Model-based object recognition from a complex binary imagery using genetic algorithm

This paper describes a technique for model-based object recognition in a noisy and cluttered environment, by extending the work presented in an earlier study by the authors. In order to accurately model small irregularly shaped objects, the model and the image are represented by their binary edge maps, rather then approximating them with straight line segments. The problem is then formulated as that of finding the best describing match between a hypothesized object and the image. A special form of template matching is used to deal with the noisy environment, where the templates are generated on-line by a Genetic Algorithm. For experiments, two complex test images have been considered and the results when compared with standard techniques indicate the scope for further research in this direction

Superquadric-Based Object Recognition

This paper proposes a technique for object recognition using superquadric built models. Superquadrics, which are three dimensional models suitable for part-level representation of objects, are reconstructed from range images using the recover-and-select paradigm. Using an interpretation three, the presence of an object in the scene from the model database can be hypothesized. These hypotheses are verified by projecting and re-fitting the object model to the range image which at the same time enables a better localization of the object in the scene

Differential Geometry, Surface Patches and Convergence Methods

The problem of constructing a surface from the information provided by the Marr-Poggio theory of human stereo vision is investigated. It is argued that not only does this theory provide explicit boundary conditions at certain points in the image, but that the imaging process also provides implicit conditions on all other points in the image. This argument is used to derive conditions on possible algorithms for computing the surface. Additional constraining principles are applied to the problem; specifically that the process be performable by a local-support parallel network. Some mathematical tools, differential geometry, Coons surface patches and iterative methods of convergence, relevant to the problem of constructing the surface are outlined. Specific methods for actually computing the surface are examined

A Computer Implementation of a Theory of Human Stereo Vision

Recently, Marr and Poggio (1979) presented a theory of human stereo vision. An implementation of that theory is presented and consists of five steps: (1) The left and right images are each filtered with masks of four sizes that increase with eccentricity; the shape of these masks is given by $abla^{2}G$, the laplacian of a gaussian function. (2) Zero-crossing in the filtered images are found along horizontal scan lines. (3) For each mask size, matching takes place between zero-crossings of the same sign and roughly the same orientation in the two images, for a range of disparities up to about the width of the mask's central region. Within this disparity range, Marr and Poggio showed that false targets pose only a simple problem. (4) The output of the wide masks can control vergence movements, thus causing small masks to come into low resolution to dealing with small disparities at a high resolution. (5) When a correspondence is achieved, it is stored in a dynamic buffer, called the 2 1/2 dimensional sketch. To support the sufficiency of the Marr-Poggio model of human stereo vision, the implementation was tested on a wide range of stereograms from the human stereopsis literature. The performance of the implementation is illustrated and compared with human perception. As well, statistical assumptions made by Marr and Poggio are supported by comparison with statistics found in practice. Finally, the process of implementing the theory has led to the clarification and refinement of a number of details within the theory; these are discussed in detail

Binocular Shading and Visual Surface Reconstruction

Zero-crossing or feature-point based stereo algorithms can, by definition, determine explicit depth information only at particular points on the image. To compute a complete surface description, this sparse depth map must be interpolated. A computational theory of this interpolation or reconstruction process, based on a surface consistency constraint, has previously been proposed. In order to provide stronger boundary conditions for the interpolation process, other visual cues to surface shape are examined in this paper. In particular, it is shown that, in principle, shading information from the two views can be used to determine the orientation of the surface normal along the feature-point contours, as well as the parameters of the reflective properties of the surface material. The numerical stability of the resulting equations is also examined

A Computational Theory of Visual Surface Interpolation

Computational theories of structure from motion [Ulman, 1979] and stereo vision [Marr and Poggio, 1979] only specify the computation of three-dimensional surface information at special points in the image. Yet, the visual perception is clearly of complete surfaces. In order to account for this, a computational theory of the interpolation of surfaces from visual information is presented

The Implicit Constraints of the Primal Sketch

Computational theories of structure-from-motion and stereo vision only specify the computation of three-dimensional surface information at points in the image at which the irradiance changes. Yet, the visual perception is clearly of complete surfaces, and this perception is consistent for different observers. Since mathematically the class of surfaces which could pass through the known boundary points provided by the stereo system is infinite and contains widely varying surfaces, the visual system must incorporate some additional constraints besides the known points in order to compute the complete surface. Using the image irradiance equation, we derive the surface consistency constraint, informally referred to as no news is good news. The constraint implies that the surface must agree with the information from stereo or motion correspondence, and not vary radically between these points. An explicit form of this surface consistency constraint is derived, by relating the probability of a zero-crossing in a region of the image to the variation in the local surface orientation of the surface, provided that the surface albedo and the illumination are roughly constant. The surface consistency constraint can be used to derive an algorithm for reconstructing the surface that "best" fits the surface information provided by stereo or motion correspondence