Application of Fast Deviation Correction Algorithm Based on Shape Matching Algorithm in Component Placement

For contradiction PC template matching between accuracy and speed, combined with the advantages of FPGA high speed parallel computing. This paper presents a FPGA-based rapid correction shape matching algorithm. Mainly in the FPGA, using shape matching and least squares method to calculate the angular deviation chip components. Use single instruction stream algorithm acceleration. Experimental results show that compared with traditional PC template matching algorithms, this algorithm to further improve the correction accuracy and greatly reducing correction time. And SMT machine vision correction can be obtained in a stable and efficient use

Memory Architecture Template for Fast Block Matching Algorithms on Field Programmable Gate Arrays

Fast Block Matching (FBM) algorithms for video compression are well suited for acceleration using parallel data-path architectures on Field Programmable Gate Arrays (FPGAs). However, designing an efficient on-chip memory subsystem to provide the required throughput to this parallel data-path architecture is a complex problem. This thesis presents a memory architecture template that can be parameterized for a given FBM algorithm, number of parallel Processing Elements (PEs), and block size. The template can be parameterized with well known exploration techniques to design efficient on-chip memory subsystems. The memory subsystems are derived for two existing FBM algorithms and are implemented on a Xilinx Virtex 4 family of FPGAs. Results show that the derived memory subsystem in the best case supports up to 27 more parallel PEs than the three existing subsystems and processes integer pixels in a 1080p video sequence up to a rate of 73 frames per second. The speculative execution of an FBM algorithm for the same number of PEs increases the number of frames processed per second by 49%

Parallel Sort-Based Matching for Data Distribution Management on Shared-Memory Multiprocessors

In this paper we consider the problem of identifying intersections between two sets of d-dimensional axis-parallel rectangles. This is a common problem that arises in many agent-based simulation studies, and is of central importance in the context of High Level Architecture (HLA), where it is at the core of the Data Distribution Management (DDM) service. Several realizations of the DDM service have been proposed; however, many of them are either inefficient or inherently sequential. These are serious limitations since multicore processors are now ubiquitous, and DDM algorithms -- being CPU-intensive -- could benefit from additional computing power. We propose a parallel version of the Sort-Based Matching algorithm for shared-memory multiprocessors. Sort-Based Matching is one of the most efficient serial algorithms for the DDM problem, but is quite difficult to parallelize due to data dependencies. We describe the algorithm and compute its asymptotic running time; we complete the analysis by assessing its performance and scalability through extensive experiments on two commodity multicore systems based on a dual socket Intel Xeon processor, and a single socket Intel Core i7 processor.Comment: Proceedings of the 21-th ACM/IEEE International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2017). Best Paper Award @DS-RT 201

Planar Object Tracking in the Wild: A Benchmark

Planar object tracking is an actively studied problem in vision-based robotic applications. While several benchmarks have been constructed for evaluating state-of-the-art algorithms, there is a lack of video sequences captured in the wild rather than in constrained laboratory environment. In this paper, we present a carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment. In particular, for each object, we shoot seven videos involving various challenging factors, namely scale change, rotation, perspective distortion, motion blur, occlusion, out-of-view, and unconstrained. The ground truth is carefully annotated semi-manually to ensure the quality. Moreover, eleven state-of-the-art algorithms are evaluated on the benchmark using two evaluation metrics, with detailed analysis provided for the evaluation results. We expect the proposed benchmark to benefit future studies on planar object tracking.Comment: Accepted by ICRA 201