2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)最新文献_第5页

Algorithm and architecture for quarter pixel motion estimation for H.264/AVC H.264/AVC中四分之一像素运动估计的算法和体系结构

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776245

S. K. Chatterjee, I. Chakrabarti

引用次数: 0

Fast registration of articulated objects from depth images 深度图像中铰接物体的快速配准

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776168

Sourabh Prajapati, P J Narayanan

引用次数: 1

Fynbos leaf online plant recognition application Fynbos叶片在线植物识别应用程序

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776220

S. Winberg, S. Katz, A. Mishra

引用次数: 3

Spatial variance of color and boundary statistics for salient object detection 显著目标检测的颜色空间方差和边界统计

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776270

Sudeshna Roy, Sukhendu Das

{"title":"Spatial variance of color and boundary statistics for salient object detection","authors":"Sudeshna Roy, Sukhendu Das","doi":"10.1109/NCVPRIPG.2013.6776270","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776270","url":null,"abstract":"Bottom-up saliency detection algorithms identify distinct regions in an image, with rare occurrence of local feature distributions. Notable among those works published recently, use local and global contrast, spectral analysis of the entire image or graph based feature mapping. Whereas, we propose a novel unsupervised method using color compactness and statistical modeling of the background cues, to segment the salient foreground region and thus the salient object. At the first stage of processing, the image is segmented into clusters using color feature. First component proposed for our saliency measure combines disparity in color and spatial distance between patches. In addition to rarity of feature, we propose another component for saliency computation that estimates the divergence of the color of a patch from those in the set of patches at the boundary of the image, representing the background. Combination of these two complementary components provides a much improved saliency map for salient object detection.We verify the performance of our proposed method of saliency detection on two popular benchmark datasets, with one or more salient regions and diverse saliency characteristics. Experimental results show that our method out-performs many existing state-of-the-art methods.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129697933","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: 2

Virtual garment simulation 虚拟服装仿真

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776167

Sailik Sengupta, P. Chaudhuri

引用次数: 8

Cross-domain clustering performed by transfer of knowledge across domains 通过跨领域的知识转移实现跨领域聚类

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776213

Suranjana Samanta, T. Selvan, Sukhendu Das

{"title":"Cross-domain clustering performed by transfer of knowledge across domains","authors":"Suranjana Samanta, T. Selvan, Sukhendu Das","doi":"10.1109/NCVPRIPG.2013.6776213","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776213","url":null,"abstract":"In this paper, we propose a method to improve the results of clustering in a target domain, using significant information from an auxiliary (source) domain dataset. The applicability of this method concerns the field of transfer learning (or domain adaptation), where the performance of a task (say, classification using clustering) in one domain is improved using knowledge obtained from a similar domain. We propose two unsupervised methods of cross-domain clustering and show results on two different categories of benchmark datasets, both having difference in density distributions over the pair of domains. In the first method, we propose an iterative framework, where the clustering in the target domain is influenced by the clusters formed in the source domain and vice-versa. Similarity/dissimilarity measures have been appropriately formulated using Euclidean distance and Bregman Divergence, for cross-domain clustering. In the second method, we perform clustering in the target domain by estimating local density computed using a non-parametric (NP) density estimator (due to less number of samples). Prior to clustering, the NP-density scattering in the target domain is modified using information of cluster density distribution in source domain. Results shown on real-world datasets suggest that the proposed methods of cross-domain clustering are comparable to the recent start-of-the-art work.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130012940","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

Feature preserving anisotropic diffusion for image restoration 特征保持各向异性扩散图像恢复

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776250

V. B. Surya Prasath, J. Moreno

引用次数: 6

Classification of hardwood species using ANN classifier 利用人工神经网络分类器对硬木树种进行分类

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776231

Arvind R. Yadav, M. Dewal, R. S. Anand, Sangeeta Gupta

引用次数: 39

Rate-invariant comparisons of covariance paths for visual speech recognition 视觉语音识别中协方差路径的率不变比较

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776200

Jingyong Su, Anuj Srivastava, F. Souza, Sudeep Sarkar

引用次数: 0

Design of Manipuri Keywords Spotting System using HMM 基于HMM的曼尼普尔关键词识别系统设计

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776249

Laishram Rahul, Salam Nandakishor, L. J. Singh, S. K. Dutta

{"title":"Design of Manipuri Keywords Spotting System using HMM","authors":"Laishram Rahul, Salam Nandakishor, L. J. Singh, S. K. Dutta","doi":"10.1109/NCVPRIPG.2013.6776249","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776249","url":null,"abstract":"This paper aims to discuss the implementation of phoneme based Manipuri Keyword Spotting System (MKWSS). Manipuri is a scheduled Indian language of Tibeto-Burman origin. Around 5 hours of read speech are collected from 4 male and 6 female speakers for development of database of MKWSS. The symbols of International Phonetic Alphabet (IPA)(revised in 2005) are used during the transcription of the data. A five state left to right Hidden Markov Model (HMM) with 32 mixture continuous density diagonal covariance Gaussian Mixture Model (GMM) per state is used to build a model for each phonetic unit. We have used HMM tool kit (HTK), version 3.4 for modeling the system. The system can recognize 29 phonemes and a non-speech event (silence) and will detect the present keywords formed by these phonemes. Continuous Speech data have been collected from 5 males and 8 females for analysing the performance of the system. The performance of the system depends on the ability of detection of the keywords. An overall performance of 65.24% is obtained from the phoneme based MKWSS.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133495676","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: 13