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This paper describes a road obstacle classification system that recognizes both vehicles and pedestrians in far-infrared images. Different local and global features based on Speeded Up Robust Features (SURF) were investigated and then selected in order to extract a discriminative signature from the infrared spectrum. First, local features representing the local appearance of an obstacle, are extracted...
Early and certain fire detection is one of the important issues to keep safe infrastructures. Especially, it becomes an urgent problem in large facilities like port facilities, large factories and power plants, due to its large harmful effect to surrounding areas. In these places, to detect the fire or flame directly is have some difficulties because they are open and hence have problem to set sensor...
This paper presents a set of simple Slit kernel suitable for regular and rotation-invariant texture analysis. The fast algorithm used to compute its feature is also presented. Extracting Slit features requires 11 to 17% of arithmetic operations when compared to that for Gabor features. Experimental results for the classification of rotated texture images indicate that Slit kernels perform as well...
Given an arbitrary image, our goal is to segment all distinct texture subimages. This is done by discovering distinct, cohesive groups of spatially repeating patterns, called texels, in the image, where each group defines the corresponding texture. Texels occupy image regions, whose photometric, geometric, structural, and spatial-layout properties are samples from an unknown pdf. If the image contains...
2D Active Appearance Models (AAM) and 3D Morphable Models (3DMM) are widely used techniques. AAM provide a fast fitting process, but may represent unwanted 3D transformations unless strictly constrained not to do so. The reverse is true for 3DMM. The two approaches also require of a pre-alignment of their 2D or 3D shapes before the modeling can be carried out which may lead to errors. Furthermore,...
In this paper, we address the problem of recovering a hyperspectral texture descriptor. We do this by viewing the wavelength-indexed bands corresponding to the texture in the image as those arising from a stochastic process whose statistics can be captured making use of the relationships between moment generating functions and Fourier kernels. In this manner, we can interpret the probability distribution...
We present a method for deriving a parametric description of a conic section (quadratic curve) in an image from the moments of the image with respect to several specially-constructed kernel functions. In contrast to Hough-transform-type methods, the moment approach requires no large accumulator array. Judicious implementation allows the parameters to be determined using five multiplication operations...
This paper proposes a learning based framework for efficient 3D face reconstruction. We transfer the 3D reconstruction into a statistical learning problem of finding appropriate mapping between texture and depth subspaces. Instead of using grayscales to directly estimate the depth, we use local binary pattern (LBP) to further encode the face texture, providing robustness for depth estimation under...
This paper presents a novel approach to pedestrian classification which involves utilizing the synthesized virtual samples of a learned generative model to enhance the classification performance of a discriminative model. Our generative model captures prior knowledge about the pedestrian class in terms of a number of probabilistic shape and texture models, each attuned to a particular pedestrian pose...
Image segmentation is a fundamental task in Computer Vision and there are numerous algorithms that have been successfully applied in various domains. There are still plenty of challenges to be met with. In this paper, we consider one such challenge, that of achieving segmentation while preserving complicated and detailed features present in the image, be it a gray level or a textured image. We present...
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