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BINK: Biological binary keypoint descriptor
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Orientador(es)
Resumo(s)
Learning robust keypoint descriptors has become an active research area in the past decade. Matching local features is not only important for computational applications, but may also play an important role in early biological vision for disparity and motion processing. Although there were already some floatingpoint descriptors like SIFT and SURF that can yield high matching rates, the need for better and faster descriptors for real-time applications and embedded devices with low computational power led to the development of binary descriptors, which are usually much faster to compute and to match. Most of these descriptors are based on purely computational methods. The few descriptors that take some inspiration from biological systems are still lagging behind in terms of performance. In this paper, we propose a new biologically inspired binary keypoint descriptor: SINK. Built on responses of cortical V1 cells, it significantly outperforms the other biologically inspired descriptors. The new descriptor can be easily integrated with a V1-based keypoint detector that we previously developed for real-time applications. (C) 2017 Elsevier B.V. All rights reserved.
Descrição
Palavras-chave
Local image descriptors V1 Recognition Responses Features Cortex Codes
Contexto Educativo
Citação
Editora
Elsevier Science