No abstract

People with color vision deficiency (CVD) cannot observe the colorful world due to the damage of color reception nerves. In this work, we present an image enhancement approach to assist colorblind people to identify the colors they are not able to distinguish naturally. An image re-coloring algorithm based on eigenvector processing is proposed for robust color separation under color deficiency transformation. It is shown that the eigenvector of color vision deficiency is distorted by an angle in the λ , Y-B, R-G color space. The experimental results show that our approach is useful for the recognition and separation of the CVD confusing colors in natural scene images. Compared to the existing techniques, our results of natural images with CVD simulation work very well in terms of RMS, HDR-VDP-2 and an IRB-approved human test. Both the objective comparison with previous works and the subjective evaluation on human tests validate the effectiveness of the proposed method.

In this paper, we present a framework for the hybrid omnidirectional and perspective robot vision system. Based on the hybrid imaging geometry, a generalized stereo approach is developed via the construction of virtual cameras. It is then used to rectify the hybrid image pair using the perspective projection model. The proposed method not only simplifies the computation of epipolar geometry for the hybrid imaging system, but also facilitates the stereo matching between the heterogeneous image formation. Experimental results for both the synthetic data and real scene images have demonstrated the feasibility of our approach.

We describe a new semantic descriptor for robots to recognize visual places. The descriptor integrates image features and color information via the hull census transform (HCT) and image histogram indexing. Our approach extracts the semantic description based on the convex hull points and statistical calculation. The color histograms are then formed by four indices and added to the descriptor. The semantic codebook consists of several places with many image descriptors. Finally, a one-versus-one (OVO) multi-class support vector machine (SVM) is used to model the places. The proposed technique is achieved by using a high-level cue integration scheme based on the learning information over the color and feature space to optimally combine the weighted cues. It is suitable for visual place recognition, particularly for the images captured by an omnidirectional camera. The experimental results show that the codebook with less vectors is as robust as most popular codebooks under varying environments. The performance is evaluated and compared with several state-of-the-art descriptors.

Mimicry is an important social skill for every person. A humanoid robot is used to play with autistic children in order to attract their attention and want to improve their concentration. Music therapy plays also an important role in this experiment. Robot plays with autistic children with music. The robot will imitate the notes which the child has played, and then the child has to imitate the robot in return. The purpose of the treatment is to improve their social skills including interpersonal synchrony and concentration can help them to adapt this society easier and make a better life in the future.