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Binary image descriptors, which derive image feature description from the local image patches directly, are widely adopted in the mobile and embedded applications due to lower computational complexity and memory requirement. With the aim of improving the computation efficiency without degrading recognition performance, a lightweight binary robust descriptor is proposed based on the analysis of the...
In comparison with the popular feature algorithms in vision applications, AFFINE-SIFT (ASIFT) achieves the highest robustness in terms of illumination, rotation, and scale in affine space but exhibits high computation complexity. This work proposes three optimization techniques, including reverse based pipelined affine computing, full parallel Gaussian pyramid computing and rotation invariant binary...
Fast and robust traffic sign recognition is very important but difficult for the safety driving assist systems. This study addresses the fast and robust traffic sign recognition to enhance safety driving. We first adopt the typical Hough transform methods to implement coarse-grained locating of the candidate regions (shapes of rectangle, triangle and circle, etc.) of the traffic signs; and then propose...
Robust feature plays an important role in many vision based applications. This paper proposes a fast extreme illumination robust feature in affine space. It inherits the techniques of extreme point location and main orientation computation from SIFT (Scale Invariant Feature Transform) algorithm, and adopts the rotation and scale invariant circular binary pattern based histograms in the affine space...
Speeded Up Robust Feature(SURF) is widely used in computer vision applications. In many recent applications like mobile devices and vision sensor network, it is extremely difficult to meet both the performance and power consumption requirements of SURF implementations, especially for CPU, GPU, DSP or FPGA based solutions. In this paper, the SURF algorithm is simplified and optimized for hardware implementation...
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