Statistical/Geometric Techniques for Object Representation and Recognition

Object modeling and recognition are key areas of research in computer vision and graphics with wide range of applications. Though research in these areas is not new, traditionally most of it has focused on analyzing problems under controlled environments. The challenges posed by real life applications demand for more general and robust solutions. The wide variety of objects with large intra-class variability makes the task very challenging. The difficulty in modeling and matching objects also vary depending on the input modality. In addition, the easy availability of sensors and storage have resulted in tremendous increase in the amount of data that needs to be processed which requires efficient algorithms suitable for large-size databases. In this dissertation, we address some of the challenges involved in modeling and matching of objects in realistic scenarios. Object matching in images require accounting for large variability in the appearance due to changes in illumination and view point. Any real world object is characterized by its underlying shape and albedo, which unlike the image intensity are insensitive to changes in illumination conditions. We propose a stochastic filtering framework for estimating object albedo from a single intensity image by formulating the albedo estimation as an image estimation problem. We also show how this albedo estimate can be used for illumination insensitive object matching and for more accurate shape recovery from a single image using standard shape from shading formulation. We start with the simpler problem where the pose of the object is known and only the illumination varies. We then extend the proposed approach to handle unknown pose in addition to illumination variations. We also use the estimated albedo maps for another important application, which is recognizing faces across age progression. Many approaches which address the problem of modeling and recognizing objects from images assume that the underlying objects are of diffused texture. But most real world objects exhibit a combination of diffused and specular properties. We propose an approach for separating the diffused and specular reflectance from a given color image so that the algorithms proposed for objects of diffused texture become applicable to a much wider range of real world objects. Representing and matching the 2D and 3D geometry of objects is also an integral part of object matching with applications in gesture recognition, activity classification, trademark and logo recognition, etc. The challenge in matching 2D/3D shapes lies in accounting for the different rigid and non-rigid deformations, large intra-class variability, noise and outliers. In addition, since shapes are usually represented as a collection of landmark points, the shape matching algorithm also has to deal with the challenges of missing or unknown correspondence across these data points. We propose an efficient shape indexing approach where the different feature vectors representing the shape are mapped to a hash table. For a query shape, we show how the similar shapes in the database can be efficiently retrieved without the need for establishing correspondence making the algorithm extremely fast and scalable. We also propose an approach for matching and registration of 3D point cloud data across unknown or missing correspondence using an implicit surface representation. Finally, we discuss possible future directions of this research

Acquisition of Surface Light Fields from Videos

La tesi presenta un nuovo approccio per la stima di Surface Light Field di oggetti reali, a partire da sequenze video acquisite in condizioni di illuminazione fisse e non controllate. Il metodo proposto si basa sulla separazione delle due componenti principali dell'apparenza superficiale dell'oggetto: la componente diffusiva, modellata come colore RGB, e la componente speculare, approssimata mediante un modello parametrico funzione della posizione dell'osservatore. L'apparenza superficiale ricostruita permette una visualizzazione fotorealistica e in real-time dell'oggetto al variare della posizione dell'osservatore, consentendo una navigazione 3D interattiva

Depth Estimation for Glossy Surfaces with Light-Field Cameras

Abstract. Light-field cameras have now become available in both consumer and industrial applications, and recent papers have demonstrated practical algorithms for depth recovery from a passive single-shot capture. However, current light-field depth estimation methods are designed for Lambertian objects and fail or degrade for glossy or specular surfaces. Because light-field cameras have an array of micro-lenses, the captured data allows modification of both focus and perspec-tive viewpoints. In this paper, we develop an iterative approach to use the benefits of light-field data to estimate and remove the specular component, improving the depth estimation. The approach enables light-field data depth estimation to sup-port both specular and diffuse scenes. We present a physically-based method that estimates one or multiple light source colors. We show our method outperforms current state-of-the-art diffuse and specular separation and depth estimation al-gorithms in multiple real world scenarios.

Laser Scanning Confocal Microscopy (LSCM): An Application for the Detection of Morphological Alterations in Skin Structure

Laser scanning confocal microscopy (LSCM) is an optical imaging technique that provides improved resolution and sensitivity over conventional methods of optical microscopy. However, the cost of most commercial LSCM systems exceeds the financial limitations of many smaller laboratories. The design of a custom LSCM created at a fraction of the cost of a commercial model is discussed in this paper. The increase in the incidence rate of skin cancer in the world today is alarming, as such, it is essential to provide an early, rapid and effective method for in vivo diagnostics of human skin tissue. LSCM is capable of detecting alterations in skin morphology and configuration, as well as providing chemical composition information which may be indicative of the development of skin cancer. If developed successfully, LSCM could replace the current invasive biopsy procedures performed today with a quick, non-invasive optical scanning method that would prove beneficial for both patients and physicians alike

Visual Computing and Machine Learning Techniques for Digital Forensics

It is impressive how fast science has improved day by day in so many different fields. In special, technology advances are shocking so many people bringing to their reality facts that previously were beyond their imagination. Inspired by methods earlier presented in scientific fiction shows, the computer science community has created a new research area named Digital Forensics, which aims at developing and deploying methods for fighting against digital crimes such as digital image forgery.This work presents some of the main concepts associated with Digital Forensics and, complementarily, presents some recent and powerful techniques relying on Computer Graphics, Image Processing, Computer Vision and Machine Learning concepts for detecting forgeries in photographs. Some topics addressed in this work include: sourceattribution, spoofing detection, pornography detection, multimedia phylogeny, and forgery detection. Finally, this work highlights the challenges and open problems in Digital Image Forensics to provide the readers with the myriad opportunities available for research