We consider the extraction of accurate silhouettes of foreground objects in combined color image and depth map data.This is of relevance for applications such as altering the contents of a scene,or changing the depths...We consider the extraction of accurate silhouettes of foreground objects in combined color image and depth map data.This is of relevance for applications such as altering the contents of a scene,or changing the depths of contents for display purposes in 3DTV,object detection,or scene understanding.To展开更多
We propose a novel scheme based on clustering analysis in color space to solve text segmentation in complex color images. Text segmentation includes automatic clustering of color space and foreground image generation....We propose a novel scheme based on clustering analysis in color space to solve text segmentation in complex color images. Text segmentation includes automatic clustering of color space and foreground image generation. Two methods are also proposed for automatic clustering: The first one is to determine the optimal number of clusters and the second one is the fuzzy competitively clustering method based on competitively learning techniques. Essential foreground images obtained from any of the color clusters are combined into foreground images. Further performance analysis reveals the advantages of the proposed methods.展开更多
基金supported by Key Project No. 61332015 of the National Natural Science Foundation of ChinaProject Nos.ZR2013FM302 and ZR2017MF057 of the Natural Science Found of Shandong
文摘We consider the extraction of accurate silhouettes of foreground objects in combined color image and depth map data.This is of relevance for applications such as altering the contents of a scene,or changing the depths of contents for display purposes in 3DTV,object detection,or scene understanding.To
文摘We propose a novel scheme based on clustering analysis in color space to solve text segmentation in complex color images. Text segmentation includes automatic clustering of color space and foreground image generation. Two methods are also proposed for automatic clustering: The first one is to determine the optimal number of clusters and the second one is the fuzzy competitively clustering method based on competitively learning techniques. Essential foreground images obtained from any of the color clusters are combined into foreground images. Further performance analysis reveals the advantages of the proposed methods.
