A Perceptually Based Comparison of Image Similarity Metrics

The assessment of how well one image matches another forms a critical component both of models of human visual processing and of many image analysis systems. Two of the most commonly used norms for quantifying image similarity are L1 and L2, which are specific instances of the Minkowski metric. However, there is often not a principled reason for selecting one norm over the other. One way to address this problem is by examining whether one metric, better than the other, captures the perceptual notion of image similarity. This can be used to derive inferences regarding similarity criteria the human visual system uses, as well as to evaluate and design metrics for use in image-analysis applications. With this goal, we examined perceptual preferences for images retrieved on the basis of the L1 versus the L2 norm. These images were either small fragments without recognizable content, or larger patterns with recognizable content created by vector quantization. In both conditions the participants showed a small but consistent preference for images matched with the L1 metric. These results suggest that, in the domain of natural images of the kind we have used, the L1 metric may better capture human notions of image similarity

Stereo imaging based particle velocimeter

Three dimensional coordinates of an object are determined from it's two dimensional images for a class of points on the object. Two dimensional images are first filtered by a Laplacian of Gaussian (LOG) filter in order to detect a set of feature points on the object. The feature points on the left and the right images are then matched using a Hopfield type optimization network. The performance index of the Hopfield network contains both local and global properties of the images. Parallel computing in stereo matching can be achieved by the proposed methodology

A robot vision system for determining 3-D coordinates of object points

A stereo disparity algorithm is implemented on an industrial robot vision system. One camera manipulated by an industrial robot is used to obtain stereo images of an object. After a pair of stereo images are processed, the points of interest are selected from each image. The similarities of three types of feature characters are used to assign initial weights of the, matching probabilities to a set of point candidates. Then a relaxation method, which is based on probabilities of local connectivity, similarity of feature characters, and smoothness of matching, is used iteratively to improve the initial probabilities of image matching. The positions of object points are determined by image disparities based on a triangulation method. Experiments performed on an AdeptOne Robot with based vision system show that the algorithm works well

Disambiguating Multi–Modal Scene Representations Using Perceptual Grouping Constraints

In its early stages, the visual system suffers from a lot of ambiguity and noise that severely limits the performance of early vision algorithms. This article presents feedback mechanisms between early visual processes, such as perceptual grouping, stereopsis and depth reconstruction, that allow the system to reduce this ambiguity and improve early representation of visual information. In the first part, the article proposes a local perceptual grouping algorithm that — in addition to commonly used geometric information — makes use of a novel multi–modal measure between local edge/line features. The grouping information is then used to: 1) disambiguate stereopsis by enforcing that stereo matches preserve groups; and 2) correct the reconstruction error due to the image pixel sampling using a linear interpolation over the groups. The integration of mutual feedback between early vision processes is shown to reduce considerably ambiguity and noise without the need for global constraints

Stereo matching algorithm by propagation of correspondences and stereo vision instrumentation

A new image processing method is described for measuring the 3-D coordinates of a complex, biological surface. One of the problems in stereo vision is known as the accuracy-precision tradeoff problem. This thesis proposes a new method that promises to solve this problem. To do so, two issues are addressed. First, stereo vision instrumentation methods are described. This instrumentation includes a camera system as well as camera calibration, rectification, matching and triangulation. Second, the approach employs an array of cameras that allow accurate computation of the depth map of a surface by propagation of correspondences through pair-wise camera views. The new method proposed in this thesis employs an array of cameras, and preserves the small baseline advantage by finding accurate correspondences in pairs of adjacent cameras. These correspondences are then propagated along the consecutive pairs of cameras in the array until a large baseline is accomplished. The resulting large baseline disparities are then used for triangulation to achieve advantage of precision in depth measurement. The matching is done by an area-based intensity correlation function called Sum of Squared Differences (SSD). In this thesis, the feasibility of using these data for further processing to achieve surface or volume measurements in the future is discussed

Sensor-based automated path guidance of a robot tool

The objective of the research is to develop a robot capability for a simultaneous measurement of the orientation (surface normal) and position of a 3-dimensional unknown object for a precise tool path guidance and control. The proposed system can guide the robot manipulator while maintaining specific orientation between the robot end-effector and the workpiece and also generate a measured geometric CAD database; The first phase involves the computer graphics simulation of an automated guidance and control of a robot tool using the proposed scheme. In the simulation, an object of known geometry is used for camera image data generation and subsequently determining the position and orientation of surface points based only on the simulated camera image information. Based on this surface geometry measurement technique, robot tool guidance and path planning algorithm is developed; The second phase involves the laboratory experiment. To demonstrate the validity of the proposed measurement method, the result of CCD image processing (grey to binary image conversion, thinning of binary image, detection of cross point, etc) and the calibration of the cameras/lighting source are performed. (Abstract shortened by UMI.)

A hypercolumn based stereo vision model.

by Lam Shu Sun.Thesis (M.Phil.)--Chinese University of Hong Kong, 1993.Includes bibliographical references (leaves [91]-94).Chapter Chapter1 --- Introduction: Binocular Depth Visual Perception of Human --- p.1Chapter 1.1 --- Introduction --- p.1Chapter 1.2 --- The visual pathway --- p.2Chapter 1.3 --- The retina --- p.3Chapter 1.4 --- The ganglion cells --- p.5Chapter 1.5 --- The lateral geniculate nucleus --- p.7Chapter 1.6 --- The visual cortex --- p.8Chapter 1.6.1 --- The cortical cells --- p.8Chapter 1.6.2 --- The organization of the visual cortex --- p.9Chapter 1.7 --- Stereopsis --- p.11Chapter 1.7.1 --- Corresponding retinal points --- p.12Chapter 1.7.2 --- Binocular fusion --- p.14Chapter 1.7.3 --- The binocular depth cells --- p.14Chapter 1.8 --- Conclusion of chapter 1 --- p.15Chapter Chapter2 --- Computational Stereo Vision --- p.15Chapter 2.1 --- Stereo image geometry --- p.16Chapter 2.1.1 --- The crossed-looking geometry --- p.17Chapter 2.1.2 --- The parallel optical axes geometry --- p.19Chapter 2.2 --- The false targets problem --- p.20Chapter 2.3 --- Feature selection --- p.21Chapter 2.3.1 --- Zero-crossing method --- p.21Chapter 2.3.2 --- A network model for ganglion cell --- p.24Chapter 2.4 --- The constraints of matching --- p.28Chapter 2.5 --- Correspondence techniques --- p.29Chapter 2.6 --- Conclusion of chapter 2 --- p.29Chapter Chapter3 --- A Hypercolumn Based Stereo Vision Model --- p.30Chapter 3.1 --- A visual model for stereo vision --- p.30Chapter 3.2 --- The model of PSVM (A Computerized Visual Model) --- p.32Chapter 3.3 --- Local orientated line extraction (Stage 1 of PSVM) --- p.34Chapter 3.3.1 --- Orientated line detection network --- p.35Chapter 3.3.2 --- On-type orientated lines and off-type orientated lines --- p.37Chapter 3.4 --- Local line matching (Stage 2 of PSVM) --- p.38Chapter 3.4.1 --- Structure of hypercolumn in PSVM --- p.39Chapter 3.4.2 --- Line length discrimination model (Part of stage 2 of PSVM) --- p.41Chapter 3.4.3 --- Orientation-length detector --- p.42Chapter 3.4.4 --- Line length selection --- p.45Chapter 3.4.5 --- The matching model --- p.46Chapter 3.4.6 --- Fusional area in PSVM --- p.48Chapter 3.4.7 --- Matching mechanism --- p.49Chapter 3.4.8 --- Disparity detection --- p.50Chapter 3.5 --- Disparity integrations (Stage 3 of PSVM) --- p.53Chapter 3.5.1 --- The voter network --- p.54Chapter 3.5.2 --- The redistributor network --- p.55Chapter 3.6 --- Conculsion of chpater 3 --- p.57Chapter Chapter4 --- Implementation and Analysis --- p.58Chapter 4.1 --- The imaging geometry of PSVM --- p.58Chapter 4.2 --- Input --- p.59Chapter 4.3 --- The hypercolumn construction --- p.59Chapter 4.4 --- Analysis of matching mechanism in PSVM --- p.59Chapter 4.4.1 --- Fusional condition --- p.61Chapter 4.4.2 --- Disparity detection --- p.61Chapter 4.5. --- Matching rules in PSVM --- p.63Chapter 4.5.1 --- The ordering constraint --- p.63Chapter 4.5.2 --- The uniqueness constraint --- p.64Chapter 4.5.3 --- The figural continuity constraint --- p.64Chapter 4.5.4 --- The smoothness assumption --- p.65Chapter 4.6. --- Use multi-lengths of oriented line to solve the occlusion problem --- p.66Chapter 4.7 --- Performance of PSVM --- p.67Chapter 4.7.1 --- Artificial scene --- p.67Chapter 4.7.2 --- Natural images --- p.71Chapter 4.8 --- Discussion --- p.83Chapter 4.9 --- Overall conclusion --- p.83Appendix: Illustration example --- p.85References --- p.9

Invariant Reconstruction of Curves and Surfaces with Discontinuities with Applications in Computer Vision

The reconstruction of curves and surfaces from sparse data is an important task in many applications. In computer vision problems the reconstructed curves and surfaces generally represent some physical property of a real object in a scene. For instance, the sparse data that is collected may represent locations along the boundary between an object and a background. It may be desirable to reconstruct the complete boundary from this sparse data. Since the curves and surfaces represent physical properties, the characteristics of the reconstruction process differs from straight forward fitting of smooth curves and surfaces to a set of data in two important areas. First, since the collected data is represented in an arbitrarily chosen coordinate system, the reconstruction process should be invariant to the choice of the coordinate system (except for the transformation between the two coordinate systems). Secondly, in many reconstruction applications the curve or surface that is being represented may be discontinuous. For example in the object recognition problem if the object is a box there is a discontinuity in the boundary curve at the comer of the box. The reconstruction problem will be cast as an ill-posed inverse problem which must be stabilized using a priori information relative to the constraint formation. Tikhonov regularization is used to form a well posed mathematical problem statement and conditions for an invariant reconstruction are given. In the case where coordinate system invariance is incorporated into the problem, the resulting functional minimization problems are shown to be nonconvex. To form a valid convex approximation to the invariant functional minimization problem a two step algorithm is proposed. The first step forms an approximation to the curve (surface) which is piecewise linear (planar). This approximation is used to estimate curve (surface) characteristics which are then used to form an approximation of the nonconvex functional with a convex functional. Several example applications in computer vision for which the invariant property is important are presented to demonstrate the effectiveness of the algorithms. To incorporate the fact that the curves and surfaces may have discontinuities the minimizing functional is modified. An important property of the resulting functional minimization problems is that convexity is maintained. Therefore, the computational complexity of the resulting algorithms are not significantly increased. Examples are provided to demonstrate the characteristics of the algorithm

Rule-Based Approach to Binocular Stereopsis

This research is motivated by a desire to integrate some of the diverse, yet complimentary, developments that have taken place during the past few years in the area of passive stereo vision. On the one hand, we have approaches based on matching zero-crossings along epipolar lines, and, on the other, people have proposed techniques that match directly higher level percepts, such as line elements and other geometrical forms. Our rule-based program is a modest attempt at integrating these different approaches into a single program. Such integration was made necessary by the fact that no single method by itself appears capable of generating usable range maps of a scene