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.

Body image and body composition: comparisons of young male elite soccer players and controls

[EN] The purpose of this study was to evaluate body composition and body image (perception and satisfaction) in a group of young elite soccer players and to compare the data with those of a control group (age and BMI matched). Participants were 56 volunteer males whose mean age and BMI were 19.6 (SD 1.3) years and 23.3 (SD 1.1) kg/m2, respectively. Results showed that soccer players have a higher lean mass and lower fat mass than controls. Moreover, body perception (difference between current and actual image) was more accurate in controls than in soccer players, and the results suggest a tendency for soccer players to aspire to have more muscle mass and body fat. Soccer players perceived an ideal image with significantly higher body-fat percentage than their current and actual images. There were no body-dissatisfaction differences between groups, however. Although the results are necessarily limited by the small sample size, the findings should be of interest to coaches of young elite soccer teams.