This paper presents an online feature selection mechanism for evaluating multiple features while tracking and adjusting the set of features used to improve tracking performance. Our hypothesis is that the features that best discriminate between object and background are also best for tracking the object. Given a set of seed features, we compute log likelihood ratios of class conditional sample densities from object and background to form a new set of candidate features tailored to the local object/background discrimination task. The two-class variance ratio is used to rank these new features according to how well they separate sample distributions of object and background pixels. This feature evaluation mechanism is embedded in a mean-shift tracking system that adaptively selects the top-ranked discriminative features for tracking. Examples are presented that demonstrate how this method adapts to changing appearances of both tracked object and scene background. We note susceptibility of the variance ratio feature selection method to distraction by spatially correlated background clutter and develop an additional approach that seeks to minimize the likelihood of distraction.

Background Low-carbohydrate diets are popular for weight loss, but their cardiovascular effects have not been well-studied, particularly in diverse populations. Objective To examine the effects of a low-carbohydrate diet compared with a low-fat diet on body weight and cardiovascular risk factors. Design A randomized, parallel-group trial. (ClinicalTrials.gov: NCT00609271) Setting A large academic medical center. Participants 148 men and women without clinical cardiovascular disease and diabetes. Intervention A low-carbohydrate (<40 g/d) or low-fat diet (<30% fat; <7% saturated fat). Both groups received dietary counseling at regular intervals throughout the trial. Measurements Data on weight, cardiovascular risk factors, and dietary composition were collected at 0, 3, 6, and 12 months. Results Sixty participants (82%) in the low-fat group and 59 (79%) in the low-carbohydrate group completed the intervention. At 12 months, participants on the low-carbohydrate diet had greater decreases in weight (mean difference in change, −3.5 kg [95% CI, −5.6 to −1.4 kg]; P < 0.001), fat mass (mean difference in change, −1.5% [CI, −2.6% to −0.4%]; P = 0.011), ratio of total to high-density lipoprotein (HDL) cholesterol (mean difference in change, −0.44 [CI, −0.71 to −0.16]; P = 0.002), and triglyceride level (mean difference in change, −0.16 mmol/L [−14.1 mg/dL] [CI, −0.31 to −0.01 mmol/L {−27.4 to −0.8 mg/dL}]; P = 0.038) and greater increases in HDL cholesterol level (mean difference in change, 0.18 mmol/L [7.0 mg/dL] [CI, 0.08 to 0.28 mmol/L {3.0 to 11.0 mg/dL}]; P< 0.001) than those on the low-fat diet. Limitation Lack of clinical cardiovascular disease end points. Conclusion The low-carbohydrate diet was more effective for weight loss and cardiovascular risk factor reduction than the low-fat diet. Restricting carbohydrate may be an option for persons seeking to lose weight and reduce cardiovascular risk factors. Primary Funding Source National Institutes of Health.

Figure 1: Top row: The regularity of the input texture is being manipulated along color and geometry dimensions, regularities decrease from left to right. Bottom row: Texture replacement where extracted geometric and lighting deformation fields from an input texture are applied to new textures. AbstractA near-regular texture deviates geometrically and photometrically from a regular congruent tiling. Although near-regular textures are ubiquitous in the man-made and natural world, they present computational challenges for state of the art texture analysis and synthesis algorithms. Using regular tiling as our anchor point, and with user-assisted lattice extraction, we can explicitly model the deformation of a near-regular texture with respect to geometry, lighting and color. We treat a deformation field both as a function that acts on a texture and as a texture that is acted upon, and develop a multimodal framework where each deformation field is subject to analysis, synthesis and manipulation. Using this formalization, we are able to construct simple parametric models to faithfully synthesize the appearance of a near-regular texture and purposefully control its regularity.

Understanding texture regularity in real images is a challenging computer vision task. We propose a higher-order feature matching algorithm to discover the lattices of near-regular textures in real images. The underlying lattice of a near-regular texture identifies all of the texels as well as the global topology among the texels. A key contribution of this paper is to formulate lattice-finding as a correspondence problem. The algorithm finds a plausible lattice by iteratively proposing texels and assigning neighbors between the texels. Our matching algorithm seeks assignments that maximize both pair-wise visual similarity and higher-order geometric consistency. We approximate the optimal assignment using a recently developed spectral method. We successfully discover the lattices of a diverse set of unsegmented, real-world textures with significant geometric warping and large appearance variation among texels.

Here we expand on this work by investigating a class of textures that have maximal formal regularity, the 17 crystallographic wallpaper groups (Fedorov, 1891). We used texture stimuli from four of the groups that differ in the maximum order of rotation symmetry they contain, and measured neural responses in human participants using functional MRI and high-density EEG. We found that cortical area V3 has a parametric representation of the rotation symmetries in the textures that is not present in either V1 or V2, the first discovery of a stimulus property that differentiates processing in V3 from that of lower-level areas. Parametric responses were also seen in higherorder ventral stream areas V4, VO1, and lateral occipital complex (LOC), but not in dorsal stream areas. The parametric response pattern was replicated in the EEG data, and source localization indicated that responses in V3 and V4 lead responses in LOC, which is consistent with a feedforward mechanism. Finally, we presented our stimuli to four well developed feedforward models and found that none of them were able to account for our results. Our results highlight structural regularity as an important stimulus dimension for distinguishing the early stages of visual processing, and suggest a previously unrecognized role for V3 in the visual form-processing hierarchy.

This study assesses the performance of public-domain automated methodologies for MRI-based segmentation of the hippocampus in elderly subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Structural MR images of 54 age- and gender-matched healthy elderly individuals, subjects with probable AD, and subjects with MCI were collected at the University of Pittsburgh Alzheimer's Disease Research Center. Hippocampi in subject images were automatically segmented by using AIR, SPM, FLIRT, and the fully deformable method of Chen to align the images to the Harvard atlas, MNI atlas, and randomly selected, manually labeled subject images ("cohort atlases"). Mixed-effects statistical models analyzed the effects of side of the brain, disease state, registration method, choice of atlas, and manual tracing protocol on the spatial overlap between automated segmentations and expert manual segmentations. Registration methods that produced higher degrees of geometric deformation produced automated segmentations with higher agreement with manual segmentations. Side of the brain, presence of AD, choice of reference image, and manual tracing protocol were also significant factors contributing to automated segmentation performance. Fully automated techniques can be competitive with human raters on this difficult segmentation task, but a rigorous statistical analysis shows that a variety of methodological factors must be carefully considered to insure that automated methods perform well in practice. The use of fully deformable registration methods, cohort atlases, and user-defined manual tracings are recommended for highest performance in fully automated hippocampus segmentation.

We analyze walking people using a gait sequence representation that bypasses the need for frame-to-frame tracking of body parts. The gait representation maps a video sequence of silhouettes into a pair of two-dimensional spatio-temporal patterns that are near-periodic along the time axis. Mathematically, such patterns are called "frieze" patterns and associated symmetry groups "frieze groups". With the help of a walking humanoid avatar, we explore variation in gait frieze patterns with respect to viewing angle, and find that the frieze groups of the gait patterns and their canonical tiles enable us to estimate viewing direction of human walking videos. In addition, analysis of periodic patterns allows us to determine the dynamic time warping and affine scaling that aligns two gait sequences from similar viewpoints. We also show how gait alignment can be used to perform human identification and model-based body part segmentation. MotivationAutomated visual measurement of human body size and pose is difficult due to nonrigid articulation and occlusion of body parts from many viewpoints. The problem is simplified during gait analysis, since we observe people performing the same activity with certain time period. Although individual gaits vary due to factors such as physical build, body weight, shoe heel height, clothing and the emotional state of the walker, at a coarse level the basic pattern of bipedal motion is the same across healthy adults, and each person's body passes through the same sequence of canonical poses while walking [6]. We have experimented with a simple, viewpoint-specific spatio-temporal representation of gait. The representation collapses a temporal sequence of body silhouette images into a periodic two-dimensional pattern. This paper explores the use of these frieze patterns for viewing angle determination, human identification, and non-rigid gait sequence alignment. Related WorkMany approaches to analyzing gait sequences are based on tracking the body as a kinematic linkage. Model-based kinematic tracking of a walking person was This work is supported in part by ONR N00014-00-1-0915 and NSF # IIS-0099597.

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