Photometric Invariant Region Detection
نویسندگان
چکیده
In this paper, we concentrate on determining homogeneously colored regions invariant to surface orientation change, illumination, shadows and highlights. To this end, the influence of various well-known color models (e.g. I , RGB, XY Z, I1I2I3, rgb, xyz, U V W , L a b and ISH) are examined, in theory, for the dichromatic reflection model and, in practice, for two distinct region-based segmentation methods: the k-means clustering technique and the split&merge algorithm. Experiments are conducted on color images taken from colored objects in real-world scenes. On the basis of the theoretical and experimental results it is concluded that l1l2l3, H , S, c1c2c3, rgb and xyz all detect regions invariant to a change in surface orientation, viewpoint of the camera, and illumination intensity. Furthermore, l1l2l3 and H also detect regions independent of highlights. I , RGB, CMY , Y IQ, XY Z, and I1I2I3 provide segmentation results which are all sensitive to surface orientation and illumination intensity as well as color models incorporating brightness into their systems: I in HSI , L in L a b , and L in Luv.
منابع مشابه
Color Edge Detection by Photometric Quasi-Invariants
Photometric invariance is used in many computer vision applications. The advantage of photometric invariance is the robustness against shadows, shading, and illumination conditions. However, the drawbacks of photometric invariance is the loss of discriminative power and the inherent instabilities caused by the non-linear transformations to compute the invariants. In this paper, we propose a new...
متن کاملRobust Color Contour Object Detection Invariant to Shadows
In this work a new robust color and contour based object detection method in images with varying shadows is presented. The method relies on a physics-based contour detector that emphasizes material changes and a contourbased boosted classifier. The method has been tested in a sequence of outdoor color images presenting varying shadows using two classifiers, one that learnt contour object featur...
متن کاملAdaptive Image Segmentation by Combining Photometric Invariant Region and Edge Information
ÐAn adaptive image segmentation scheme is proposed employing the Delaunay triangulation for image splitting. The tessellation grid of the Delaunay triangulation is adapted to the semantics of the image data by combining region and edge information. To achieve robustness against imaging conditions (e.g., shading, shadows, illumination, and highlights), photometric invariant similarity measures, ...
متن کاملTowards Invariant Interest Point Detection of an Objects
Detection of some interest points on an object is useful for many applications, such as local shape description of the object, recognition of the object in clutter environment etc. The same object present in different images can have some geometric and photometric transformations with respect to one another. The detection method should be robust to all these transformations. We describe relativ...
متن کاملπ-SIFT: A Photometric and Scale Invariant Feature Transform
For many years, various local descriptors that are insensitive to geometric changes such as viewpoint, rotation, and scale changes, have been attracting attention due to their promising performance. However, most existing local descriptors including the SIFT (Scale Invariant Feature Transform) are based on luminance information rather than color information thereby resulting in instability to p...
متن کاملInvariant salient regions based image retrieval under viewpoint and illumination variations
In this paper, we present a novel image retrieval technique based on salient regions that are invariant under viewpoint and illumination variations. The salient regions are detected according to local entropy and scale selection. The detected regions have very high repeatability under various viewpoint and illumination changes. We apply the invariant region detector on content-based image retri...
متن کامل