A novel approach to the detection of small objects with low contrast

Authors:
Feng-Yang Hsieh;Chin-Chuan Han;Nai-Shen Wu;Thomas C. Chuang;Kuo-Chin Fan
Affiliations:
Institute of Computer Science and Information Engineering, National Central University, Chung-Li, Taiwan, ROC;Department of Computer Science and Information Engineering, National United University, Miao-Li, Taiwan, ROC;Institute of Computer Science and Information Engineering, National Central University, Chung-Li, Taiwan, ROC;Department of Computer Science and Information Engineering, Vanung University, Chung-Li, Taiwan, ROC;Institute of Computer Science and Information Engineering, National Central University, Chung-Li, Taiwan, ROC
Venue:
Signal Processing
Year:
2006

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Abstract

This paper proposes an effective approach to the detection of small objects by employing watershed-based transformation. In our work, moving objects with small size and low contrast are first detected from an image sequence which was captured from a video camera. The proposed detection system includes two main modules, region of interest (ROI) locating and contour extraction. In the former module, an image differencing technique is first employed on two contiguous image frames to generate rough candidate objects appearing in the images. A novel neighboring encoding technique along with the image differencing technique is devised here to effectively reduce noise which usually affects the performance of detection results, especially for small objects. Next, we find the bounded rectangles enclosing the denoised candidate objects, which in turn generate ROI. However, the results of the previous process fail to characterize object contours. To do this, we need to devise a contour extraction technique. Unfortunately, satisfactory results cannot be obtained by applying traditional contour extraction methods. To solve this problem, the watershed-based transformation along with the region matching technique is employed to obtain better object contours. Experimental results validate that the proposed approach is indeed feasible and effective in detecting objects with small size and low contrast.