In computer vision, the majority of research works covering the subject of vision through participating media are based on the concept of single scattering of light rays. Only few works deal with multiple scattering and they do so under restrictive constraints. In this paper we introduce a new multiple-scattering based polychromatic model (PM) for vision through participating media. This model involves two basic concepts, namely attenuation and ambient illumination. The resulting model can be applied to a wide range of media. For instance, it can be devoted to the modeling of atmospheric vision, underwater vision and vision through misty glass. We show that it can be used to accurately restore the original versions of degraded images taken through atmosphere. Experimental results confirm that the proposed model is both in good agreement with the theory, and useful in practice.

Real images can contain geometric distortions as well as photometric degradations. Analysis and characterization of those images without recourse to either restoration or geometric standardization is of great importance for the computer vision community as those two processes are often ill-posed problems. To this end, it is necessary to implement image descriptors that make it possible to identify the original image in a simple way independently of the imaging system and imaging conditions. Ideally, descriptors that capture image characteristics must be invariant to the whole range of geometric distortions and photometric degradations, such as blur, that may affect the image. In this paper, we introduce two new classes of radiometric and/or geometric invariant descriptors. The first class contains two types of radiometric invariant descriptors. The first of these type is based on the Mellin transform and the second one is based on central moments. Both descriptors are invariant to contrast changes and to convolution with any kernel having a symmetric form with respect to the diagonals. The second class contains two subclasses of combined invariant descriptors. The first subclass includes central-moment-based descriptors invariant simultaneously to horizontal and vertical translations, to uniform and anisotropic scaling, to stretching, to convolution, and to contrast changes. The second subclass contains central-complex-moment-based descriptors that are simultaneously invariant to similarity transformation and to contrast changes. We apply these invariant descriptors to the matching of geometric transformed and/or blurred images. Experimental results confirm both the robustness and the effectiveness of the proposed invariants.

Near-periodic textures are all around us, in brick walls, fabrics, mosaics, and many other manifestations. They are made up of a basic motif, which can be extracted, yielding a small image known as a tile. In particular, such textures are used in 3D scenes in virtual environments like games. However, the memory allocated for textures on a video card is limited. Generating texture by tiling allows scenes to be rendered in a realistic manner while using very little memory. This paper presents a simple method for extracting a representative tile from a near-periodic texture, working from a photo. The period of the texture is calculated to determine the size of the tile. Then the representative tile is chosen based on two criteria: avoiding color discontinuities at the junction of tiles, and recreating a texture that is as faithful as possible to the original.

This paper describes an optimal line detector for the one-dimensional case which is derived from Canny's criteria, and an efficient approach for the detection of line junctions and line terminations. The line detector is extended to the two-dimensional case by operating separately in the x and y directions. An efficient implementation using an infinite impulse response (IIR) filter is provided. This implementation has the additional advantage that increasing the filter scale affects neither temporal nor spatial complexity. The detection algorithm for junctions and terminations is divided into two steps. First, given the lines extracted from the original image, a local measure of line curvature is estimated using the mean of the dot products of orientation vectors within a given neighbourhood. The second step involves the localization of junctions and terminations. Experimental results using several synthetic and real images demonstrate the validity of the two methods.

This paper presents a homotopy-based algorithm for a simultaneous recovery of defocus blur and the affine parameters of apparent shifts between planar patches of two pictures. These parameters are recovered from two images of the same scene acquired by a camera evolving in time and/or space and for which the intrinsic parameters are known. Using limited Taylor's expansion one of the images (and its partial derivatives) is expressed as a function of the partial derivatives of the two images, the blur difference, the affine parameters and a continuous parameter derived from homotopy methods. All of these unknowns can thus be directly computed by resolving a system of equations at a single scale. The proposed algorithm is tested using synthetic and real images. The results confirm that dense and accurate estimation of the previously mentioned parameters can be obtained.

This paper presents a homotopy-based algorithm for the recovery of depth cues in the spatial domain. The algorithm specifically deals with defocus blur and spatial shifts, that is 2D motion, stereo disparities and/or zooming disparities. These cues are estimated from two images of the same scene acquired by a camera evolving in time and/or space. We show that they can be simultaneously computed by resolving a system of equations using a homotopy method. The proposed algorithm is tested using synthetic and real images. The results confirm that the use of a homotopy method leads to a dense and accurate estimation of depth cues. This approach has been integrated into an application for relief estimation from remotely sensed images.

We present a homotopy-based algorithm for a cooperative and simultaneous estimation of defocus blur and spatial shifts (2D motion, stereo disparities and/or zooming disparities) in the spatial domain. These cues are estimated from two images of the same scene acquired by a camera evolving in time and/or space and for which the intrinsic parameters are known. We show that these depth cues can be directly computed by resolving a system of equations embedded in a family of systems using a homotopy method. The results confirm that the use of homotopies reduces approximation errors and thus leads to a denser and more accurate estimation of depth cues.

This paper describes an efficient approach for the detection of line junctions in gray-level images. The algorithm is divided into two steps. First, given the lines extracted from the original image, local line curvature is estimated. For this purpose, two different measures of curvature are proposed: the rate of change of direction of the orientation vector along the line, and the mean of the dot products of orientation vectors within a given neighborhood. The second step involves the localization of junctions. Examples are provided based on experiments with synthetic and real images

Describes an efficient approach for the detection of line junctions in gray-level images. The algorithm is divided into two steps. First, given the lines extracted from the original image, local line curvature is estimated. For this purpose, two different measures of curvature are proposed: The rate of change of direction of the orientation vector along the line and the mean of the dot products of orientation vectors within a given neighborhood. The second step involves the localization of junctions. Examples are provided based on experiments with remotely sensed images containing road intersections

This paper describes an efficient approach for the detection of line junctions and line terminations. The algorithm is divided into two steps. First, given the lines extracted from the original image, a local measure of line curvature is estimated. Two different measures of curvature were tried out – the rate of change of direction of the orientation vector along the line and the mean of the dot products of orientation vectors within a given neighborhood. The second step involves the localization of junctions and terminations. The algorithm is validated on several synthetic and real images.