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손 동작 인식을 위한 Optical Flow Orientation Histogram
Dhi Aurrahman,Nurul Arif Setiawan,Chi-Min Oh,Chil Woo Lee 한국HCI학회 2008 한국HCI학회 학술대회 Vol.2008 No.2
Hand motion classification problem is considered as basis for sign or gesture recognition. We promote optical flow as main feature extracted from images sequences to simultaneously segment the motion's area by its magnitude and characterize the motion's directions by its orientation. We manage the flow orientation histogram as motion descriptor, A motion is encoded by concatenating the flow orientation histogram from several frames. We utilize simple histogram matching to classify the motion sequences. Attempted experiments show the feasibility of our method for hand motion localization and classification.
PCA-SVM for HOG-family Evaluation in Human Detection Problem
Dhi Aurrahman,Nurul Arif Setiawan,Dae-Jin Kim,Ki-Tae Bae,Chil-Woo Lee 대한전자공학회 2008 ICEIC:International Conference on Electronics, Inf Vol.1 No.1
Support Vector Machine (SVM) is one of powerful learning machine and has been applied to varying task with generally acceptable performance. The success of SVM for classification tasks in one domain is affected by features which represent the instance of specific class. Given the representative and discriminative features, SVM learning will give good generalization and consequently we can obtain good classifier. In this paper, we will assess the problem of feature choices for human detection tasks and measure the performance of each feature. Here we will consider HOG-family feature. We proposed the multi-scale HOG as a newly family member in this feature group. We also combine SVM with Principal Component Analysis (PCA) to reduce dimension of features and enhance the evaluation speed while retaining most of discriminative feature vectors.
Human Detection을 위한 Bayesian Logistic Regression
Dhi Aurrahman,Nurul Arif Setiawan,Chil Woo Lee(이칠우) 한국HCI학회 2008 한국HCI학회 학술대회 Vol.2008 No.2
The possibility to extent the solution in human detection problem for plug-in on vision-based Human Computer Interaction domain is very attractive, since the successful of the machine leaning theory and computer vision marriage. Bayesian logistic regression is a powerful classifier performing sparseness and high accuracy. The difficulties of finding people in an image will be conquered by implementing this Bayesian model as classifier. The comparison with other massive classifier e.g. SVM and RVM will introduce acceptance of this method for human detection problem. Our experimental results show the good performance of Bayesian logistic regression in human detection problem, both in trade-off curves (ROC, DET) and real-implementation compare to SVM and RVM.
Chamfer Matching을 이용한 실시간 템플릿 기반 개체 검출 및 추적
( Md. Zahidul Islam ),( Nurul Arif Setiawan ),김형관 ( Hyung-kwan Kim ),이칠우 ( Chil-woo Lee ) 한국정보처리학회 2008 한국정보처리학회 학술대회논문집 Vol.15 No.1
In this paper we describe an approach for template based detection and tracking of objects by chamfer matching in real time video. Detecting and tracking of any objects is the key problem in computer vision. In our case we try for hand and head of human for detection and tracking by chamfer matching technique. Matching involves correlating the templates with the distance transformed scene and determining the locations where the mismatch is below a certain user defined threshold.