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We test a number of the leading computational color constancy algorithms using a comprehensive set of images. These were of 33 different scenes under 11 different sources representative of common illumination conditions. The algorithms studied include two gray world methods, a version of the Retinex method, several variants of Forsyth's gamut-mapping method, Cardei et al.'s neural net method, and Finlayson et al.'s Color by Correlation method. We discuss a number of issues in applying color constancy ideas to image data, and study in depth the effect of different preprocessing strategies. We compare the performance of the algorithms on image data with their performance on synthesized data. All data used for this study are available online at http://www.cs.sfu.ca/(tilde)color/data, and implementations for most of the algorithms are also available (http://www.cs.sfu.ca/(tilde)color/code). Experiments with synthesized data (part one of this paper) suggested that the methods which emphasize the use of the input data statistics, specifically color by correlation and the neural net algorithm, are potentially the most effective at estimating the chromaticity of the scene illuminant. Unfortunately, we were unable to realize comparable performance on real images. Here exploiting pixel intensity proved to be more beneficial than exploiting the details of image chromaticity statistics, and the three-dimensional (3-D) gamut-mapping algorithms gave the best performance.

Abstract. This paper presents a negative result: current machine colour constancy algorithms are not good enough for colour-based object recognition. This result has surprised us since we have previously used the better of these algorithms successfully to correct the colour balance of images for display. Colour balancing has been the typical application of colour constancy, rarely has it been actually put to use in a computer vision system, so our goal was to show how well the various methods would do on an obvious machine colour vision task, namely, object recognition. Although all the colour constancy methods we tested proved insufficient for the task, we consider this an important finding in itself. In addition we present results showing the correlation between colour constancy performance and object recognition performance, and as one might expect, the better the colour constancy the better the recognition rate.

Refeeding patients with AN using a hospital-based, behavioral protocol may be accomplished safely and more rapidly than generally recognized, weight restoring most patients by discharge. Helpful elements may include the program's integrated, step-down structure; multidisciplinary team approach emphasizing group therapy to effect behavior change; and close medical monitoring for those with BMI < 15.

Bone is highly dynamic and responsive. Bone location, bone type and gender can influence bone responses (positive, negative or none) and magnitude. Type I diabetes induces bone loss and increased marrow adiposity in the tibia. We tested if this response exhibits gender and location dependency by examining femur, vertebrae and calvaria of male and female, control and diabetic BALB/c mice. Non-diabetic male mice exhibited larger body, muscle, and fat mass, and increased femur BMD compared to female mice, while vertebrae and calvarial bone parameters did not exhibit gender differences. Streptozotocin-induced diabetes caused a reduction in BMD at all sites examined irrespective of gender. Increased marrow adiposity was evident in diabetic femurs and calvaria (endochondrial and intramembranous formed bones, respectively), but not in vertebrae. Leptin-deficient mice also exhibit location dependent bone responses and we found that serum leptin levels were significantly lower in diabetic compared to control mice. However, in contrast to leptin-deficient mice, the vertebrae of T1-diabetic mice exhibit bone loss, not gain. Taken together, our findings indicate that TI-diabetic bone loss in mice is not gender, bone location or bone type dependent, while increased marrow adiposity is location dependent.