2014 Fifth International Conference on Signal and Image Processing最新文献_第3页

Entropy Based Fuzzy C Means Clustering and Key Frame Extraction for Sports Video Summarization 基于熵的模糊C均值聚类和关键帧提取运动视频摘要

2014 Fifth International Conference on Signal and Image Processing Pub Date : 2014-01-08 DOI: 10.1109/ICSIP.2014.49

S. Angadi, Vilas Naik

{"title":"Entropy Based Fuzzy C Means Clustering and Key Frame Extraction for Sports Video Summarization","authors":"S. Angadi, Vilas Naik","doi":"10.1109/ICSIP.2014.49","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.49","url":null,"abstract":"Recent advances in technology have made tremendous amount of multimedia information available to the general population. To access the needed information in this scenario there is a need for automatic tools to filter and present information summary. Summarization techniques will give a choice to users to browse and select the multimedia documents of their choice for complete viewing later. In this work a new summarization technique to collect frames of importance in a video is presented. The method is based on selection of frames typically different from their immediate neighbors as key frames from group of similar frames. It uses the process of clustering, where visually similar frames are collected into one group using Fuzzy C means clustering algorithm. When clusters are formed, the frames that exhibit a change ratio which is a measure of the content variation, greater than the average value of the cluster are treated as Key frames. The summary is created by merging Key frames on the basis of their timeline. This method ensures that video summary represents the most unique frames of the input video and gives equal attention to preserving continuity of the summarized video. The robustness of the algorithm is validated by average values of performance parameters. The average compression ratio of 92% is indication of higher conciseness. The average fidelity of 95% is an indicative of comprehensive representation of video by the key frames selected using proposed algorithm.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131918181","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}

引用次数: 19

An Efficient Classification Algorithm for Music Mood Detection in Western and Hindi Music Using Audio Feature Extraction 一种基于音频特征提取的西方和印度音乐情绪检测的高效分类算法

2014 Fifth International Conference on Signal and Image Processing Pub Date : 2014-01-08 DOI: 10.1109/ICSIP.2014.63

A. S. Bhat, Namrata S. Prasad, Amith V S, M. Murali

{"title":"An Efficient Classification Algorithm for Music Mood Detection in Western and Hindi Music Using Audio Feature Extraction","authors":"A. S. Bhat, Namrata S. Prasad, Amith V S, M. Murali","doi":"10.1109/ICSIP.2014.63","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.63","url":null,"abstract":"Over the past decade, a lot of research has been done in audio content analysis for extracting various kinds of information, especially the moods it denotes, from an audio signal, because music expresses emotions in a concise and succinct way, yet in an effective way. People select music in congruence to their moods and emotions, making the need to classify music in accordance to moods more of a demand. Since different individuals have different perceptions about classifying music according to mood, it becomes a much more difficult task. This paper proposes an automated and efficient method to perceive the mood of any given music piece, or the \"emotions\" related to it, by drawing out a link between the spectral and harmonic features and human perception of music and moods. Features such as rhythm, harmony, spectral feature, and so on, are studied in order to classify the songs according to its mood, based on Thayer's model. The values of the quantified features are then compared against the threshold value using neural networks before classifying them according to different mood labels. The method analyzes many different features of the music piece, including spectra of beat and roughness, before classifying it under any mood. A total of 8 different moods are considered. In particular, the paper classifies both western and Indian Hindi film music, taking into consideration, a database of over 100 songs in total. The efficiency of this method was found to reach 94.44% at the best.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121919487","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}

引用次数: 39

Automated Real Time Video Quality Measurement Using Levenberg-Marquardt Backpropagation Algorithm 基于Levenberg-Marquardt反向传播算法的自动实时视频质量测量

2014 Fifth International Conference on Signal and Image Processing Pub Date : 2014-01-08 DOI: 10.1109/ICSIP.2014.61

P. Archana, S. Kulkarni

引用次数: 2

Stereo Image Rectification Using Focal Length Adjustment 立体声图像校正使用焦距调整

2014 Fifth International Conference on Signal and Image Processing Pub Date : 2014-01-08 DOI: 10.1109/ICSIP.2014.45

M. S. Kumar, N. Avinash

引用次数: 4

Digital Restoration of Deteriorated Mural Images 退化壁画图像的数字修复

2014 Fifth International Conference on Signal and Image Processing Pub Date : 2014-01-08 DOI: 10.1109/ICSIP.2014.10

M. Prasanth, K. K, Mayank Kumar, Bhargav B V S, Mrinmoy Ghorai

引用次数: 4

A Novel Approach to Circular Edge Detection for Iris Image Segmentation 一种用于虹膜图像分割的圆形边缘检测方法

2014 Fifth International Conference on Signal and Image Processing Pub Date : 2014-01-08 DOI: 10.1109/ICSIP.2014.56

Shashidhara H R, A. Aswath

{"title":"A Novel Approach to Circular Edge Detection for Iris Image Segmentation","authors":"Shashidhara H R, A. Aswath","doi":"10.1109/ICSIP.2014.56","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.56","url":null,"abstract":"This paper details about segmentation of iris region for iris recognition as a biometrical personal identification and verification. Human iris is unique and differs from one individual to another. Just as finger prints, biomedical proves human irises are distinct. Also, iris can be easily accessed from any visual capturing device. The two dimensional structure of iris further assists the technology. This paper describes the extraction of iris region from an image of the human eye. The proposed algorithm defines a new method to segment Iris from the image. It's a new technique for circular edge detection particularly for Iris recognition. An image undergoes various operations like black and white conversion, edge detection and filtering. The fact that the intensity of iris lies between the intensities of pupil and rest of the eye is the key here to extract iris. A simple vertical and horizontal scan is done over the image to get the tangents of the circles. A mathematical analysis is done on the images to get the radius and the center of the circle and hence the inner and outer circles of the iris are drawn or Hough transform can be done using the obtained values for more accuracy. We are constructed the circles after obtaining the values.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"48 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126844515","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}

引用次数: 11

Content Based Image Retrieval with Relevance Feedback Using Riemannian Manifolds 黎曼流形相关反馈的基于内容的图像检索

2014 Fifth International Conference on Signal and Image Processing Pub Date : 2014-01-08 DOI: 10.1109/ICSIP.2014.9

Pushpa B. Patil, M. Kokare

引用次数: 4

VIMARSHAK -- A Web Based Subjective Image Evaluation System VIMARSHAK——一个基于Web的主观图像评价系统

2014 Fifth International Conference on Signal and Image Processing Pub Date : 2014-01-08 DOI: 10.1109/ICSIP.2014.16

Gazal Garg, P. Mondal, S. M. Aswatha, Jit Mukherjee, Tapas Maji, J. Mukherjee

引用次数: 2

An Active Contour Method for MR Image Segmentation of Anterior Cruciate Ligament (ACL) 前交叉韧带(ACL) MR图像分割的主动轮廓法

2014 Fifth International Conference on Signal and Image Processing Pub Date : 2014-01-08 DOI: 10.1109/ICSIP.2014.28

N. A. Vinay, H. Vinay, T. Narendra

{"title":"An Active Contour Method for MR Image Segmentation of Anterior Cruciate Ligament (ACL)","authors":"N. A. Vinay, H. Vinay, T. Narendra","doi":"10.1109/ICSIP.2014.28","DOIUrl":"https://doi.org/10.1109/ICSIP.2014.28","url":null,"abstract":"Image segmentation is a fundamental task in image analysis which is responsible for partitioning an image into multiple sub-regions based on a desired feature. Active contours have been widely used as attractive image segmentation methods because they always produce sub-regions with continuous boundaries, while the kernel-based edge detection methods, e.g. Sobel edge detectors, often produce discontinuous boundaries. The use of level set theory has provided more flexibility and convenience in the implementation of active contours. However, traditional edge-based active contour models have been applicable to only relatively simple images whose sub-regions are uniform without internal edges. Here in this paper we attempt to brief the taxonomy and current state of the art in Image segmentation and usage of Active Contours. The goal of medical image segmentation is to partition a medical image in to separate regions, usually anatomic structures that are meaningful for a specific task. In many medical applications, such as diagnosis, surgery planning, and radiation treatment planning determining of the volume and position of an anatomic structure is required and plays a critical role in the treatment outcome.","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122364273","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}

引用次数: 3

Vehicle Data Acquisition and Telemetry 车辆数据采集和遥测

2014 Fifth International Conference on Signal and Image Processing Pub Date : 2014-01-08 DOI: 10.1109/ICSIP.2014.35

J. Chandiramani, Sanjam Bhandari, S. Hariprasad

引用次数: 8