The choice of a color model is of great importance for many computer vision algorithms (e.g., feature detection, object recognition, and tracking) as the chosen color model induces the equivalence classes to the actual algorithms. As there are many color models available, the inherent difficulty is how to automatically select a single color model or, alternatively, a weighted subset of color models producing the best result for a particular task. The subsequent hurdle is how to obtain a proper fusion scheme for the algorithms so that the results are combined in an optimal setting. To achieve proper color model selection and fusion of feature detection algorithms, in this paper, we propose a method that exploits nonperfect correlation between color models or feature detection algorithms derived from the principles of diversification. As a consequence, a proper balance is obtained between repeatability and distinctiveness. The result is a weighting scheme which yields maximal feature discrimination. The method is verified experimentally for three different image feature detectors. The experimental results show that the fusion method provides feature detection results having a higher discriminative power than the standard weighting scheme. Further, it is experimentally shown that the color model selection scheme provides a proper balance between color invariance (repeatability) and discriminative power (distinctiveness).

The choice of a color space is of great importance for many computer vision algorithms (e.g. edge detection and object recognition). It induces the equivalence classes to the actual algorithms. However, the problem is how to automatically select the color space that produces the best result for a particular task. The subsequent difficulty then is how to obtain a proper weighting scheme for the algorithms so that the results are combined in an optimal setting. To achieve proper color space selection and fusion of feature detectors, in this paper, we propose a method that exploits non-perfect correlation between the color models derived from the principles of diversification. As a consequence, the weighting scheme yields maximal color discrimination. The method is verified experimentally for two different feature detectors. The experimental results show that the model provides feature detection results having a discriminative power of 30 percent higher than the standard weighting scheme.

We aim at using color information to classify the physical nature of edges in video. To achieve physics-based edge classification, we first propose a novel approach to color edge detection by automatic noise-adaptive thresholding derived from sensor noise analysis. Then, we present a taxonomy on color edge types. As a result, a parameter-free edge classifier is obtained labeling color transitions into one of the following types: 1) shadow-geometry, 2) highlight edges, and 3) material edges. The proposed method is empirically verified on images showing complex real world scenes.

This paper aims for color constant identification of object colors through the analysis of spectral color data. New computational color models are proposed which are not only invariant to illumination variations (color constancy) but also robust to a change in viewpoint and object geometry (color invariance). Color constancy and invariance is achieved by spectral imaging using a white reference, and based on color ratio’s (without a white reference). From the theoretical and experimental results it is concluded that the proposed computational methods for color constancy and invariance are highly robust to a change in SPD of the light source as well as a change in the pose of the object.

We aim at using color information to classify the physical nature of a color edge: that is whether the transition is due to shadows, abrupt surface orientation changes, illumination, highlights or material changes. To achieve a physics-based edge classification, we propose a taxonomy of color invariant edges. The taxonomy is based upon the sensitivity of the various color edges with respect to different imaging dependencies i.e. shadows, object shape, shading (i.e. illumination intensity changes), highlights and material characteristics. From this taxonomy, the edge classifier is derived labeling color transitions into the following types: (1) shadow, geometry or shading edges, (2) highlight edges, (3) material edges. Experiments conducted with the edge classification technique on color and hyperspectral images show that the proposed method successfully discriminates the different edge types.

Intensity-based edge detectors cannot distinguish whether an edge is caused by material changes, shadows, surface orientation changes or by highlights. Therefore, our aim is to classify the physical cause of an edge using hyperspectra obtained by a spectrograph. Methods are presented to detect edges in hyperspectral images. In theory, the effect of varying imaging conditions is analyzed for "raw" hyper-spectra, for normalized hyper-spectra, and for hue computed from hyper-spectra. From this analysis, an edge classifier is derived which distinguishes hyper-spectral edges into the following types: (1) a shadow or geometry edge, (2) a highlight edge, (3) a material edge. 1 Introduction Edge information from an image can be used to measure or recognize objects in images. Edges correspond to significant changes in the image, ideally at the boundary between two different regions. However, false edges are often detected, and (parts of) important edges are missing. Thus, after edge d...

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. , , , , , , , and ) 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 , , , , and all detect regions invariant to a change in surface orientation, viewpoint of the camera, and illumination intensity. Furthermore, and also detect regions independent of highlights. , , , , ,a nd provide segmentation results which are all sensitive to surface orientation and illumination intensity as well as color models incorporating brightness into their systems: in , in ,a nd in .