Abstract
This article deals with the 2D image-based recognition of industrial parts. Methods based on histograms are well known and widely used, but it is hard to find the best combination of histograms, most distinctive for instance, for each situation and without a high user expertise. We proposed a descriptor subset selection technique that automatically selects the most appropriate descriptor combination, and that outperforms approach involving single descriptors. We have considered both backward and forward mechanisms. Furthermore, to recognize the industrial parts a supervised classification is used with the global descriptors as predictors. Several class approaches are compared. Given our application, the best results are obtained with the Support Vector Machine with a combination of descriptors increasing the F1 by 0.031 with respect to the best descriptor alone.
Highlights
Computer vision, in the last years, has gained much interest in many fields, such as autonomous driving [1], medical [2], face recognition [3], object detection [4], and object segmentation [5].Perception is regarded as one of the key enabling technologies for extending the robot capabilities, preferentially targeting flexibility, adaptation, and robustness, as required for fulfilling the industry4.0 paradigm [6]
The aim of this paper is to present a method to improve the accuracy on reduced datasets of texture-less objects
In order to prove that our method improves the score of the descriptors by their own, we have created a small dataset composed by seven different random industrial parts (Figure 5)
Summary
In the last years, has gained much interest in many fields, such as autonomous driving [1], medical [2], face recognition [3], object detection [4], and object segmentation [5].Perception is regarded as one of the key enabling technologies for extending the robot capabilities, preferentially targeting flexibility, adaptation, and robustness, as required for fulfilling the industry4.0 paradigm [6]. Sometimes there is a need of detecting diverse, complex, and tiny objects [7] and lack of time to generate a robust dataset (taking pictures and labeling). The analysis of images usually relies on the extraction of visual features. Such an approach can be observed in classification [12], object detection [4], and segmentation [5]. Local features extractors are characteristic local primitives as points focusing on a close neighborhood. Some examples of those features are SIFT [13], SURF [14], and LBP [15]. Global features instead are more used for classification and object detection as they achieve a more global representation
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