A fuzzy structural matching scheme for space robotics vision

In this paper, we propose a new fuzzy structural matching scheme for space stereo vision which is based on the fuzzy properties of regions of images and effectively reduces the computational burden in the following low level matching process. Three dimensional distance images of a space truss structural model are estimated using this scheme from stereo images sensed by Charge Coupled Device (CCD) TV cameras

Disparity refinement process based on RANSAC plane fitting for machine vision applications

This paper presents a new disparity map refinement process for stereo matching algorithm and the refinement stage that will be implemented by partitioning the place or mask image and re-projected to the preliminary disparity images. This process is to refine the noise and sparse of initial disparity map from weakly textured. The plane fitting algorithm is using Random Sample Consensus. Two well-known stereo matching algorithms have been tested on this framework with different filtering techniques applied at disparity refinement stage. The framework is evaluated on three Middlebury datasets. The experimental results show that the proposed framework produces better-quality and more accurate than normal flow state-of-the-art stereo matching algorithms. The performance evaluations are based on standard image quality metrics i.e. structural similarity index measure, peak signal-to-noise ratio and mean square error.Keywords: computer vision; disparity refinement; image segmentation; RANSAC; stereo.

Three dimensional reconstruction of the cell cytoskeleton from stereo images

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1998.Includes bibliographical references (leaves 80-83).Besides its primary application to robot vision, stereo vision also appears promising in the biomedical field. This study examines 3D reconstruction of the cell cytoskeleton. This application of stereo vision to electron micrographs extracts information about the interior structure of cells at the nanometer scale level. We propose two different types of stereo vision approaches: the line-segment and wavelet multiresolution methods. The former is primitive-based and the latter is a point-based approach. Structural information is stressed in both methods. Directional representation is employed to provide an ideal description for filament-type structures. In the line-segment method, line-segments are first extracted from directional representation and then matching is conducted between two line-segment sets of stereo images. A new search algorithm, matrix matching, is proposed to determine the matching globally. In the wavelet multiresolution method, a pyramidal architecture is presented. Bottom-up analysis is first performed to form two pyramids, containing wavelet decompositions and directional representations. Subsequently, top-down matching is carried out. Matching at a high level provides guidance and constraints to the matching at a lower level. Our reconstructed results reveal 3D structure and the relationships of filaments which are otherwise hard to see in the original stereo images. The method is sufficiently robust and accurate to allow the automated analysis of cell structural characteristics from electron microscopy pairs. The method may also have application to a general class of stereo images.by Yuan Cheng.S.M

Development Of Double Stage Filter (DSF) On Stereo Matching Algorithm For 3D Computer Vision Applications

In the field of stereo vision, some of existing stereo matching algorithms are designed with less accuracy of algorithm. Thus, a new hybrid algorithm with higher accuracy of computation is developed in this project. This thesis will present the design, development and analysis of performance on a developed Double Stage Filter (DSF) algorithm and other existing stereo matching algorithms. DSF algorithm is a hybrid stereo matching algorithm which divided into two phases. Phase 1 is consists of the part on Sum of Absolute Differences from basic block matching and the part of Scanline Optimization (SO) from dynamic programming approches while phase 2 includes segmentation, merging and basic median filter process. The main feature of DSF algorithm is mainly on the phase 2 or as post-processing in which to remove the unwanted aspects like random noises and horizontal streaks, which is obtained from the raw disparity depth map on the step of optimization. In order to remove the unwanted aspects, two stages filtering process are needed along with the developed approaches in the phase 2 of DSF algorithm. There are two categorized evaluations done on the disparity maps obtained by the algorithms : objective evaluation and subjective evaluation. The objective evaluation includes the evaluation system of Middlebury Stereo Vision website page, computation analysis and traditional methods of Mean Square Errors (MSE), Peak to Signal Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM). Besides, for subjective evaluation, the datasets are captured from LNC IP camera and the results obtained by the selected algorithms are evaluated by human's eyes perception. Based on the results of evaluations, the results obtained by DSF is better than the algorithms, basic block matching and dynamic programming

Performance Analysis on Stereo Matching Algorithms Based on Local and Global Methods for 3D Images Application

Stereo matching is one of the methods in computer vision and image processing. There have numerous algorithms that have been found associated between disparity maps and ground truth data. Stereo Matching Algorithms were applied to obtain high accuracy of the depth as well as reducing the computational cost of the stereo image or video. The smoother the disparity depth map, the better results of triangulation can be achieved. The selection of an appropriate set of stereo data is very important because these stereo pairs have different characteristics. This paper discussed the performance analysis on stereo matching algorithm through Peak Signal to Noise Ratio (PSNR in dB), Structural Similarity (SSIM), the effect of window size and execution time for different type of techniques such as Sum Absolute Differences (SAD), Sum Square Differences (SSD), Normalized Cross Correlation (NCC), Block Matching (BM), Global Error Energy Minimization by Smoothing Functions, Adapting BP and Dynamic Programming (DP). The dataset of stereo images that used for the experimental purpose is obtained from Middlebury Stereo Datasets

Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture

Deep neural networks are applied to a wide range of problems in recent years. In this work, Convolutional Neural Network (CNN) is applied to the problem of determining the depth from a single camera image (monocular depth). Eight different networks are designed to perform depth estimation, each of them suitable for a feature level. Networks with different pooling sizes determine different feature levels. After designing a set of networks, these models may be combined into a single network topology using graph optimization techniques. This "Semi Parallel Deep Neural Network (SPDNN)" eliminates duplicated common network layers, and can be further optimized by retraining to achieve an improved model compared to the individual topologies. In this study, four SPDNN models are trained and have been evaluated at 2 stages on the KITTI dataset. The ground truth images in the first part of the experiment are provided by the benchmark, and for the second part, the ground truth images are the depth map results from applying a state-of-the-art stereo matching method. The results of this evaluation demonstrate that using post-processing techniques to refine the target of the network increases the accuracy of depth estimation on individual mono images. The second evaluation shows that using segmentation data alongside the original data as the input can improve the depth estimation results to a point where performance is comparable with stereo depth estimation. The computational time is also discussed in this study.Comment: 44 pages, 25 figure

Stereo Computation for a Single Mixture Image

This paper proposes an original problem of \emph{stereo computation from a single mixture image}-- a challenging problem that had not been researched before. The goal is to separate (\ie, unmix) a single mixture image into two constitute image layers, such that the two layers form a left-right stereo image pair, from which a valid disparity map can be recovered. This is a severely illposed problem, from one input image one effectively aims to recover three (\ie, left image, right image and a disparity map). In this work we give a novel deep-learning based solution, by jointly solving the two subtasks of image layer separation as well as stereo matching. Training our deep net is a simple task, as it does not need to have disparity maps. Extensive experiments demonstrate the efficacy of our method.Comment: Accepted by European Conference on Computer Vision (ECCV) 201

Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots

Safety is paramount for mobile robotic platforms such as self-driving cars and unmanned aerial vehicles. This work is devoted to a task that is indispensable for safety yet was largely overlooked in the past -- detecting obstacles that are of very thin structures, such as wires, cables and tree branches. This is a challenging problem, as thin objects can be problematic for active sensors such as lidar and sonar and even for stereo cameras. In this work, we propose to use video sequences for thin obstacle detection. We represent obstacles with edges in the video frames, and reconstruct them in 3D using efficient edge-based visual odometry techniques. We provide both a monocular camera solution and a stereo camera solution. The former incorporates Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter enjoys a novel, purely vision-based solution. Experiments demonstrated that the proposed methods are fast and able to detect thin obstacles robustly and accurately under various conditions.Comment: Appeared at IEEE CVPR 2017 Workshop on Embedded Visio