In this paper, we propose a method for detecting digital processing of video such as compositing. Our method is based on a realistic model of charge coupled device (CCD) sensor noise. It allows the identification of an object that has been composited with an image. This is accomplished through the comparison of local characteristics of noise. These characteristics should be constant for an image captured by one camera. However, when an element captured with a different camera is composited with an image, the local noise characteristics of the foreign element can help us identify it. Experimental results validate our model.

Cylindrical panoramic mosaics can be created by aligning and stitching images from a series, captured by a camera rotating around its optical center. The transformation between two images must then be found. Existing methods compute a global transformation for the whole image starting from pixels in the overlapping region. This global transformation, due to local distortions often results in ghost problems in the overlapping region which have to be handled with some deghosting algorithm. This paper proposes to directly use optical flow in order to find a pixelwise registration in the overlapping region, reducing ghost problems. The optical flow is computed using a multiresolution computational algebraic topology approach.

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.

In this paper we introduce a new filter to approximate multiple scattering of light rays within a participating media. This filter is derived from the generalized Gaussian distribution GGD. It characterizes the Atmospheric Point Spread Function (APSF) and thus makes it possible to introduce three new approaches. First, it allows us to accurately simulate various weather conditions that induce multiple scattering including fog, haze, rain, etc. Second, it allows us to propose a new method for a cooperative and simultaneous estimation of visual cues, i.e., the identification of weather degradations and the estimation of optical thickness between two images of the same scene acquired under unknown weather conditions. Third, by combining this filter with two new sets of invariant features we recently developed, we obtain invariant features that can be used for the matching of atmospheric degraded images. The first set leads to atmospheric invariant features while the second one simultaneously provides atmospheric and geometric invariance.

This paper presents a homotopy-based algorithm for simultaneous recovery of defocus blur and the affine transformation between two images of the same scene. One of the images (and its partial derivatives) is expressed as a function of the second image, partial derivatives of the two images, blur difference, 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. The proposed algorithm is tested using synthetic and real images. The results confirm that dense and accurate estimation can be obtained.

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.