1000 frame/sec Stereo Matching VLSI Processor with Adaptive Window-Size Control

2006 ◽  
Author(s):  
Masanori Hariyama ◽  
Naoto Yokoyama ◽  
Michitaka Kameyama
Keyword(s):  
Stereo Matching ◽  
Size Control ◽  
Window Size ◽  
Adaptive Window

Stereo Vision VLSI Processor Based on Pixel-Serial and Window-Parallel Architecture

2000 ◽  
Vol 12 (5) ◽  
pp. 521-526
Author(s):  
Masanori Hariyama ◽  
◽  
Michitaka Kameyama
Keyword(s):  
Stereo Vision ◽  
Stereo Matching ◽  
Parallel Architecture ◽  
Window Size ◽  
Input Image ◽  
General Purpose ◽  
Image Size ◽  
Matching Algorithm ◽  
Processing Elements ◽  
General Purpose Processor

This article presents a stereo-matching algorithm to establish reliable correspondence between images by selecting a desirable window size for SAD (Sum of Absolute Differences) computation. In SAD computation, parallelism between pixels in a window changes depending on its window size, while parallelism between windows is predetermined by the input-image size. Based on this consideration, a window-parallel and pixel-serial architecture is proposed to achieve 100% utilization of processing elements. Performance of the VLSI processor is evaluated to be more than 10,000 times higher than that of a general-purpose processor.

ROBUST AND FAST TRACKING ALGORITHM IN VIDEO SEQUENCES BY ADAPTIVE WINDOW SIZING USING A NOVEL ANALYSIS ON SPATIOTEMPORAL GRADIENT POWERS

2007 ◽  
Vol 16 (02) ◽  
pp. 305-317 ◽  
Author(s):  
PAYMAN MOALLEM ◽  
ALIREZA MEMARMOGHADDAM ◽  
MOHSEN ASHOURIAN
Keyword(s):  
Window Size ◽  
Image Sequences ◽  
Tracking Algorithm ◽  
Video Sequences ◽  
Occupancy Rate ◽  
Tracking Method ◽  
Fast Tracking ◽  
Adaptive Window ◽  
Real Video ◽  
Adaptively Adjusting

Success of a tracking method depends largely on choosing the suitable window size as soon as the target size changes in image sequences. To achieve this goal, we propose a fast tracking algorithm based on adaptively adjusting tracking window. Firstly, tracking window is divided into four edge subwindows, and a background subwindow around it. Then, by calculating the spatiotemporal gradient power ratios of the target in each subwindow, four proper expansion vectors are associated with any tracking window sides such that the occupancy rate of the target in tracking window should be maintained within a specified range. In addition, since temporal changing of target is evaluated in calculating these vectors, we estimate overall target displacement by sum of expansion vectors. Experimental results using various real video sequences show that the proposed algorithm successfully track an unknown textured target in real time, and is robust to dynamic occlusions in complex noisy backgrounds.

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