Bayesian recognition of local 3-d shape by approximating image intensity functions with quadric polynomials

The recognition in image data of viewed patches of spheres, cylinders, and planes in the 3-D world is discussed as a first step to complex object recognition or complex object location and orientation estimation. Accordingly, an image is partitioned into small square windows, each of which is a view of a piece of a sphere, or of a cylinder, or of a plane. Windows are processed in parallel for recognition of content. New concepts and techniques include approximations of the image within a window by 2-D quadric polynomials where each approximation is constrained by one of the hypotheses that the 3-D surface shape seen is either planar, cylindrical, or spherical; a recognizer based upon these approximations to determine whether the object patch viewed is a piece of a sphere, or a piece of a cylinder, or a piece of a plane; lowpass filtering of the image by the approximation. The shape recognition is computationally simple, and for large windows is approximately Bayesian minimum-probability-of-error recognition. These classifications are useful for many purposes. One such purpose is to enable a following processor to use an appropriate estimator to estimate shape, and orientation and location parameters for the 3-D surface seen within a window.     

Estimation of moving vehicle locations using wheel shape information in single 2-D lateral vehicle images by 3-D computer vision techniques

An approach to the estimation of moving lateral vehicle locations for driving assistance using wheel shape information in single 2-D vehicle images by 3-D computer vision techniques is proposed. The location scheme is supposed to be performed on a vehicle with a camera mounted on the front bumper. An analytical solution is applied to estimate locations of the lateral vehicle. Firstly, the rear wheel shape of a lateral vehicle moving in a nearby lane is imaged. By using the Hough transform, the projected wheel shape, which is an ellipse, is detected. Secondly, the equation of the detected ellipse is used to infer the orientation angle of the lateral vehicle with respect to the camera view direction. Finally, the center of the ellipse shape is used to determine the relative position of the lateral vehicle with respect to the camera lens center. Moreover, an edge-point verification algorithm is utilized to extract the ellipse shape more precisely in the image processing stage. Both computer simulated and real images are tested and good experimental results show the effectiveness of the proposed approach for estimating lateral vehicle locations. The results are useful for driving assistance and vehicle collision avoidance and are discussed in detail.     

Perception of 3-D surfaces from 2-D contours

Inference of 3-D shape from 2-D contours in a single image is an important problem in machine vision. The authors survey classes of techniques proposed in the past and provide a critical analysis. They show that two kinds of symmetries in figures, which are known as parallel and skew symmetries, give significant information about surface shape for a variety of objects. They derive the constraints imposed by these symmetries and show how to use them to infer 3-D shape. They also discuss the zero Gaussian curvature (ZGC) surfaces in depth and show results on the recovery of surface orientation for various ZGC surfaces     

Road shape reconstruction by local flatness approximation

A new algorithm is presented for reconstructing the three-dimensional (3-D) road shape from camera images for the purposes of navigating autonomous land vehicles. The approximation that the road surface is locally flat enables us to determine a one-to-one correspondence between the two road boundaries, which together with our knowledge about roads (the 'model' of roads) determines the 3-D road shape. In order to cope with the inaccuracy of image data, a least-squares curve-fitting technique is proposed, and the behaviours of image noise are analysed. Some examples based on real images are given.     

Local surface shape estimation of 3-D textured surfaces usingGaussian Markov random fields and stereo windows

The problem of extracting the local shape information of a 3-D texture surface from a single 2-D image by tracking the perceived systematic deformations the texture undergoes by virtue of being present on a 3-D surface and by virtue of being imaged is examined. The surfaces of interest are planar and developable surfaces. The textured objects are viewed as originating by laying a rubber planar sheet with a homogeneous parent texture on it onto the objects. The homogeneous planar parent texture is modeled by a stationary Gaussian Markov random field (GMRF). A probability distribution function for the texture data obtained by projecting the planar parent texture under a linear camera model is derived, which is an explicit function of the parent GMRF parameters, the surface shape parameters. and the camera geometry. The surface shape parameter estimation is posed as a maximum likelihood estimation problem. A stereo-windows concept is introduced to obtain a unique and consistent parent texture from the image data that, under appropriate transformations, yields the observed texture in the image. The theory is substantiated by experiments on synthesized as well as real images of textured surfaces     

3-d space location and orientation parameter estimation of lambertian spheres and cylinders from a single 2-d image by fitting lines and ellipses to thresholded data

An approach to object location and orientation estimation is discussed in which objects in 3-D space are approximated by chunks of spheres, cylinders, and planes. The surface-shape parameters of these chunks of primitive subobjects are estimated in real time from a single 2-D image assuming a Lambertian reflection model. This processing is realized by partitioning an image into small square windows and processing the windows in parallel. It is assumed that a small window views a portion of one of the spherical, cylindrical or planar chunks. The paper applies standard statistical estimators in new ways to the estimation of the 3-D shape parameters for spherical and cylindrical surfaces. Linear regression and scatter matrix eigenvalue analysis techniques are used here. The algorithms are computationally simple yet are robust and can handle noisy highly variable data.     

A methodology for extracting objective color from images

We present a methodology for correcting color images taken in practical indoor environments, such as laboratories, factories, and studios, that explicitly models illuminant location, surface reflectance and geometry, and camera responsivity. We explicitly model surfaces by taking our color images with corresponding registered three-dimensional (3-D) range images, which provide surface orientation and location information for every point in the scene. We automatically detect regions where color correction should not be applied, such as specularities, coarse texture regions, and jump edges. This correction results in objective color measures of the imaged surfaces. This kind of integrated, comprehensive system of color correction has not existed until now. i.e., it is the first of its kind in computer vision. We demonstrate results of applying this methodology to real images for applications in photorealistic rerendering, skin lesion detection, burn scar color measurement, and general color image enhancement. We also have tested the method under different lighting configurations and with three different range scanners.     

Surface reconstruction using deformable models with interior andboundary constraints

The authors introduce a technique for 3D surface reconstruction using elastic deformable-models. The model used is an imaginary elastic grid, which is made of membranous, thin-plate-type material. The elastic grid can bent, twisted, compressed, and stretched into any desired 3D shape, which is specified by the shape constraints derived automatically from images of a real 3D object. Shape reconstruction is guided by a set of imaginary springs that enforce the consistency in the position, orientation, and/or curvature measurements of the elastic grid and the desired shape. The dynamics of a surface reconstruction process is regulated by Hamilton's principle or the principle of the least action. Furthermore, a 1D deformable template that borders the elastic grid may be used. This companion boundary template is attracted/repelled by image forces to conform with the silhouette of the imaged object. Implementation results using simple analytic shapes and images of real objects are presented     

Moment feature-based three-dimensional tracking using two high-speed vision systems

When considering real-world applications of robot control with visual servoing, both three-dimensional (3-D) information and a high feedback rate are required. We have developed a 3-D target-tracking system with a 1-ms feedback rate using two high-speed vision systems called Column Parallel Vision (CPV) systems. To obtain 3-D information, such as position, orientation and shape parameters of the target object, a feature-based algorithm has been introduced using moment feature values extracted from vision systems for a spheroidal object model. Also, we propose a new 3-D self-windowing method to extract the target in 3-D space using epipolar geometry, which is an extension of the conventional self-windowing method in 2-D images.     

广义的以物体为中心的行程编码：一种新的三维物体模型

三维物体的表达和识别是图象理解和场景分析的核心问题,三维模型在三维物体的识别和场景分析中具有十分重要的作用.三维模型应该是以物体为中心的,能够提供该场景的所有有用信息.物体的大小,形状及朝向应均可从该模型中提取得到.本文提出了一种新的三维物体模型——广义的以物体为中心的行程编码.它包括物体的GORC物理数据结构,详细的形状描述和抽象描述.物体的高层次的表达可以通过以GORC编码的物理数据直接提取得到.三维的GORC是二维的以物体为中心的行程编码在三维上的推广,它兼有物体的体积表达和表面表达的优点.三维物体的GORC模型可以很容易地由其深度信息构造得出,基于GORC的投影运算,图象代数运算以及特征提取均可非常有效地实现.     

Representation and shape matching of 3-d objects

A three-dimensional scene analysis system for the shape matching of real world 3-D objects is presented. Various issues related to representation and modeling of 3-D objects are addressed. A new method for the approximation of 3-D objects by a set of planar faces is discussed. The major advantage of this method is that it is applicable to a complete object and not restricted to single range view which was the limitation of the previous work in 3-D scene analysis. The method is a sequential region growing algorithm. It is not applied to range images, but rather to a set of 3-D points. The 3-D model of an object is obtained by combining the object points from a sequence of range data images corresponding to various views of the object, applying the necessary transformations and then approximating the surface by polygons. A stochastic labeling technique is used to do the shape matching of 3-D objects. The technique matches the faces of an unknown view against the faces of the model. It explicitly maximizes a criterion function based on the ambiguity and inconsistency of classification. It is hierarchical and uses results obtained at low levels to speed up and improve the accuracy of results at higher levels. The objective here is to match the individual views of the object taken from any vantage point. Details of the algorithm are presented and the results are shown on several unknown views of a complicated automobile casting.     

Knowledge-driven ultrasonic three-dimensional organ modeling

A representation is described for nonstructured biologic objects which are single-valued distortions of a sphere. The representation is implemented in a model-driven system for extracting three-dimensional (3-D) organ reconstructions from a series of arbitrarily oriented ultrasound slices. A training set of ultrasonic reconstructions of similarly shaped objects is used to give the computer generic knowledge of a given shape class. This knowledge is in the form of local slope constraints defined on an object coordinate system. The combination of constraints, interacting together via a relaxation process on continuous label sets, attempts to capture the essential shape and range of variation for an organ class. An initial tolerance region and ``bestguess' organ surface are established by the interaction of the learned shape knowledge with manually input organ landmarks. A hypothesize-verify paradigm is employed to alternately request new data and to update the tolerance region and bestguess surface. Examples from runs on two balloon classes are presented. These examples show: 1) the local constraints interact to produce a reasonable global depiction of the essential shape and range of variation, 2) the use of shape knowledge permits accurate results from only one third of the available data, and 3) the 3-D shape knowledge provides a two-dimensional (2-D) tolerance region for plan-guided edge detection.     

Retrieving shape information from multiple images of a specularsurface

In many remote sensing and machine vision applications, the shape of a specular surface such as water, glass, or polished metal must be determined instantaneously and under natural lighting conditions. Most image analysis techniques, however, assume surface reflectance properties or lighting conditions that are incompatible with these situations. To retrieve the shape of smooth specular surfaces, a technique known as specular surface stereo was developed. The method analyzes multiple images of a surface and finds a surface shape that results in a set of synthetic images that match the observed ones. An image synthesis model is used to predict image irradiance values as a function of the shape and reflectance properties of the surface, camera geometry, and radiance distribution of the illumination. The specular surface stereo technique was tested by processing four numerical simulations-a water surface illuminated by a low- and high-contrast extended light source, and a mirrored surface illuminated by a low- and high-contrast extended light source. Under these controlled circumstances, the recovered surface shape showed good agreement with the known input     

Approximation-Based Similarity Search for 3-D Surface Segments

The issue of finding similar 3-D surface segments arises in many recent applications of spatial database systems, such as molecular biology, medical imaging, CAD, and geographic information systems. Surface segments being similar in shape to a given query segment are to be retrieved from the database. The two main questions are how to define shape similarity and how to efficiently execute similarity search queries. We propose a new similarity model based on shape approximation by multi-parametric surface functions that are adaptable to specific application domains. We then define shape similarity of two 3-D surface segments in terms of their mutual approximation errors. Applying the multi-step query processing paradigm, we propose algorithms to efficiently support complex similarity search queries in large spatial databases. A new query type, called the ellipsoid query, is utilized in the filter step. Ellipsoid queries, being specified by quadratic forms, represent a general concept for similarity search. Our major contribution is the introduction of efficient algorithms to perform ellipsoid queries on multidimensional index structures. Experimental results on a large 3-D protein database containing 94,000 surface segments demonstrate the successful application and the high performance of our method.     

Shape from shading through photometric motion

We present a new shape from shading algorithm, extending to the single-input case, a recently introduced approach to the photometric motion process. As proposed by Pentland, photometric motion is based on the intensity variation, due to the motion, at a given point on a rotating surface. Recently, an alternative formulation has also appeared, based on the intensity change at a fixed image location. Expressing this as a function of reflectance-map and motion-field parameters, a constraint on the shape of the imaged surface can be obtained. Coupled with an affine matching constraint, this has been shown to yield a closed-form expression for the surface function. Here, we extend such formulation to the single-input case, by using the Green’s function of an affine matching equation to generate an artificial pair to the input image, corresponding to an approximate rendition of the imaged surface under a rotated view. Using this, we are able to obtain high quality shape-from-shading estimates, even under conditions of unknown reflectance map and light source direction, as demonstrated here by an extensive experimental study.     

A new generalized computational framework for finding objectorientation using perspective trihedral angle constraint

This paper investigates a fundamental problem of determining the position and orientation of a three-dimensional (3-D) object using a single perspective image view. The technique is focused on the interpretation of trihedral angle constraint information. A new closed form solution based on Kanatani's formulation is proposed. The main distinguishing feature of the authors' method over the original Kanatani formulation is that their approach gives an effective closed form solution for a general trihedral angle constraint. The method also provides a general analytic technique for dealing with a class of problem of shape from inverse perspective projection by using “angle to angle correspondence information.” A detailed implementation of the authors' technique is presented. Different trihedral angle configurations were generated using synthetic data for testing the authors' approach of finding object orientation by angle to angle constraint. The authors performed simulation experiments by adding some noise to the synthetic data for evaluating the effectiveness of their method in a real situation. It has been found that the authors' method worked effectively in a noisy environment which confirms that the method is robust in practical application     

Design and recovery of 2-D and 3-D shapes using rational Gaussian curves and surfaces

A new representation for parametric curves and surfaces is introduced here. It is in rational form and uses rational Gaussian bases. This representation allows design of 2-D and 3-D shapes, and makes recovery of shapes from noisy image data possible. The standard deviations of Gaussians in a curve or surface control the smoothness of a recovered shape. The control points of a surface in this representation are not required to form a regular grid and a scattered set of control points is sufficient to reconstruct a surface. Examples of shape design, shape recovery, and image segmentation using the proposed representation are given.