Abstract
This paper studies the challenging problem of object detection using rich image and depth features. An invariant Hough random ferns framework for RGB-D images is proposed here, which primarily consists of a rotation-invariant RGB-D local binary feature, random ferns classifier training, Hough mapping and voting, searches for the maxima, and back projection. In comparison with traditional three-dimensional local feature extraction techniques, this method is effective in reducing the amount of computation required for feature extraction and matching. Moreover, the detection results showed that the proposed method is robust against rotation and scale variations, changes in illumination, and part-occlusions. The authors believe that this method will facilitate the use of perception in fields such as robotics.
Highlights
In the field of computer vision, object detection has long been a challenging and important task
In order to solve the problems of rotation and scale, partocclusions, and nonrigid transformations, recent RGB-D object detectors have featured the following tools:[4] feature extraction using a rotation-invariant descriptor,[5] part-based coding scheme using the generalized Hough transform,[6] feature matching using machine learning frameworks,[7] and so on
For invariant Hough random ferns (IHRF), the settings were as follows: the random ferns classifier (RFC) consisted of K 1⁄4 10 ferns, and picked S 1⁄4 13 pairwise pixels for RGB-D local binary feature (LBF)
Summary
In the field of computer vision, object detection has long been a challenging and important task. State-of-the-art algorithms deliver satisfactory results for two-dimensional images These methods suffer from limited variation and cluttered backgrounds.[1] Highly accurate RGB-D cameras that have recently been developed can provide high-quality three-dimensional (3-D) information (color and depth information).[2] Objects can be examined by acquiring color and depth information together, which is better than using only raw color images to learn feature representations.[3]. In order to solve the problems of rotation and scale, partocclusions, and nonrigid transformations, recent RGB-D object detectors have featured the following tools:[4] feature extraction using a rotation-invariant descriptor,[5] part-based coding scheme using the generalized Hough transform,[6] feature matching using machine learning frameworks,[7] and so on
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