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The paper presents an approach to multimodal image registration. The method is developed for aligning infrared (IR) and visual (RGB) images of facades. It is based on mapping clouds of points extracted by a corner detector applied to both images. The experiments show that corners are suitable features for our application. In the alignment process a number of transformation hypotheses is generated and evaluated. The evaluation is performed by measuring similarity between the RGB corners and the transformed corners from IR image. Directed partial Hausdorff distance is used as a robust similarity measure. The implemented system has been tested on various IR-RGB pairs of images of buildings. The results show that the method can be used for image registration, but also expose some typical problems.

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The problem of restoring images blurred by circular motion is considered in the paper. In order to simplify the process of deblurring, the original image in the (x,y) plane is transformed into polar plane ( p , 4). This simplifies the blur model, resulting in one-dimensional integration.

Abstract. We introduce a novel local spatio-temporal descriptor intended to model the spatio-temporal behavior of a tracked object of interest in a general manner. The basic idea of the descriptor is the accumulation of histograms of an image function value through time. The histograms are calculated over a regular grid of patches inside the bounding box of the object and normalized to represent empirical probability distributions. The number of grid patches is fixed, so the descriptor is invariant to changes in spatial scale. Depending on the temporal complexity/details at hand, we introduce "first order STA descriptors" that describe the average distribution of a chosen image function over time, and "second order STA descriptors" that model the distribution of each histogram bin over time. We discuss entropy and χ 2 as well-suited similarity and saliency measures for our descriptors. Our experimental validation ranges from the patch-to the object-level. Our results show that STA, this simple, yet powerful novel description of local space-time appearance is well-suited to machine learning and will be useful in videoanalysis, including potential applications of object detection, tracking, and background modeling.