2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)最新文献_第7页

Iris biometrie: Is the near-infrared spectrum always the best? 虹膜生物识别:近红外光谱总是最好的吗?

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486616

Mohammed A. M. Abdullah, J. Chambers, W. L. Woo, S. Dlay

引用次数: 11

Offline handwritten Devanagari word recognition: Information fusion at feature and classifier levels 离线手写德文字识别:特征和分类器层次的信息融合

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486597

Bikash Shaw, U. Bhattacharya, S. K. Parui

{"title":"Offline handwritten Devanagari word recognition: Information fusion at feature and classifier levels","authors":"Bikash Shaw, U. Bhattacharya, S. K. Parui","doi":"10.1109/ACPR.2015.7486597","DOIUrl":"https://doi.org/10.1109/ACPR.2015.7486597","url":null,"abstract":"This article presents our recent study on fusion of information at feature and classifier output levels for improved performance of offline handwritten Devanagari word recognition. We consider here two state-of-the-art features, viz., Directional Distance Distribution (DDD) and Gradient-Structural-Concavity (GSC) features along with multi-class SVM classifiers. Here, we study various combinations of DDD features along with one or more features from the GSC feature set. We experiment by presenting different combined feature vectors as input to SVM classifiers. Also, the output vectors of different SVM classifiers fed with different feature vectors are combined by another SVM classifier. The combination of the outputs of two SVMs each being fed with a different feature vector provides superior performance to the performance of a single SVM classifier fed with the combined feature vector. Experimental results are obtained on a large handwritten Devanagari word sample image database of 100 Indian town names. The recognition results on its test samples show that SVM recognition output of DDD features combined with the SVM output of GSC features improves the final recognition accuracy significantly.","PeriodicalId":240902,"journal":{"name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115034751","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: 12

Privacy-conscious human detection using low-resolution video 使用低分辨率视频的具有隐私意识的人类检测

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486519

Nobuhiro Miyazaki, K. Tsuji, Mingxie Zheng, M. Nakashima, Yuji Matsuda, E. Segawa

引用次数: 9

WA-ICP algorithm for tackling ambiguous correspondence 用于处理模糊通信的WA-ICP算法

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486469

Rong Wang, Z. Geng

引用次数: 3

Script independent scene text segmentation using fast stroke width transform and GrabCut 脚本独立的场景文本分割使用快速笔画宽度变换和GrabCut

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486484

Jay H. Bosamiya, Palash Agrawal, P. Roy, B. Raman

引用次数: 14

Segmentation and recognition of text written in 3D using Leap motion interface 基于Leap运动界面的三维文本分割与识别

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486561

Chelsi Agarwal, D. P. Dogra, Rajkumar Saini, P. Roy

{"title":"Segmentation and recognition of text written in 3D using Leap motion interface","authors":"Chelsi Agarwal, D. P. Dogra, Rajkumar Saini, P. Roy","doi":"10.1109/ACPR.2015.7486561","DOIUrl":"https://doi.org/10.1109/ACPR.2015.7486561","url":null,"abstract":"In this paper, we present a word extraction and recognition methodology from online cursive handwritten text-lines recorded by Leap motion controller The online text, drawn by 3D gesture in air, is distinct from usual online pen-based strokes. The 3D gestures are recorded in air, hence they produce often non-uniform text style and jitter-effect while writing. Also, due to the constraint of writing in air, the pause of stroke-flow between words is missing. Instead all words and lines are connected by a continuous stroke. In this paper, we have used a simple but effective heuristic to segment words written in air. Here, we propose a segmentation methodology of continuous 3D strokes into text-lines and words. Separation of text lines is achieved by heuristically finding the large gap-information between end and start-positions of successive text lines. Word segmentation is characterized in our system as a two class problem. In the next phase, we have used Hidden Markov Model-based approach to recognize these segmented words. Our experimental validation with a large dataset consisting with 320 sentences reveals that the proposed heuristic based word segmentation algorithm performs with accuracy as high as 80.3%c and an accuracy of 77.6% has been recorded by HMM-based word recognition when these segmented words are fed to HMM. The results show that the framework is efficient even with cluttered gestures.","PeriodicalId":240902,"journal":{"name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114719080","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: 26

Principal affinity based cross-modal retrieval 基于主亲和力的跨模态检索

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486479

Jian Liang, Dong Cao, R. He, Zhenan Sun, T. Tan

引用次数: 3

Multiple-dataset traffic sign classification with OneCNN 基于OneCNN的多数据集交通标志分类

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486576

Fran Jurisic, Ivan Filkovic, Z. Kalafatić

引用次数: 22

Skeleton based action recognition with convolutional neural network 基于骨架的卷积神经网络动作识别

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486569

Yong Du, Y. Fu, Liang Wang

{"title":"Skeleton based action recognition with convolutional neural network","authors":"Yong Du, Y. Fu, Liang Wang","doi":"10.1109/ACPR.2015.7486569","DOIUrl":"https://doi.org/10.1109/ACPR.2015.7486569","url":null,"abstract":"Temporal dynamics of postures over time is crucial for sequence-based action recognition. Human actions can be represented by the corresponding motions of articulated skeleton. Most of the existing approaches for skeleton based action recognition model the spatial-temporal evolution of actions based on hand-crafted features. As a kind of hierarchically adaptive filter banks, Convolutional Neural Network (CNN) performs well in representation learning. In this paper, we propose an end-to-end hierarchical architecture for skeleton based action recognition with CNN. Firstly, we represent a skeleton sequence as a matrix by concatenating the joint coordinates in each instant and arranging those vector representations in a chronological order. Then the matrix is quantified into an image and normalized to handle the variable-length problem. The final image is fed into a CNN model for feature extraction and recognition. For the specific structure of such images, the simple max-pooling plays an important role on spatial feature selection as well as temporal frequency adjustment, which can obtain more discriminative joint information for different actions and meanwhile address the variable-frequency problem. Experimental results demonstrate that our method achieves the state-of-art performance with high computational efficiency, especially surpassing the existing result by more than 15 percentage on the challenging ChaLearn gesture recognition dataset.","PeriodicalId":240902,"journal":{"name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124869399","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: 352

Semi-global depth from focus 半全局深度的焦点

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI: 10.1109/ACPR.2015.7486578

Wentao Liu, Xihong Wu

引用次数: 1