Shape-from-image via cross-sections

International Conference on Pattern Recognition (ICPR), 2000, Barcelona (España)Using structural geometry, Whiteley (1991) showed that a line drawing is a correct projection of a spherical polyhedron if and only if it has a cross-section compatible with it. We extend the class of drawings to which this test applies, including those of polyhedral disks. Our proof is constructive, showing how to derive all spatial interpretations; it relies on elementary synthetic geometric arguments, and, as a by-product, it yields a simpler and shorter proof of Whiteley's result. Moreover, important properties of line drawings are visually derived as corollaries: realizability is independent of the adopted projection, it is an invariant projective property, and for trihedral drawings it can be checked with a pencil and an unmarked ruler alone.Peer Reviewe

Shape-from-Image via Cross-Sections

Abstrac

Designing a Contactless, AI System to Measure the Human Body using a Single Camera for the Clothing and Fashion Industry

Using a single RGB camera to obtain accurate body dimensions rather than measuring these manually or via more complex multi-camera or more expensive 3D scanners, has a high application potential for the apparel industry. In this thesis, a system that estimates upper human body measurements using a set of computer vision and machine learning techniques. The main steps involve: (1) using a portable camera; (2) improving image quality; (3) isolating the human body from the surrounding environment; (4) performing a calibration step; (5) extracting body features from the image; (6) indicating markers on the image; (7) producing refined final results. In this research, a unique geometric shape is favored, namely the ellipse, to approximate human body main cross sections. We focus on the upper body horizontal slices (i.e. from head to hips) which, we show, can be well represented by varying an ellipse’s eccentricity, this per individual. Then, evaluating each fitted ellipse’s perimeter allows us to obtain better results than the current state-of-the-art for use in the fashion and online retail industry. In our study, I selected a set of two equations, out of many other possible choices, to best estimate upper human body horizontal cross sections via perimeters of fitted ellipses. In this study, I experimented with the system on a diverse sample of 78 participants. The results for the upper human body measurements in comparison to the traditional manual method of tape measurements, when used as a reference, show ±1cm average differences, sufficient for many applications, including online retail

The Impact of Baryonic Cooling on Giant Arc Abundances

Using ray tracing for simple analytic profiles, we demonstrate that the lensing cross section for producing giant arcs has distinct contributions due to arcs formed through image distortion only, and arcs form from the merging of two or three images. We investigate the dependence of each of these contributions on halo ellipticity and on the slope of the density profile, and demonstrate that at fixed Einstein radius, the lensing cross section increases as the halo profile becomes steeper. We then compare simulations with and without baryonic cooling of the same cluster for a sample of six clusters, and demonstrate that cooling can increase the overall abundance of giant arcs by factors of a few. The net boost to the lensing probability for individual clusters is mass dependent, and can lower the effective low mass limit of lensing clusters. This last effect can potentially increase the number of lensing clusters by an extra 50%. While the magnitude of these effects may be overestimated due to the well known overcooling problem in simulations, it is evident that baryonic cooling has a non-negligible impact on the expected abundance of giant arcs, and hence cosmological constraints from giant arc abundances may be subject to large systematic errors.Comment: ApJ Submitte

Part-to-whole Registration of Histology and MRI using Shape Elements

Image registration between histology and magnetic resonance imaging (MRI) is a challenging task due to differences in structural content and contrast. Too thick and wide specimens cannot be processed all at once and must be cut into smaller pieces. This dramatically increases the complexity of the problem, since each piece should be individually and manually pre-aligned. To the best of our knowledge, no automatic method can reliably locate such piece of tissue within its respective whole in the MRI slice, and align it without any prior information. We propose here a novel automatic approach to the joint problem of multimodal registration between histology and MRI, when only a fraction of tissue is available from histology. The approach relies on the representation of images using their level lines so as to reach contrast invariance. Shape elements obtained via the extraction of bitangents are encoded in a projective-invariant manner, which permits the identification of common pieces of curves between two images. We evaluated the approach on human brain histology and compared resulting alignments against manually annotated ground truths. Considering the complexity of the brain folding patterns, preliminary results are promising and suggest the use of characteristic and meaningful shape elements for improved robustness and efficiency.Comment: Paper accepted at ICCV Workshop (Bio-Image Computing