Sign language is the main type of communication of the deaf community. However, most people do not know this language, which causes communication problems for many people. Many technological solutions have been proposed to overcome this issue. Some use the concept of wearable devices, but gesture recognition in a video sequence is a cheaper and less intrusive solution. In this work, we address the problem of gesture recognition in video. To do so, we employ a two-step method with feature space mapping and classification. First, the body parts of each subject in a video are segmented through a deep neural network architecture. Then, we use Gait Energy Image to encode the motion of the body parts in a compact feature space. Small datasets are usually a problem in this type of application, leading to sparse representation in the feature space. To contour this problem, we evaluate SMOTE as a data augmentation technique in the feature space and classical dimensionality reduction techniques. We evaluate our method on three challenging Brazilian sign language (Libras) datasets, CEFET/RJ-Libras, MINDS-Libras, and LIBRAS-UFOP, achieving global accuracies of 85.40±3.13%, 84.66±1.78%, and 64.91±3.79%, respectively, withsingular value decompositionandsupport vector machine.

The fusion of visible-light and infrared videos has applications in several areas, and is an active research topic. To this end, it is common to employ complex fusion methods that take into account spatial and/or temporal information in the videos. In this paper we propose a video fusion method that is based on a motion-compensated, two-band temporal sub-band decomposition. The alignment provided by the motion vectors, besides providing a reduction in the registration errors of the input images, allows the use of a simple fusion rule to the temporal sub-bands. The results indicate that the use of the proposed exploration of the temporal information alone is quite effective, and gives objective fusion quality results that compare favorably to more sophisticated methods based on complete spatiotemporal information.

In this paper, we investigate how visualization factors, such as disparity, mobility, angular resolution, and viewpoint interpolation, influence the quality of experience (QoE) in a stereoscopic multiview environment. In order to do so, we set up a dedicated testing room and conducted subjective experiments. We also developed a framework that emulates a supermultiview environment. This framework can be used to investigate and assess the effects of angular resolution and viewpoint interpolation on the QoE produced by multiview systems, and provide relevant cues as to how the baselines of cameras and interpolation strategies in such systems affect user experience. Aspects such as visual comfort, model fluidity, sense of immersion, and the three-dimensional (3D) experience as a whole have been assessed for several test cases. Obtained results suggest that user experience in an motion parallax environment is not as critically influenced by configuration parameters such as disparity as initially thought. In addition, extensive subjective tests have indicated that while users are very sensitive to angular resolution in multiview 3D systems, this sensitivity tends not to be as critical when a user is performing a task that involves a great amount of interaction with the multiview content. These tests have also indicated that interpolating intermediate viewpoints can be effective in reducing the required view density without degrading the perceived QoE.

The accurate geometric calibration of thermal infrared (IR) cameras is of vital importance in many computer vision applications. In general, the calibration procedure consists of localizing a set of calibration points within a calibration image. This set is subsequently used to solve for the camera parameters. However, due to the physical limitations of the IR acquisition process, the localization of the calibration points often poses difficulties, subsequently leading to unsatisfying calibration results. In this work a novel IR camera calibration approach is introduced. It is able to localize the calibration points within the images of a conventional calibration board consisting of miniature light-bulbs with improved accuracy. Our algorithm models the radiation pattern of each light bulb as an ellipse and considers the center of mass of the extracted ellipsoidal region as the starting calibration point, which is refined iteratively using alternating mappings to and from an undistorted grid model. The proposed processing chain leads to a significantly reduced calibration error when compared to the state-of-the-art. Furthermore, the proposed methodology can also be used to calibrate visible-light cameras thus being suitable for the calibration of multiple camera rigs involving both visible-light and IR cameras.