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This paper proposes a real-time method for the detection and elimination of brightness in color images. We use a 2D-histogram that allows us to relate the signals of luminance and saturation of a color image and to identify the specularities in a given area of the histogram. This is known as the MS diagram and it is constructed from a polar color model. We use a new connected vectorial filter based on color morphology to eliminate the brightness. This filter operates only in the bright zones previously detected, reducing the high cost of processing of connected filters and avoiding over-simplification, in single-processing and multiprocessing environments.

This paper deals with the use of morphological filters by reconstruction of the mathematical morphology for Gaussian noise removal in color images. These new vector connected have the property of suppressing details preserving the contours of the objects. For the extension of the mathematical morphology to color images we chose a new polar color space, the l1-norme. This color model guarantees the formation of the complete lattice necessary in mathematical morphology avoiding the drawbacks of others polar spaces. Finally, after having defined the vectorial geodesic operators, the opening and closing by reconstruction are then employed for the Gaussian noise elimination.

A new method for obtaining the optimal path to robot navigation in 2-D environments is presented in this paper. To obtain the optimal path we use mathematical morphology in binary worlds and the geodesic distance. The navigation algorithm is based on the search for a path of minimum cost by using the wave-front of the geodesic distance of the mathematical morphology. The optimal path will be the one that minimize the direction changes of the robot. The algorithm of optimal path will be applied in several and complex 2-D environments.

In this paper, we present a new application of the mathematical morphology: a single-image approach for the automatic detection and elimination of highlights in colour images. We use a 2D-histogram that allows us to relate the achromatic and saturation signals of a colour image and to identify interior brightness. To eliminate the highlights detected, we use an image-inpainting method, by means of connected vectorial filters of the mathematical morphology. This new filter operates exclusively on bright zones, reducing the high cost of processing the connected filters and avoiding over-simplification. The new method proposed here achieves good results, which are similar to those obtained from other multimedia techniques, yet does not require either costly multiple-view systems or stereo images.

In this paper, we present a comparative study of detection and elimination of highlights in real color images of any type of material. We use different polar color spaces for the automatic highlight detection (HLS, HSV and L1-norme). To eliminate the highlights detected, we use a new connected vectorial filter of color mathematical morphology which it operates exclusively on bright zones, reducing the high cost of processing of the connected filter and avoiding over-simplification. The new method proposed here achieves good results and it not requires costly multiple-view systems or stereo images.

In this paper we present a new method for the identification of specular reflectance in colour images. We have developed a bi-dimensional histogram which allows the exploitation of the relations between the signals of intensity and saturation of a colour image. Once the diagram has been constructed, it is possible to verify that the pixels of the specular reflectance are located in a well-defined region. The brightness is automatically identified by means of the extraction of pixels present in this region of the diagram, independently of their hue values. The effectiveness of the method in a variety of real chromatic images has been proven.