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Multiple View Geometry in Computer Vision.

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

This paper presents a new reflectance model for rendering computer synthesized images. The model accounts for the relative brightness of different materials and light sources in the same scene. It describes the directional distribution of the reflected light and a color shift that occurs as the reflectance changes with incidence angle. The paper presents a method for obtaining the spectral energy distribution of the light reflected from an object made of a specific real material and discusses a procedure for accurately reproducing the color associated with the spectral energy distribution. The model is applied to the simulation of a metal and a plastic.

A reflectance model for computer graphics

http://www1.cs.columbia.edu/~allen/PHOTOPAPERS/flynn.3D.survey.pdf

A Survey Of Free-Form Object Representation and Recognition Techniques

This paper demonstrates a content-based retrieval strategy that can tell whether there are naked people present in an image. No manual intervention is required. The approach combines color and texture properties to obtain an effective mask for skin regions. The skin mask is shown to be effective for a wide range of shades and colors of skin. These skin regions are then fed to a specialized grouper, which attempts to group a human figure using geometric constraints on human structure. This approach introduces a new view of object recognition, where an object model is an organized collection of grouping hints obtained from a combination of constraints on geometric properties such as the structure of individual parts, and the relationships between parts, and constraints on color and texture. The system is demonstrated to have 60% precision and 52% recall on a test set of 138 uncontrolled images of naked people, mostly obtained from the internet, and 1401 assorted control images, drawn from a wide collection of sources.

Finding Naked People

In any object recognition system a major and primary task is to associate those image features, within an image of a complex scene, that arise from an individual object. The key idea here is that a geometric class defined in 3D induces relationships in the image which must hold between points on the image outline (the perspective projection of the object). The resulting image constraints enable both identification and grouping of image features belonging to objects of that class. The classes include surfaces of revolution, canal surfaces (pipes) and polyhedra. Recognition proceeds by first recognising an object as belonging to one of the classes (for example a surface of revolution) and subsequently identifying the object (for example as a particular vase). This differs from conventional object recognition systems where recognition is generally targetted at particular objects. These classes also support the computation of 3D invariant descriptions including symmetry axes, canonical coordinate frames and projective signatures. The constraints and grouping methods are viewpoint invariant, and proceed with no information on object pose. We demonstrate the effectiveness of this class-based grouping on real, cluttered scenes using grouping algorithms developed for rotationally symmetric surfaces, canal-surfaces and polyhedra. >

Class-based grouping in perspective images

The process of grouping and subsequently recognising objects in cluttered images is one laden with difficulties; however, results can be greatly enhanced if the inherent uncertainty of image-features is taken into account. This paper shows that starting with the individual edgel's uncertainty it is possible to calculate covariance-information for all derived quantities. This information can be used to choose between competing algorithms, selecting the one that produces the more reliable results, but also as an aid during the recognition process. The consequent application of error-propagation leads to a new formulation for the calculation of the cross-ratio, which is both robust and efficient in dealing with measured lines, and does not require knowledge about the vanishing point. Extensive Monte-Carlo simulations as well as the application to images of cluttered street-scenes demonstrate the robustness and suitability of the approach.

Grouping based on projective geometry constraints and uncertainty

Viewpoint-invariant representation of generalized cylinders using the symmetry set

This paper describes an experimental investigation of the recognition performance of two approaches to the representation of objects for recognition. The first representation, generally known as appearance modelling, describes an object by a set of images. The image set is acquired for a range of views and illumination conditions which are expected to be encountered in subsequent recognition. This image database provides a description of the object. Recognition is carried out by constructing an eigenvector space to compute efficiently the distance between a new image and any image in the database. The second representation is a geometric description based on the projected boundary of an object. General object classes such as planar objects, surfaces of revolution and repeated structures support the construction of invariant descriptions and invariant index functions for recognition.

An Experimental Comparison of Appearance and Geometric Model Based Recognition

This paper addresses the problem of recovering the generating curve of a surface of revolution from a single uncalibrated perspective view, based solely on the object’s outline and two (partly) visible cross-sections. Without calibration of the camera’s internal parameters such recovery is only possible up to a particular transformation of the true shape. This is however sufficient for 3D reconstruction up to a 2 DOF transformation, for recognition of objects, and for transfer between views. We will describe the basic algorithm and show some examples.

/pdf/projective-reconstruction-of-surfaces-of-revolution-fhcc8qqk8o.pdf

Sven Utcke

Papers

Class-based grouping in perspective images

Grouping based on projective geometry constraints and uncertainty

Viewpoint-invariant representation of generalized cylinders using the symmetry set

An Experimental Comparison of Appearance and Geometric Model Based Recognition

Projective Reconstruction of Surfaces of Revolution