Automatic Segmentation and Classification of Resistors in Digital Images
Computer vision systems are frequently used for inspection and classification of products during manufacturing. Image processing and analysis allows non-invasive extraction of object features within an image and the classification of objects based on the extracted data. Shape, texture and color are typical features that can be extracted from an image and used for object recognition. In this paper, a method of detection, segmentation and classification of resistors captured in digital image, based on their nominal values, is presented. The process consists of the following steps: image segmentation, morphological image processing, representation and description of objects, object features extraction, classification of extracted data using support vector machines (SVM). Experimental results show that the proposed method exhibits solid performance and real-time operating capabilities.