Denis Simakov

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We propose a principled approach to summarization of visual data (images or video) based on optimization of a well-defined similarity measure. The problem we consider is re-targeting (or summarization) of image/video data into smaller sizes. A good ldquovisual summaryrdquo should satisfy two properties: (1) it should contain as much as possible visual(More)
We present a method for shape reconstruction from several images of a moving object. The reconstruction is dense (up to image resolution). The method assumes that the motion is known, e.g., by tracking a small number of feature points on the object. The object is assumed Lambertian (completely matte), light sources should not be very close to the object but(More)
The space of images is known to be a nonlinear sub-space that is difficult to model. This paper derives an algorithm that walks within this space. We seek a manifold through the video volume that is constrained to lie locally in this space. Every local neighborhood within the manifold resembles some image patch. We call this the scene manifold because the(More)
Various problems in Computer Vision become difficult due to a strong influence of lighting on the images of an object. Recent work showed analytically that the set of all images of a convex, Lambertian object can be accurately approximated by the low-dimensional linear subspace constructed using spherical harmonic functions. In this paper we present two(More)
In this thesis we address the problem of summarizing visual data in images and video sequences. We show how this problem can be solved by optimizing purely visual criteria, based on comparison of image or video patches. As the first step, we show how the well-studiedmosaicing problem (summarizing a video using a single image) can be solved without motion(More)
This paper studies the problem of matching two unsynchronized video sequences of the same dynamic scene, recorded by different stationary uncalibrated video cameras. The matching is done both in time and in space, where the spatial matching can be modeled by a homography (for 2D scenarios) or by a fundamental matrix (for 3D scenarios). Our approach is based(More)
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