Depth Map Improvement by Combining Passive and Active Scanning Methods

The paper presents a new method of more precise estimation of the depth map in 3D videos. The novelty of the proposed approach lies in sophisticated combination of partial results obtained by selected existing passive and active 3D scanning methods. The aim of the combination is to overcome drawbacks of individual methods and this way to improve the accessible precision of the final depth map. The active method used is incoherent profilometry scanning which fails on surface discontinuities. As a passive method, a stereo pair matching is used. This method is currently the most widely applied method of depth map estimation in the field of 3D capturing and is available in various implementations. Unfortunately, it fails if there is a lack of identifiable corresponding points in the scanned scene. The paper provides a specific way of combining these methods to improve the accuracy and usability. The proposed innovative technique exploits the advantages of both approaches. Specifically, the more accurate depth profiles of individual discontinuous objects obtained from the active method, and information about mean depths of the objects from the stereo pair are combined. Two implementations of the passive method have been tested for combination with active scanning: matching from stereo pair, and SIFT. The paper includes a brief description of the active and passive methods used and a thorough explanation of their combination. As an example, the proposed method is tested on a simple scene whose nature enables straight assessment of the achieved accuracy. The choice of a suitable implementation of the passive component is also shown and discussed. The obtained results of individual existing methods used and of the proposed combined method are given and compared. To demonstrate the contribution of the proposed combined method, also a comparison with the results obtained with a commercial solution is presented with significantly good results

Design and management of image processing pipelines within CPS: Acquired experience towards the end of the FitOptiVis ECSEL Project

Cyber-Physical Systems (CPSs) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, characterized for being smart, adaptive, predictive and react in real-time. Indeed, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis, we are developing novel methods and tools to integrate complex image- and video-processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPSs both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints

Design and management of image processing pipelines within CPS : Acquired experience towards the end of the FitOptiVis ECSEL Project

Cyber-Physical Systems (CPSs) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, characterized for being smart, adaptive, predictive and react in real-time. Indeed, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis, we are developing novel methods and tools to integrate complex image- and video-processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPSs both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints.Peer reviewe

Real-Time High-Resolution Multiple-Camera Depth Map Estimation Hardware and Its Applications

Depth information is used in a variety of 3D based signal processing applications such as autonomous navigation of robots and driving systems, object detection and tracking, computer games, 3D television, and free view-point synthesis. These applications require high accuracy and speed performances for depth estimation. Depth maps can be generated using disparity estimation methods, which are obtained from stereo matching between multiple images. The computational complexity of disparity estimation algorithms and the need of large size and bandwidth for the external and internal memory make the real-time processing of disparity estimation challenging, especially for high resolution images. This thesis proposes a high-resolution high-quality multiple-camera depth map estimation hardware. The proposed hardware is verified in real-time with a complete system from the initial image capture to the display and applications. The details of the complete system are presented. The proposed binocular and trinocular adaptive window size disparity estimation algorithms are carefully designed to be suitable to real-time hardware implementation by allowing efficient parallel and local processing while providing high-quality results. The proposed binocular and trinocular disparity estimation hardware implementations can process 55 frames per second on a Virtex-7 FPGA at a 1024 x 768 XGA video resolution for a 128 pixel disparity range. The proposed binocular disparity estimation hardware provides best quality compared to existing real-time high-resolution disparity estimation hardware implementations. A novel compressed-look up table based rectification algorithm and its real-time hardware implementation are presented. The low-complexity decompression process of the rectification hardware utilizes a negligible amount of LUT and DFF resources of the FPGA while it does not require the existence of external memory. The first real-time high-resolution free viewpoint synthesis hardware utilizing three-camera disparity estimation is presented. The proposed hardware generates high-quality free viewpoint video in real-time for any horizontally aligned arbitrary camera positioned between the leftmost and rightmost physical cameras. The full embedded system of the depth estimation is explained. The presented embedded system transfers disparity results together with synchronized RGB pixels to the PC for application development. Several real-time applications are developed on a PC using the obtained RGB+D results. The implemented depth estimation based real-time software applications are: depth based image thresholding, speed and distance measurement, head-hands-shoulders tracking, virtual mouse using hand tracking and face tracking integrated with free viewpoint synthesis. The proposed binocular disparity estimation hardware is implemented in an ASIC. The ASIC implementation of disparity estimation imposes additional constraints with respect to the FPGA implementation. These restrictions, their implemented efficient solutions and the ASIC implementation results are presented. In addition, a very high-resolution (82.3 MP) 360°x90° omnidirectional multiple camera system is proposed. The hemispherical camera system is able to view the target locations close to horizontal plane with more than two cameras. Therefore, it can be used in high-resolution 360° depth map estimation and its applications in the future