Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real time stereo vision using exponential step cost aggregation on GPU
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
An Auto-tuning Solution to Data Streams Clustering in OpenCL
CSE '11 Proceedings of the 2011 14th IEEE International Conference on Computational Science and Engineering
High Performance Stereo Vision Designed for Massively Data Parallel Platforms
IEEE Transactions on Circuits and Systems for Video Technology
Implementation of Motion Estimation Based on Heterogeneous Parallel Computing System with OpenCL
HPCC '12 Proceedings of the 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems
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Heterogeneous computing systems increase the performance of parallel computing in many domains of general purpose computing with CPU, GPU and other accelerators. With Hardware developments, the software developments like Compute Unified Device Architecture (CUDA) and Open Computing Language (OpenCL) try to offer a simple and visual framework for parallel computing. But it turns out to be more difficult than programming on CPU platform for optimization of performance. For one kind of parallel computing application, there are different configurations and parameters for various hardware platforms. In this paper, we apply the Hybrid Multi-cores Parallel Programming (HMPP) to automatically generate tunable code for GPU platform and show the results of implementation of Stereo Matching with detailed comparison with C code version and manual CUDA version. The experimental results show that default and optimized HMPP have approximately the same performance and the better quality of disparity map compared with CUDA implementation.