Parallel Image Processing On A Massively Parallel Pyramid Architecture For Robotic Vision Systems
| dc.contributor.author | M Rabti, Al-Asaad | |
| dc.date.accessioned | 2016-10-06T02:11:51Z | |
| dc.date.available | 2016-10-06T02:11:51Z | |
| dc.date.issued | 1994-11 | |
| dc.description.abstract | Robots will not be able to do useful work until they can wander within and interact with dynamic environments. To do that they must be able to recognize and perceive realworld objects as they move about and change in real-time. F.,o r real-time vision, objects must be recognized within milliseconds. Real-time image processing and analysis is computationally intensive, and it requires the use of massively parallel machines. Conventional parallel machines consist of an array of identical processors organized in either single instruction multiple data (SIMD) or multiple instruction multiple data (MIMD) configurations. Machines of this type generally only operate effectively on parts of the image analysis problem. SIMD operates on low-level processing and MIMD operates on high-level processing. This thesis considers the implementation and the development of real-time parallel image processing algorithms for use in robotic- vision systems on a massively parallel pyramid machine, called SPHINX. This machine consists of both SIMD and MIMD parts in a multiple-SIMD organization which can operate effectively at all levels of the image analysis problem. It can provide fast local and global properties compu41tion of an image. The complexity of an operation is reduced by O(210gn), where (n x n) is the input image size. A successful general-purpose simulator based on the prototype SPHINX pyramid machine has been upgraded and realized, using *Lisp data parallel language. Thus, the functionality of the parallel algorithms developed for "real-time" execution is tested. A number of parallel common vision algorithms has been implemented and developed on the pyramid machine. These algorithms address the stages of enhancement, edge detection, segmentation, and detection of straight lines and circles. Their performance is analyzed using the characteristics of the actual pyramid machine. They demonstrate the power of the multi-SIMD architecture for performing image analysis in real-time. The Hough transform is a well-known medium-level image recognition technique, for the detection of straight lines and circles. Its adoption has been slow due to its computational and storage complexity. Various parallel algorithms based on its technique are developed and implemented on the pyramid machine, to obtain higher speed. Since the conventional Hou~h transform technique requires" floating point arithmetic which is time consuming, a new efficient scheme for straight lines detection is designed and implemented to reduce the computation time. It u.,s es only integer arithmetic. Results on the analytic and empirical performance of these algorithms, and a comparison of their performances and computation times are presented. The final result of this work is a user friendly, modular parallel digital image processing package, named parallel ALIRA. It combines "the SPHINX pyramid machine simulator, the high performance parallel algorithms, and the graphical user interface. It is potentially capable of performing in real-time many tasks relevant to image analysis. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/2678 | |
| dc.subject | Parallel Pyramid Architecture | en_US |
| dc.subject | For Robotic Vision Systems | en_US |
| dc.title | Parallel Image Processing On A Massively Parallel Pyramid Architecture For Robotic Vision Systems | en_US |
| dc.type | Thesis | en_US |
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