation for threshoding and give a detailed comparison of
Niblack and sauvola local thresholding algorithm. Niblack and sauvola thresholding algorithm is
implemented on medical images. The quality of segmented image is measured by statistical parameters:
Jaccard Similarity Coefficient, Peak Signal to Noise Ratio (PSNR).
An evaluation of two popular segmentation algorithms, the mean shift-based segmentation algorithm and a graph-based segmentation scheme. We also consider a hybrid method which combines the other two methods.
Marker Controlled Segmentation Technique for Medical applicationRushin Shah
Medical image segmentation is a very important field for the medical science. In medical images, edge detection is an important work for object recognition of the human organs such as brain, heart or kidney etc. and it is an essential pre-processing step in medical image segmentation.
Medical images such as CT, MRI or X-Ray visualizes the various information’s of internal organs which is very important for doctors diagnoses as well as medical teaching, learning and research.
It is a tough job to locate the internal organs if images contains noise or rough structure of human body organs.
This is about Image segmenting.We will be using fuzzy logic & wavelet transformation for segmenting it.Fuzzy logic shall be used because of the inconsistencies that may occur during segementing or
Improvement oh the recognition rate by random forestYoussef Rachidi
In this paper; we introduce a system of automatic recognition of characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal and of the Zernike moment. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the multi-layer perceptron (MLP) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures.
Improvement of the Recognition Rate by Random ForestIJERA Editor
In this paper; we introduce a system of automatic recognition of characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal and of the Zernike moment. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the multi-layer perceptron (MLP) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures
Image segmentation is an important image processing step, and it is used everywhere if we want to analyze what is inside the image. Image segmentation, basically provide the meaningful objects of the image.
Image fusion is an image enhancement approach for increasing the visual perception from two or more image into a single image. Each image is obtained from different object in focus. This process is now broadly used in various application of image processing such as medical imaging such as MRI, CT [18] and PET, remote sensing, satellite imaging, in design of intelligent robot etc. In this paper we have gone through the literature work done by the various researchers to obtain high quality improved image by combining important and desirable features from two or more images into a single image. Different image fusion rules, like maximum selection scheme, weighted average scheme and window based verification scheme are discussed. We also discuss about the image fusion techniques using DWT. Distinct blurred images are fused using DWT, SWT and using local correlation and their results are compared. The role of fusion in image enhancement is also taken into consideration. The results of different fusion technique are also compared, which are furnished in pictures and tables.
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An evaluation of two popular segmentation algorithms, the mean shift-based segmentation algorithm and a graph-based segmentation scheme. We also consider a hybrid method which combines the other two methods.
Marker Controlled Segmentation Technique for Medical applicationRushin Shah
Medical image segmentation is a very important field for the medical science. In medical images, edge detection is an important work for object recognition of the human organs such as brain, heart or kidney etc. and it is an essential pre-processing step in medical image segmentation.
Medical images such as CT, MRI or X-Ray visualizes the various information’s of internal organs which is very important for doctors diagnoses as well as medical teaching, learning and research.
It is a tough job to locate the internal organs if images contains noise or rough structure of human body organs.
This is about Image segmenting.We will be using fuzzy logic & wavelet transformation for segmenting it.Fuzzy logic shall be used because of the inconsistencies that may occur during segementing or
Improvement oh the recognition rate by random forestYoussef Rachidi
In this paper; we introduce a system of automatic recognition of characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal and of the Zernike moment. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the multi-layer perceptron (MLP) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures.
Improvement of the Recognition Rate by Random ForestIJERA Editor
In this paper; we introduce a system of automatic recognition of characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal and of the Zernike moment. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the multi-layer perceptron (MLP) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures
Image segmentation is an important image processing step, and it is used everywhere if we want to analyze what is inside the image. Image segmentation, basically provide the meaningful objects of the image.
Image fusion is an image enhancement approach for increasing the visual perception from two or more image into a single image. Each image is obtained from different object in focus. This process is now broadly used in various application of image processing such as medical imaging such as MRI, CT [18] and PET, remote sensing, satellite imaging, in design of intelligent robot etc. In this paper we have gone through the literature work done by the various researchers to obtain high quality improved image by combining important and desirable features from two or more images into a single image. Different image fusion rules, like maximum selection scheme, weighted average scheme and window based verification scheme are discussed. We also discuss about the image fusion techniques using DWT. Distinct blurred images are fused using DWT, SWT and using local correlation and their results are compared. The role of fusion in image enhancement is also taken into consideration. The results of different fusion technique are also compared, which are furnished in pictures and tables.
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EFFICIENT IMAGE RETRIEVAL USING REGION BASED IMAGE RETRIEVALsipij
Early image retrieval techniques were based on textual annotation of images. Manual annotation of images
is a burdensome and expensive work for a huge image database. It is often introspective, context-sensitive
and crude. Content based image retrieval, is implemented using the optical constituents of an image such
as shape, colour, spatial layout, and texture to exhibit and index the image. The Region Based Image
Retrieval (RBIR) system uses the Discrete Wavelet Transform (DWT) and a k-means clustering algorithm
to segment an image into regions. Each region of the image is represented by a set of optical
characteristics and the likeness between regions and is measured using a particular metric function on
such characteristics
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.
Local Phase Oriented Structure Tensor To Segment Texture Images With Intensit...CSCJournals
This paper proposed the active contour based texture image segmentation scheme using the linear structure tensor and tensor oriented steerable Quadrature filter. Linear Structure tensor (LST) is a popular method for the unsupervised texture image segmentation where LST contains only horizontal and vertical orientation information but lake in other orientation information and also in the image intensity information on which active contour is dependent. Therefore in this paper, LST is modified by adding intensity information from tensor oriented structure tensor to enhance the orientation information. In the proposed model, these phases oriented features are utilized as an external force in the region based active contour model (ACM) to segment the texture images having intensity inhomogeneity and noisy images. To validate the results of the proposed model, quantitative analysis is also shown in terms of accuracy using a Berkeley image database.
Image Segmentation Using Pairwise Correlation ClusteringIJERA Editor
A pairwise hypergraph based image segmentation framework is formulated in a supervised manner for various images. The image segmentation is to infer the edge label over the pairwise hypergraph by maximizing the normalized cuts. Correlation clustering which is a graph partitioning algorithm, was shown to be effective in a number of applications such as identification, clustering of documents and image segmentation.The partitioning result is derived from a algorithm to partition a pairwise graph into disjoint groups of coherent nodes. In the pairwise correlation clustering, the pairwise graph which is used in the correlation clustering is generalized to a superpixel graph where a node corresponds to a superpixel and a link between adjacent superpixels corresponds to an edge. This pairwise correlation clustering also considers the feature vector which extracts several visual cues from a superpixel, including brightness, color, texture, and shape. Significant progress in clustering has been achieved by algorithms that are based on pairwise affinities between the datasets. The experimental results are shown by calculating the typical cut and inference in an undirected graphical model and datasets.
Density Driven Image Coding for Tumor Detection in mri ImageIOSRjournaljce
The significant of multi spectral band resolution is explored towards selection of feature coefficients based on its energy density. Toward the feature representiaon in transformed domain, multi wavelet transformations were used for finer spectral representation. However, due to a large feature count these features are not optimal under low resource computing system. In the recognition units, running with low resources a new coding approach of feature selection, considering the band spectral density is developed. The effective selection of feature element, based on its spectral density achieve two objective of pattern recognition, the feature coefficient representiaon is minimized, hence leading to lower resource requirement, and dominant feature representation, resulting in higher retrieval performance.
Performance of Efficient Closed-Form Solution to Comprehensive Frontier Exposureiosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Image Registration using NSCT and Invariant MomentCSCJournals
Image registration is a process of matching images, which are taken at different times, from different sensors or from different view points. It is an important step for a great variety of applications such as computer vision, stereo navigation, medical image analysis, pattern recognition and watermarking applications. In this paper an improved feature point selection and matching technique for image registration is proposed. This technique is based on the ability of nonsubsampled contourlet transform (NSCT) to extract significant features irrespective of feature orientation. Then the correspondence between the extracted feature points of reference image and sensed image is achieved using Zernike moments. Feature point pairs are used for estimating the transformation parameters mapping the sensed image to the reference image. Experimental results illustrate the registration accuracy over a wide range for panning and zooming movement and also the robustness of the proposed algorithm to noise. Apart from image registration proposed method can be used for shape matching and object classification. Keywords: Image Registration, NSCT, Contourlet Transform, Zernike Moment.
SEMANTIC IMAGE RETRIEVAL USING MULTIPLE FEATUREScscpconf
In Content Based Image Retrieval (CBIR) some problem such as recognizing the similar
images, the need for databases, the semantic gap, and retrieving the desired images from huge
collections are the keys to improve. CBIR system analyzes the image content for indexing,
management, extraction and retrieval via low-level features such as color, texture and shape.
To achieve higher semantic performance, recent system seeks to combine the low-level features
of images with high-level features that conation perceptual information for human beings.
Performance improvements of indexing and retrieval play an important role for providing
advanced CBIR services. To overcome these above problems, a new query-by-image technique
using combination of multiple features is proposed. The proposed technique efficiently sifts through the dataset of images to retrieve semantically similar images.
Modified adaptive bilateral filter for image contrast enhancementeSAT 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
An Unsupervised Change Detection in Satellite IMAGES Using MRFFCM ClusteringEditor IJCATR
This paper presents a new approach for change detection in synthetic aperture radar images by incorporating Markov random field (MRF) within the framework of FCM. The objective is to partition the difference image which is generated from multitemporal satellite images into changed and unchanged regions. The difference image is generated from log ratio and mean ratio images by image fusion technique. The quality of difference image depends on image fusion technique. In the present work; we have proposed an image fusion method based on stationary wavelet transform. To process the difference image is to discriminate changed regions from unchanged regions using fuzzy clustering algorithms. The analysis of the DI is done using Markov random field (MRF) approach that exploits the interpixel class dependency in the spatial domain to improve the accuracy of the final change-detection areas. The experimental results on real synthetic aperture radar images demonstrate that change detection results obtained by the MRFFCM exhibits less error than previous approaches. The goodness of the proposed fusion algorithm by well-known image fusion measures and the percentage correct classifications are calculated and verified.
PERFORMANCE EVALUATION OF DIFFERENT TECHNIQUES FOR TEXTURE CLASSIFICATION cscpconf
Texture is the term used to characterize the surface of a given object or phenomenon and is an
important feature used in image processing and pattern recognition. Our aim is to compare
various Texture analyzing methods and compare the results based on time complexity and
accuracy of classification. The project describes texture classification using Wavelet Transform
and Co occurrence Matrix. Comparison of features of a sample texture with database of
different textures is performed. In wavelet transform we use the Haar, Symlets and Daubechies
wavelets. We find that, thee ‘Haar’ wavelet proves to be the most efficient method in terms of
performance assessment parameters mentioned above. Comparison of Haar wavelet and Cooccurrence
matrix method of classification also goes in the favor of Haar. Though the time
requirement is high in the later method, it gives excellent results for classification accuracy
except if the image is rotated.
A comparative study of dimension reduction methods combined with wavelet tran...ijcsit
In this paper, we present a comparative study of dimension reduction methods combined with wavelet
transform. This study is carried out for mammographic image classification. It is performed in three stages:
extraction of features characterizing the tissue areas then a dimension reduction was achieved by four
different methods of discrimination and finally the classification phase was carried. We have late compared
the performance of two classifiers KNN and decision tree.
Results show the classification accuracy in some cases has reached 100%. We also found that generally the
classification accuracy increases with the dimension but stabilizes after a certain value which is
approximately d=60.
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We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
1. International Journal of Engineering and Technical Research (IJETR)
ISSN: 2321-0869, Volume-1, Issue-9, November 2013
6 www.erpublication.org
Abstract— This paper presents an unsupervised texture image
segmentation algorithm using clustering. Two criteria are
proposed in order to construct a feature space of reduced
dimensions for texture image segmentation, based on selected
Gabor filter set. An unsupervised clustering algorithm is applied
to the reduced feature space to obtain the number of clusters, i.e.
the number of texture regions [1]. A simple Euclidean distance
classification scheme is used to group the pixels into
corresponding texture regions. Experiments on a mixture of
mosaic of textures generated by random field model show the
proposed algorithm of using the clustering gives satisfactory
results in terms of the number of regions and region shapes. A
mathematical programming based clustering approach that is
applied to a digital platform of segmentation problem involving
demographic and transactional attributes related to the images.
It is the collection of sub images corresponding to different
image regions and scales is obtained. From the experiments, it is
found that the multistage sub image matching method is an
efficient way to achieve effective texture retrieval for image
segmentation.
Keywords- Image segmentation, Texture feature extraction,
sub-images matching, Clustering, Texture segmentation, Gabor
filter set.
I. INTRODUCTION
The multistage approach to extract colour features using
the Multistage Colour Coherence Vector (MCCV), manages
to handle the problem of sub-image colour retrieval
effectively. The idea of multistage feature extraction
technique has found much success in the area of colour
analysis. The multistage approach can be used to extract
texture information using any texture feature extraction
method, the Discrete Wavelet Frames (DWF). Wavelet-based
feature extractors are chosen because of their high
classification accuracy and low computational load [1].
In this fundamental process in digital image processing
which has found extensive application in areas such as image
retrieval, medical image processing, and remote sensing [2].
Texture is an essential key to the segmentation of image and
originates from the scale dependent spatial variability, and is
observed as the fluctuation of gray levels in images [3]. It is
well known that another source of the fluctuations exists in
images. Even though the human interpreter often is superior in
identifying strange details and phenomena in images, there is
still a need to automate this process.
Manuscript received November 01, 2013.
Mr.S.Raja is an Assistant Prof. of CSE, Panimalar Institute of Technolog
y, Chennai, TamilNadu, India.
Dr.S.Sankar is a Faculty in Department of EEE, Panimalar Institute of
Technology,Chennai.Tamil Nadu ,India.
Dr.S.Saravanakumar is a Prof. of IT, Panimalar Institute of Technology,
Chennai, TamilNadu, India.
The clustering leads to the concept of unsupervised
segmentation or classification of images. The notion of scale
gives an important hint to texture analysis. Wavelet packet
transform have received much attention as a promising tool
for texture analysis, because they have the ability to examine a
signal at different scales. The texture feature set derived from
the wavelet de-composition where the sampling between
wavelet levels is omitted is superior to that derived from the
standard wavelet decomposition [4], [5].The trend probably
therefore seems to be a concentration on the wavelet packet
transform which basically is the wavelet transform with sub
band decompositions not restricted to be dyadic. The discrete
wavelet packet transform are critically sampled multi rate
filter banks. However, critically sampled filter banks typically
imply inaccurate texture edge localization. Therefore, in this
paper, wavelet packet decomposition is employed to extract
local texture features from image.
From the sub-band filtering point of view, the difference
between the wavelet packet transform and the conventional
wavelet transform is that the former also recursively
decomposes the high frequency components, thus
constructing tree structured multi-band extension of the
wavelet transform wavelet packet transform decomposes an
image into sub images[6,7]. In the Fig.1 shows the standard
decomposition. H and L in Fig. 1 denote a high pass and low
pass filter respectively. The approximated image LL is
obtained by low pass filtering in both row and column
directions. The detail images, LH, HL, and HH, contain high
frequency components.
Fig.1 Decomposition of images into sub-images
Sub-image classification is done by indexing sub regions
and no specific objects. The image is partitioned in
overlapping rectangles of different sizes and this allows
dealing with the different scales of objects in the image. Each
of these regions is represented by global information and, for
efficiency reasons, instead of storing the whole global
features, only the differences of the feature vectors are stored.
The segmentation process is repeated at several different
resolutions or scales of the image. The result of this process is
a collection of sub-images each corresponding to different
regions of the parent image.
Investigation of Compressed Image Segmentation
S.Raja, S.Sankar, S.Saravanakumar
2. Investigation of Compressed Image Segmentation
7 www.erpublication.org
A. Sub-Image Querying
The sub-image querying problem can be defined as
follows: Given as input query a sub-image Q of an image I and
an image set S, retrieve from S those images Q’ in which
query Q appears (denoted Q<= Q’).The problem is made
more difficult than image retrieval by a wide variety of effects
(such as changing viewpoint, camera noise, etc.) that cause
the same object to appear different in different images . The
patterns belonging to each category will form a cluster in the
feature space which is compact and isolated from clusters
corresponding to other texture categories.
First stage: original image
Sub-image tilling
Fig.2 Sub-image querying
The above condition however might be suitable for
colour features, but might not work for texture features. By
non-linearly re-scaling the resolution of the input image, the
texture characteristics of the image might be affected, and
features extracted from it might not reflects an accurate
representation of the texture, hence resulting in incorrect
retrieval. Because of this, multiscale resolution is used from
the original size of the input image in the proposed algorithm.
Because of the possibly non-dyadic nature of the image size,
the algorithm ends up with a collection of slightly overlapping
(64×64) sub-images at the current resolution.
A suitable texture feature extraction can be performed
on all sub-images separately. Each time a feature vector of a
sub-image is produced, it is added to the final feature vector,
which represents the multiscale feature vector of the original
image. After all sub-images of the current scale have been
processed, the image is linearly rescaled and the whole
process is repeated.
B. Salient Location Identification
The wavelet decomposition is used because of its
computational simplicity. The decomposition algorithm is
based on the non-standard decomposition procedure.
Input image Salient Location Neighborhood
Identification Extraction
Sub-image
Descriptor Assignment
Fig.3 Neighborhood identification and extraction
The wavelet decomposition of the feature extraction
process from the sub-images are obtained from the
neighborhood region, and the descriptor vectors are
computed from each sub-image.
An input image yields three coefficient matrices, namely
the Horizontal (H ), Vertical (V ), Diagonal (D ), and the
compressed image to a quarter of the size of the original
image. The decomposition can be recursively performed by
using the output image from the previous iteration as the
input. At each decomposition level, wavelet coefficient
matrices are combined into one signal matrix S:
S (n, m) = | H (n, m)| +|V (n, m)|+|D (n, m) | (1)
The salient locations are identified by each element of the
last signal matrix (last wavelet decomposition) to the
untransformed input image. The localization approach
determines the location of a android when it travels near the
place that a reference image was taken as in (1). Hence, the
current query image is different from the corresponding
reference image by certain rotation angles. The neighborhood
region around each salient location is extracted to make it
rotationally invariant using the coordinates transformation in
equation (2). As a result, the sub-images of the same salient
point extracted from this method are having the same
appearance regardless of rotational deviations from an
original image.
Xn=Xf+ (u-M/2) cosaf (2)
Yn=Yf+ (v-M/2) sinaf
Where (u, v) are the transformed (new) coordinates,
(xn,yn) are untransformed coordinates in the original image,
and (fx, fy) is the coordinate of the salient point af=tan
-1 (
Xf / Yf
).
Classically, image segmentation is defined as the partitioning
of an image into overlapping, constituent regions which are
homogeneous with respect to some characteristic such as
intensity or texture. If the domain of the image is given by I,
then the segmentation problem is to determine the sets S<=I
whose union is the entire image I.
II. BACK PROPOGATION SYSTEM
In this back-propagation algorithm is a widely used learning
algorithm in Artificial Neural Networks. The Feed-Forward
3. International Journal of Engineering and Technical Research (IJETR)
ISSN: 2321-0869, Volume-1, Issue-9, November 2013
8 www.erpublication.org
Neural Network architecture is capable of approximating
most problems with high accuracy and generalization ability.
This algorithm is based on the error-correction learning rule.
Error propagation consists of two passes through the different
layers of the network, a forward pass and a backward pass. In
the forward pass the input vector is applied to the sensory
nodes of the network and its effect propagates through the
network layer by layer. The actual response of the network is
subtracted from the desired response to produce an error
signal. This error signal is then propagated backward through
the network against the direction of synaptic conditions. The
synaptic weights are adjusted to make the actual response of
the network move closer to the desired response.
Fig.4. Input layer Hidden layers Output layer
The back propagation neural network is essentially a
network of simple processing elements working together to
produce a complex output. These elements or nodes are
arranged into different layers: input, middle and output. The
output from a back propagation neural network is computed
using a procedure known as the forward pass. The circuit of
propagation network is as shown in the Fig.4. The pictorial
representation of different steps as follows;
1.The input layer propagates a particular input vector’s
components to each node in the hidden layers.
2. Hidden layer nodes compute output values, which become
inputs to the nodes of the output layer.
3. The output layer nodes compute the network output for the
particular input vector. The forward pass produces an
output vector for a given input vector based on the current
state of the network weights.
4.Since the network weights are initialized to random
values, it is unlikely that reasonable outputs will result
before training. The weights are adjusted to reduce the error
by propagating the output error backward through the
network. This process is where the back propagation neural
network gets its name and is known as the backward pass:
5. Compute error values for each node in the output layer.
This can be computed because the desired output for each
node is known.
6. Compute the error for the middle layer nodes. This is done
by attributing a portion of the error at each output layer node
to the middle layer node, which feed that output node. The
amount of error due to each middle layer node depends on the
size of the weight assigned to the connection between the two
nodes.
7. Adjust the weight values to improve network performance
using the Delta rule.
8. Compute the overall error to test network performance.
The training set is repeatedly presented to the network
and the weight values are adjusted until the overall error is
below a predetermined tolerance. Since the Delta rule follows
the path of greatest decent along the error surface, local
minima can impede training. The momentum term
compensates for this problem to some degree.
Neighborhoods from the sub-band images and with the same
centre coordinates are represented. This process is repeated
for all unknown wavelet coefficients at the highest level. Once
all of the wavelet coefficients have been generated, the
synthesised image is inverse transformed to give an image that
should resemble that of the sample texture.
Note that, in order to avoid problems with boundary
conditions, it is necessary to pad each sub image with zeros
before performing the algorithm. This padding should be
removed prior to inverse transform.
Input image low poss 75 degree 45 degree
15 degree -15 degree -45 degree -75 degree
Fig.5 Synthesized image transformation
The unsupervised texture segmentation algorithm, in
terms of low computational load is considerable. The Fig.5 of
size m * n and the image to be synthesised Is of size M * N. Let
p 2 Is be a pixel to be synthesised and let w be the width of the
neighbourhood of the square spatial neighbourhood
surrounding p. For each pixel to be synthesised in algorithm
needs to search up to nm locations.
At each of those locations, 4w2 operations need to be
performed to calculate the weighted sum squared difference.
This is 4nmw2 operations in total for each searched region.
Therefore to generate Is of size M* N the algorithm will have
to perform 4NMnmw2
operations. In comparison, the
algorithm proposed in this paper synthesises texture at the
level of the complex wavelet transform.
At this level the dimensions of the sample image are
nm=16 and the image to be synthesised are NM=16. Since all
the sub images at this level must be searched, the total number
of operations is given as NM=16 /nm=16 /4w1
2
*6. Here w1 is
the neighbourhood size for this process and is typically
smaller than the needed.
The multi-resolution compositing technique is to turn
into an image which is a combination of an original image and
a synthetic texture derived from target; the combination is
controlled by a mask. The algorithm remains the same as
before, except that at each iteration of the algorithm, the
original is composite back into the noise image according to
the mask using that avoids blurring and aliasing.
For nonuniform textures, the texels within a region may
vary randomly in orientation and the position and shape of the
texels may be perturbed by the end of the algorithm, noise will
have been converted into a synthetic texture. First,
4. Investigation of Compressed Image Segmentation
9 www.erpublication.org
Match-Histogram is used to force the intensity distribution of
noise to match the intensity distribution of target. Then an
analysis is constructed from the target texture. The pyramid
representation can be chosen to capture features of various
sizes by using the features of various orientations. Then, the
noise are modified in an iterative manner. At each iterations, a
synthesis is constructed from noise, and Match-Histogram is
done on each of the sub-bands of the synthesis and analysis.
Fig.6 matching of sub-textures.
In the Fig. 6, shows that sub-texture field corresponding
to the concrete between the bricks. That show some of the
connected components have been used to generate this
sub-texture field left of Fig 6. The sub-texture fields are
synthesized in preprocess by using any existing texture
synthesis technique.
III.RESULTS AND DISCUSSIONS
In this section, the results obtained with the previously
described texture synthesis and sub image matching
technique. Fig.5 illustrates an example of synthesis result. The
top row shows from left to right: the input, the resulting cells
and the corresponding mesh. The second row shows the
arrangement map and the resulting synthesized mesh, that row
is the resulting texture for a low (left) and high (right) amount
of classes. Using a high amount of classes causes connected
components to be very small, which results in nearly no
computed sub textures. The left column represents the model,
the middle shows existing techniques from top to bottom:
texture quilting the feature matching synthesis technique of
pixel the right column the result shows. In all cases, the
pre-processing time required only once for a given texture is
below m*n this includes segmentation, texture-mesh
generation and sub texture field synthesis.
Simulations have been carried out using a database of
aquatic images. The proposed scheme for indexing the still
texture content is employed on the image database.
IV. CONCLUSIONS
This method of each textured regions are obtained.
Simulation results show that the proposed algorithm is
effective in texture image retrieval on the bases of texture
content. The segmentation and it allows efficient proposed
algorithm can be applied to large real-world image database
for efficient textured-based image indexing. In our
propagation algorithm, features of the segmented regions are
calculated as the mean of features of all blocks it contains,
features more efficient for indexing purpose can be found
later.
BIOGRAPHY
Mr.S.Raja completed his B.Tech degree in
Information Technology at Sri Balaji Chokalingam
Engineering College, Arni and M.Tech degree in
Computer Science and Engineering at Dr.M.G.R
University, Chennai. He has presented number of
papers in National and International Conferences.
His areas of interests are Computer Networks, Data
Mining, Java Programming & Database
Technologies. He is working as an Assistant
Professor in Department of Computer Science and
Engineering at Panimalar Institute of Technology,
Chennai.
Dr.S.Sankar obtained his B.E Degree in Electrical
& Electronics Engineering at Sri Venkateswara
College of Engineering, from Madras University
and M.E (Power System) Degree from Annamalai
University Chidambaram. He has done his Ph.D in
the area of FACTS controllers in 2011. His
research interests are in the area of FACTS,
Electrical Machines, Voltage stability, power
quality, Power system security and Power System
Analysis.
Dr S.SARAVANAKUMAR has more than 10
years of teaching and research experience. He did
his Postgraduate in ME in Computer Science and
Engineering at Bharath engineering college,anna
university chennai, and Ph.D in Computer Science
and Engineering at Bharath University, Chennai.
He has guiding a number of research scholars in the
area Adhoc Network, ANN, Security in Sensor
Networks, Mobile Database and Data Mining
under Bharath University Chennai, , Sathayabama
University and Bharathiyar University.
References
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4.B. S. Manjunath and W. Y. Ma, "Texture Features for Browsing
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using Color, Texture and Shape", In SPIE
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