Markov random field modeling in computer vision
Markov random field modeling in computer vision
Quantitative planar region detection
International Journal of Computer Vision
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision: Three-Dimensional Data from Images
Computer Vision: Three-Dimensional Data from Images
Computer and Robot Vision
Calculating Dense Disparity Maps from Color Stereo Images, an Efficient Implementation
International Journal of Computer Vision
Autocalibration from Planar Scenes
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Stereo Vision-Based Obstacle Detection for Partially Sighted People
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
Scene Classification from Dense Disparity Maps in Indoor Environments
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Markov random field modeled range image segmentation
Pattern Recognition Letters
Range image segmentation based on randomized Hough transform
Pattern Recognition Letters
Two-View Multibody Structure-and-Motion with Outliers through Model Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Embedded Image Processing on the TMS320C6000 DSP: Examples in Code Composer Studio and MATLAB
Embedded Image Processing on the TMS320C6000 DSP: Examples in Code Composer Studio and MATLAB
Disparity/segmentation analysis: matching with an adaptive window and depth-driven segmentation
IEEE Transactions on Circuits and Systems for Video Technology
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An embedded system is developed to segment stereo images using disparity. The recent developments in the embedded system architecture have allowed real time implementation of low-level vision tasks such as stereo disparity computation. At the same time, an intermediate level task such as segmentation is rarely attempted in an embedded system. To solve the planar surface segmentation problem, which is iterative in nature, our system implements a Segmentation–Estimation framework. In the segmentation phase, segmentation labels are assigned based on the underlying plane parameters. Connected component analysis is carried out on the segmentation result to select the largest spatially connected area for each plane. From the largest areas, the parameters for each plane are reestimated. This iterative process was implemented on TMS320DM642 based embedded system that operates at 3–5 frames per second on images of size 320 × 240.