2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)最新文献_第10页

A Deep Learning Approach to Appearance-Based Gaze Estimation under Head Pose Variations 头部姿态变化下基于外观的凝视估计的深度学习方法

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.155

Hsin-Pei Sun, Cheng-Hsun Yang, S. Lai

引用次数: 3

Local Behavior Analysis for Trajectory Classification Using Graph Embedding 基于图嵌入的轨迹分类局部行为分析

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.27

Rajkumar Saini, Pradeep Kumar, S. Dutta, P. Roy, U. Pal

{"title":"Local Behavior Analysis for Trajectory Classification Using Graph Embedding","authors":"Rajkumar Saini, Pradeep Kumar, S. Dutta, P. Roy, U. Pal","doi":"10.1109/ACPR.2017.27","DOIUrl":"https://doi.org/10.1109/ACPR.2017.27","url":null,"abstract":"Understanding motion patterns is of great importance to analyze the behavior of objects in the vigilance area. Grouping the motion patterns into clusters in such a way that similar motion patterns lie in same cluster and the inter-cluster variance is maximized. Variation in the duration of trajectory patterns in terms of time or number of points in them (even in the trajectories from same cluster) make it more difficult to correctly classify in respective clusters as a bijective mapping is not possible in such cases. In this paper, we have formulated the trajectory classification problem into graph based similarity problem using Douglas-Peucker (DP) algorithm and complete bipartite graphs. Local behavior of objects has been analyzed using their motion segments and Dynamic Time Warping (DTW) has been used for finding similarity among motion trajectories. Class-wise global and local costs have been computed using DTW and their fusion has been done using Particle Swarm Optimization (PSO) to improve the classification rate. Experiments have been performed using two public trajectory datasets, namely T15 and LabOmni. The proposed method yields encouraging results and outperforms the state of the art techniques.","PeriodicalId":426561,"journal":{"name":"2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132542400","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

Holistic Handwritten Uyghur Word Recognition Using Convolutional Neural Networks 基于卷积神经网络的整体手写维吾尔语词识别

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.104

Wujiahemaiti Simayi, A. Hamdulla, Cheng-Lin Liu

引用次数: 1

Simple and Effective Speech Enhancement for Visual Microphone 简单有效的视觉麦克风语音增强

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.41

Juhyun Ahn, Daijin Kim

{"title":"Simple and Effective Speech Enhancement for Visual Microphone","authors":"Juhyun Ahn, Daijin Kim","doi":"10.1109/ACPR.2017.41","DOIUrl":"https://doi.org/10.1109/ACPR.2017.41","url":null,"abstract":"Visual microphone is a technique that recovers the sound from a silent video. The simplest way to improve sound recovery performance of the visual microphone is by applying the traditional speech enhancement algorithms which are based on complicated filter designs or sound models. This paper proposes a simple and effective speech enhancement for visual microphone (SEVM) that suppress spectrum components with small amplitude than a predefined threshold value, which exploits the unique properties that the sound spectrum recovered from the visual microphone is relatively high and the noise spectrum generated motion estimation error and damped oscillation is relatively low. The proposed SEVM method can also be easily extended to a multichannel case that multiple speech signals are recovered from multiple cameras. Experimental results show the proposed SEVM method better performance than the traditional speech enhancement algorithms in terms of log-likelihood ratio (LLR), signal to noise ratio (SNR), segmental SNR (SegSNR) and cepstral distance (CEP). From these results, we convince that the proposed SEVM method that is adapted to the visual microphone is really simple and effective than the traditional speech enhancement methods that are just extended to the visual microphone as a post-processing.","PeriodicalId":426561,"journal":{"name":"2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126015463","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: 5

Fabric Defect Detection Based on Gabor Filter and Tensor Low-Rank Recovery 基于Gabor滤波和张量低秩恢复的织物缺陷检测

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.37

Guangshuai Gao, Chaodie Liu, Zhoufeng Liu, Chunlei Li, Ruimin Yang

{"title":"Fabric Defect Detection Based on Gabor Filter and Tensor Low-Rank Recovery","authors":"Guangshuai Gao, Chaodie Liu, Zhoufeng Liu, Chunlei Li, Ruimin Yang","doi":"10.1109/ACPR.2017.37","DOIUrl":"https://doi.org/10.1109/ACPR.2017.37","url":null,"abstract":"Fabric defect detection plays a curial step in the quality control of textiles. Existing fabric defect detection methods are lack of adaptability and have a poor detection performance. A novel fabric defect detection method based on Gabor filter and tensor low-rank recovery was proposed in this paper. Defect-free fabric images have the specified direction, while defects damage their regularity of direction. Therefore, the direction feature is curial for fabric defect detection. For different kinds of fabric image, the direction information is also distinct. In order to characterize the direction information for all kinds of fabric image, we adopted a bank of Gabor directional filters to extract directional information, and generated the directional Gabor filtered maps. Thereafter, an efficient TRPCA model is proposed to decompose the feature tensor which is generated by stacking the feature vector of all the feature maps into a low-rank tensor and a sparse tensor by the alternating direction method of multipliers according to the tensor recovery (ADMM-TR) techniques. Finally, the saliency map generated by the sparse tensor part is segmented via the improved adaptive thresholding algorithm to locate the defective regions. Experimental results demonstrate that our algorithm is superior to the state-of-the-art.","PeriodicalId":426561,"journal":{"name":"2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126698205","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

Occlusion Object Detection via Collaborative Sensing Deep Convolution Network 基于协同感知深度卷积网络的遮挡目标检测

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.90

Ce Li, Xinyu Zhao, Hao Liu, Limei Xiao

引用次数: 2

Enhancing Protein-ATP and Protein-ADP Binding Sites Prediction Using Supervised Instance-Transfer Learning 利用监督实例迁移学习增强蛋白质- atp和蛋白质- adp结合位点预测

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.9

Junda Hu, Zi Liu, Dong-Jun Yu

{"title":"Enhancing Protein-ATP and Protein-ADP Binding Sites Prediction Using Supervised Instance-Transfer Learning","authors":"Junda Hu, Zi Liu, Dong-Jun Yu","doi":"10.1109/ACPR.2017.9","DOIUrl":"https://doi.org/10.1109/ACPR.2017.9","url":null,"abstract":"Protein-ATP and protein-ADP interactions are ubiquitous in a wide variety of biological processes. Accurately identifying ATP-binding and ADP-binding sites or pockets is of significant importance for both protein function analysis and drug design. Although much progress has been made, challenges remain, especially in the post-genome era where large volume of proteins without being functional annotated are quickly accumulated. In this study, we report an instance-transfer-learning-based predictor, ATP&ADPsite, to target both ATP-binding and ADP-binding residues from protein sequence and structural information. ATP&ADPsite first employs evolutionary information, predicted secondary structure, and predicted solvent accessibility to represent each residue sample. In the above feature space, a supervised instance-transfer-learning method is proposed to improve the ATP-binding/ADP-binding residues prediction by combining ATP-binding and ADP-binding proteins. Random under-sampling is lastly employed to solve the imbalanced data learning problem. Experimental results demonstrate that the proposed ATP&ADPsite achieves a better prediction performance and outperforms many existing sequence-based predictors. The ATP&ADPsite web-server is available at http://csbio.njust.edu.cn/bioinf/ATP&ADPsite.","PeriodicalId":426561,"journal":{"name":"2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124386134","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: 1

Detecting Driver's Braking Intention Using Recurrent Convolutional Neural Networks Based EEG Analysis 基于循环卷积神经网络的脑电分析检测驾驶员制动意图

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.86

Suk-Min Lee, Jeong-Woo Kim, Seong-Whan Lee

{"title":"Detecting Driver's Braking Intention Using Recurrent Convolutional Neural Networks Based EEG Analysis","authors":"Suk-Min Lee, Jeong-Woo Kim, Seong-Whan Lee","doi":"10.1109/ACPR.2017.86","DOIUrl":"https://doi.org/10.1109/ACPR.2017.86","url":null,"abstract":"Driving assistance system has been recently studied to prevent emergency braking situations by combining external information on radar or camera devices and internal information on driver's intention. Electroencephalography (EEG) is an effective method to read user's intention with high temporal resolution. Our proposed system is mainly contributed to detecting driver's braking intention prior to stepping on the brake pedal in the emergency situation. We investigated early event-related potential (ERP) curves evoked by visual sensory process in emergency situation by using recurrent convolutional neural networks (RCNN) model. RCNN model has advantages to capture contextual and spatial patterns of brain signal. RCNN model is composed of a convolutional layer, two recurrent convolutional layers (RCLs), and a softmax layer. Fourteen participants drove for 120 minutes with two types of emergency situations and a normal driving situation in a virtual driving environment. In this article, early ERP showed a potential to be used for classifying the driver's braking intention. The classification performances based on RCNN and regularized linear discriminant analysis (RLDA) at 200 ms post-stimulus time were 0.86 AUC score and 0.61 AUC score respectively. Following the results, braking intention was recognized at 380 ms earlier based on early ERP patterns using RCNN model than the brake pedal. Our system could be applied to other brain-computer interface (BCI) system for minimizing detection time by capturing early ERP curves based on RCNN model.","PeriodicalId":426561,"journal":{"name":"2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124506264","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: 5

Estimating Food Calories for Multiple-Dish Food Photos 估算多道菜食物照片的食物卡路里

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.145

Takumi Ege, Keiji Yanai

引用次数: 32

CNN-Based Pedestrian Orientation Estimation from a Single Image 基于cnn的单幅图像行人方向估计

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.10

Kojiro Kumamoto, K. Yamada

引用次数: 4