2017 International Conference on Signal Processing and Communication (ICSPC)最新文献

{"title":"Novel local binary textural pattern for analysis and classification of mammogram using support vector machine","authors":"Narain Ponraj, J. Winston, Poongodi, M. Mercy","doi":"10.1109/CSPC.2017.8305874","DOIUrl":"https://doi.org/10.1109/CSPC.2017.8305874","url":null,"abstract":"Breast cancer is one of the most devastating and deadly diseases for women. It is estimated that between one in eight and one in twelve women will develop breast cancer during their lifetime. The most convenient practical method to detect breast cancer is mammography, because it allows the detection of the cancer at its early stages, a crucial issue for a high survival rate. Mammography is the only technique that has demonstrated the ability to detect breast cancer at an early stage and with high sensitivity and specificity. The search for features in this kind of image is complicated by the higher frequency textural variations in image intensity. In this paper, we have proposed few novel local binary textural patterns for classification of mammogram which was found to have consistent accuracy rate.","PeriodicalId":123773,"journal":{"name":"2017 International Conference on Signal Processing and Communication (ICSPC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134577645","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":"Realization of epileptic seizure detection in EEG signal using wavelet transform and SVM classifier","authors":"D. Selvathi, V. Meera","doi":"10.1109/CSPC.2017.8305848","DOIUrl":"https://doi.org/10.1109/CSPC.2017.8305848","url":null,"abstract":"The objective of this work is to identity the occurrence of seizure in an epileptic patient from his/her Electroencephalogram (EEG) signals and also to avoid aggressive situations during their seizure. In this paper an efficient method is proposed for detecting the presence of seizure in EEG signal using wavelet transform and Support Vector Machine (SVM) classifier. In this work, EEG signal is decomposed into seven levels using discrete wavelet transform to obtain the delta, alpha, theta, beta and gamma subbands. Among the five subbands, alpha wave has the very high amplitude in the range of 100μv which is mostly used to detect the seizure. Then the statistical features are extracted from the alpha band and finally classification of EEG signal has been done using SVM classifier. This method is applied for two groups of EEG signal: 1) Normal EEG dataset; 2) seizure dataset during a seizure period. The implementation of the proposed method utilized 76% of LUTs and 20% of registers. Total power analyzed for implementing this proposed work is 0.017W and classification accuracy is 95.6%.","PeriodicalId":123773,"journal":{"name":"2017 International Conference on Signal Processing and Communication (ICSPC)","volume":"315 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133360990","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":"Edge preserving image enhancement for color and gray scale images using local mean and local standard deviation","authors":"Shivaprasad, Clitus Neil Dsouza","doi":"10.1109/CSPC.2017.8305812","DOIUrl":"https://doi.org/10.1109/CSPC.2017.8305812","url":null,"abstract":"The aim of image enhancement is to produce a processed image which is more suitable than the original image for specific application. Application can be edge detection, boundary detection, image fusion, segmentation etc. In this paper different types of image enhancement algorithms in spatial domain are presented for gray scale as well as for color images. Quantitative analysis like AMBE (Absolute mean brightness error), MSE (Mean square error) and PSNR (Peak signal to noise ratio) for the different algorithms are evaluated. For gray scale image Weighted histogram equalization, Linear contrast stretching (LCS), Non linear contrast stretching logarithmic (NLLCS), Non linear contrast stretching exponential (NLECS), Bi Histogram Equalization (BHE) algorithms are discussed and compared. For color image (RGB) Linear contrast stretching, Non linear contrast stretching logarithmic and Non linear contrast stretching exponential algorithms are discussed. During result analysis, it has been observed that some algorithms does give considerably highly distinct values(MSE or AMBE) for different images. To stabilize these parameters, had proposed the new enhancement scheme Local mean and local standard deviation(LMLS) which will take care of these issues. By experimental analysis It has been observed that proposed method gives better AMBE (should be less) and PSNR (should be high) values compared with other algorithms, also these values are not highly distinct for different images.","PeriodicalId":123773,"journal":{"name":"2017 International Conference on Signal Processing and Communication (ICSPC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115894754","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":"Unsupervised classification of remote sensing imagery using multi-sensor data fusion","authors":"Ashish Kumar Agarwalla, S. Minz","doi":"10.1109/CSPC.2017.8305844","DOIUrl":"https://doi.org/10.1109/CSPC.2017.8305844","url":null,"abstract":"Remotely sensed imagery accounts for sensor specific information. The following paper deals with making use of data from multiple sources with similar temporal resolution to improve classification accuracy. This was done by clustering five masks or samples of 100 × 100 pixels selected randomly from multispectral data from Landsat TM and evaluation of cluster quality to find the number of naturally occurring clusters. This was followed by clustering the entire study area Landsat TM data using k-means algorithm and evaluation of the resulting cluster quality using silhouette coefficient to identify loosely classified pixels and mean silhouette value (threshold of the scene). Hyper-spectral data from Hyperion was used for only the loosely classified pixels identified above and was clustered using the k-means algorithm. Finally, soft decision level fusion method was applied to the clustering output from HS data with good quality clusters (clusters with silhouette coefficient above the mean) from the multi-spectral imagery to produce final classification maps. In the fused imagery, the overall Classification accuracy and Kappa Statistics increased significantly as compared to the multispectral imagery. Cluster validity indices like Silhouette coefficient is used to evaluate cluster quality and predict naturally occurring clusters. The decision level fusion of selective data from multiple sources has exhibited better classification results at reduced computational overheads.","PeriodicalId":123773,"journal":{"name":"2017 International Conference on Signal Processing and Communication (ICSPC)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125068167","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":"Computational prediction tool for Leafy Cotyledon Proteins (LEC) in oil palms and date palms","authors":"N. Hemalatha, Manasa, P. Kavyashree, T. Anusha, M. K. Rajesh","doi":"10.1109/CSPC.2017.8305865","DOIUrl":"https://doi.org/10.1109/CSPC.2017.8305865","url":null,"abstract":"Members of the palm family (Arecaceae) constitute one of the most economically essential crops used by mankind. Even though genome sequences of oil palm (Elaeis guineensis) and date palm (Phoenix dactylifera) are available, genome wide analysis has not been undertaken yet in these crops. In the present study we have analyzed the genome sequence of Elaeis guineensis and Phoenix dactylifera for the presence of protein Leafy Cotyledon (LEC), which has been implicated in the control of embryo development and maturation. The resultant LEC protein sequences from the two palms were then used to create predictive model using different computational algorithms like Naive Bayes, SMO, MLP and Random Forest based on their motif pattern and amino acid properties such as charged amino acids and basic amino acids. Performance testing of the computational models developed in this study resulted in 100% accuracy for motif feature using Naive Bayes, MLP and Random Forest algorithm.","PeriodicalId":123773,"journal":{"name":"2017 International Conference on Signal Processing and Communication (ICSPC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132967518","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":"Supervised classification of type of crowd motion in video surveillance system","authors":"Gauri Deshmukh, Manasi Pathade, M. Khambete","doi":"10.1109/CSPC.2017.8305816","DOIUrl":"https://doi.org/10.1109/CSPC.2017.8305816","url":null,"abstract":"Automated surveillance is of vital importance in public places which has large extent of dynamics to be addressed. The complexity of analysis of such surveillance increases as the size of crowd goes on increasing. This paper attempts to propose an algorithm to analyze and classify the type of motion in a crowd. The analysis is based on texture analysis of video sequence. Nearest neighbor classification is used to classify the motion into predefined classes. The algorithm is tested on standard PETS database.","PeriodicalId":123773,"journal":{"name":"2017 International Conference on Signal Processing and Communication (ICSPC)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116651745","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":"Mammography classification using modified hybrid SVM-KNN","authors":"Poonam Sonar, U. Bhosle, Chandrajit Choudhury","doi":"10.1109/CSPC.2017.8305858","DOIUrl":"https://doi.org/10.1109/CSPC.2017.8305858","url":null,"abstract":"Today leading cause of cancer deaths for women is the Breast cancer. For early and accurate detection of breast cancer, mammography is found to be the most reliable and effective technique. In this context, computer aided diagnosis of breast cancer from mammograms is gaining high importance and priority for many researchers. In this paper machine learning based mammogram classification using modified hybrid SVM-KNN is proposed. The idea is to map the feature points to kernel space using kernel and find the K nearest neighbors among the training dataset for a given test data point. Doing this we narrow down the search for support vectors. Mammogram images are preprocessed and region of interest is extracted using Fuzzy C Means clustering and Active Counter technique. GLCM (grey level covariance matrix) based texture features are extracted from segmented ROI. These features are used to train modified hybrid SVM-KNN classifier proposed by authors. The trained classifier is used to classify breast tissues in normal/abnormal classes and further abnormal class into benign/malignant. Proposed technique is experimented on two standard MIAS and DDSM databases. The proposed classifier reports classification accuracy of 100 % for DDSM and 94% for MIAS database for benign and malignant class. Results are compared with SVM, KNN and Random Forest classifiers.","PeriodicalId":123773,"journal":{"name":"2017 International Conference on Signal Processing and Communication (ICSPC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131229339","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":"Modified codebook algorithm with Kalman filter for foreground segmentation in video sequences","authors":"S. Aung, Zin Mar Kyu","doi":"10.1109/CSPC.2017.8305864","DOIUrl":"https://doi.org/10.1109/CSPC.2017.8305864","url":null,"abstract":"Background subtraction method is widely used in most of the video motion detection algorithms especially for video surveillance application. Background subtraction is used to extract moving or static foreground objects from the background scene. The efficiency of foreground-background segmentation heavily relies on background model which must be able to cope with changes in the scene and granularity of the foreground objects. The robust background model can produce good foreground segmentation results and it is still a great challenge to get accurate and high performance result today. In this paper, a video foreground-background segmentation approach is proposed. This approach is based on Codebook (CB) model with Kalman Filter. This approach can be used to extract foreground objects from the video stream. The Lab color space is used in this approach to calculate color difference between two pixels using CIEDE2000 color difference formula. Extracted foreground object from video sequence using this approach is useful for object detection in video surveillance applications. This approach has been tested with PETS and CDnet2014 datasets and segmentation results accuracy are evaluated compare with ground truth.","PeriodicalId":123773,"journal":{"name":"2017 International Conference on Signal Processing and Communication (ICSPC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131287208","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":"A voice activity detector using SVM and Naïve Bayes classification algorithm","authors":"N. Selvakumari, V. Radha","doi":"10.1109/CSPC.2017.8305815","DOIUrl":"https://doi.org/10.1109/CSPC.2017.8305815","url":null,"abstract":"Voice or Speech Pathology analysis performs a significant role in the recent record of medical experts. The need for research is the recognition and classifications of tone of pathological voices are believed as a challenging work in the field of speech analysis still now. Commonly Patients are in a position to identify a change in voice parameters, such as hoarseness; however the voice pathologies can result from a wide spectral range of causes, like common cold to a malicious tumor. Medical experts like otolaryngologists were discovering a genuine quantity and range of speech pathologies from the Patients conversation. Unluckily, the current classification rate of voice pathology by the human experts is merely about 60–70%. Thus tone of voice or speech pathologies can be found by the endoscopy techniques and strategies like laryngostroboscopy or medical micro laryngoscopy, which distress the individual to a great scope which is expensive also. The primary objective of the research work is to assist this speech pathology finding process with computer structured diagnostic tools. This speech pathology diagnosis system works predicated on the support of the medical clinic based mostly professional otolaryngologists, by determining and figuring out the chance of the pathology automatically without the endoscopy which escalates the detection of speech pathology at the initial stage. In this research work, the conversation signal is examined by the acoustic guidelines and variables like transmission energy, pitch, Silence removal, Windowing, Mel consistency and occurrence Cepstrum, and Jitter. At the final end, the classification strategy i.e Support Vector Machine is employed to classify the standard and pathology speech, predicated on the features extracted in the last phase. Predicated on the results & conversation and dialogue pointed out below, thus the Speech pathology recognition system successfully categorized and labelled the normal tone of voice and the pathological speech which assists with diagnosing and examining the patient.","PeriodicalId":123773,"journal":{"name":"2017 International Conference on Signal Processing and Communication (ICSPC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131856140","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":"DCT-SVD domain feature vector for image retrieval","authors":"R. Patil","doi":"10.1109/CSPC.2017.8305895","DOIUrl":"https://doi.org/10.1109/CSPC.2017.8305895","url":null,"abstract":"The novel approach combines Cosine Transform (DCT) and Singular Value Decomposition (SVD) for content based image retrieval (CBIR). DCT coefficients are mapped into four, eight, sixteen, thirty two and sixty four quadrants and then SVD is applied on each quadrant. The singular values from each quadrant are used as a feature vector for each image. Further image is divided into blocks and DCT applied on each block. Each block DCT coefficients are mapped into different quadrants and then SVD apply on each block. These SVD coefficients are used as a feature vector for each image in the database. Proposed algorithm tested over database of 1200 images having 15 different categories. Results are compared using grayscale image, RGB color plane and YCbCr color plane. Two similarity measures are used Bray Curtis Distance (BCD) and Euclidean Distance(ED). Performance evaluation of proposed method calculated by using overall average precision and overall average recall.","PeriodicalId":123773,"journal":{"name":"2017 International Conference on Signal Processing and Communication (ICSPC)","volume":"9 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114095886","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}