Detecting Man-Made Objects in Gray-Level Images

• Main Authors: Yen-Bo Huang, 黃彥博
• Other Authors: Young-Chang Hou
• Format: Others
• Language: en_US
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/67844884759774496510

碩士 === 國立中央大學 === 資訊管理研究所 === 90 === Reconnaissance has for centuries been at the heart of all thinking about infantry tactics. Nowadays, reconnaissance is increasingly assigned to machines. These machines are equipped with build-in sensors and automatic target recognition system (ATR) in it. We proposed a framework to perform the detecting phase in ATR systems. This system can label approximate man-made object contours in gray-level images via gradient image analysis and straight lines detection. We first use the Sobel operator to produce a gradient image. Then, use local fuzzy image contrast enhancement with a region criterion to degrade background and enhance both weak and strong edges. After the processes of binarization, small component removal, and edge thinning, we apply the modified Hough transform to detect long straight lines. Via these lines, we can label the region of interest and use them to produce initial object contours. At last of all, we apply the adaptive active contour model to perform contour approximation. Our experiment is performed on a PC and the experimental result shows that it works well under most environmental condition.