We present in this paper a study of unsupervised evaluation criteria that enable the quantification of the quality of an image segmentation result. These evaluation criteria compute some statistics for each region or class in a segmentation result. Such an evaluation criterion can be useful for different applications: the comparison of segmentation results, the automatic choice of the best fitted parameters of a segmentation method for a given image, or the definition of new segmentation methods by optimization. We first present the state of art of unsupervised evaluation, and then, we compare six unsupervised evaluation criteria. For this comparative study, we use a database composed of 8400 synthetic gray-level images segmented in four different ways. Vinet's measure (correct classification rate) is used as an objective criterion to compare the behavior of the different criteria. Finally, we present the experimental results on the segmentation evaluation of a few gray-level natural images.

While the RGB color model refers to the biological processing of colors in the human visual system, the HSV color model corresponds to the human perception of color similarity. In this paper we formulate a projection of RGB vectors within the RGB color space, which separates achromatic from chromatic information. The projection is the mathematical equivalent to Hue and Saturation of the HSV color space in the RGB space. It integrates the psycho-visual concept of human differentiation between colors of the HSV space into the physiological-visual based concept of the RGB space. With the projection it is, contrary to the prevailing opinion, possible to differentiate between colors based on human perception in the linear geometry of the RGB color space. This opens new possibilities in many fields of color image processing, especially in the domain of color image segmentation, where color similarity plays a major role.

We present in this article a study of some unsupervised evaluation criteria of an image segmentation result. The goal of this work is to be able to automatically choose the parameters of a segmentation method best fitted for an image or to fusion different segmentation results. We compared six unsupervised evaluation criteria on a database composed of 100 synthetic gray-level images segmented by four methods. Vinet's measure is used as an objective function to compare the behavior of the different criteria. We finally apply these criteria to evaluate segmentation results of multi-components images. We present in this article some experimental results of evaluation of gray-level and multicomponents natural images.

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Many works in the literature focus on the definition of evaluation metrics and criteria that enable to quantify the performance of an image processing algorithm. These evaluation criteria can be used to define new image processing algorithms by optimizing them. In this paper, we propose a general scheme to segment images by a genetic algorithm. The developed method uses an evaluation criterion which quantifies the quality of an image segmentation result. The proposed segmentation method can integrate a local ground truth when it is available in order to set the desired level of precision of the final result. A genetic algorithm is then used in order to determine the best combination of information extracted by the selected criterion. Then, we show that this approach can either be applied for grey-levels or multi-components images in a supervised context or in an unsupervised one. Last, we show the efficiency of the proposed method through some experimental results on several gray-levels and multi-components images.

Cultured pearls are human creations formed by inserting a nucleus and a small piece of mantle tissue into a living shelled mollusc, usually a pearl oyster. Although many pearl observations intuitively suggest a possible rotation of the nucleated pearl inside the oyster, no experimental demonstration of such a movement has ever been done. This can be explained by the difficulty of observation of such a phenomenon in the tissues of a living animal. To investigate this question of pearl rotation, a magnetometer system was specifically engineered to register magnetic field variations with magnetic sensors from movements of a magnetic nucleus inserted in the pearl oyster. We demonstrated that a continuous movement of the nucleus inside the oyster starts after a minimum of 40 days post-grafting and continues until the pearl harvest. We measured a mean angular speed of 1.27° min−1 calculated for four different oysters. Rotation variability was observed among oysters and may be correlated to pearl shape and defects. Nature's ability to generate so amazingly complex structures like a pearl has delivered one of its secrets.

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The objective of this study was to observe the impact of temperature on pearl formation using an integrative approach describing the rotation of the pearls, the rate of nacre deposition, the thickness of the aragonite tablets and the biomineralizing potential of the pearl sac tissue though the expression level of some key genes. Fifty pearl oysters were grafted with magnetized nuclei to allow the rotation of the pearls to be described. Four months later, 32 of these pearl oysters were exposed to four temperatures (22, 26, 30 and 34°C) for 2 weeks. Results showed that the rotation speed differed according to the movement direction: pearls with axial movement had a significantly higher rotation speed than those with random movement. Pearl growth rate was influenced by temperature, with a maximum between 26 and 30°C but almost no growth at 34°C. Lastly, among the nine genes implicated in the biomineralization process, only expression was significantly modified by temperature. These results showed that the rotation speed of the pearls was not linked to pearl growth or to the expression profiles of biomineralizing genes targeted in this study. On the basis of our results, we consider that pearl rotation is a more complex process than formerly thought. Mechanisms involved could include a strong environmental forcing in immediate proximity to the pearl. Another implication of our findings is that, in the context of ocean warming, pearl growth and quality can be expected to decrease in pearl oysters exposed to temperatures above 30°C.