
000 | 01418camuuu200361 a 4500 | |
001 | 000000584432 | |
003 | OCoLC | |
005 | 19980318151647.0 | |
008 | 900601s1990 maua b 001 0 eng | |
010 | ▼a 90004889 | |
015 | ▼a GB91-20307 | |
020 | ▼a 0792390784 | |
040 | ▼a DLC ▼c DLC ▼d UKM ▼d FPU | |
049 | ▼a ACSL ▼l 121030282 ▼l 121030283 | |
050 | 0 0 | ▼a TA1632 ▼b .C47 1990 |
082 | 0 0 | ▼a 006.3/7 ▼2 20 |
090 | ▼a 006.37 ▼b C552p | |
100 | 1 | ▼a Choudhary, Alok N. ▼q (Alok Nidhi), ▼d 1961- |
245 | 1 0 | ▼a Parallel architectures and parallel algorithms for integrated vision systems / ▼c by Alok N. Choudhary and Janak H. Patel. |
260 | ▼a Boston : ▼b Kluwer Academic Publishers, ▼c c1990. | |
300 | ▼a xvii, 157 p. : ▼b ill. ; ▼c 25 cm. | |
490 | 1 | ▼a The Kluwer international series in engineering and computer science ; ▼v SECS 108. ▼a Robotics |
504 | ▼a Includes bibliographical references (p. [147]-151) and index. | |
650 | 7 | ▼a Vision par ordinateur. ▼2 ram |
650 | 7 | ▼a Parall?lisme (Informatique). ▼2 ram |
650 | 7 | ▼a Ordinateurs ▼x Architecture. ▼2 ram |
650 | 0 | ▼a Computer vision. |
650 | 0 | ▼a Parallel processing (Electronic computers) |
650 | 0 | ▼a Computer architecture. |
653 | ▼a Machine vision | |
700 | 1 | ▼a Patel, Janak H., ▼d 1948- |
830 | 0 | ▼a Kluwer international series in engineering and computer science ; ▼v SECS 108. |
830 | 0 | ▼a Kluwer international series in engineering and computer science. ▼p Robotics. |
소장정보
No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
---|---|---|---|---|---|---|---|
No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.37 C552p | 등록번호 121030282 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
No. 2 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.37 C552p | 등록번호 121030283 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
목차
CONTENTS List of Figures = ⅸ List of Tables = xiii Preface = xv Acknowledgements = xviii 1 Introduction = 1 1.1 Computational Complexities in Vision = 1 1.2 Review of Multiprocessor Architectures = 4 1.2.1. Mesh connected computers = 5 1.2.2. Pyramid computers = 7 1.2.3. Hypercube multiprocessors = 9 1.2.4. Shared memory machines = 11 1.2.5. Systolic arrays = 12 1.2.6. Partitionable and hierarchical architectures = 14 1.3 Organization = 17 2 Model of Computation = 19 2.1 Parallelism in IVSs = 20 2.2 Data Dependencies = 21 2.3 Features and Capabilities of Parallel Architectures for IVSs = 25 2.4 Examples of Integrated Vision Systems = 25 2.4.1 Image understanding benchmark system = 25 2.4.2 Motion estimation and object recognition = 26 3 Architecture of NETRA = 37 3.1 Processor Clusters = 37 3.1.1 Crossbar design = 40 3.1.2 Scalability of crossbar = 40 3.2 The DSP Hierarchy = 41 3.3 Global Memory = 41 3.4 Global interconnection = 43 3.4.1 Interconnection network = 43 3.4.2 Global bus = 43 3.5 IVS Computation Requirements and NETRA = 44 3.6 Comparison of NETRA with Other Architectures = 51 4 Parallel Algorithms on a Cluster = 55 4.1 Classification of Common Vision Algorithms = 56 4.2 Issues in Mapping an Algorithms = 57 4.3 Performance Evaluation of Parallel Algorithms = 59 4.3.1 2-D convolution = 60 4.3.2 Separable convolution = 64 4.3.3 Two-dimensional FFT = 65 4.3.4 Hough transform = 69 4.4 Parallel Implementation Results = 76 4.4.1 2-D FFT = 78 4.4.2 Separable convolution = 79 4.4.3 Benchmark Algorithms = 80 4.5 Summary = 82 5 Inter-Cluster Communication In NETRA = 83 5.1 Alternatives for Inter-cluster Communication = 83 5.1.1 Multistage interconnection network and global memory = 83 5.1.2 DSP tree links = 84 5.1.3 Global bus = 85 5.2 Analysis of Inter-cluster Communication = 86 5.3 Approach to Performance Evaluation = 90 5.4 Performance of Parallel Algorithms on Multiple Clusters = 91 5.4.1 Two-dimensional Fast Fourier Transform(2-D FFT) = 91 5.4.2 2-D separable convolution = 102 5.4.3 Hough transform = 109 5.5 Summary = 116 6 Load Balancing and Scheduling Techniques = 119 6.1 Need for Efficient Load Balancing Techniques = 119 6.2 Load Balancing and Scheduling Techniques for Parallel Implementation = 120 6.2.1 Uniform partitioning = 123 6.2.2 Static scheduling(First-order scheduling) = 123 6.2.3 Weighted static scheduling(second-order scheduling) = 125 6.2.4 Dynamic = 126 6.3 Parallel Implementation and Performance Evaluation = 128 6.3.1 Feature extraction = 128 6.3.2 Matching features = 131 6.3.3 Time match = 137 6.3.4 Second stereo match = 139 6.3.5 Summary = 141 7 Concluding Remarks = 143 7.1 Summary and Discussion = 143 7.2 Extensions = 145 References = 147 Index = 153