Browsing by Author "Su, Tzung-Min"

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3-D human posture recognition system using 2-D shape features
(2007)
Hu, Jwu-Sheng
;
Su, Tzung-Min
;
Lin, Pei-Ching
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper presents an integrated framework for recognizing 3D human posture from 2D images. A flexible combinational algorithm motivated by the novel view expressed by Cyr and Kimia [1] is proposed to generate the aspects of 3D human postures as the posture prototype using features extracted from the collected 2D images sampled at random intervals from the viewing sphere. Frequency and phase information of the posture are calculated from the Fourier descriptors (FDs) of the sampled points on the posture contour as the main and assistant features to extract the characteristic views as the aspects. Moreover, a modified particle filter is applied to improve the robustness of human posture recognition for continuous monitoring. Experimental trials on synthetic and real sequences have shown the effectiveness of the proposed method.
3D object tracking using mean-shift and similarity-based aspect-graph modeling
(2007)
Hu, Jwu-Sheng
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Su, Tzung-Min
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Juan, Chung-Wei
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Wang, George
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電控工程研究所
The mean shift algorithm is a popular method in the field of 2D object tracking due to its simplicity and robustness over slight variations of lighting condition, scale and view-point over time. However, the appearance of 3D object might have distinctive variations for different viewpoints over time. In this work, a novel method for tracking 3D objects using mean-shift algorithm and a 3D object database is proposed to achieve a more precise tracking. A 3D obctject database using similarity-based aspect-graph is built from 2D images sampled at random intervals from the viewing sphere. Contour and color features of each 2D image are used for modeling the 3D object database. To conduct tracking, a suitable object model is selected from the database and the mean-shift tracking is applied to find the local minima of a similarity measure between the color histourams of the object model and the target image. The effectiveness of the proposed method is demonstrated by experiments with objects rotating and translating in space.
FLEXIBLE 3D OBJECT RECOGNITION FRAMEWORK USING 2D VIEWS VIA A SIMILARITY-BASED ASPECT-GRAPH APPROACH
(2008-09-01)
Hu, Jwu-Sheng
;
Su, Tzung-Min
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This work presents a flexible framework for recognizing 3D objects from 2D views. Similarity-based aspect-graph, which contains a set of aspects and prototypes for these aspects, is employed to represent the database of 3D objects. An incremental database construction method that maximizes the similarity of views in the same aspect and minimizes the similarity of prototypes is proposed as the core of the framework to build and update the aspect-graph using 2D views randomly sampled from a viewing sphere. The proposed framework is evaluated on various object recognition problems, including 3D object recognition, human posture recognition and scene recognition. Shape and color features are employed in different applications with the proposed framework and the top three matching rates show the efficiency of the proposed method.
Robust background subtraction with shadow and highlight removal for indoor surveillance
(2007)
Hu, Jwu-Sheng
;
Su, Tzung-Min
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This work describes a robust background subtraction scheme involving shadow and highlight removal for indoor environmental surveillance. Foreground regions can be precisely extracted by the proposed scheme despite illumination variations and dynamic background. The Gaussian mixture model (GMM) is applied to construct a color-based probabilistic background model (CBM). Based on CBM, the short-term color-based background model (STCBM) and the long-term color-based background model (LTCBM) can be extracted and applied to build the gradient-based version of the probabilistic background model (GBM). Furthermore, a new dynamic cone-shape boundary in the RGB color space, called a cone-shape illumination model (CSIM), is proposed to distinguish pixels among shadow, highlight, and foreground. A novel scheme combining the CBM, GBM, and CSIM is proposed to determine the background which can be used to detect abnormal conditions. The effectiveness of the proposed method is demonstrated via experiments with several video clips collected in a complex indoor environment.
Robust background subtraction with shadow and highlight removal for indoor surveillance
(2006)
Hu, Jwu-Sheng
;
Su, Tzung-Min
;
Jeng, Shr-Chi
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This work describes a new 3D cone-shape illumination model (CSIM) and a robust background subtraction scheme involving shadow and highlight removal for indoor-environmental surveillance. Foreground objects can be precisely extracted for various post-processing procedures such as recognition. Gaussian mixture model (GMM) is applied to construct a color-based probabilistic background model (CBM) that contains the short-term color-based background model (STCBM) and the long-term color-based background model (LTCBM). STCBM and LTCBM are then proposed to build the gradient-based version of the probabilistic background model (GBM) and the CSIM. In the CSIM, a new dynamic cone-shape boundary in the RGB color space is proposed to distinguish pixels among shadow, highlight and foreground. Furthermore, CBM can be used to determine the threshold values of CSIM. A novel scheme combining the CBM, GBM and CSIM is proposed to determine the background. The effectiveness of the proposed method is demonstrated via experiments in a complex indoor environment.
Robust environmental change detection using PTZ camera via spatial-temporal probabilistic modeling
(2007-06-01)
Hu, Jwu-Sheng
;
Su, Tzung-Min
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes a novel procedure for detecting environmental changes by using a pan-tilt-zoom (PTZ) camera. Conventional approaches based on pixel space and stationary cameras need time-consuming image registration to yield pixel statistics. This work proposes an alternative approach to describe each scene with a Gaussian mixture model (GMM) via a spatial-temporal statistical method. Although details of the environment covered by the camera are lost; this model is efficient and robust in recognizing scene and detecting scene changes in the environment. Moreover, the threshold selection for separating different environmental changes is convenient by using the proposed framework. The effectiveness of the proposed method is demonstrated experimentally in an office environment.
Shape Memorization and Recognition of 3D Objects Using a Similarity-Based Aspect-Graph Approach
(2009-01-01)
Hu, Jwu-Sheng
;
Su, Tzung-Min
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This work proposes an incremental combinational algorithm to generate the prototype of a 3D object using 2D images randomly sampled from a viewing sphere. Similarity-based aspect-graph, which contains a set of aspects and prototypes for these aspects, is employed to represent the database of 3D objects. Furthermore, the proposed algorithm is based on low-level features and similarity measures between the features. In this work, the Fourier descriptor and point-to-point lengths are adopted as features, and three similarity measures, called the 1-norm, 2-norm, and K-L distance, are adopted to extract characteristic views. The effectiveness of the proposed algorithm is demonstrated by experiments with an updating mechanism.
Vision-based indoor scene cognition using a spatial probabilistic modeling method
(2006)
Hu, Jwu-Sheng
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Su, Tzung-Min
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Huang, Heng-Chia
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Lin, Pei-Ching
;
電控工程研究所
This work describes a vision-based approach to recognize scene in the indoor environment. The proposed method represents each scene captured by a Pan-Tilt-Zoom (PTZ) camera with a blob model using spatial probabilistic modeling. Although the details of the scene covered by the camera are lost, this model is efficient in memorizing the scene characteristics and is robust against image distortions. Furthermore, multi-view recognition is studied to increase the precision of scene cognition via a partial knowledge of the scene. The images captured in the same location with different view angles are collected to extract the scene characteristics in order to decrease the memory storage size for each location. The effectiveness of the method is demonstrated by experiments in an unstructured indoor environment.