This paper describes the sequence of construction of a cell nuclei classification model by the analysis, the characterization and the classification of shape and texture. We describe first the elaboration of dedicated shape indexes and second the construction of the associated classification submodel. Then we present a new method of texture characterization, based on the construction and the analysis of statistical matrices encoding the texture. The various characterization techniques developed in this paper are systematically compared to previous approaches. In particular, we paid special attention to the results obtained by a versatile classification method using a large range of descriptors dedicated to the characterization of shapes and textures. Finally, the last classifier built with our methods achieved 88% of classification out of the 94% possible.

Light absorption and scattering lead to underwater image showing low contrast, fuzzy, and color cast. To solve these problems presented in various shallow-water images, we propose a simple but effective shallow-water image enhancement method-relative global histogram stretching (RGHS) based on adaptive parameter acquisition. The proposed method consists of two parts: contrast correction and color correction. The contrast correction in RGB color space firstly equalizes G and B channels and then redistributes each R-G-B channel histogram with dynamic parameters that relate to the intensity distribution of original image and wavelength attenuation of different colors under the water. The bilateral filtering is used to eliminate the effect of noise while still preserving valuable details of the shallow-water image and even enhancing local information of the image. The color correction is performed by stretching the 'L' component and modifying 'a' and 'b' components in CIE-Lab color space. Experimental results demonstrate that the proposed method can achieve better perceptual quality, higher image information entropy, and less noise, compared to the state-of-the-art underwater image enhancement methods.

The ability to associate labels to colors is very natural for human beings. Though, this apparently simple task hides very complex and still unsolved problems, spreading over many different disciplines ranging from neurophysiology to psychology and imaging. In this paper, we propose a discrete model for computational color categorization and naming. Starting from the 424 color specimens of the OSA-UCS set, we propose a fuzzy partitioning of the color space. Each of the 11 basic color categories identified by Berlin and Kay is modeled as a fuzzy set whose membership function is implicitly defined by fitting the model to the results of an ad hoc psychophysical experiment (Experiment 1). Each OSA-UCS sample is represented by a feature vector whose components are the memberships to the different categories. The discrete model consists of a three-dimensional Delaunay triangulation of the CIELAB color space which associates each OSA-UCS sample to a vertex of a 3D tetrahedron. Linear interpolation is used to estimate the membership values of any other point in the color space. Model validation is performed both directly, through the comparison of the predicted membership values to the subjective counterparts, as evaluated via another psychophysical test (Experiment 2), and indirectly, through the investigation of its exploitability for image segmentation. The model has proved to be successful in both cases, providing an estimation of the membership values in good agreement with the subjective measures as well as a semantically meaningful color-based segmentation map.