Inner Space Preserving Generative Pose Machine

Image-based generative methods, such as generative adversarial networks (GANs) have already been able to generate realistic images with much context control, specially when they are conditioned. However, most successful frameworks share a common procedure which performs an image-to-image translation with pose of figures in the image untouched. When the objective is reposing a figure in an image while preserving the rest of the image, the state-of-the-art mainly assumes a single rigid body with simple background and limited pose shift, which can hardly be extended to the images under normal settings. In this paper, we introduce an image "inner space" preserving model that assigns an interpretable low-dimensional pose descriptor (LDPD) to an articulated figure in the image. Figure reposing is then generated by passing the LDPD and the original image through multi-stage augmented hourglass networks in a conditional GAN structure, called inner space preserving generative pose machine (ISP-GPM). We evaluated ISP-GPM on reposing human figures, which are highly articulated with versatile variations. Test of a state-of-the-art pose estimator on our reposed dataset gave an accuracy over 80% on PCK0.5 metric. The results also elucidated that our ISP-GPM is able to preserve the background with high accuracy while reasonably recovering the area blocked by the figure to be reposed.Comment: http://www.northeastern.edu/ostadabbas/2018/07/23/inner-space-preserving-generative-pose-machine

CONFIGR: A Vision-Based Model for Long-Range Figure Completion

CONFIGR (CONtour FIgure GRound) is a computational model based on principles of biological vision that completes sparse and noisy image figures. Within an integrated vision/recognition system, CONFIGR posits an initial recognition stage which identifies figure pixels from spatially local input information. The resulting, and typically incomplete, figure is fed back to the “early vision” stage for long-range completion via filling-in. The reconstructed image is then re-presented to the recognition system for global functions such as object recognition. In the CONFIGR algorithm, the smallest independent image unit is the visible pixel, whose size defines a computational spatial scale. Once pixel size is fixed, the entire algorithm is fully determined, with no additional parameter choices. Multi-scale simulations illustrate the vision/recognition system. Open-source CONFIGR code is available online, but all examples can be derived analytically, and the design principles applied at each step are transparent. The model balances filling-in as figure against complementary filling-in as ground, which blocks spurious figure completions. Lobe computations occur on a subpixel spatial scale. Originally designed to fill-in missing contours in an incomplete image such as a dashed line, the same CONFIGR system connects and segments sparse dots, and unifies occluded objects from pieces locally identified as figure in the initial recognition stage. The model self-scales its completion distances, filling-in across gaps of any length, where unimpeded, while limiting connections among dense image-figure pixel groups that already have intrinsic form. Long-range image completion promises to play an important role in adaptive processors that reconstruct images from highly compressed video and still camera images.Air Force Office of Scientific Research (F49620-01-1-0423); National Geospatial-Intelligence Agency (NMA 201-01-1-0216); National Science Foundation (SBE-0354378); Office of Naval Research (N000014-01-1-0624

The Relationship Between Perceived and Ideal Body Size and Body Mass Index Among 9th Grade Students in an Urban High School

This study examined the perceptions of actual and ideal body size and how it related to body mass index in 82 ethnically and socioeconomically diverse 9th-graders from an urban high school. The height and weight were measured, and BMI was calculated for each of the 82 participants. Using the Childress Silhouette Scale (CSS), each student self-reported Perceived Actual Self Image (P ASI) and Perceived Ideal Self Image (PISI). The CSS was modified to offer 7 figure drawings that illustrated various examples of underweight, healthy weight, overweight, and obese. Results showed that 54% ofthe students had a healthy weight, 21% were overweight, and 15% were obese. The students\u27 PASI and PISI showed a small positive correlation. Students with a higher BJv.fl had a greater tendency to choose a higher P ASI and PIS I. The main difference between boys and girls regarding perceived ideal self-image (PISI) was that girls chose an underweight figure whereas boys chose a healthy weight figure

The Newton Polytope of the Implicit Equation

We apply tropical geometry to study the image of a map defined by Laurent polynomials with generic coefficients. If this image is a hypersurface then our approach gives a construction of its Newton polytope.Comment: 18 pages, 3 figure

Improving Spatial Codification in Semantic Segmentation

This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem. We propose to partition the image into three regions for each object to be described: Figure, Border and Ground. This partition aims at minimizing the influence of the image context on the object description and vice versa by introducing an intermediate zone around the object contour. Furthermore, we also propose a richer visual descriptor of the object by applying a Spatial Pyramid over the Figure region. Two novel Spatial Pyramid configurations are explored: Cartesian-based and crown-based Spatial Pyramids. We test these approaches with state-of-the-art techniques and show that they improve the Figure-Ground based pooling in the Pascal VOC 2011 and 2012 semantic segmentation challenges.Comment: Paper accepted at the IEEE International Conference on Image Processing, ICIP 2015. Quebec City, 27-30 September. Project page: https://imatge.upc.edu/web/publications/improving-spatial-codification-semantic-segmentatio

Super-resolution image transfer by a vortex-like metamaterial

We propose a vortex-like metamaterial device that is capable of transferring image along a spiral route without losing subwavelength information of the image. The super-resolution image can be guided and magnified at the same time with one single design. Our design may provide insights in manipulating super-resolution image in a more flexible manner. Examples are given and illustrated with numerical simulations.Comment: 7 pages, 6 figure