BIND: Binary Integrated Net Descriptors for Texture-Less Object Recognition
| Autor: | Jimmy Lee, Jacob Chan, Qian Kemao |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
business.industry
Detector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Cognitive neuroscience of visual object recognition Binary number 020207 software engineering Pattern recognition 02 engineering and technology ENCODE Discriminative model Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Clutter 020201 artificial intelligence & image processing Computer vision Artificial intelligence Invariant (mathematics) business Mathematics |
| Zdroj: | CVPR |
| DOI: | 10.1109/cvpr.2017.322 |
| Popis: | This paper presents BIND (Binary Integrated Net Descriptor), a texture-less object detector that encodes multi-layered binary-represented nets for high precision edge-based description. Our proposed concept aligns layers of object-sized patches (nets) onto highly fragmented occlusion resistant line-segment midpoints (linelets) to encode regional information into efficient binary strings. These lightweight nets encourage discriminative object description through their high-spatial resolution, enabling highly precise encoding of the objects edges and internal texture-less information. BIND achieved various invariant properties such as rotation, scale and edge-polarity through its unique binary logical-operated encoding and matching techniques, while performing remarkably well in occlusion and clutter. Apart from yielding efficient computational performance, BIND also attained remarkable recognition rates surpassing recent state-of-the-art texture-less object detectors such as BORDER, BOLD and LINE2D. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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