NDLI: Curve description by histograms of tangent directions

Content Provider	IET Digital Library
Author	Köksal, Ali Özuysal, Mustafa
Abstract	The authors propose a novel approach for the description of objects based on contours in their images using real-valued feature vectors. The approach is particularly suitable when objects of interest have high contrast and texture-free images or when the texture variations are high so textural cues are nuisance factors for classification. The proposed descriptor is suitable for nearest neighbour classification still popular in embedded vision applications when the power considerations outweigh the performance requirements. They describe object outlines purely based on the histograms of contour tangent directions mimicking many of the design heuristics of texture-based descriptors such as scale-invariant feature transform (SIFT). However, unlike SIFT and its variants, the proposed approach is directly designed to work with contour data and it is robust to variations inside and outside the object outline as well as the sampling of the contour itself. They show that relying on tangent direction estimation as opposed to gradient computation yields a more robust description and higher nearest neighbour classification rates in a variety of classification problems.
Starting Page	507
Ending Page	514
Page Count	8
ISSN	17519632
Volume Number	13
e-ISSN	17519640
Issue Number	Issue 5, Aug (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/13/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5613
Journal	IET Computer Vision
Publisher Date	2019-04-05
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Embedded Vision Application Feature Extraction Gradient Method Image Classification Image Recognition Image Sampling Image Texture Integral Transforms Nearest Neighbour Classification Nearest Neighbour Method Optimisation Technique Scale-invariant Feature Transform SIFT Statistics Tangent Direction Estimation Textural Cues Texture Variations Texture-based Descriptors Texture-free Image Transform
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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