A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Computer Vision, Graphics, and Image Processing
Pixel-planes 5: a heterogeneous multiprocessor graphics system using processor-enhanced memories
SIGGRAPH '89 Proceedings of the 16th annual conference on Computer graphics and interactive techniques
Refining edges detected by a LoG operator
Computer Vision, Graphics, and Image Processing
Operating system concepts (3rd ed.)
Operating system concepts (3rd ed.)
International Journal of Computer Vision
A guided tour of computer vision
A guided tour of computer vision
Optimal surface reconstruction from planar contours
Communications of the ACM
Communication and Computation Patterns of Large Scale Image Convolutions on Parallel Architectures
Proceedings of the 8th International Symposium on Parallel Processing
Massively parallel isosurface extraction
VIS '92 Proceedings of the 3rd conference on Visualization '92
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In this paper, we propose a parallel convolution algorithm for estimating the partial derivatives of 2D and 3D images on distributed-memory MIMD architectures. Exploiting the separable characteristics of the Gaussian filter, the proposed algorithm consists of multiple phases such that each phase corresponds to a separated filter. Furthermore, it exploits both the task and data parallelism, and reduces communication through data redistribution. We have implemented the proposed algorithm on the Intel Paragon and obtained a substantial speedup using more than 100 processors. The performance of the algorithm is also evaluated analytically. The analytical results confirming with the experimental results indicate that the proposed algorithm scales very well with the problem size and number of processors. We have also applied our algorithm to the design and implementation of an efficient parallel scheme for the 3D surface tracking process. Although our focus is on 3D image data, the algorithm is also applicable to 2D image data, and can be useful for a myriad of important applications including medical imaging, magnetic resonance imaging, ultrasonic imagery, scientific visualization, and image sequence analysis.