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Efficient detection and reliable matching of image features constitute a fundamental task in computer vision. When real-time operation is required, the solution to this problem becomes a real challenge, because of increased processing requirements. Scale Invariant Feature Transform (SIFT) is considered as a stable and robust algorithm for the extraction of invariant features, however special hardware...
Aiming at the characteristics of SIFT (Scale Invariant Feature Transform) algorithm which has large amount of calculation and can be highly paralleled, we propose an optimized FPGA implementation so that it can be accelerated on hardware. In this method, we firstly simplify the process of filtering image and generating Gaussian pyramids through selecting appropriate parameters and hardware structure,...
Feature extraction is the most important and essential part in any image matching algorithm. Features are obtained by quantifying the characteristics of an image like illumination, corner, orientation and view angle etc. Image matching techniques consist of features extraction and their matching with other features. The inherent mathematical steps involved in calculation of these features make the...
SIFT (Scale Invariant Feature Transform) is one of most popular approach for feature detection and matching. Many parallelized algorithms have been proposed to accelerate SIFT to apply into real-time systems. This paper divides the researches into three different categories, that is, optimizing parallel algorithms based on general purpose multi-core processors, designing customized multi-core processor...
SIFT is regarded as one of the most powerful feature point detection algorithms in the world. The Orientation Calculation Part, defining major orientation of feature points, enables selected image features to be invariant to rotation changes. In this paper, we propose an FPGA-implementable hardware accelerator for this part. By introducing LUT-Based Square Root Computation and Shifting-Based Orientation...
SIFT is regarded as one of the most robust feature point detection algorithms in CV field. The feature point detection part, allocating final positions of all feature points, majorly defines the accuracy and stability of the whole system. In this paper, we propose an FPGA-implementable hardware accelerator for this part. By introducing dual-pixel processing and the 3-stage-interpolation pipelined...
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