Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The desired local feature descriptor should be distinctive, compact and fast to compute and match. Therefore, many computer vision applications use binary keypoint descriptors instead of floating-point, rich techniques. , an optimisation approach to the design of a binary descriptor is proposed, in which the detected keypoint is described using several, scale-dependent patches. Each such patch is divided into disjoint blocks of pixels, and then, binary tests beten blocks’ intensities, as ll as their gradients, are used to obtain the binary string. Since the number of image patches and their relative sizes influence the descriptor creation pipeline, a simulated annealing algorithm is used to determine them, optimising recall and precision of keypoint matching. The simulated annealing is also used for dimensionality reduction in long binary strings. The proposed approach is extensively evaluated and compared with SIFT, SURF and BRIEF on public benchmarks. Obtained results show that the binary descriptor created using the resulted pipeline is faster to compute and yields comparable or better performance than the state-of-the-art descriptors under different image transformations.
['Mariusz Oszust']
I am writing a research paper on a idea An optimisation approach to the design of a fast, compact and distinctive binary descriptor
[{"from": "human", "value": "I am writing a research paper on a idea An optimisation approach to the design of a fast, compact and distinctive binary descriptor"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The desired local feature descriptor should be distinctive, compact and fast to compute and match. Therefore, many computer vision applications use binary keypoint descriptors instead of floating-point, rich techniques. , an optimisation approach to the design of a binary descriptor is proposed, in which the detected keypoint is described using several, scale-dependent patches. Each such patch is divided into disjoint blocks of pixels, and then, binary tests beten blocks\u2019 intensities, as ll as their gradients, are used to obtain the binary string. Since the number of image patches and their relative sizes influence the descriptor creation pipeline, a simulated annealing algorithm is used to determine them, optimising recall and precision of keypoint matching. The simulated annealing is also used for dimensionality reduction in long binary strings. The proposed approach is extensively evaluated and compared with SIFT, SURF and BRIEF on public benchmarks. Obtained results show that the binary descriptor created using the resulted pipeline is faster to compute and yields comparable or better performance than the state-of-the-art descriptors under different image transformations."}]