Levelset and B-spline deformable model techniques for image segmentation: a pragmatic comparative study

International audienceDeformable contours are now widely used in image segmentation, using different models, criteria and numerical schemes. Some theoretical comparisons between some deformable model methods have already been published. Yet, very few experimental comparative studies on real data have been reported. In this paper,we compare a levelset with a B-spline based deformable model approach in order to understand the mechanisms involved in these widely used methods and to compare both evolution and results on various kinds of image segmentation problems. In general, both methods yield similar results. However, specific differences appear when considering particular problems

A Diverse and Flexible Teaching Toolkit Facilitates the Human Capacity for Cumulative Culture

© 2017, The Author(s). Human culture is uniquely complex compared to other species. This complexity stems from the accumulation of culture over time through high- and low-fidelity transmission and innovation. One possible reason for why humans retain and create culture, is our ability to modulate teaching strategies in order to foster learning and innovation. We argue that teaching is more diverse, flexible, and complex in humans than in other species. This particular characteristic of human teaching rather than teaching itself is one of the reasons for human’s incredible capacity for cumulative culture. That is, humans unlike other species can signal to learners whether the information they are teaching can or cannot be modified. As a result teaching in humans can be used to support high or low fidelity transmission, innovation, and ultimately, cumulative culture

Breast cancer resistance protein identifies clonogenic keratinocytes in human interfollicular epidermis

INTRODUCTION: There is a practical need for the identification of robust cell-surface markers that can be used to enrich for living keratinocyte progenitor cells. Breast cancer resistance protein (ABCG2), a member of the ATP binding cassette (ABC) transporter family, is known to be a marker for stem/progenitor cells in many tissues and organs. METHODS: We investigated the expression of ABCG2 protein in normal human epidermis to evaluate its potential as a cell surface marker for identifying and enriching for clonogenic epidermal keratinocytes outside the pilosebaceous tract. RESULTS: Immunofluorescence and immunoblotting studies of human skin showed that ABCG2 is expressed in a subset of basal layer cells in the epidermis. Flow cytometry analysis showed approximately 2-3% of keratinocytes in non-hair-bearing epidermis expressing ABCG2; this population also expresses p63, β1 and α6 integrins and keratin 14, but not CD34, CD71, C-kit or involucrin. The ABCG2-positive keratinocytes showed significantly higher colony forming efficiency when co-cultured with mouse 3T3 feeder cells, and more extensive long-term proliferation capacity in vitro, than did ABCG2-negative keratinocytes. Upon clonal analysis, most of the freshly isolated ABCG2-positive keratinocytes formed holoclones and were capable of generating a stratified differentiating epidermis in organotypic culture models. CONCLUSIONS: These data indicate that in skin, expression of the ABCG2 transporter is a characteristic of interfollicular keratinocyte progentior cells and suggest that ABCG2 may be useful for enriching keratinocyte stem cells in human interfollicular epidermis

2D Articulated Human Pose Estimation and Retrieval in (Almost) Unconstrained Still Images

Abstract We present a technique for estimating the spatial layout of humans in still images—the position of the head, torso and arms. The theme we explore is that once a person is localized using an upper body detector, the search for their body parts can be considerably simplified using weak constraints on position and appearance arising from that detection. Our approach is capable of estimating upper body pose in highly challenging uncontrolled images, without prior knowledge of background, clothing, lighting, or the location and scale of the person in the image. People are only required to be upright and seen from the front or the back (not side). We evaluate the stages of our approach experimentally using ground truth layout annotation on a variety of challenging material, such as images from the PASCAL VOC 2008 challenge and video frames from TV shows and feature films. We also propose and evaluate techniques for searching a video dataset for people in a specific pose. To this end, we develop three new pose descriptors and compare their clas

Data for: How children use accuracy information to infer informant intentions and to make reward decisions

This contains the data files for Study 1 and Study 2 as well as the syntax files for the analyses reported in the manuscript and supplementary materials

Data for: How children use accuracy information to infer informant intentions and to make reward decisions

This contains the data files for Study 1 and Study 2 as well as the syntax files for the analyses reported in the manuscript and supplementary materials.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Human upper body pose estimation in static images

Abstract. Estimating human pose in static images is challenging due to the high dimensional state space, presence of image clutter and ambiguities of image observations. We present an MCMC framework for estimating 3D human upper body pose. A generative model, comprising of the human articulated structure, shape and clothing models, is used to formulate likelihood measures for evaluating solution candidates. We adopt a data-driven proposal mechanism for searching the solution space efficiently. We introduce the use of proposal maps, which is an efficient way of implementing inference proposals derived from multiple types of image cues. Qualitative and quantitative results show that the technique is effective in estimating 3D body pose over a variety of images. 1 Estimating Pose in Static Image This paper proposes a technique for estimating human upper body pose in static images. Specifically, we want to estimate the 3D body configuration defined by a set of parameters that represent the global orientation of the body and body joint angles. We are focusing on middle resolution images, where a person’s upper body length is about 100 pixels or more. Images of people in meetings or other indoor environment are usually of this resolution. We are currently only concerned with estimating the upper body pose, which is relevant for indoor scene. In this situation the lower body is often occluded and the upper body conveys most of a person’s gestures. We do not make any restrictive assumptions about the background and the human shape and clothing, except for not wearing any head wear nor gloves. 1.1 Issues There are two main issues in pose estimation with static images, the high dimension state space and pose ambiguity

Human pose estimation using learnt probabilistic region similarities and partial configurations

A model of human appearance is presented for e#cient pose estimation from real-world images. In common with related approaches, a high-level model defines a space of configurations which can be associated with image measurements and thus scored. A search is performed to identify good configuration(s). Such an approach is challenging because the configuration space is high dimensional, the search is global, and the appearance of humans in images is complex due to background clutter, shape uncertainty and texture