Advanced Feature Extraction Workflow for Few Shot Object Recognition

Abstract

AbstractObject recognition is well known to have a high importance in various fields. Example applications are anomaly detection and object sorting. Common methods for object recognition in images divide into neural and non-neural approaches: Neural-based concepts, e.g. using deep learning techniques, require a lot of training data and involve a resource intensive learning process. Additionally, when working with a small number of images, the development effort increases. Common non-neural feature detection approaches, such as SIFT, SURF or AKAZE, do not require these steps for preparation. They are computationally less expensive and often more efficient than the neural-based concepts. On the downside, these algorithms usually require grey-scale images as an input. Thus, information about the color of the reference image cannot be considered as a determinant for recognition. Our objective is to achieve an object recognition approach by eliminating the “color blindness” of key point extraction methods by using a combination of SIFT, color histograms and contour detection algorithms. This approach is evaluated in context of object recognition on a conveyor belt. In this scenario, objects can only be recorded while passing the camera’s field of vision. The approach is divided into three stages: In the first step, Otsu’s method is applied among other computer vision algorithms to perform automatic edge detection for object localization. Within the subsequent second stage, SIFT extracts key points out of the previously identified region of interest. In the last step, color histograms of the specified region are created to distinguish between objects that feature a high similarity in the extracted key points. Only one image is sufficient to serve as a template. We are able to show that developing and applying a concept with a combination of SIFT, histograms and edge detection algorithms successfully compensates the color blindness of the SIFT algorithm. Promising results in the conducted proof of concept are achieved without the need for implementing complex and time consuming methods.