Combine color and shape in real-time detection of texture-less objects

•Augmenting Hinterstoisser et al.’s DOT method with color naming.•Efficiently implementing the proposed method using SIMD operations.•Combining the color and shape matching scores based on logistic regression.•An acceleration strategy to fast eliminate unpromising cluster templates.•Extensive experiments on three different datasets containing more than 40 objects.

Object instance detection is a fundamental problem in computer vision and has many applications. Compared with the problem of detecting a texture-rich object, the detection of a texture-less object is more involved because it is usually based on matching the shape of the object with the shape primitives extracted from an image, which is not as discriminative as matching appearance-based local features, such as the SIFT features. The Dominant Orientation Templates (DOT) method proposed by Hinterstoisser et al. is a state-of-the-art method for the detection of texture-less objects and can work in real time. However, it may well generate false detections in a cluttered background. In this paper, we propose a new method which has three contributions. Firstly, it augments the DOT method with a type of illumination insensitive color information. Since color is complementary to shape, the proposed method significantly outperforms the original DOT method in the detection of texture-less object in cluttered scenes. Secondly, we come up with a systematic way based on logistic regression to combine the color and shape matching scores in the proposed method. Finally, we propose a speed-up strategy to work with the proposed method so that it runs even faster than the original DOT method. Extensive experimental results are presented in this paper to compare the proposed method directly with the original DOT method and the LINE-2D method, and indirectly with another two state-of-the-art methods.