Textureless object detection using cumulative orientation feature

We propose a novel image feature for textureless object detection. The feature is based on quantized gradient orientations those have been shown to be robust to cluttered backgrounds and illumination changes. We make this feature robust to the appearance changes of a targeted object itself induced by its transformations and small deformations. In our proposed method, we add small random values to the similarity transformation parameters and synthesize many model images. Then quantized orientations are extracted on these images and the orientations are cumulated at each pixel. The frequencies of selected features are utilized as weights when calculating scores. Our proposed feature is evaluated on publicly available dataset and achieve top-class performance both in speed and detection accuracy compared to state-of-the-art techniques.