Edge Detection for Pattern Recognition: A Survey <s> Object recognition <s> A moving object segmentation algorithm based on edge detection is put forward in this paper. The algorithm reconstructs background from image sequence. It uses edge detection to eliminate the disturbance of shadow, and uses Gaussian filter to eliminate noise. The background subtraction is used to extract moving object. The experimental results show that this algorithm effectually avoids the influence of noise and shadow and accurately extracts the moving objects. <s> BIB001 </s> Edge Detection for Pattern Recognition: A Survey <s> Object recognition <s> As the usage of multimedia data increasing rapidly, how to get the video data we need efficiently become so important. Recent advances in multimedia technologies allow the capture and storage of video data with relatively inexpensive computers. Problem Statement: However, without appropriate search techniques all these data are hardly usable. Users want to query the content instead of the raw video data. Today research is focused on video retrieval. Content-based search and retrieval of video data becomes a challenging and important problem. To retrieve the content of the video the user need automatic classification and categorization of the visual content. Approach: In this study a novel algorithm is proposed for shot detection using Genetic Edge Histogram and 2-D discreate cosine transform as a feature and multiple features like color, motion, shape and SIFT are used to retrieve the similar shots. Results and Conclusion: The combination of proposed features yields good results interms of precision and recall. <s> BIB002
The boundary of the objects in movements represented as directional edges have important cues for the recognition and tracking (Das, Dipankar, Yoshinori, & Yoshinori, 2009; G. Wang, Guangwei, Zenggang, Yihua, & Conghuan, 2013 . The objects detected can be used for various application such as video retrieval BIB002 , change detection (Niemeyer, Marpu, & Nussbaum, n.d.) , segmentation (P. BIB001 , in perception of motion  and category level detection (Hock & Nichols, 2013; Ponce et al., 2007 . Local edge features and region based methods seems to indicate a better quality of edge features (Hock & Nichols, 2013; Ponce et al., 2007; Tang Xusheng et al., 2009 . The the edge detection methods on color cue has proved to be a useful tool for object recognition in images (Jiqiang Song, Jiqiang, Min, & Lyu, n.d.; Tsang & Tsang, n.d.) . Grouping and fusion of edge features have also a positive impact on the detection of objects (Amit, 2002; Antoniu & Eduard, 1994; Chunxin, Wang, & Xu, 2009; J. Kim & Chen, 2003; Mednieks & Ints, 2008; Meurant, 1992; Mu, Nan, Xin, & Ziheng, 2015; Srikantha, Abhilash, & Juergen, 2014; Stathaki, 2011; Torralba, Antonio, Murphy, & Freeman, 2006 . Object tracking is another important area in object detection research that make use of a wide range of edge detection techniques (Chunxin et al., 2009; Gao et al., n.d.; Wei Jyh Heng & Ngan, 2001; Mukherjee et al., 2010 . In tracking problems, the speed of tracking and detection of edges become an important problem, and that leads to development of hardware friendly approaches (W. J. Heng & Ngan, n.d.) . Optical flow algorithms have been used along with particle filters to implement realtime tracking (Mae & Shirai, n.d.; Shirai et al., 1996) .