Normal variation for adaptive feature size

The change in the normal between any two nearby points on a closed, smooth surface is bounded with respect to the local feature size (distance to the medial axis). An incorrect proof of this lemma appeared as part of the analysis of the "crust" algorithm of Amenta and Bern

Assessment of Waist-to-Hip Ratio Attractiveness in Women: An Anthropometric Analysis of Digital Silhouettes

The low proportion of waist to hip size in females is a unique and adaptive human feature. In contemporary human populations, the waist-to-hip ratio (WHR) is negatively associated with women’s health, fecundity, and cognitive ability. It is, therefore, hypothesized that men will prefer women with low WHR. Although this prediction is supported by many studies, considerable disagreement persists about which WHR values are the most attractive and the importance of WHR for attractiveness of the female body. Unfortunately, the methods applied thus far are flawed in several ways. In the present study, we investigated male preferences for female WHR using a high precision assessment procedure and digitally manufactured, high quality, anthropometrically informed stimuli which were disentangled from body mass covariation. Forty men were requested to choose the most attractive silhouette consecutively from six series (2 levels of realism × 3 levels of body mass), each consisting of 26 female images that varied in WHR (from .60 to .85 by .01). Substantial inter-individual variation in the choices made was observed. Nevertheless, low and average WHR values were chosen more frequently than above-average values or values below the normal variation of the trait. This preference pattern mirrors the relationship between WHR and mate value, suggesting that the preferences are adaptive

Deformable Object Tracking with Gated Fusion

The tracking-by-detection framework receives growing attentions through the integration with the Convolutional Neural Networks (CNNs). Existing tracking-by-detection based methods, however, fail to track objects with severe appearance variations. This is because the traditional convolutional operation is performed on fixed grids, and thus may not be able to find the correct response while the object is changing pose or under varying environmental conditions. In this paper, we propose a deformable convolution layer to enrich the target appearance representations in the tracking-by-detection framework. We aim to capture the target appearance variations via deformable convolution, which adaptively enhances its original features. In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance. The enriched feature representation through deformable convolution facilitates the discrimination of the CNN classifier on the target object and background. Extensive experiments on the standard benchmarks show that the proposed tracker performs favorably against state-of-the-art methods

Discriminative Scale Space Tracking

Accurate scale estimation of a target is a challenging research problem in visual object tracking. Most state-of-the-art methods employ an exhaustive scale search to estimate the target size. The exhaustive search strategy is computationally expensive and struggles when encountered with large scale variations. This paper investigates the problem of accurate and robust scale estimation in a tracking-by-detection framework. We propose a novel scale adaptive tracking approach by learning separate discriminative correlation filters for translation and scale estimation. The explicit scale filter is learned online using the target appearance sampled at a set of different scales. Contrary to standard approaches, our method directly learns the appearance change induced by variations in the target scale. Additionally, we investigate strategies to reduce the computational cost of our approach. Extensive experiments are performed on the OTB and the VOT2014 datasets. Compared to the standard exhaustive scale search, our approach achieves a gain of 2.5% in average overlap precision on the OTB dataset. Additionally, our method is computationally efficient, operating at a 50% higher frame rate compared to the exhaustive scale search. Our method obtains the top rank in performance by outperforming 19 state-of-the-art trackers on OTB and 37 state-of-the-art trackers on VOT2014.Comment: To appear in TPAMI. This is the journal extension of the VOT2014-winning DSST tracking metho