Abstract
This paper presents a fast algorithm for texture-less object recognition, which is designed to be robust to cluttered backgrounds and small transformations. At its core, the proposed method demonstrates a two-stage template-based procedure using an orientation compressing map and discriminative regional weight (OCM-DRW) to effectively detect texture-less objects. In the first stage, the proposed method quantizes and compresses all the orientations in a neighborhood to obtain the orientation compressing map which then is used to generate a set of possible object locations. To recognize the object in these possible object locations, the second stage computes the similarity of each possible object location with the learned template by using discriminative regional weight, which can effectively distinguish different categories of objects with similar parts. Experiments on publiclyavailable, texture-less object datasets indicate that apart from yielding efficient computational performance, the proposed method also attained remarkable recognition rates surpassing recent state-of-the-art texture-less object detectors in the presence of high-clutter, occlusion and scale-rotation changes. It improves the accuracy and speed by 8% and 370% respectively, relative to the previous best result on D-Textureless dataset.
Highlights
Object detection is one of the most fundamental problems in computer vision
We propose a method having general purpose, fast, and high object detection rates by using binarized orientation compressing map and discriminative regional weight
In order to evaluate the performance of orientation compressing map and discriminative regional weight (OCM-DRW) for object instance detection, we include in our comparison template-based methods for texture-less object detection such detection, we include in our comparison template-based methods for texture-less object detection as LINE2D [33], as well as popular descriptor-based keypoint detectors like SIFT [16], Bunch of Lines Descriptor (BOLD) [23] and such as LINE2D [33], as well as popular descriptor-based keypoint detectors like SIFT [16], BOLD
Summary
Object detection is one of the most fundamental problems in computer vision. Recognizing object instances in natural scenes is crucial for many real applications such as Robotic systems [1], Image retrieval [2], Augmented Reality [3] and 3D reconstruction [4]. The object detection methods based on the manual designed features methods could not automatically extract abundant representative features from the vast training samples as deep leaning methods These methods are attractive for object detection because they need neither a large training set nor a time-consuming training stage, which can be implemented efficiently. The DOT utilizes only the dominant gradient orientations as the matching feature and cannot adequately describe a texture-less object, resulting in the massive loss of information of the texture-less object and severe degradation of performance or even failure in the presence of occlusion and clutter. We propose a method having general purpose, fast, and high object detection rates by using binarized orientation compressing map and discriminative regional weight. Experimental results illustrate that the proposed method is suitable for real-time texture-less object detection, and its performance is competitive with other state-of-the-art texture-less object detectors in homogenous conditions
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