Scene Analysis under Variable Illumination using Gradient Domain Methods

The goal of this research is to develop algorithms for reconstruction and manipulation of gradient fields for scene analysis, from intensity images captured under variable illumination. These methods utilize gradients or differential measurements of intensity and depth for analyzing a scene, such as estimating shape and intrinsic images, and edge suppression under variable illumination. The differential measurements lead to robust reconstruction from gradient fields in the presence of outliers and avoid hard thresholds and smoothness assumptions in manipulating image gradient fields. Reconstruction from gradient fields is important in several applications including shape extraction using Photometric Stereo and Shape from Shading, image editing and matting, retinex, mesh smoothing and phase unwrapping. In these applications, a non-integrable gradient field is available, which needs to be integrated to obtain the final image or surface. Previous approaches for enforcing integrability have focused on least square solutions which do not work well in the presence of outliers and do not locally confine errors during reconstruction. I present a generalized equation to represent a continuum of surface reconstructions of a given non-integrable gradient field. This equation is used to derive new types of feature preserving surface reconstructions in the presence of noise and outliers. The range of solutions is related to the degree of anisotropy of the weights applied to the gradients in the integration process. Traditionally, image gradient fields have been manipulated using hard thresholds for recovering reflectance/illumination maps or to remove illumination effects such as shadows. Smoothness of reflectance/illumination maps is often assumed in such scenarios. By analyzing the direction of intensity gradient vectors in images captured under different illumination conditions, I present a framework for edge suppression which avoids hard thresholds and smoothness assumptions. This framework can be used to manipulate image gradient fields to synthesize computationally useful and visually pleasing images, and is based on two approaches: (a) gradient projection and (b) affine transformation of gradient fields using cross-projection tensors. These approaches are demonstrated in the context of several applications such as removing shadows and glass reflections, and recovering reflectance/illumination maps and foreground layers under varying illumination

Color Dipole Moments for Edge Detection

Dipole and higher moments are physical quantities used to describe a charge distribution. In analogy with electromagnetism, it is possible to define the dipole moments for a gray-scale image, according to the single aspect of a gray-tone map. In this paper we define the color dipole moments for color images. For color maps in fact, we have three aspects, the three primary colors, to consider. Associating three color charges to each pixel, color dipole moments can be easily defined and used for edge detection.Comment: 8 page

CVABS: Moving Object Segmentation with Common Vector Approach for Videos

Background modelling is a fundamental step for several real-time computer vision applications that requires security systems and monitoring. An accurate background model helps detecting activity of moving objects in the video. In this work, we have developed a new subspace based background modelling algorithm using the concept of Common Vector Approach with Gram-Schmidt orthogonalization. Once the background model that involves the common characteristic of different views corresponding to the same scene is acquired, a smart foreground detection and background updating procedure is applied based on dynamic control parameters. A variety of experiments is conducted on different problem types related to dynamic backgrounds. Several types of metrics are utilized as objective measures and the obtained visual results are judged subjectively. It was observed that the proposed method stands successfully for all problem types reported on CDNet2014 dataset by updating the background frames with a self-learning feedback mechanism.Comment: 12 Pages, 4 Figures, 1 Tabl

Dipole Vectors in Images Processing

Instead of evaluating the gradient field of the brightness map of an image, we propose the use of dipole vectors. This approach is obtained by adapting to the image gray-tone distribution the definition of the dipole moment of charge distributions. We will show how to evaluate the dipoles and obtain a vector field, which can be a good alternative to the gradient field in pattern recognition

Patterning nonisometric origami in nematic elastomer sheets

Nematic elastomers dramatically change their shape in response to diverse stimuli including light and heat. In this paper, we provide a systematic framework for the design of complex three dimensional shapes through the actuation of heterogeneously patterned nematic elastomer sheets. These sheets are composed of \textit{nonisometric origami} building blocks which, when appropriately linked together, can actuate into a diverse array of three dimensional faceted shapes. We demonstrate both theoretically and experimentally that: 1) the nonisometric origami building blocks actuate in the predicted manner, 2) the integration of multiple building blocks leads to complex multi-stable, yet predictable, shapes, 3) we can bias the actuation experimentally to obtain a desired complex shape amongst the multi-stable shapes. We then show that this experimentally realized functionality enables a rich possible design landscape for actuation using nematic elastomers. We highlight this landscape through theoretical examples, which utilize large arrays of these building blocks to realize a desired three dimensional origami shape. In combination, these results amount to an engineering design principle, which we hope will provide a template for the application of nematic elastomers to emerging technologies

The History, Development and Impact of Computed Imaging in Neurological Diagnosis and Neurosurgery: CT, MRI, and DTI

A steady series of advances in physics, mathematics, computers and clinical imaging science have progressively transformed diagnosis and treatment of neurological and neurosurgical disorders in the 115 years between the discovery of the X-ray and the advent of high resolution diffusion based functional MRI. The story of the progress in human terms, with its battles for priorities, forgotten advances, competing claims, public battles for Nobel Prizes, and patent priority litigations bring alive the human drama of this remarkable collective achievement in computed medical imaging