A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

1 Passive Methods for Depth Recovery.- 1.1 Introduction.- 1.2 Different Methods of Depth Recovery.- 1.2.1 Depth from Stereo.- 1.2.2 Structure from Motion.- 1.2.3 Shape from Shading.- 1.2.4 Range from Focus.- 1.2.5 Depth from Defocus.- 1.3 Difficulties in Passive Ranging.- 1.4 Organization of the Book.- 2 Depth Recovery from Defocused Images.- 2.1 Introduction.- 2.2 Theory of Depth from Defocus.- 2.2.1 Real Aperture Imaging.- 2.2.2 Modeling the Camera Defocus.- 2.2.3 Depth Recovery.- 2.2.4 Sources of Errors.- 2.3 Related Work.- 2.4 Summary of the Book.- 3 Mathematical Background.- 3.1 Introduction.- 3.2 Time-Frequency Representation.- 3.2.1 The Complex Spectrogram.- 3.2.2 The Wigner Distribution.- 3.3 Calculus of Variations.- 3.4 Markov Random Fields and Gibbs Distributions.- 3.4.1 Theory of MRF.- 3.4.2 Gibbs Distribution.- 3.4.3 Incorporating Discontinuities.- 4 Depth Recovery with a Block Shift-Variant Blur Model.- 4.1 Introduction.- 4.2 The Block Shift-Variant Blur Model.- 4.2.1 Estimation of Blur.- 4.2.2 Special Cases.- 4.3 Experimental Results.- 4.4 Discussion.- 5 Space-Variant Filtering Models for Recovering Depth.- 5.1 Introduction.- 5.2 Space-Variant Filtering.- 5.3 Depth Recovery Using the Complex Spectrogram.- 5.4 The Pseudo-Wigner Distribution for Recovery of Depth.- 5.5 Imposing Smoothness Constraint.- 5.5.1 Regularized Solution Using the Complex Spectrogram..- 5.5.2 The Pseudo-Wigner Distribution and Regularized Solution.- 5.6 Experimental Results.- 5.7 Discussion.- 6 ML Estimation of Depth and Optimal Camera Settings.- 6.1 Introduction.- 6.2 Image and Observation Models.- 6.3 ML-Based Recovery of Depth.- 6.4 Computation of the Likelihood Function.- 6.5 Optimality of Camera Settings.- 6.5.1 The Cramer-Rao Bound.- 6.5.2 Optimality Criterion.- 6.6 Experimental Results.- 6.7 Discussion.- 7 Recursive Computation of Depth from Multiple Images.- 7.1 Introduction.- 7.2 Blur Identification from Multiple Images.- 7.3 Minimization by Steepest Descent.- 7.4 Recursive Algorithm for Computing the Likelihood Function.- 7.4.1 Single Observation.- 7.4.2 Two Observations.- 7.4.3 General Case of M Observations.- 7.5 Experimental Results.- 7.6 Discussion.- 8 MRF Model-Based Identification of Shift-Variant PSF.- 8.1 Introduction.- 8.2 A MAP-MRF Approach.- 8.3 The Posterior Distribution and Its Neighborhood.- 8.4 MAP Estimation by Simulated Annealing.- 8.5 Experimental Results.- 8.6 Discussion.- 9 Simultaneous Depth Recovery and Image Restoration.- 9.1 Introduction.- 9.2 Depth Recovery and Restoration using MRF Models.- 9.3 Locality of the Posterior Distribution.- 9.4 Parameter Estimation.- 9.5 Experimental Results.- 9.6 Discussion.- 10 Conclusions.- A Partial Derivatives of Various Quantities in CRB.- References.

https://www.springer.com/productFlyer_978-0-387-98635-7.pdf?SGWID=0-0-1297-2012782-0

Depth From Defocus: A Real Aperture Imaging Approach

This paper describes a novel technique to recover large similarity transformations (rotation/scale/translation) and moderate perspective deformations among image pairs We introduce a hybrid algorithm that features log-polar mappings and nonlinear least squares optimization The use of log-polar techniques in the spatial domain is introduced as a preprocessing module to recover large scale changes (eg, at least four-fold) and arbitrary rotations Although log-polar techniques are used in the Fourier-Mellin transform to accommodate rotation and scale in the frequency domain, its use in registering images subjected to very large scale changes has not yet been exploited in the spatial domain In this paper, we demonstrate the superior performance of the log-polar transform in featureless image registration in the spatial domain We achieve subpixel accuracy through the use of nonlinear least squares optimization The registration process yields the eight parameters of the perspective transformation that best aligns the two input images Extensive testing was performed on uncalibrated real images and an array of 10,000 image pairs with known transformations derived from the Corel Stock Photo Library of royalty-free photographic images

/pdf/image-registration-using-log-polar-mappings-for-recovery-of-5061m2sn9d.pdf

Image registration using log-polar mappings for recovery of large-scale similarity and projective transformations

We discuss calibration and removal of “vignetting” (radial falloff) and exposure (gain) variations from sequences of images. Even when the response curve is known, spatially varying ambiguities prevent us from recovering the vignetting, exposure, and scene radiances uniquely. However, the vignetting and exposure variations can nonetheless be removed from the images without resolving these ambiguities or the previously known scale and gamma ambiguities. Applications include panoramic image mosaics, photometry for material reconstruction, image-based rendering, and preprocessing for correlation-based vision algorithms.

Vignette and Exposure Calibration and Compensation

Image-based rendering (IBR) is unique in that it requires computer graphics, computer vision, and image processing to join forces to solve a common goal, namely photorealistic rendering through the use of images. IBR as an area of research has been around for about ten years, and substantial progress has been achieved in effiectively capturing, representing, and rendering scenes. In this article, we survey the techniques used in IBR. Our survey shows that representations and rendering techniques can differ radically, depending on design decisions related to ease of capture, use of geometry, accuracy of geometry (if used), number and distribution of source images, degrees of freedom for virtual navigation, and expected scene complexity.

/pdf/image-based-rendering-2ajv8z1bew.pdf

Image-based rendering

An approach to stereo feature matching is presented with the introduction of a similarity measure for evaluating and confirming a stereo match. The contributions of this study are reflected in (1) the development of a similarity measure which evaluates a stereo match based on feature locality and gray-level gradient associated with the feature; and (2) the use of a matching procedure that integrates local and global matching strategies based on matching first those features with the highest similarity measure among the set of all highest similarities found locally under confined search spaces, ensuring that each feature is matched with a high degree of certainty. A left-to-right and right-to-left consistency check is used for each feature to comply with the uniqueness constraint and to confirm if a potential match can be declared a correct match.

A similarity measure for stereo feature matching

This paper describes an approach whereby comparametric analysis is used in jointly registering image pairs in their domain and range, i.e., in their spatial coordinates and pixel values, respectively. This is accomplished by approximating a camera's nonlinear comparametric function with a constrained piecewise linear one. The optimal fitting of this approximation to comparagram data is then used in a re-parameterized version of the camera's comparametric function to estimate the exposure difference between images. Doing this allows the inherently nonlinear problem of joint domain and range registration to be performed using a computationally attractive least squares formalism. The paper first presents the range registration process and then describes the strategy for performing the joint registration. The models used allow for the pair-wise registration of images taken from a camera that can automatically adjust its exposure as well as tilt, pan, rotate and zoom about its optical center. Results concerning the joint registration as well as range-only registration are provided to demonstrate the method's effectiveness.

Jointly registering images in domain and range by piecewise linear comparametric analysis

An approach is presented that can simultaneously align multiple exposure-adjusted pictures of the same scene both in their spatial coordinates as well as in their pixel values. The approach is featureless and produces an image mosaic at a common spatial and exposure reference and also addresses the misalignment problem common to methods that compose mosaics from only pair-wise registered image pairs. The objective function considered minimizes the sum of the collective variance over pixels of a global coordinate grid on which to create the final image. The models employed relate images spatially by homographic transformations and tonally by comparametric functions. The importance of performing joint spatial and tonal registration on exposure-adjusted images is emphasized by providing two examples in which spatial-only registration fails. The performance between pair-wise and simultaneous registration under both spatial-only and joint registration procedures is discussed.

Simultaneous homographic and comparametric alignment of multiple exposure-adjusted pictures of the same scene

A fundamentally new approach that accurately estimates the camera response function from comparametric data, i.e., pixel data from two differently exposed images over a common field of view, is presented. It does so by solving for the camera response function from its associated comparametric relation. The approach offers several advantageous features, including having a complexity that is independent of the number of pixel data considered, allowing for the modeling of saturated pixels, enabling an inherently constrained optimization problem to be solved in an unconstrained manner, and the easy incorporation into an existing framework for joint image registration. This is accomplished by approximating the camera response function with a constrained piecewise linear model so that its solution, within the comparametric camera relation, can be obtained. This results in a semiparametric comparametric model, optimally determined from pixel data, which is directly parameterized in terms of the exposure parameter. Subsequently, it is shown how this semiparametric model is used for exposure estimation from captured images. Finally, we incorporate the semiparametric model within an existing and previously published framework for simultaneous and joint spatial and tonal image registration in order to illustrate the developed model's performance.

A semiparametric model for accurate camera response function modeling and exposure estimation from comparametric data

Describes a new feature-based stereo matching technique which exploits the Walsh transformation. The established matching strategy adopts and integrates the fundamental steps of the stereo vision problem: (a) detecting and locating feature points, (b) searching for potential matches, (c) validating a match through a global consistency check, and (d) determining the disparity of the matched feature points. The unique representation of stereo images into Walsh-based attributes unites the aforementioned steps into an integrated process which yields accurate disparity extraction. It is shown that the first and second Walsh attributes are used as operators approximating the first and second derivatives for the extraction and localization of feature points. The complete set of these attributes are then used as matching primitives contributing equally to the decision-making process and providing relevant information on both the characterization of a potential match and its validation through a consistency check. Computer results using images of varying complexities prove the soundness and the relatively fast processing time of this stereo matching technique. >

F.M. Candocia

Papers

A similarity measure for stereo feature matching

Jointly registering images in domain and range by piecewise linear comparametric analysis

Simultaneous homographic and comparametric alignment of multiple exposure-adjusted pictures of the same scene

A semiparametric model for accurate camera response function modeling and exposure estimation from comparametric data

A stereo matching paradigm based on the Walsh transformation