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     

3-D reconstruction from tomographic data using 2-D active contours.

Reconstructing three-dimensional (3-D) shapes of structures like internal organs from tomographic data is an important problem in medical imaging. Various forms of the deformable surface model have been proposed to tackle it, but they are either computationally expensive or limited to tubular shapes. In this paper a 3-D reconstruction mechanism that requires only 2-D deformations is proposed. Advantages of the proposed model include that it is conformable to any 3-D shape, efficient, and highly parallelizable. Most importantly, it requires from the user an initial 2-D contour on only one of the tomograph slices to start with. Experimental results are shown to illustrate the performance of the model.     

Detecting 3-D motion field from range image sequences

An algorithm for computing three-dimensional (3-D) velocity field and motion parameters from a range image sequence is presented. It is based on a new integral 3-D rigid motion invariant feature-the trace of a 3x3 "feature matrix" related to the moment of inertia tensor. This trace can be computed at every point of a moving surface and provides a quantitative measure of the local shape of the surface. Based on the feature's conservation along the trajectory of every moving point, a 3-D Flow Constraint Equation is formulated and solved for the velocity field. The robustness of the feature in presence of noise and discontinuity is analyzed.     

Acquiring 3D models of non-rigid moving objects from time andviewpoint varying image sequences: a step toward left ventricle recovery

This paper describes a method for the accurate recovery of time-varying 3D shapes with known cycle from images with different viewpoints as well as times; aiming at the recovery of the left ventricular shapes. Our recovery method is based on the integration of apparent contours from different viewpoints. We perform direct fitting to a 4D closed surface model based on B-splines so as to deal with fragmented contours such as extracted from X-ray cineangiocardiograms. The method is quantitatively evaluated using synthesized and real image sequences     

Acquiring domain models

Whereas a Learning Apprentice System stresses the generation and refinement of shallow rules of a performance program, presupposing a domain theory, BLIP3 is mainly concerned with the construction of a domain theory as the first phase of the knowledge-acquisition process. In this paper the BLIP approach to machine learning is described. The system design is presented and the already implemented knowledge sources are shown with their formalisms and functions for the learning process.     

Acquiring user models

Existing machine techniques for acquiring user models are characterized along five orthogonal dimensions: passive/active, user-initiated/automatic, logical/plausible, direct/indirect, and on-line/off-line. Passive techniques observe users whereas active techniques query users. User-initiated techniques require that users volunteer information; automatic techniques do not. The logical/plausible dimension measures the accuracy of derived user model data. Indirect techniques build upon data gathered by more direct methods. On-line techniques acquire user models in real-time during user interaction, while off-line techniques work after the user interaction is finished. Commonalities and differences in capabilities and features of different user model acquisition techniques are analyzed along the above dimensions, and the relationship of these techniques to similar techniques in other areas of artificial intelligence are discussed.     

On 3-D scene flow and structure recovery from multiview image sequences

Two novel systems computing dense three-dimensional (3-D) scene flow and structure from multiview image sequences are described in this paper. We do not assume rigidity of the scene motion, thus allowing for nonrigid motion in the scene. The first system, integrated model-based system (IMS), assumes that each small local image region is undergoing 3-D affine motion. Non-linear motion model fitting based on both optical flow constraints and stereo constraints is then carried out on each local region in order to simultaneously estimate 3-D motion correspondences and structure. The second system is based on extended gradient-based system (EGS), a natural extension of two-dimensional (2-D) optical flow computation. In this method, a new hierarchical rule-based stereo matching algorithm is first developed to estimate the initial disparity map. Different available constraints under a multiview camera setup are further investigated and utilized in the proposed motion estimation. We use image segmentation information to adopt and maintain the motion and depth discontinuities. Within the framework for EGS, we present two different formulations for 3-D scene flow and structure computation. One formulation assumes that initial disparity map is accurate, while the other does not. Experimental results on both synthetic and real imagery demonstrate the effectiveness of our 3-D motion and structure recovery schemes. Empirical comparison between IMS and EGS is also reported.     

An interactive framework for acquiring vision models of 3-D objects from 2-D images.

This paper presents a human-computer interaction (HCI) framework for building vision models of three-dimensional (3-D) objects from their two-dimensional (2-D) images. Our framework is based on two guiding principles of HCI: 1) provide the human with as much visual assistance as possible to help the human make a correct input; and 2) verify each input provided by the human for its consistency with the inputs previously provided. For example, when stereo correspondence information is elicited from a human, his/her job is facilitated by superimposing epipolar lines on the images. Although that reduces the possibility of error in the human marked correspondences, such errors are not entirely eliminated because there can be multiple candidate points close together for complex objects. For another example, when pose-to-pose correspondence is sought from a human, his/her job is made easier by allowing the human to rotate the partial model constructed in the previous pose in relation to the partial model for the current pose. While this facility reduces the incidence of human-supplied pose-to-pose correspondence errors, such errors cannot be eliminated entirely because of confusion created when multiple candidate features exist close together. Each input provided by the human is therefore checked against the previous inputs by invoking situation-specific constraints. Different types of constraints (and different human-computer interaction protocols) are needed for the extraction of polygonal features and for the extraction of curved features. We will show results on both polygonal objects and object containing curved features.     

Constructing 3-D object models using multiple simulated 2.5-D sketches

Many applications involve the construction of 3-D object models from which images, often requiring a high degree of realism, are later produced. Constructing such models frequently involves considerable human intervention, even in cases where a physical model or the actual object to be modelled exists. This paper describes an approach to the automatic construction of 3-D object models using images of scenes. This method employs a representation of the visible surfaces in a scene called the 2.5-D sketch and a model construction process is described that utilizes multiple simulated 2.5-D sketches.