A Novel Tiny Object Recognition Algorithm Based on Unit Statistical Curvature Feature
To recognize tiny objects whose sizes are in the range of 15\(\times \) 15 to 40\(\times \) 40 pixels, a novel image feature descriptor, unit statistical curvature feature (USCF), is proposed based on the statistics of unit curvature distribution. USCF ca
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Abstract. To recognize tiny objects whose sizes are in the range of 15×15 to 40×40 pixels, a novel image feature descriptor, unit statistical curvature feature (USCF), is proposed based on the statistics of unit curvature distribution. USCF can represent the local general invariant features of the image texture. Due to the curvature features are independent of image sizes, USCF algorithm had high recognition rate for object images in any size including tiny object images. USCF is invariant to rotation and linear illumination variation, and is partially invariant to viewpoint variation. Experimental results showed that the recognition rate of USCF algorithm was the highest for tiny object recognition compared to other nine typical object recognition algorithms under complex test conditions with simultaneous rotation, illumination, viewpoint variation and background interference. Keywords: Object recognition · Tiny object · Feature descriptor · Unit Statistical Curvature Feature
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Introduction
Recognition of tiny image objects taken by digital cameras is a key subject in machine vision. Recognizing an object accurately and quickly when it is very small and in a distance provides more time to take appropriate actions for a system that relies on machine vision, such as a robot, an Unmanned Aerial Vehicle (UAV), etc. However, automatic recognition gets more and more difficult when the objects are getting smaller, due to the tiny objects have very few pixels and texture information. There are limited studies focused on this subject. Torralba et al. [1] implemented tiny objects classification whose sizes are 32×32 color pixels by using the nearest neighbor matching schemes and image indexing techniques. They showed that the 32×32 color pixel tiny images already seem to contain most of the relevant information needed to support reliable recognition. However, the approach is only used to classify objects but not to distinguish a tiny object from other objects in an image. Multiple approaches can be used to recognize a big image object whose size is larger than 40×40 pixels by matching the image features, e.g. edge features [2–4], c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part V, LNCS 9909, pp. 762–777, 2016. DOI: 10.1007/978-3-319-46454-1 46
A Novel Tiny Object Recognition Algorithm
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invariant features [5–10], statistical features [11–13], etc. Tiny object whose size is smaller than 40×40 pixels has vague contours, and the algorithms based on edge features cannot work on tiny object recognition. Some recognition algorithms based on invariant features are commonly used to recognize objects. SIFT [5] constructed feature descriptors based on histogram of magnitude and direction of gradients to characterize an object. To improve the calculation efficiency of SIFT, SURF [6] built feature descriptors based on sum of Haar wavelet responses. Rani et al. [7] found that the number of keypoints detected by using SIFT is more than that of SURF through a set of experiments. Rublee et al. promp
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