The document lists potential final year projects in computer science and engineering and provides details about some of the projects. It discusses projects on 3D facial modeling, wood defect recognition using self organizing maps, image recovery and denoising using contourlet transforms, texture segmentation using wavelets and rotational invariant moments, transmitter placement optimization using genetic algorithms, surveillance robots for human tracking with neural networks, software security using neural networks, secure transactions with steganography, steganalysis techniques, and texture segmentation using Gabor filters.
Image Compression Using Hybrid Svd Wdr And Svd AswdrMelanie Smith
This document discusses two new image compression techniques that combine existing techniques:
1) SVD is combined with WDR (SVD-WDR) to achieve better image quality and higher compression rate.
2) SVD is then combined with an advanced version of WDR, called ASWDR (SVD-ASWDR), again to improve image quality and compression rate. These two new hybrid techniques are tested on several images and their results, such as PSNR, MSE, and CR, are compared.
Sign Language Detection using Action RecognitionIRJET Journal
This document presents a sign language detection system using action recognition. It aims to enhance current systems' performance in terms of response time and accuracy. The proposed system uses machine learning algorithms like LSTM neural networks trained on data sets to classify sign language gestures in real-time video. It segments hand regions, extracts features, and recognizes signs with 98% accuracy for 26 gestures. The system is intended to help deaf individuals communicate through translating signs to text in real-world applications.
IRJET- Visual Question Answering using Combination of LSTM and CNN: A SurveyIRJET Journal
This document discusses using a combination of long short-term memory (LSTM) and convolutional neural networks (CNN) for visual question answering (VQA). It proposes extracting image features from CNNs and encoding question semantics with LSTMs. A multilayer perceptron would then combine the image and question representations to predict answers. The methodology aims to reduce statistical biases in VQA datasets by focusing attention on relevant image regions. It was implemented in Keras with TensorFlow using pre-trained CNNs for images and word embeddings for questions. The proposed approach analyzes local image features and question semantics to improve VQA classification accuracy over methods relying solely on language.
This document is a resume for Manoj Alwani providing his contact information, education history, professional experience, skills, projects, publications, and courses. It details that he has a M.S. in Computer Science from Stony Brook University and a B.Tech from India. His professional experience includes research roles at Element Inc and Stony Brook University focused on deep learning and computer vision. His skills and projects involve areas such as deep learning, computer vision, parallel computing, robotics, and natural language processing.
A Secure & Optimized Data Hiding Technique Using DWT With PSNR ValueIJERA Editor
Multimedia applications are becoming increasingly significant in modern world. The mushroom growth of multimedia data of these applications, particularly over the web has increased the demand for protection of copyright. Digital watermarking is much more acceptable as a solution to the problem of copyright protection and authentication of multimedia data while working in a networked environment. In this paper, a DWT based watermarking scheme is proposed. We have used Genetic Algorithm (GA) in order to make an optimum tradeoff between imperceptibility and robustness by choosing an optimum watermarking level for each coefficient of the cover image. In addition to the suitable watermarking strength, the selection of best block size is also necessary for superior perceptual shaping functions. To achieve this goal we have trained and used GA to pick the best block size to tailor the watermark in one of the coefficients of the DWT. The fitness function criterion for the genetic algorithm decision making is based on PSNR values
This document describes a novel algorithm for image steganography using discrete wavelet transformation on a Beagle Board-XM. The algorithm uses discrete wavelet transformation and a modified AES technique to encrypt and hide a secret payload image in the LH, HL, and HH subbands of a cover image. The discrete wavelet transformation decomposes the cover image into frequency subbands. The secret image is encrypted using a modified AES algorithm before being embedded. This approach aims to provide better image quality and increased security compared to other steganography methods. The algorithm is implemented using the Beagle Board-XM and Open CV for reduced processing delays, costs, and resource requirements.
Two level data security using steganography and 2 d cellular automataeSAT 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
Integrated Hidden Markov Model and Kalman Filter for Online Object Trackingijsrd.com
Visual prior from generic real-world images study to represent that objects in a scene. The existing work presented online tracking algorithm to transfers visual prior learned offline for online object tracking. To learn complete dictionary to represent visual prior with collection of real world images. Prior knowledge of objects is generic and training image set does not contain any observation of target object. Transfer learned visual prior to construct object representation using Sparse coding and Multiscale max pooling. Linear classifier is learned online to distinguish target from background and also to identify target and background appearance variations over time. Tracking is carried out within Bayesian inference framework and learned classifier is used to construct observation model. Particle filter is used to estimate the tracking result sequentially however, unable to work efficiently in noisy scenes. Time sift variance were not appropriated to track target object with observer value to prior information of object structure. Proposal HMM based kalman filter to improve online target tracking in noisy sequential image frames. The covariance vector is measured to identify noisy scenes. Discrete time steps are evaluated for identifying target object with background separation. Experiment conducted on challenging sequences of scene. To evaluate the performance of object tracking algorithm in terms of tracking success rate, Centre location error, Number of scenes, Learning object sizes, and Latency for tracking.
Image Compression Using Hybrid Svd Wdr And Svd AswdrMelanie Smith
This document discusses two new image compression techniques that combine existing techniques:
1) SVD is combined with WDR (SVD-WDR) to achieve better image quality and higher compression rate.
2) SVD is then combined with an advanced version of WDR, called ASWDR (SVD-ASWDR), again to improve image quality and compression rate. These two new hybrid techniques are tested on several images and their results, such as PSNR, MSE, and CR, are compared.
Sign Language Detection using Action RecognitionIRJET Journal
This document presents a sign language detection system using action recognition. It aims to enhance current systems' performance in terms of response time and accuracy. The proposed system uses machine learning algorithms like LSTM neural networks trained on data sets to classify sign language gestures in real-time video. It segments hand regions, extracts features, and recognizes signs with 98% accuracy for 26 gestures. The system is intended to help deaf individuals communicate through translating signs to text in real-world applications.
IRJET- Visual Question Answering using Combination of LSTM and CNN: A SurveyIRJET Journal
This document discusses using a combination of long short-term memory (LSTM) and convolutional neural networks (CNN) for visual question answering (VQA). It proposes extracting image features from CNNs and encoding question semantics with LSTMs. A multilayer perceptron would then combine the image and question representations to predict answers. The methodology aims to reduce statistical biases in VQA datasets by focusing attention on relevant image regions. It was implemented in Keras with TensorFlow using pre-trained CNNs for images and word embeddings for questions. The proposed approach analyzes local image features and question semantics to improve VQA classification accuracy over methods relying solely on language.
This document is a resume for Manoj Alwani providing his contact information, education history, professional experience, skills, projects, publications, and courses. It details that he has a M.S. in Computer Science from Stony Brook University and a B.Tech from India. His professional experience includes research roles at Element Inc and Stony Brook University focused on deep learning and computer vision. His skills and projects involve areas such as deep learning, computer vision, parallel computing, robotics, and natural language processing.
A Secure & Optimized Data Hiding Technique Using DWT With PSNR ValueIJERA Editor
Multimedia applications are becoming increasingly significant in modern world. The mushroom growth of multimedia data of these applications, particularly over the web has increased the demand for protection of copyright. Digital watermarking is much more acceptable as a solution to the problem of copyright protection and authentication of multimedia data while working in a networked environment. In this paper, a DWT based watermarking scheme is proposed. We have used Genetic Algorithm (GA) in order to make an optimum tradeoff between imperceptibility and robustness by choosing an optimum watermarking level for each coefficient of the cover image. In addition to the suitable watermarking strength, the selection of best block size is also necessary for superior perceptual shaping functions. To achieve this goal we have trained and used GA to pick the best block size to tailor the watermark in one of the coefficients of the DWT. The fitness function criterion for the genetic algorithm decision making is based on PSNR values
This document describes a novel algorithm for image steganography using discrete wavelet transformation on a Beagle Board-XM. The algorithm uses discrete wavelet transformation and a modified AES technique to encrypt and hide a secret payload image in the LH, HL, and HH subbands of a cover image. The discrete wavelet transformation decomposes the cover image into frequency subbands. The secret image is encrypted using a modified AES algorithm before being embedded. This approach aims to provide better image quality and increased security compared to other steganography methods. The algorithm is implemented using the Beagle Board-XM and Open CV for reduced processing delays, costs, and resource requirements.
Two level data security using steganography and 2 d cellular automataeSAT 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
Integrated Hidden Markov Model and Kalman Filter for Online Object Trackingijsrd.com
Visual prior from generic real-world images study to represent that objects in a scene. The existing work presented online tracking algorithm to transfers visual prior learned offline for online object tracking. To learn complete dictionary to represent visual prior with collection of real world images. Prior knowledge of objects is generic and training image set does not contain any observation of target object. Transfer learned visual prior to construct object representation using Sparse coding and Multiscale max pooling. Linear classifier is learned online to distinguish target from background and also to identify target and background appearance variations over time. Tracking is carried out within Bayesian inference framework and learned classifier is used to construct observation model. Particle filter is used to estimate the tracking result sequentially however, unable to work efficiently in noisy scenes. Time sift variance were not appropriated to track target object with observer value to prior information of object structure. Proposal HMM based kalman filter to improve online target tracking in noisy sequential image frames. The covariance vector is measured to identify noisy scenes. Discrete time steps are evaluated for identifying target object with background separation. Experiment conducted on challenging sequences of scene. To evaluate the performance of object tracking algorithm in terms of tracking success rate, Centre location error, Number of scenes, Learning object sizes, and Latency for tracking.
Image Captioning Generator using Deep Machine Learningijtsrd
Technologys scope has evolved into one of the most powerful tools for human development in a variety of fields.AI and machine learning have become one of the most powerful tools for completing tasks quickly and accurately without the need for human intervention. This project demonstrates how deep machine learning can be used to create a caption or a sentence for a given picture. This can be used for visually impaired persons, as well as automobiles for self identification, and for various applications to verify quickly and easily. The Convolutional Neural Network CNN is used to describe the alphabet, and the Long Short Term Memory LSTM is used to organize the right meaningful sentences in this model. The flicker 8k and flicker 30k datasets were used to train this. Sreejith S P | Vijayakumar A "Image Captioning Generator using Deep Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42344.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42344/image-captioning-generator-using-deep-machine-learning/sreejith-s-p
ON THE IMAGE QUALITY AND ENCODING TIMES OF LSB, MSB AND COMBINED LSB-MSBijcsit
The Least Significant Bit (LSB) algorithm and the Most Significant Bit (MSB) algorithm are stenography algorithms with each one having its demerits. This work therefore proposed a Hybrid approach and compared its efficiency with LSB and MSB algorithms. The Least Significant Bit (LSB) and Most
Significant Bit (MSB) techniques were combined in the proposed algorithm. Two bits (the least significant bit and the most significant bit) of the cover images were replaced with a secret message. Comparisons were made based on Mean-Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and the encoding time between the proposed algorithm, LSB and MSB after embedding in digital images. The combined
technique produced a stego-image with minimal distortion in image quality than MSB technique independent of the nature of data that was hidden. However, LSB algorithm produced the best stego-image quality. Large cover images however made the combined algorithm’s quality better improved. The combined algorithm had lesser time of image and text encoding. Therefore, a trade-off exists between the encoding time and the quality of stego-image as demonstrated in this work.
IRJET- Spot Me - A Smart Attendance System based on Face RecognitionIRJET Journal
The article discusses international issues. It mentions that globalization has increased economic interdependence between nations while also raising tensions over immigration and trade. Solutions will require cooperation and compromise and a recognition that isolationism is not a viable strategy in an interconnected world.
Sign Language Recognition using Deep LearningIRJET Journal
The document discusses using deep learning techniques like MobileNet V2 to develop a model for sign language recognition. It aims to classify sign language gestures to help communicate with deaf people. The model was trained on a dataset of sign language images and achieved an accuracy of 70% in recognizing letters, numbers, and gestures.
AN ANN BASED BRAIN ABNORMALITY DETECTION USING MR IMAGEScscpconf
The Main purpose of this paper is to design, implement and evaluate a strong automatic diagnostic system that increases the accuracy of tumor diagnosis in brain using MR images.This presented work classifies the brain tissues as normal or abnormal automatically, usingcomputer vision. This saves lot of radiologist time to carryout monotonous repeated job. The
acquired MR images are processed using image preprocessing techniques. The preprocessed images are then segmented, and the various features are extracted. The extracted features are
fed to the artificial neural network as input that trains the network using error back propagation algorithm for correct decision making.
IRJET - Threat Prediction using Speech AnalysisIRJET Journal
This document describes a proposed system to analyze audio files and detect potential threats by performing speech recognition and sentiment analysis. The system would have three main steps: 1) Convert speech to text using machine learning algorithms like recurrent neural networks, 2) Perform sentiment analysis on the text using natural language processing techniques like naïve Bayes classification to determine if the text is positive, negative, or neutral, 3) Calculate an overall threat percentage and issue a warning if it exceeds a threshold. The goal is to automate audio surveillance and analysis to reduce time and human error compared to manual processes. Artificial neural networks would be used for both speech recognition and sentiment classification.
SIGNIFICANCE OF RATIONAL 6TH ORDER DISTORTION MODEL IN THE FIELD OF MOBILE’S ...P singh
The document discusses a proposed method for video watermarking that uses spatial and frequency domain techniques for embedding watermark information, and tests the method's robustness against rational 6th order distortion. The key steps are: (1) extracting frames from a video and selecting the highest entropy frame, (2) using spread spectrum and LSB techniques to embed a watermark in the spatial domain and DWT in the frequency domain, (3) applying rational 6th order distortion to test the effect on the watermarked video, (4) calculating metrics like correlation, SSIM, PSNR, BER and MSE to evaluate the method and detect the watermark from the distorted video. The results show the values of correlation and SSIM
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Comparative Performance of Image Scrambling in Transform Domain using Sinusoi...CSCJournals
With the rapid development of technology, and the popularization of internet, communication is been greatly promoted. The communication is not limited only to information but also includes multimedia information like digital Images. Therefore, the security of digital images has become a very important and practical issue, and appropriate security technology is used for those digital images containing confidential or private information especially. In this paper a novel approach of Image scrambling has been proposed which includes both spatial as well as Transform domain. Experimental results prove that correlation obtained in scrambled images is much lesser then the one obtained in transformed images.
IRJET- Chest Abnormality Detection from X-Ray using Deep LearningIRJET Journal
This document proposes using a convolutional neural network (CNN) to detect abnormalities in chest x-rays. It discusses developing a CNN model with an input of chest x-ray images labeled as normal or abnormal. The model would use techniques like pre-processing, data augmentation, and a network architecture with convolutional and pooling layers to classify images as normal or abnormal. The goal is to build an accurate system for detecting various chest diseases from x-ray images to help doctors with diagnosis.
IRJET- Chest Abnormality Detection from X-Ray using Deep LearningIRJET Journal
This document summarizes a research paper that developed a deep learning system to detect abnormalities in chest x-rays. The system used a convolutional neural network (CNN) trained on 5000 chest x-ray images labeled as normal or abnormal. The CNN architecture included convolutional and pooling layers. Pre-processing techniques like histogram equalization and data augmentation like image flipping were used. The trained CNN could classify x-rays as normal, abnormal, or detect 14 specific diseases with 1% error rate. While large datasets improve accuracy, they also increase training time. The authors conclude CNN is effective for chest x-ray analysis but note room for improvements like including more diseases and optimized architectures.
A SURVEY ON DEEP LEARNING METHOD USED FOR CHARACTER RECOGNITIONIJCIRAS Journal
The field of Artificial Intelligence is very fashionable today, especially neural networks that work well in various areas such as speech recognition and natural language processing. This Research Article briefly describes how deep learning models work and what different techniques are used in text recognition. It also describes the great progress that has been made in the field of medicine, the analysis of forensic documents, the recognition of license plates, banking, health and the legal industry. The recognition of handwritten characters is one of the research areas in the field of artificial intelligence. The individual character recognition has a higher recognition accuracy than the complete word recognition. The new method for categorizing Freeman strings is presented using four connectivity events and eight connectivity events with a deep learning approach.
Real-Time Pertinent Maneuver Recognition for SurveillanceIRJET Journal
This document presents a real-time system for recognizing pertinent maneuvers and detecting weapons using video surveillance. The system uses a ResNet-34 model trained on the Kinetics 400 dataset to recognize human activities in real-time video streams. It also uses a YOLOv4 model trained on a custom dataset to detect weapons. The ResNet-34 model can analyze still images and live video streams to classify activities. YOLOv4 is used for fast and accurate object detection of weapons. The system is designed to be deployed in CCTV surveillance to recognize activities and detect if individuals are carrying weapons. It aims to provide timely notifications or advanced user information for real-time monitoring.
SIGN LANGUAGE INTERFACE SYSTEM FOR HEARING IMPAIRED PEOPLEIRJET Journal
The document describes a proposed sign language interface system for hearing impaired people. The system aims to use machine learning algorithms like convolutional neural networks to classify hand gestures captured by a webcam into corresponding letters or words. The system would preprocess the images, extract features, then use a trained CNN model to predict the sign and output it as text and speech for better understanding by users. The goal is to help bridge communication between deaf/mute and normal people without requiring specialized gloves or sensors.
This document describes a wearable AI device that uses computer vision and speech synthesis to help blind individuals. The device uses a Raspberry Pi with a camera to perform three main functions: facial recognition using convolutional neural networks and linear discriminant analysis, optical character recognition (OCR) to convert text to speech using a text-to-speech system, and object detection. The facial recognition and text are conveyed to the blind user through a speaker. The system is designed to be portable and help blind people identify faces, read text, and detect objects to assist them in daily life.
Partial Object Detection in Inclined Weather ConditionsIRJET Journal
This document provides a comprehensive analysis of imbalance problems in object detection. It presents a taxonomy to classify different types of imbalances and discusses solutions proposed in literature. The analysis highlights significant gaps including existing imbalances that require further attention, as well as entirely new imbalances that have never been addressed before. A survey of imbalance problems caused by weather conditions and common object imbalances is conducted. Methods for addressing imbalances include data augmentation using GANs and balancing training based on class performance.
A Review of Comparison Techniques of Image SteganographyIOSR Journals
This document reviews and compares three common techniques for hiding information in digital images: Least Significant Bit (LSB) steganography, Discrete Cosine Transform (DCT) steganography, and Discrete Wavelet Transform (DWT) steganography. LSB is implemented in the spatial domain by replacing the least significant bits of cover image pixels with payload bits. DCT and DWT are implemented in the frequency domain by transforming the cover image and embedding payload bits in the transformed coefficients. The document evaluates and compares the performance of these three techniques based on metrics like mean squared error, peak signal-to-noise ratio, embedding capacity, and robustness.
IRJET- Review on Text Recognization of Product for Blind Person using MATLABIRJET Journal
This document summarizes a research paper that proposes a system to help blind people read text on product labels and documents using a camera and MATLAB software. The system uses image processing techniques like converting images to grayscale, binarization, and filtering to isolate text from complex backgrounds. It then applies optical character recognition to identify the text and provide information to blind users. The proposed system aims to address limitations of prior methods that struggled with non-horizontal text, complex backgrounds, and positioning objects in the camera view. It extracts a region of interest around a product using motion detection and recognizes text regardless of orientation.
A Study on Surf & Hog Descriptors for Alzheimer’s Disease DetectionIRJET Journal
This document presents a study on using SURF and HOG descriptors to detect Alzheimer's disease from brain MRI scans. It proposes combining SURF and HOG features to match keypoints in MRI images for improved accuracy over using SURF alone. Performance is evaluated using measures like sensitivity, specificity, and ROC. Results show the combined SURF+HOG approach achieves better accuracy than SURF alone, identifying more true positives and fewer false negatives. This combined approach is thus effective for Alzheimer's disease detection from brain MRI images.
Off-line English Character Recognition: A Comparative Surveyidescitation
It has been decades since the evolution of idea that
human brain can be mimicked by artificial neuron like
mathematical structures. Till date, the development of this
endeavor has not reached the threshold of excellence. Neural
networks are commonly used to solve sample-recognition
problems. One of these is character recognition. The solution
of this problem is one of the easier implementations of neural
networks. This paper presents a detailed comparative
literature survey on the research accomplished for the last
few decades. The comparative literature review will help us
understand the platform on which we stand today to achieve
the highest efficiency in terms of Character Recognition
accuracy as well as computational resource and cost.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Image Captioning Generator using Deep Machine Learningijtsrd
Technologys scope has evolved into one of the most powerful tools for human development in a variety of fields.AI and machine learning have become one of the most powerful tools for completing tasks quickly and accurately without the need for human intervention. This project demonstrates how deep machine learning can be used to create a caption or a sentence for a given picture. This can be used for visually impaired persons, as well as automobiles for self identification, and for various applications to verify quickly and easily. The Convolutional Neural Network CNN is used to describe the alphabet, and the Long Short Term Memory LSTM is used to organize the right meaningful sentences in this model. The flicker 8k and flicker 30k datasets were used to train this. Sreejith S P | Vijayakumar A "Image Captioning Generator using Deep Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42344.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42344/image-captioning-generator-using-deep-machine-learning/sreejith-s-p
ON THE IMAGE QUALITY AND ENCODING TIMES OF LSB, MSB AND COMBINED LSB-MSBijcsit
The Least Significant Bit (LSB) algorithm and the Most Significant Bit (MSB) algorithm are stenography algorithms with each one having its demerits. This work therefore proposed a Hybrid approach and compared its efficiency with LSB and MSB algorithms. The Least Significant Bit (LSB) and Most
Significant Bit (MSB) techniques were combined in the proposed algorithm. Two bits (the least significant bit and the most significant bit) of the cover images were replaced with a secret message. Comparisons were made based on Mean-Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and the encoding time between the proposed algorithm, LSB and MSB after embedding in digital images. The combined
technique produced a stego-image with minimal distortion in image quality than MSB technique independent of the nature of data that was hidden. However, LSB algorithm produced the best stego-image quality. Large cover images however made the combined algorithm’s quality better improved. The combined algorithm had lesser time of image and text encoding. Therefore, a trade-off exists between the encoding time and the quality of stego-image as demonstrated in this work.
IRJET- Spot Me - A Smart Attendance System based on Face RecognitionIRJET Journal
The article discusses international issues. It mentions that globalization has increased economic interdependence between nations while also raising tensions over immigration and trade. Solutions will require cooperation and compromise and a recognition that isolationism is not a viable strategy in an interconnected world.
Sign Language Recognition using Deep LearningIRJET Journal
The document discusses using deep learning techniques like MobileNet V2 to develop a model for sign language recognition. It aims to classify sign language gestures to help communicate with deaf people. The model was trained on a dataset of sign language images and achieved an accuracy of 70% in recognizing letters, numbers, and gestures.
AN ANN BASED BRAIN ABNORMALITY DETECTION USING MR IMAGEScscpconf
The Main purpose of this paper is to design, implement and evaluate a strong automatic diagnostic system that increases the accuracy of tumor diagnosis in brain using MR images.This presented work classifies the brain tissues as normal or abnormal automatically, usingcomputer vision. This saves lot of radiologist time to carryout monotonous repeated job. The
acquired MR images are processed using image preprocessing techniques. The preprocessed images are then segmented, and the various features are extracted. The extracted features are
fed to the artificial neural network as input that trains the network using error back propagation algorithm for correct decision making.
IRJET - Threat Prediction using Speech AnalysisIRJET Journal
This document describes a proposed system to analyze audio files and detect potential threats by performing speech recognition and sentiment analysis. The system would have three main steps: 1) Convert speech to text using machine learning algorithms like recurrent neural networks, 2) Perform sentiment analysis on the text using natural language processing techniques like naïve Bayes classification to determine if the text is positive, negative, or neutral, 3) Calculate an overall threat percentage and issue a warning if it exceeds a threshold. The goal is to automate audio surveillance and analysis to reduce time and human error compared to manual processes. Artificial neural networks would be used for both speech recognition and sentiment classification.
SIGNIFICANCE OF RATIONAL 6TH ORDER DISTORTION MODEL IN THE FIELD OF MOBILE’S ...P singh
The document discusses a proposed method for video watermarking that uses spatial and frequency domain techniques for embedding watermark information, and tests the method's robustness against rational 6th order distortion. The key steps are: (1) extracting frames from a video and selecting the highest entropy frame, (2) using spread spectrum and LSB techniques to embed a watermark in the spatial domain and DWT in the frequency domain, (3) applying rational 6th order distortion to test the effect on the watermarked video, (4) calculating metrics like correlation, SSIM, PSNR, BER and MSE to evaluate the method and detect the watermark from the distorted video. The results show the values of correlation and SSIM
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Comparative Performance of Image Scrambling in Transform Domain using Sinusoi...CSCJournals
With the rapid development of technology, and the popularization of internet, communication is been greatly promoted. The communication is not limited only to information but also includes multimedia information like digital Images. Therefore, the security of digital images has become a very important and practical issue, and appropriate security technology is used for those digital images containing confidential or private information especially. In this paper a novel approach of Image scrambling has been proposed which includes both spatial as well as Transform domain. Experimental results prove that correlation obtained in scrambled images is much lesser then the one obtained in transformed images.
IRJET- Chest Abnormality Detection from X-Ray using Deep LearningIRJET Journal
This document proposes using a convolutional neural network (CNN) to detect abnormalities in chest x-rays. It discusses developing a CNN model with an input of chest x-ray images labeled as normal or abnormal. The model would use techniques like pre-processing, data augmentation, and a network architecture with convolutional and pooling layers to classify images as normal or abnormal. The goal is to build an accurate system for detecting various chest diseases from x-ray images to help doctors with diagnosis.
IRJET- Chest Abnormality Detection from X-Ray using Deep LearningIRJET Journal
This document summarizes a research paper that developed a deep learning system to detect abnormalities in chest x-rays. The system used a convolutional neural network (CNN) trained on 5000 chest x-ray images labeled as normal or abnormal. The CNN architecture included convolutional and pooling layers. Pre-processing techniques like histogram equalization and data augmentation like image flipping were used. The trained CNN could classify x-rays as normal, abnormal, or detect 14 specific diseases with 1% error rate. While large datasets improve accuracy, they also increase training time. The authors conclude CNN is effective for chest x-ray analysis but note room for improvements like including more diseases and optimized architectures.
A SURVEY ON DEEP LEARNING METHOD USED FOR CHARACTER RECOGNITIONIJCIRAS Journal
The field of Artificial Intelligence is very fashionable today, especially neural networks that work well in various areas such as speech recognition and natural language processing. This Research Article briefly describes how deep learning models work and what different techniques are used in text recognition. It also describes the great progress that has been made in the field of medicine, the analysis of forensic documents, the recognition of license plates, banking, health and the legal industry. The recognition of handwritten characters is one of the research areas in the field of artificial intelligence. The individual character recognition has a higher recognition accuracy than the complete word recognition. The new method for categorizing Freeman strings is presented using four connectivity events and eight connectivity events with a deep learning approach.
Real-Time Pertinent Maneuver Recognition for SurveillanceIRJET Journal
This document presents a real-time system for recognizing pertinent maneuvers and detecting weapons using video surveillance. The system uses a ResNet-34 model trained on the Kinetics 400 dataset to recognize human activities in real-time video streams. It also uses a YOLOv4 model trained on a custom dataset to detect weapons. The ResNet-34 model can analyze still images and live video streams to classify activities. YOLOv4 is used for fast and accurate object detection of weapons. The system is designed to be deployed in CCTV surveillance to recognize activities and detect if individuals are carrying weapons. It aims to provide timely notifications or advanced user information for real-time monitoring.
SIGN LANGUAGE INTERFACE SYSTEM FOR HEARING IMPAIRED PEOPLEIRJET Journal
The document describes a proposed sign language interface system for hearing impaired people. The system aims to use machine learning algorithms like convolutional neural networks to classify hand gestures captured by a webcam into corresponding letters or words. The system would preprocess the images, extract features, then use a trained CNN model to predict the sign and output it as text and speech for better understanding by users. The goal is to help bridge communication between deaf/mute and normal people without requiring specialized gloves or sensors.
This document describes a wearable AI device that uses computer vision and speech synthesis to help blind individuals. The device uses a Raspberry Pi with a camera to perform three main functions: facial recognition using convolutional neural networks and linear discriminant analysis, optical character recognition (OCR) to convert text to speech using a text-to-speech system, and object detection. The facial recognition and text are conveyed to the blind user through a speaker. The system is designed to be portable and help blind people identify faces, read text, and detect objects to assist them in daily life.
Partial Object Detection in Inclined Weather ConditionsIRJET Journal
This document provides a comprehensive analysis of imbalance problems in object detection. It presents a taxonomy to classify different types of imbalances and discusses solutions proposed in literature. The analysis highlights significant gaps including existing imbalances that require further attention, as well as entirely new imbalances that have never been addressed before. A survey of imbalance problems caused by weather conditions and common object imbalances is conducted. Methods for addressing imbalances include data augmentation using GANs and balancing training based on class performance.
A Review of Comparison Techniques of Image SteganographyIOSR Journals
This document reviews and compares three common techniques for hiding information in digital images: Least Significant Bit (LSB) steganography, Discrete Cosine Transform (DCT) steganography, and Discrete Wavelet Transform (DWT) steganography. LSB is implemented in the spatial domain by replacing the least significant bits of cover image pixels with payload bits. DCT and DWT are implemented in the frequency domain by transforming the cover image and embedding payload bits in the transformed coefficients. The document evaluates and compares the performance of these three techniques based on metrics like mean squared error, peak signal-to-noise ratio, embedding capacity, and robustness.
IRJET- Review on Text Recognization of Product for Blind Person using MATLABIRJET Journal
This document summarizes a research paper that proposes a system to help blind people read text on product labels and documents using a camera and MATLAB software. The system uses image processing techniques like converting images to grayscale, binarization, and filtering to isolate text from complex backgrounds. It then applies optical character recognition to identify the text and provide information to blind users. The proposed system aims to address limitations of prior methods that struggled with non-horizontal text, complex backgrounds, and positioning objects in the camera view. It extracts a region of interest around a product using motion detection and recognizes text regardless of orientation.
A Study on Surf & Hog Descriptors for Alzheimer’s Disease DetectionIRJET Journal
This document presents a study on using SURF and HOG descriptors to detect Alzheimer's disease from brain MRI scans. It proposes combining SURF and HOG features to match keypoints in MRI images for improved accuracy over using SURF alone. Performance is evaluated using measures like sensitivity, specificity, and ROC. Results show the combined SURF+HOG approach achieves better accuracy than SURF alone, identifying more true positives and fewer false negatives. This combined approach is thus effective for Alzheimer's disease detection from brain MRI images.
Off-line English Character Recognition: A Comparative Surveyidescitation
It has been decades since the evolution of idea that
human brain can be mimicked by artificial neuron like
mathematical structures. Till date, the development of this
endeavor has not reached the threshold of excellence. Neural
networks are commonly used to solve sample-recognition
problems. One of these is character recognition. The solution
of this problem is one of the easier implementations of neural
networks. This paper presents a detailed comparative
literature survey on the research accomplished for the last
few decades. The comparative literature review will help us
understand the platform on which we stand today to achieve
the highest efficiency in terms of Character Recognition
accuracy as well as computational resource and cost.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
2. List of final year BSc, BE projects for
Computer science(CS) and Computer science
and Engineering(CSE)
3D Facial Modeling
Wood Defect Recognition
The Contourlet Based Image Recovery And Denoising
Texture Segmentation Using Rotational Invariant Moment Technique
Transmitter Placement Using Genetic Algorithm
Surveillance Robot For Tracking Multiple Moving Targets Using Artificial Neural
Networks (ANN)
Software Security Using Neural Networks
Secure Transaction Using Steganography
Steganalysis
Segmentation Of Textures Using Gabor Filter
Dr.S.Purushothaman & Dr.P.Rajeswari 2
3. 3D Facial Modeling
This work focuses on 3D facial modeling using three images of a face. Each image
is taken at 90o.
Each image is segmented to identify skin, to locate eye centres, nose profile,
mouth profiles.
All the three images are combined to form a 3D facial model.
The segmented portions of the images are placed on a standard computer plastic
model.
Subsequently, the various features of the plastic model are animated
corresponding to the various positions of the features in the subsequent images.
Dr.S.Purushothaman & Dr.P.Rajeswari 3
4. Wood Defect Recognition
The quality of a board or a sheet of veneer determines its
potential uses and the price for the saw mills.
Automatic detection of the defects in the wood surface and
grading of the products is one of the key interest areas in
the mechanical wood industry.
In this work, intelligent methods for knot recognition is
implemented using Self organizing map(SOM).
Defective wood is used as template.
During training of SOM, the SOM learns defective wood.
During testing of the processed a test picture is given as
input that contains a combination of all defects
Dr.S.Purushothaman & Dr.P.Rajeswari 4
5. The Contourlet Based Image Recovery And
Denoising
The contourlet transform consists of two modules: the Laplacian Pyramid
and the Directional Filter Bank.
When both of them use perfect reconstruction filters, the contourlet
expansion and reconstruction is a Perfect dual.
Therefore, the contourlet transform can be employed as a coding scheme.
The contourlet coefficients derived above can be transmitted through the
wireless channel in the same way as transmitting the original image,
where the transmission is prone to noise and block loss.
However, the reconstruction at the receiver performs differently if the
image is transmitted directly or coded by the contourlet Transform.
The project studies the performance of the contourlet coding in image
recovery and denoising.
The simulation results show that for general images the contourlet
transform is quite competitive to the wavelet transform in the signal to
noise ration(SNR) sense and in visual effect.
Dr.S.Purushothaman & Dr.P.Rajeswari 5
6. Texture Segmentation Using Rotational
Invariant Moment Technique
Texture segmentation plays an important role in recognizing
and identifying a material, type of characteristic for
particular image.
Wavelets are employed for the computation of single and
multi-scale roughness features because of their ability to
extract information at different resolutions.
Features are extracted in multiple directions using
directional wavelet obtained from partial derivative of
Gaussian distribution function.
The first and second derivative wavelets are used to obtain
the features of the textured image at different orientations
like 0o , 45o , 90o and 135o and scales such as 1, 2 and 4.
Dr.S.Purushothaman & Dr.P.Rajeswari 6
7. ……continued
The feature extraction part consists of two stages, the
extraction of directional roughness feature and the
percentage of energy feature.
The directional roughness features results in high quality
texture segmentation performance, whereas the use of
percentage of energy feature retains the important properties
of fractal dimension based features like insensitivity to
absolute illumination and contrast.
The percentage energy feature computed using exponential
wavelets is used for segmenting different features in a given
image by using k-means algorithm.
For classification purpose database has been created from
different texture samples and classification is done using
template matching technique.
Dr.S.Purushothaman & Dr.P.Rajeswari 7
8. Transmitter Placement Using Genetic
Algorithm
Wireless communication is part and parcel of modern
communication methods.
Cell phones have become handy to everyone for instant
communication.
During such communication, clarity, continuity and less noise
are the major parameters considered.
To communicate between two or more cell phone users, a
transceiver should be erected, called base station.
The base station depending upon quality, capacity will decide
the number of base stations in a given area.
The base station is similar to a telephone exchange.
What is the optimal number of base station required so that
they can communicate with their service providers called nodes
effectively without any redundancy or duplication or more
number of base stations than that is required to satisfy all
the service providers.
Dr.S.Purushothaman & Dr.P.Rajeswari 8
9. …….continued
The desire to make computers think, as human being do,
resulted in the development of Genetic algorithm.
Genetic algorithm is one of the artificial intelligent
methods which proves to be superior in many aspects when
compared to other classical methods in solving character
recognition problem.
The solution generated out of training the algorithm is
positive and definite.
Dr.S.Purushothaman & Dr.P.Rajeswari 9
10. Surveillance Robot For Tracking Multiple
Moving Targets Using Artificial Neural
Networks (ANN)
Object tracking is a challenging task in spite of all sophisticated methods that have
been developed.
The major challenge is to keep track of the object of a particular choice.
In this work, a new video moving object-tracking method is proposed.
The segmentation of the video is done by contextual clustering.
The features of the segmented image is further processed by the imfeature properties
of the matlab.
The imfeature provides 24 properties.
In this work, two important properties are used to process the features of the
segmented image for highlighting the presence of the human.
The co-ordinates of the human in the video are given as input for the Kalman filter.
This process is further repeated for the successive video frames.
An artificial neural network with supervised backpropagation algorithm learns the
input output scenarios of the Kalman filter and provides a better estimate of the
movement of the human in the video frame.
A multitarget human tracking is attempted.
Dr.S.Purushothaman & Dr.P.Rajeswari 10
11. Software Security Using Neural Networks
This project involves the development of security for software.
The project involves key generation and key recovery procedures.
These two procedures avoid pirating any newly developed marketable
package.
Key generation and key recovery software involves intelligent
techniques to create secured keys.
These keys play an important role in providing access to the licensed
software.
During generation of keys Expiry date, Version and Ethernet number of
the customer are taken into consideration and keys are developed.
The keys are used as inputs to the artificial neural network.
The outputs of artificial neural network are encoded as floating
values that plays important role in avoiding piracy.
In addition, two sets of final weight files are developed.
Altogether, four files along with the required software will be
provided to the customer by the vendor.
Dr.S.Purushothaman & Dr.P.Rajeswari 11
12. ….continued
The customer will install all the software in the computer.
During the execution of the software, the recovery program
will convert the floating-point values into characters that
are then reassembled to get Ethernet number and Expiry date.
Subsequently a separate program will access the system date
and existing Ethernet number.
Comparison between the obtained and the existing Ethernet
number is done.
If the strings are matching then a check is made if the
system date is less than expiry date of the original
software.
If everything matches then the execution of the software
continues.
Dr.S.Purushothaman & Dr.P.Rajeswari 12
13. Secure Transaction Using
Steganography
The work aims at incorporating a concept of hiding message
into another message.
The includes hiding image into image, text into image, text
into text, sound into sound and so on.
A preferred image is taken as a standard for hiding secret
images. Logic has been developed to hide message and unhide
the message.
The steganography is possible among people who have hiding
and unhiding message software.
In addition to standard hiding procedure in steganography,
different other combination of methods can be implemented.
In addition, cryptography can also be used.
Dr.S.Purushothaman & Dr.P.Rajeswari 13
14. Steganalysis
Sequential steganography as those class of embedding algorithms
that hide messages in consecutive (time, spatial or frequency
domain) features of a host signal.
The project presents a steganalysis method that estimates the
secret key used in sequential steganography.
A theory is developed for detecting abrupt jumps in the
statistics of the stego signal during steganalysis.
Stationary and non-stationary host signals with low, medium and
high SNR embedding are considered.
A locally most powerful steganalysis detector for the low SNR
case is also derived. Several techniques to make the
steganalysis algorithm work for non-stationary digital image
steganalysis are also presented.
Extensive experimental results are shown to illustrate the
strengths and weaknesses of the proposed steganalysis algorithm.
Dr.S.Purushothaman & Dr.P.Rajeswari 14
15. Segmentation Of Textures Using Gabor
Filter
The work involves segmenting textured images used in textile industries.
Many conventional algorithms are available for segmentation but Gabor
segmentation is used.
Gabor filter is a set of filter banks obtained at different orientation
corresponding to the size of actual image texture.
Each filter bank is convolved with the actual image to obtain features of
the image.
The features obtained are almost same when different Gabor filters are
convolved with the original image.
In order to make sure that the convolved image has the correct features,
all the convolved matrices are averaged.
The averages matrix is assigned class label based on the range of values
predefined.
The class label is in the form of alphabets.
When the labeled matrix is viewed, it shows the segmented portion of the
image.
Subsequently, kronecker delta rule is applied to refine the segmentation
process.
Dr.S.Purushothaman & Dr.P.Rajeswari 15
16. ….continued
The nature of the project involves in taking an image texture
as input and getting the partitioned or segmented textures as
output.
It can be very well justified in doing this project as
automatic segmentation, identification and classification
plays an important role in image processing.
The future of the project is unlimited.
There is lot of demand in segmentation of 3D objects,
especially in magnetic resonance imaging, computer
tomography.
Dr.S.Purushothaman & Dr.P.Rajeswari 16
17. Thank you
Contact dr.s.purushothamann@gmail.com for further clarifications
Dr.S.Purushothaman 17