Video Segmentation and Its Applications by King Ngi Ngan, Hongliang Li

By King Ngi Ngan, Hongliang Li

Video segmentation has develop into one of many center components in visible sign processing examine. the target of Video Segmentation and Its purposes is to offer the newest advances in video segmentation and research innovations whereas protecting the theoretical methods, actual functions and strategies being constructed within the desktop imaginative and prescient and video research group. The publication also will offer researchers and practitioners a entire knowing of state of the art of video segmentation recommendations and a source for power functions and winning practice.

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By King Ngi Ngan, Hongliang Li

Video segmentation has develop into one of many center components in visible sign processing examine. the target of Video Segmentation and Its purposes is to offer the newest advances in video segmentation and research innovations whereas protecting the theoretical methods, actual functions and strategies being constructed within the desktop imaginative and prescient and video research group. The publication also will offer researchers and practitioners a entire knowing of state of the art of video segmentation recommendations and a source for power functions and winning practice.

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4 Towards Object Driven Segmentation A typical work of object driven segmentation is called “co-segmentation”, which aims to segment the similar object from images. The main idea of co-segmentation is to discover common objects from image pairs based on the assumption that each image contains the foregrounds with similar color, texture, or shape. The first row of Fig. 11 shows two image pairs with similar objects in the foreground but different 18 H. N. Ngan backgrounds, which have been used in many works [55–58].

Comparing the Mitq segmentation results in Figs. 17, it can be shown that the SHVS algorithm is better than the other two search algorithms. 39): (a) α = 2, β = 1, and γ = 1 and (b) α = 2, β = 1, and γ = 0. Fig. 17 (a) SSS (b) FQS Segmentation Results of Mitq sequence Fig. 18 (a) Original sequences (Desired color objects are marked with arrows) (b) Segmentation results by SHVS Methods 2 Image Segmentation with Eigen-Subspace Projections 45 Fig. 4 Color Object Segmentation Using Fuzzy C-Means (FCM) with Eigen-Subspace Projection In this section, two eigen-based FCM methods by combing both PCT and FCM concepts together to achieve effective color segmentation were proposed.

Mean shift, mode seeking, and clustering, IEEE Trans. Pattem Anal. , 17, 790–799 (1995) 34. , Meer P. (1997). Robust analysis of feature spaces: color image segmentation, IEEE Conf. Comp. Vis. , Puerto Rico, 750–755. 35. : Mean shift: a robust analysis of feature spaces, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 603–619 (2002) 36. Li Y. : Video object cut and paste. SIGGRAPH 2005, 24, 595–600 (2005) 37. : Recovering Human body configurations: combining segmentation and recognition, IEEE Conf.

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