Prevalence and magnitude of body weight and image dissatisfaction among women in dietetics majors

[EN] Some authors have suggested that body weight dissatisfaction may be high in students majoring in dietetics. Therefore, this study was conducted to examine the extent of body weight and image dissatisfaction in a sample of women in dietetics major. Additionally, predictors of magnitude of body weight dissatisfaction were analyzed. Participants were 62 volunteers with normalweight whose mean age was 21.87±1.89 years old (nonrandom sample). The assessment instruments included anthropometric measurements, a somatomorphic matrix test and an eating disorders inventory (EDI-2). Data were analyzed using SPSS vs. 15.0. A larger proportion of students chose an ideal body weight lower than actual weight (67.7%) and body image with less body fat and more muscle mass than actual values (56.4%). The magnitude of body weight dissatisfaction was associated with muscle mass and body fat dissatisfaction, and with the subscale of EDI-2 “body dissatisfaction”. So, from a public health standpoint, we consider important to continue working in this line of research with the aim of better understanding the extent of body weight dissatisfaction in women dietitians, and how this dissatisfaction could interfere with their professional practice.

Reduced deep-tissue image degradation in three-dimensional multiphoton microscopy with concentric two-color two-photon fluorescence excitation

We theoretically demonstrate that enhanced penetration depth in three-dimensional multiphoton microscopy can be achieved using concentric two-color two-photon (C2C2P) fluorescence excitation in which the two excitation beams are separated in space before reaching their common focal spot. Monte Carlo simulation shows that, in comparison with the one-color two-photon excitation scheme, the C2C2P fluorescence microscopy provides a significantly greater penetration depth for imaging into a highly scattering medium. (C) 2008 Optical Society of America.

Computational modeling and psychophysics in low- and mid-level vision

This thesis addresses a series of topics related to the question of how people find the foreground objects from complex scenes. With both computer vision modeling, as well as psychophysical analyses, we explore the computational principles for low- and mid-level vision.

We first explore the computational methods of generating saliency maps from images and image sequences. We propose an extremely fast algorithm called Image Signature that detects the locations in the image that attract human eye gazes. With a series of experimental validations based on human behavioral data collected from various psychophysical experiments, we conclude that the Image Signature and its spatial-temporal extension, the Phase Discrepancy, are among the most accurate algorithms for saliency detection under various conditions.

In the second part, we bridge the gap between fixation prediction and salient object segmentation with two efforts. First, we propose a new dataset that contains both fixation and object segmentation information. By simultaneously presenting the two types of human data in the same dataset, we are able to analyze their intrinsic connection, as well as understanding the drawbacks of today’s “standard” but inappropriately labeled salient object segmentation dataset. Second, we also propose an algorithm of salient object segmentation. Based on our novel discoveries on the connections of fixation data and salient object segmentation data, our model significantly outperforms all existing models on all 3 datasets with large margins.

In the third part of the thesis, we discuss topics around the human factors of boundary analysis. Closely related to salient object segmentation, boundary analysis focuses on delimiting the local contours of an object. We identify the potential pitfalls of algorithm evaluation for the problem of boundary detection. Our analysis indicates that today’s popular boundary detection datasets contain significant level of noise, which may severely influence the benchmarking results. To give further insights on the labeling process, we propose a model to characterize the principles of the human factors during the labeling process.

The analyses reported in this thesis offer new perspectives to a series of interrelating issues in low- and mid-level vision. It gives warning signs to some of today’s “standard” procedures, while proposing new directions to encourage future research.