Disparity Map Computation Research Articles

Accurate Real-Time Disparity Map Computation Based on Variable Support Window

We propose in this paper a real-time dense stereo matching algorithm using variable support window for disparity map computing. Basic real-time local algorithms relying on a fixed and rectangular correlation window suffer from the difficulty for window-based methods lies in determining the best window shape and size for each pixel. This work proposes a novel local approach using a combination of the DSI (Disparity Space Image) structure and gradient information. Two improvements are introduced so that an accurate and fast result will be reached. The first one concerns the proposition of a new strategy in order to optimize the computation time of the initial disparity map. The second one, concerns the pixel similarity measure for matching score computation and it consists to use in addition to the traditional pixel intensities, the magnitude and orientation of the gradients providing more accuracy. Experimental results on real data sets are conducted and a comparative evaluation of the obtained results relatively to the state-of-art methods is presented.

FPGA based disparity map computation with vergence control

Depth estimation in a scene using image pairs acquired by a stereo camera setup, is one of the important tasks of stereo vision systems. The disparity between the stereo images allows for 3D information acquisition which is indispensable in many machine vision applications. Practical stereo vision systems involve wide ranges of disparity levels. Considering that disparity map extraction of an image is a computationally demanding task, practical real-time FPGA based algorithms require increased device utilization resource usage, depending on the disparity levels operational range, which leads to significant power consumption. In this paper a new hardware-efficient real-time disparity map computation module is developed. The module constantly estimates the precisely required range of disparity levels upon a given stereo image set, maintaining this range as low as possible by verging the stereo setup cameras axes. This enables a parallel-pipelined design, for the overall module, realized on a single FPGA device of the Altera Stratix IV family. Accurate disparity maps are computed at a rate of more than 320 frames per second, for a stereo image pair of 640 × 480 pixels spatial resolution with a disparity range of 80 pixels. The presented technique provides very good processing speed at the expense of accuracy, with very good scalability in terms of disparity levels. The proposed method enables a suitable module delivering high performance in real-time stereo vision applications, where space and power are significant concerns.

A real-time fuzzy hardware structure for disparity map computation

Stereo images acquired by a stereo camera setup provide depth estimation of a scene. Numerous machine vision applications deal with retrieval of 3D information. Disparity map recovery from a stereo image pair involves computationally complex algorithms. Previous methods of disparity map computation are mainly restricted to software-based techniques on general-purpose architectures, presenting relatively high execution time. In this paper, a new hardware-implemented real-time disparity map computation module is realized. This enables a hardware-based fuzzy inference system parallel-pipelined design, for the overall module, implemented on a single FPGA device with a typical operating frequency of 138 MHz. This provides accurate disparity map computation at a rate of nearly 440 frames per second, given a stereo image pair with a disparity range of 80 pixels and 640 × 480 pixels spatial resolution. The proposed method allows a fast disparity map computational module to be built, enabling a suitable module for real-time stereo vision applications.

Real-time disparity map computation using the cell broadband engine

In this paper, we present what we believe to be the first implementation of a disparity map computation algorithm, using a local block-based matching technique, on the Cell Broadband Engine, a new processor released by Sony, Toshiba and IBM. The paper discusses the architecture of the Cell Broadband Engine from an image processing perspective. The results of the implementation are also presented and compared to the results obtained using desktop processing devices such as CPUs and GPUs, and shows that the computational power of the Cell Broadband Engine can be used to implement image processing algorithms in real-time.

Real-time disparity map computation module

Stereo vision deals with images acquired by a stereo camera setup, where the disparity between the stereo images allows depth estimation within a scene. 3D information, hence, is retrieved which is essential in many machine vision applications. Disparity map extraction of an image is a computationally demanding task. Previous work on disparity map computation is mainly limited to software based techniques on general-purpose architectures. In this paper a new hardware-efficient real-time disparity map computation module is developed. This enables a hardware-based cellular automata (CA) parallel-pipelined design, for the overall module, realized on a single FPGA device, the typical operating frequency of which is 256 MHz. Accurate disparity maps are computed at a rate of nearly 275 per second, for a stereo image pair with a disparity range of 80 pixels and 640 × 480 pixels spatial resolution. The presented hardware-based algorithm provides very good processing speed at the expense of accuracy, with very good scalability in terms of disparity levels. The proposed method allows the fastest disparity map computational module to be built, to the best of the authors’ knowledge so far, enabling a suitable module for real-time stereo vision applications.

Solving da Vinci stereopsis with depth-edge-selective V2 cells

We propose a new model for da Vinci stereopsis based on a coarse-to-fine disparity energy computation in V1 and disparity-boundary-selective units in V2. Unlike previous work, our model contains only binocular cells, relies on distributed representations of disparity, and has a simple V1-to-V2 feedforward structure. We demonstrate with random-dot stereograms that the V2 stage of our model is able to determine the location and the eye-of-origin of monocularly occluded regions, and improve disparity map computation. We also examine a few related issues. First, we argue that since monocular regions are binocularly defined, they cannot generally be detected by monocular cells. Second, we show that our coarse-to-fine V1 model for conventional stereopsis explains double matching in Panum’s limiting case. This provides computational support to the notion that the perceived depth of a monocular bar next to a binocular rectangle may not be da Vinci stereopsis per se [Gillam, B., Cook, M., & Blackburn, S. (2003). Monocular discs in the occlusion zones of binocular surfaces do not have quantitative depth—a comparison with Panum’s limiting case. Perception 32, 1009–1019.]. Third, we demonstrate that some stimuli previously deemed invalid have simple, valid geometric interpretations. Our work suggests that studies of da Vinci stereopsis should focus on stimuli more general than the bar-and-rectangle type and that disparity-boundary-selective V2 cells may provide a simple physiological mechanism for da Vinci stereopsis.

Phase-Based Binocular Vergence Control and Depth Reconstruction Using Active Vision

We present a technique for guiding vergence movements for an active stereo camera system and for calculating dense disparity maps. Both processes are described in the same theoretical framework based on phase differences in complex Gabor filter responses, modeling receptive field properties in the visual cortex. While the camera movements are computed with input images of coarse spatial resolution, the disparity map calculation uses a finer resolution in the scale space. The correspondence problem is solved implicitly by restricting the disparity range around zero disparity (Panum′s area in the human visual system). The vergence process is interpreted as a mechanism to minimize global disparity, thereby setting a 3D region of interest for subsequent disparity detection. The disparity map represents smaller local disparities as an important cue for depth perception. Experimental data for the integrated performance of vergence in natural scenes followed by disparity map calculations are presented.

A new approach for disparity map determination

This article presents a new method to determine disparity map useful for three-dimensional (3D) scene reconstruction. The main task behind the computation of disparity map is stereo correspondence matching. In recent years, several stereo matching algorithms have been developed to find corresponding pairs in two images: left and right images captured by a stereo camera. But these algorithms exhibit a very high computational cost. With a view to reduce the computation time and produce a smooth and detailed disparity map, a fast and new approach based on average disparity estimation is proposed in this research, which can tackle additive noise. Experimental results confirm that the method achieves a substantial gain in accuracy with less expense of computation time. Key Words: Disparity map, Stereo correspondence, Stereo Vision, 3D Scene Reconstruction. DOI: 10.3329/diujst.v4i1.4348 Daffodil International University Journal of Science and Technology Vol.4(1) 2009 pp.9-13