Chung-Yuan Lin

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The parallel connected component labeling used in binary image analysis is reconsidered in this paper for the high throughput and intermediate memory requirements problem on high dimensional image sequence. It is based on a proposed dual-parallel connected component labeling method. The main idea is to break the sequentiality of the labeling procedure by(More)
Automatic understanding of events happening at a site is the ultimate goal for many visual surveillance systems. Understanding of events requires that certain lower level computer vision tasks be performed. These include foreground detection, labeling foreground parts, and tracking targets. To achieve these tasks, it is necessary to build background(More)
This paper presents a new method for auto-focus digital camera system. Our approach differs from other common auto-focus approaches, since a focal window will be searched and tracked before applying auto-focus algorithm to move the lens toward the proper position. The advantages are increasing the focus reliability and flexibility than common auto-focus(More)
—Objects in the world exhibit complex interactions. When captured in a video sequence, some interactions manifest themselves as occlusions. A visual tracking system must be able to track objects which are partially or even fully occluded. Occlusion is a difficult problem in target tracking, especially when users need to track multiple targets simultaneously(More)
This paper presents an architecture design for a low cost and low complexity foreground object detection based on Multi-model Background Maintenance (MBM) algorithm [1]. The MBM framework basically contains two principal features. These features consist of static and dynamic pixels to represent the characteristic of background. Under this framework, a pure(More)
Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity(More)