Feature extraction is important in image matching. However, the perspective deformations, especially the anisotropic scaling deformations will affect the performances of feature extraction ...algorithms. To improve the image matching results when notable perspective deformations exist, an algorithm for extracting feature points and covariant regions is introduced in this paper. We propose using a new type of feature points, the "inside corner points" as seed points. And we propose using a multi-scale seeded region growing method to find the local support regions for feature points. Based on the shapes of local support regions, an image patch around a feature point can be rectified by doing shape normalization, and the anisotropic scaling deformations can be reduced by the rectification. By doing image matching with these rectified image patches, the matching results are notably improved.
This paper presents a vision-based approach for mobile robot localization. The model of the environment is topological. The new approach characterizes a place using a signature. This signature ...consists of a constellation of descriptors computed over different types of local affine covariant regions extracted from an omnidirectional image acquired rotating a standard camera with a pan-tilt unit. This type of representation permits a reliable and distinctive environment modelling. Our objectives were to validate the proposed method in indoor environments and, also, to find out if the combination of complementary local feature region detectors improves the localization versus using a single region detector. Our experimental results show that if false matches are effectively rejected, the combination of different covariant affine region detectors increases notably the performance of the approach by combining the different strengths of the individual detectors. In order to reduce the localization time, two strategies are evaluated: re-ranking the map nodes using a global similarity measure and using standard perspective view field of 45°.
In order to systematically test topological localization methods, another contribution proposed in this work is a novel method to see the degradation in localization performance as the robot moves away from the point where the original signature was acquired. This allows to know the robustness of the proposed signature. In order for this to be effective, it must be done in several, variated, environments that test all the possible situations in which the robot may have to perform localization.
Feature-based image watermarking schemes, which aim to survive various geometric distortions, have attracted great attention in recent years. Existing schemes have shown robustness against rotation, ...scaling, and translation, but few are resistant to cropping, nonisotropic scaling, random bending attacks (RBAs), and affine transformations. Seo and Yoo present a geometrically invariant image watermarking based on affine covariant regions (ACRs) that provide a certain degree of robustness. To further enhance the robustness, we propose a new image watermarking scheme on the basis of Seo's work, which is insensitive to geometric distortions as well as common image processing operations. Our scheme is mainly composed of three components: 1) feature selection procedure based on graph theoretical clustering algorithm is applied to obtain a set of stable and nonoverlapped ACRs; 2) for each chosen ACR, local normalization, and orientation alignment are performed to generate a geometrically invariant region, which can obviously improve the robustness of the proposed watermarking scheme; and 3) in order to prevent the degradation in image quality caused by the normalization and inverse normalization, indirect inverse normalization is adopted to achieve a good compromise between the imperceptibility and robustness. Experiments are carried out on an image set of 100 images collected from Internet, and the preliminary results demonstrate that the developed method improves the performance over some representative image watermarking approaches in terms of robustness.
Feature extraction is important in image matching. However, the perspective deformations, especially the anisotropic scaling deformations will affect the performances of feature extraction ...algorithms. To improve the image matching results when notable perspective deformations exist, an algorithm for extracting feature points and covariant regions is introduced in this paper. We propose using a new type of feature points, the "inside corner points" as seed points. And we propose using a multi-scale seeded region growing method to find the local support regions for feature points. Based on the shapes of local support regions, an image patch around a feature point can be rectified by doing shape normalization, and the anisotropic scaling deformations can be reduced by the rectification. By doing image matching with these rectified image patches, the matching results are notably improved.
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