{"title":"An Enhanced Block Based Edge Detection Technique Using Hysteresis Thresholding","authors":"M. Jayasree, K. NarayananN, Kabeer, R. ArunC","doi":"10.5121/SIPIJ.2018.9202","DOIUrl":"https://doi.org/10.5121/SIPIJ.2018.9202","url":null,"abstract":"","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"14 1","pages":"15-26"},"PeriodicalIF":0.0,"publicationDate":"2018-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/SIPIJ.2018.9202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72512047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Sagar, Abidali Munna Nc, K. Sureshbabu, B. RajaK, R. VenugopalK.
{"title":"Multi Scale ICA Based IRIS Recognition Using BSIF and HOG","authors":"G. Sagar, Abidali Munna Nc, K. Sureshbabu, B. RajaK, R. VenugopalK.","doi":"10.5121/SIPIJ.2017.8602","DOIUrl":"https://doi.org/10.5121/SIPIJ.2017.8602","url":null,"abstract":"","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"121 1","pages":"11-31"},"PeriodicalIF":0.0,"publicationDate":"2017-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82482367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Analysis of the Kalman, Extended Kalman, Uncented Kalman and Particle Filters with Application to DOA Tracking","authors":"M. VenuMadhava, N. JagadeeshaS, T. Yerriswamy","doi":"10.5121/SIPIJ.2017.8603","DOIUrl":"https://doi.org/10.5121/SIPIJ.2017.8603","url":null,"abstract":"","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"41 1","pages":"33-51"},"PeriodicalIF":0.0,"publicationDate":"2017-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87898990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Human action recognition is still a challenging problem and researchers are focusing to investigate this problem using different techniques. We propose a robust approach for human action recognition. This is achieved by extracting stable spatio-temporal features in terms of pairwise local binary pattern (P-LBP) and scale invariant feature transform (SIFT). These features are used to train an MLP neural network during the training stage, and the action classes are inferred from the test videos during the testing stage. The proposed features well match the motion of individuals and their consistency, and accuracy is higher using a challenging dataset. The experimental evaluation is conducted on a benchmark dataset commonly used for human action recognition. In addition, we show that our approach outperforms individual features i.e. considering only spatial and only temporal feature.
{"title":"Human action recognition in videos using stable features","authors":"M. Ullah, H. Ullah, Ibrahim M Alseadonn","doi":"10.5121/SIPIJ.2017.8601","DOIUrl":"https://doi.org/10.5121/SIPIJ.2017.8601","url":null,"abstract":"Human action recognition is still a challenging problem and researchers are focusing to investigate this problem using different techniques. We propose a robust approach for human action recognition. This is achieved by extracting stable spatio-temporal features in terms of pairwise local binary pattern (P-LBP) and scale invariant feature transform (SIFT). These features are used to train an MLP neural network during the training stage, and the action classes are inferred from the test videos during the testing stage. The proposed features well match the motion of individuals and their consistency, and accuracy is higher using a challenging dataset. The experimental evaluation is conducted on a benchmark dataset commonly used for human action recognition. In addition, we show that our approach outperforms individual features i.e. considering only spatial and only temporal feature.","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"18 1","pages":"01-10"},"PeriodicalIF":0.0,"publicationDate":"2017-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81251762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jonathan M. Branham, B. Myers, Zachary Garner, Dale Hamiton
A variety of machine learning algorithms have been used to map wildland fire effects, but previous attempts to map post-fire effects have been conducted using relatively low-resolution satellite imagery. Small unmanned aircraft systems (sUAS) provide opportunities to acquire imagery with much higher spatial resolution than is possible with satellites or manned aircraft. This effort investigates improvements achievable in the accuracy of post-fire effects mapping with machine learning algorithms that use hyperspatial (sub-decimeter) drone imagery. Spatial context using a variety of texture metrics were also evaluated in order to determine the inclusion of spatial context as an additional input to the analytic tools along with the three-color bands. This analysis shows that the addition of texture as an additional fourth input increases classifier accuracy when mapping post-fire effects.
{"title":"Evaluation of Texture as an Input of Spatial Context for Machine Learning Mapping of Wildland Fire Effects","authors":"Jonathan M. Branham, B. Myers, Zachary Garner, Dale Hamiton","doi":"10.5121/sipij.2017.8501","DOIUrl":"https://doi.org/10.5121/sipij.2017.8501","url":null,"abstract":"A variety of machine learning algorithms have been used to map wildland fire effects, but previous attempts to map post-fire effects have been conducted using relatively low-resolution satellite imagery. Small unmanned aircraft systems (sUAS) provide opportunities to acquire imagery with much higher spatial resolution than is possible with satellites or manned aircraft. This effort investigates improvements achievable in the accuracy of post-fire effects mapping with machine learning algorithms that use hyperspatial (sub-decimeter) drone imagery. Spatial context using a variety of texture metrics were also evaluated in order to determine the inclusion of spatial context as an additional input to the analytic tools along with the three-color bands. This analysis shows that the addition of texture as an additional fourth input increases classifier accuracy when mapping post-fire effects.","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"128 1","pages":"01-11"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85742954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Face image is an efficient biometric trait to recognize human beings without expecting any co-operation from a person. In this paper, we propose HVDLP: Horizontal Vertical Diagonal Local Pattern based face recognition using Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). The face images of different sizes are converted into uniform size of 108×990and color images are converted to gray scale images in pre-processing. The Discrete Wavelet Transform (DWT) is applied on pre-processed images and LL band is obtained with the size of 54×45. The Novel concept of HVDLP is introduced in the proposed method to enhance the performance. The HVDLP is applied on 9×9 sub matrix of LL band to consider HVDLP coefficients. The local Binary Pattern (LBP) is applied on HVDLP of LL band. The final features are generated by using Guided filters on HVDLP and LBP matrices. The Euclidean Distance (ED) is used to compare final features of face database and test images to compute the performance parameters.
{"title":"HVDLP : Horizontal Vertical Diagonal Local Pattern Based Face Recognition","authors":"Chandrakala, V. Kumar, K. Sureshbabu, B. RajaK","doi":"10.5121/SIPIJ.2017.8502","DOIUrl":"https://doi.org/10.5121/SIPIJ.2017.8502","url":null,"abstract":"Face image is an efficient biometric trait to recognize human beings without expecting any co-operation from a person. In this paper, we propose HVDLP: Horizontal Vertical Diagonal Local Pattern based face recognition using Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). The face images of different sizes are converted into uniform size of 108×990and color images are converted to gray scale images in pre-processing. The Discrete Wavelet Transform (DWT) is applied on pre-processed images and LL band is obtained with the size of 54×45. The Novel concept of HVDLP is introduced in the proposed method to enhance the performance. The HVDLP is applied on 9×9 sub matrix of LL band to consider HVDLP coefficients. The local Binary Pattern (LBP) is applied on HVDLP of LL band. The final features are generated by using Guided filters on HVDLP and LBP matrices. The Euclidean Distance (ED) is used to compare final features of face database and test images to compute the performance parameters.","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"82 1","pages":"13-28"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81484687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ultrasonograms refer to images generated through ultrasonography, a technique that applies ultrasound pulses to delineate internal structures of the body. Despite being useful in medicine, ultrasonograms usually suffer from multiplicative noises that may limit doctors to analyse and interpret them. Attempts to address the challenge have been made from previous works, but denoising ultrasonograms while preserving semantic features remains an open-ended problem. In this work, we have proposed a diffusion-steered model that gives an effective interplay between total variation and Perona-Malik models. Two parameters have been introduced into the framework to convexify our energy functional. Also, to deal with multiplicative noise, we have incorporated a log-based prior into the framework. Empirical results show that the proposed method generates sharper and detailed images. Even more importantly, our framework can be evolved over a longer time without smudging critical image features.
{"title":"Hybrid Diffusion Steered Model for Suppressing Multiplicative Noise in Ultrasonograms","authors":"S. Kessy, B. Maiseli, Michael Kisangiri","doi":"10.5121/SIPIJ.2017.8401","DOIUrl":"https://doi.org/10.5121/SIPIJ.2017.8401","url":null,"abstract":"Ultrasonograms refer to images generated through ultrasonography, a technique that applies ultrasound pulses to delineate internal structures of the body. Despite being useful in medicine, ultrasonograms usually suffer from multiplicative noises that may limit doctors to analyse and interpret them. Attempts to address the challenge have been made from previous works, but denoising ultrasonograms while preserving semantic features remains an open-ended problem. In this work, we have proposed a diffusion-steered model that gives an effective interplay between total variation and Perona-Malik models. Two parameters have been introduced into the framework to convexify our energy functional. Also, to deal with multiplicative noise, we have incorporated a log-based prior into the framework. Empirical results show that the proposed method generates sharper and detailed images. Even more importantly, our framework can be evolved over a longer time without smudging critical image features.","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"130 1","pages":"01-13"},"PeriodicalIF":0.0,"publicationDate":"2017-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77006509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sonu Pratap Singh Gurjar, Shivam Gupta, R. Srivastava
In this digital world, artificial intelligence has provided solutions to many problems, likewise to encounter problems related to digital images and operations related to the extensive set of images. We should learn how to analyze an image, and for that, we need feature extraction of the content of that image. Image description methods involve natural language processing and concepts of computer vision. The purpose of this work is to provide an efficient and accurate image description of an unknown image by using deep learning methods. We propose a novel generative robust model that trains a Deep Neural Network to learn about image features after extracting information about the content of images, for that we used the novel combination of CNN and LSTM. We trained our model on MSCOCO dataset, which provides set of annotations for a particular image, and after the model is fully automated, we tested it by providing raw images. And also several experiments are performed to check efficiency and robustness of the system, for that we have calculated BLEU Score.
{"title":"Automatic Image Annotation Model Using LSTM Approach","authors":"Sonu Pratap Singh Gurjar, Shivam Gupta, R. Srivastava","doi":"10.5121/SIPIJ.2017.8403","DOIUrl":"https://doi.org/10.5121/SIPIJ.2017.8403","url":null,"abstract":"In this digital world, artificial intelligence has provided solutions to many problems, likewise to encounter problems related to digital images and operations related to the extensive set of images. We should learn how to analyze an image, and for that, we need feature extraction of the content of that image. Image description methods involve natural language processing and concepts of computer vision. The purpose of this work is to provide an efficient and accurate image description of an unknown image by using deep learning methods. We propose a novel generative robust model that trains a Deep Neural Network to learn about image features after extracting information about the content of images, for that we used the novel combination of CNN and LSTM. We trained our model on MSCOCO dataset, which provides set of annotations for a particular image, and after the model is fully automated, we tested it by providing raw images. And also several experiments are performed to check efficiency and robustness of the system, for that we have calculated BLEU Score.","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"103 1","pages":"25-37"},"PeriodicalIF":0.0,"publicationDate":"2017-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85972870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Compression Based Face Recognition Using Transform Domain Features Fused at Matching Level","authors":"Srinivas Halvi","doi":"10.5121/SIPIJ.2017.8404","DOIUrl":"https://doi.org/10.5121/SIPIJ.2017.8404","url":null,"abstract":"","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"3501 1","pages":"39-58"},"PeriodicalIF":0.0,"publicationDate":"2017-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86651272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aim of this paper is reformulation of global image thresholding problem as a well-founded statistical method known as change-point detection (CPD) problem. Our proposed CPD thresholding algorithm does not assume any prior statistical distribution of background and object grey levels. Further, this method is less influenced by an outlier due to our judicious derivation of a robust criterion function depending on Kullback-Leibler (KL) divergence measure. Experimental result shows efficacy of proposed method compared to other popular methods available for global image thresholding. In this paper we also propose a performance criterion for comparison of thresholding algorithms. This performance criteria does not depend on any ground truth image. We have used this performance criterion to compare the results of proposed thresholding algorithm with most cited global thresholding algorithms in the literature.
{"title":"Optimal Global Threshold Estimation Using Statistical Change Point Detection","authors":"R. Chatterjee, A. Kar","doi":"10.5121/SIPIJ.2017.8402","DOIUrl":"https://doi.org/10.5121/SIPIJ.2017.8402","url":null,"abstract":"Aim of this paper is reformulation of global image thresholding problem as a well-founded statistical method known as change-point detection (CPD) problem. Our proposed CPD thresholding algorithm does not assume any prior statistical distribution of background and object grey levels. Further, this method is less influenced by an outlier due to our judicious derivation of a robust criterion function depending on Kullback-Leibler (KL) divergence measure. Experimental result shows efficacy of proposed method compared to other popular methods available for global image thresholding. In this paper we also propose a performance criterion for comparison of thresholding algorithms. This performance criteria does not depend on any ground truth image. We have used this performance criterion to compare the results of proposed thresholding algorithm with most cited global thresholding algorithms in the literature.","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"9 1","pages":"15-24"},"PeriodicalIF":0.0,"publicationDate":"2017-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89214705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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