Multimodal Spatial Calibration for Accurately Registering EEG Sensor Positions

This paper proposes a fast and accurate calibration method to calibrate multiple multimodal sensors using a novel photogrammetry system for fast localization of EEG sensors. The EEG sensors are placed on human head and multimodal sensors are installed around the head to simultaneously obtain all EEG sensor positions. A multiple views’ calibration process is implemented to obtain the transformations of multiple views. We first develop an efficient local repair algorithm to improve the depth map, and then a special calibration body is designed. Based on them, accurate and robust calibration results can be achieved. We evaluate the proposed method by corners of a chessboard calibration plate. Experimental results demonstrate that the proposed method can achieve good performance, which can be further applied to EEG source localization applications on human brain

Kinect Depth Recovery via the Cooperative Profit Random Forest Algorithm

The depth map captured by Kinect usually contain missing depth data. In this paper, we propose a novel method to recover the missing depth data with the guidance of depth information of each neighborhood pixel. In the proposed framework, a self-taught mechanism and a cooperative profit random forest (CPRF) algorithm are combined to predict the missing depth data based on the existing depth data and the corresponding RGB image. The proposed method can overcome the defects of the traditional methods which is prone to producing artifact or blur on the edge of objects. The experimental results on the Berkeley 3-D Object Dataset (B3DO) and the Middlebury benchmark dataset show that the proposed method outperforms the existing method for the recovery of the missing depth data. In particular, it has a good effect on maintaining the geometry of objects

Extended patch prioritization for depth filling within constrained exemplar-based RGB-D image completion.

We address the problem of hole filling in depth images, obtained from either active or stereo sensing, for the purposes of depth image completion in an exemplar-based framework. Most existing exemplar-based inpainting techniques, designed for color image completion, do not perform well on depth information with object boundaries obstructed or surrounded by missing regions. In the proposed method, using both color (RGB) and depth (D) information available from a common-place RGB-D image, we explicitly modify the patch prioritization term utilized for target patch ordering to facilitate improved propagation of complex texture and linear structures within depth completion. Furthermore, the query space in the source region is constrained to increase the efficiency of the approach compared to other exemplar-driven methods. Evaluations demonstrate the efficacy of the proposed method compared to other contemporary completion techniques

A Novel Inpainting Framework for Virtual View Synthesis

Multi-view imaging has stimulated significant research to enhance the user experience of free viewpoint video, allowing interactive navigation between views and the freedom to select a desired view to watch. This usually involves transmitting both textural and depth information captured from different viewpoints to the receiver, to enable the synthesis of an arbitrary view. In rendering these virtual views, perceptual holes can appear due to certain regions, hidden in the original view by a closer object, becoming visible in the virtual view. To provide a high quality experience these holes must be filled in a visually plausible way, in a process known as inpainting. This is challenging because the missing information is generally unknown and the hole-regions can be large. Recently depth-based inpainting techniques have been proposed to address this challenge and while these generally perform better than non-depth assisted methods, they are not very robust and can produce perceptual artefacts. This thesis presents a new inpainting framework that innovatively exploits depth and textural self-similarity characteristics to construct subjectively enhanced virtual viewpoints. The framework makes three significant contributions to the field: i) the exploitation of view information to jointly inpaint textural and depth hole regions; ii) the introduction of the novel concept of self-similarity characterisation which is combined with relevant depth information; and iii) an advanced self-similarity characterising scheme that automatically determines key spatial transform parameters for effective and flexible inpainting. The presented inpainting framework has been critically analysed and shown to provide superior performance both perceptually and numerically compared to existing techniques, especially in terms of lower visual artefacts. It provides a flexible robust framework to develop new inpainting strategies for the next generation of interactive multi-view technologies