A novel depth-from-focus technique is introduced that needs only a single image. It is based on a precise knowledge of the 3-D point spread function and requires objects of uniform brightness and simple shapes. Using ...
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A novel depth-from-focus technique is introduced that needs only a single image. It is based on a precise knowledge of the 3-D point spread function and requires objects of uniform brightness and simple shapes. Using adequate low-level image processing techniques, the true area of the object and the distance from the focal plane is obtained from parameters such as the apparent (blurred) area of the object and the mean brightness in this area. The technique has been applied to measure the size distribution of bubbles submerged by breaking waves. A depth criterion is used to define a virtual measuring volume that is roughly proportional to the size of the bubbles.< >
A new method for generating X-Y separable, steerable, scalable approximations of filter kernels is proposed which is based on a generalization of the singular value decomposition (SVD) to three dimensions. This "...
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A new method for generating X-Y separable, steerable, scalable approximations of filter kernels is proposed which is based on a generalization of the singular value decomposition (SVD) to three dimensions. This "pseudo-SVD" improves upon a previous scheme due to Perona (1992) in that it reduces convolution time and storage requirements. An adaptation of the pseudo-SVD is proposed to generate steerable and scalable kernels which are suitable for use with a Laplacian pyramid. The properties of this method are illustrated experimentally in generating steerable and scalable approximations to an early vision edge-detection kernel.< >
A given (overcomplete) discrete oriented pyramid may be converted into a steerable pyramid by interpolation. We present a technique for deriving the optimal interpolation functions (otherwise called 'steering coef...
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A given (overcomplete) discrete oriented pyramid may be converted into a steerable pyramid by interpolation. We present a technique for deriving the optimal interpolation functions (otherwise called 'steering coefficients'). The proposed scheme is demonstrated on a computationally efficient oriented pyramid, which is a variation on the Burt and Adelson (1983) pyramid. We apply the generated steerable pyramid to orientation-invariant texture analysis in order to demonstrate its excellent rotational isotropy. High classification rates and precise rotation identification are demonstrated.< >
Accurately segmenting and quantifying structures is a key issue in biomedical image analysis. The two conventional methods of image segmentation, region-based segmentation and boundary finding, often suffer from a var...
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Accurately segmenting and quantifying structures is a key issue in biomedical image analysis. The two conventional methods of image segmentation, region-based segmentation and boundary finding, often suffer from a variety of limitations. We propose a method which endeavors to integrate the two approaches in an effort to form a unified approach that is robust to noise and poor initialization. Our approach uses Green's theorem to derive the boundary of a homogeneous region-classified area in the image and integrates this with a grey-level-gradient-based boundary finder. This combines the perceptual notions of edge/shape information with gray level homogeneity.< >
We propose an affine framework for perspective views, captured by a single extremely simple equation based on a viewer-centered invariant we call relative affine structure. Via a number of corollaries of our main resu...
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We propose an affine framework for perspective views, captured by a single extremely simple equation based on a viewer-centered invariant we call relative affine structure. Via a number of corollaries of our main results we show that our framework unifies previous work-including Euclidean, projective and affine-in a natural and simple way. Finally, the main results were applied to a real image sequence for purpose of 3D reconstruction from 2D views.< >
This paper is concerned with estimation of the reflectance map and its use in surface shape estimation by a method that combines stereo and shading information. There are many advantages to the integrated approach. Ho...
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This paper is concerned with estimation of the reflectance map and its use in surface shape estimation by a method that combines stereo and shading information. There are many advantages to the integrated approach. However, shape from shading algorithms are limited in their applicability by the assumption of idealized reflectance and lighting models involving many preset or hard to estimate parameters. In this paper, it is shown that the difficulties involved in estimation of the reflectance function and light source distribution can be eliminated through direct estimation of the reflectance map using adaptive polynomial models. The reflectance map estimate is used with a surface estimate provided by stereo in an integrated approach to surface shape estimation.< >
This paper presents a new framework for the computation of shape and motion from a sequence of images taken under perspective projection. The framework is based on two abstractions, the picture and trail loci, that re...
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This paper presents a new framework for the computation of shape and motion from a sequence of images taken under perspective projection. The framework is based on two abstractions, the picture and trail loci, that represent respectively the set of all pictures of the same scene and the set of all trails that a point in the world can leave on the image for a given camera trajectory. These abstractions lead to a remarkably clean relation between perspective and orthography. A shape and motion reconstruction method is developed for the case of a two-dimensional world but all concepts also hold in three dimensions. Experiments show that the method is rather immune to noise but critically dependent on camera calibration.< >
No feature-based vision system can work unless good features can be identified and tracked from frame to frame. Although tracking itself is by and large a solved problem, selecting features that can be tracked well an...
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No feature-based vision system can work unless good features can be identified and tracked from frame to frame. Although tracking itself is by and large a solved problem, selecting features that can be tracked well and correspond to physical points in the world is still hard. We propose a feature selection criterion that is optimal by construction because it is based on how the tracker works, and a feature monitoring method that can detect occlusions, disocclusions, and features that do not correspond to points in the world. These methods are based on a new tracking algorithm that extends previous Newton-Raphson style search methods to work under affine image transformations. We test performance with several simulations and experiments.< >
This paper addresses the problem of illumination planning for robust object recognition in structured environments. Given a set of objects, the goal is to determine the illumination for which the objects are most dist...
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This paper addresses the problem of illumination planning for robust object recognition in structured environments. Given a set of objects, the goal is to determine the illumination for which the objects are most distinguishable in appearance from each other. For each object, a large number of images is automatically obtained by varying pose and illumination. Images of all objects, together, constitute the planning image set. The planning set is compressed using the Karhunen-Loeve transform to obtain a low-dimensional subspace. For any given illumination, objects are represented as parametrized manifolds in the subspace. The minimum distance between the manifolds of too objects represents the similarity between the objects in the correlation sense. The optimal illumination is therefore one that maximizes the shortest distance between object manifolds. Results produced by the illumination planner heave been used to enhance the performance of an object recognition system.< >
This paper describes an investigation into the use of parametric 2D models describing the movement of edges for the determination of possible 3D shape and hence function of an object. An assumption of this research is...
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This paper describes an investigation into the use of parametric 2D models describing the movement of edges for the determination of possible 3D shape and hence function of an object. An assumption of this research is that the camera can foveate and track particular features. It is argued that simple 2D analytic descriptions of the movement of edges can infer 3D shape while the camera is moved. This uses an advantage of foveation i.e. The problem becomes object centred. The problem of correspondence for numerous edge points is overcome by the use of a tree based representation for the competing hypotheses. Numerous hypothesis are maintained simultaneously and it does not rely on a single kinematic model which assumes constant velocity or acceleration. The numerous advantages of this strategy are described.< >
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