Vision based counting of texture-less objects using shape and color features

Automatic object recognition for texture-less objects using computer vision is a difficult task in comparison of textured one since class discriminative information is rarely available. Herein, an algorithm to count such objects using shape and color attributes for recognition with scale, rotation and illumination invariance is proposed. Initially, the algorithm extracts shape and color features of the prototype image to find its instance in the real-time pre-processed scene image captured by the vision interface. The pre-processing is achieved by morphological boundary extraction and segmentation techniques. Color and shape features are extracted based on mean hue value and Hu-moments respectively from the obtained segments. SVM, kNN, neural network and tree-bagging are then applied for classification. Tree-bagging is found to eclipse over the other classifiers in terms of accuracy. Finally, the classified objects are counted and localized in the image by drawing bounding boxes around them. A desktop application of the proposed algorithm is also developed. To assess the performance of the proposed algorithm, experimentation has been carried out for various objects having different shapes and colors. The algorithm proved out to be robust and effective for recognition and counting of the texture-less objects.