On the Use of Large Area Tactile Feedback for Contact Data Processing and Robot Control

The progress in microelectronics and embedded systems has recently enabled the realization of devices for robots functionally similar to the human skin, providing large area tactile feedback over the whole robot body. The availability of such kind of systems, commonly referred to as extit{robot skins}, makes possible to measure the contact pressure distribution applied on the robot body over an arbitrary area. Large area tactile systems open new scenarios on contact processing, both for control and cognitive level processing, enabling the interpretation of physical contacts. The contents proposed in this thesis address these topics by proposing techniques exploiting large area tactile feedback for: (i) contact data processing and classification; (ii) robot control

Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints

The continuous stream of videos that are uploaded and shared on the Internet has been leveraged by computer vision researchers for a myriad of detection and retrieval tasks, including gesture detection, copy detection, face authentication, etc. However, the existing state-of-the-art event detection and retrieval techniques fail to deal with several real-world challenges (e.g., low resolution, low brightness and noise) under adversary constraints. This dissertation focuses on these challenges in realistic scenarios and demonstrates practical methods to address the problem of robustness and efficiency within video event detection and retrieval systems in five application settings (namely, CAPTCHA decoding, face liveness detection, reconstructing typed input on mobile devices, video confirmation attack, and content-based copy detection). Specifically, for CAPTCHA decoding, I propose an automated approach which can decode moving-image object recognition (MIOR) CAPTCHAs faster than humans. I showed that not only are there inherent weaknesses in current MIOR CAPTCHA designs, but that several obvious countermeasures (e.g., extending the length of the codeword) are not viable. More importantly, my work highlights the fact that the choice of underlying hard problem selected by the designers of a leading commercial solution falls into a solvable subclass of computer vision problems. For face liveness detection, I introduce a novel approach to bypass modern face authentication systems. More specifically, by leveraging a handful of pictures of the target user taken from social media, I show how to create realistic, textured, 3D facial models that undermine the security of widely used face authentication solutions. My framework makes use of virtual reality (VR) systems, incorporating along the way the ability to perform animations (e.g., raising an eyebrow or smiling) of the facial model, in order to trick liveness detectors into believing that the 3D model is a real human face. I demonstrate that such VR-based spoofing attacks constitute a fundamentally new class of attacks that point to a serious weaknesses in camera-based authentication systems. For reconstructing typed input on mobile devices, I proposed a method that successfully transcribes the text typed on a keyboard by exploiting video of the user typing, even from significant distances and from repeated reflections. This feat allows us to reconstruct typed input from the image of a mobile phone’s screen on a user’s eyeball as reflected through a nearby mirror, extending the privacy threat to include situations where the adversary is located around a corner from the user. To assess the viability of a video confirmation attack, I explored a technique that exploits the emanations of changes in light to reveal the programs being watched. I leverage the key insight that the observable emanations of a display (e.g., a TV or monitor) during presentation of the viewing content induces a distinctive flicker pattern that can be exploited by an adversary. My proposed approach works successfully in a number of practical scenarios, including (but not limited to) observations of light effusions through the windows, on the back wall, or off the victim’s face. My empirical results show that I can successfully confirm hypotheses while capturing short recordings (typically less than 4 minutes long) of the changes in brightness from the victim’s display from a distance of 70 meters. Lastly, for content-based copy detection, I take advantage of a new temporal feature to index a reference library in a manner that is robust to the popular spatial and temporal transformations in pirated videos. My technique narrows the detection gap in the important area of temporal transformations applied by would-be pirates. My large-scale evaluation on real-world data shows that I can successfully detect infringing content from movies and sports clips with 90.0% precision at a 71.1% recall rate, and can achieve that accuracy at an average time expense of merely 5.3 seconds, outperforming the state of the art by an order of magnitude.Doctor of Philosoph

Object Part Localization Using Exemplar-based Models

​Object part localization is a fundamental problem in computer vision, which aims to let machines understand object in an image as a configuration of parts. As the visual features at parts are usually weak and misleading, spatial models are needed to constrain the part configuration, ensuring that the estimated part locations respect both image cue and shape prior. Unlike most of the state-of-the-art techniques that employ parametric spatial models, we turn to non-parametric exemplars of part configurations. The benefit is twofold: instead of assuming any parametric yet imprecise distributions on the spatial relations of parts, exemplars literally encode such relations present in the training samples; exemplars allow us to prune the search space of part configurations with high confidence. This thesis consists of two parts: fine-grained classification and object part localization. We first verify the efficacy of parts in fine-grained classification, where we build working systems that automatically identify dog breeds, fish species, and bird species using localized parts on the object. Then we explore multiple ways to enhance exemplar-based models, such that they can be well applied to deformable objects such as bird and human body. Specifically, we propose to enforce pose and subcategory consistency in exemplar matching, thus obtaining more reliable hypotheses of configuration. We also propose part-pair representation that features novel shape composing with multiple promising hypotheses. In the end, we adapt exemplars to hierarchical representation, and design a principled formulation to predict the part configuration based on multi-scale image cues and multi-level exemplars. These efforts consistently improve the accuracy of object part localization

Browse-to-search

This demonstration presents a novel interactive online shopping application based on visual search technologies. When users want to buy something on a shopping site, they usually have the requirement of looking for related information from other web sites. Therefore users need to switch between the web page being browsed and other websites that provide search results. The proposed application enables users to naturally search products of interest when they browse a web page, and make their even causal purchase intent easily satisfied. The interactive shopping experience is characterized by: 1) in session - it allows users to specify the purchase intent in the browsing session, instead of leaving the current page and navigating to other websites; 2) in context - -the browsed web page provides implicit context information which helps infer user purchase preferences; 3) in focus - users easily specify their search interest using gesture on touch devices and do not need to formulate queries in search box; 4) natural-gesture inputs and visual-based search provides users a natural shopping experience. The system is evaluated against a data set consisting of several millions commercial product images. © 2012 Authors

Polyclonal architecture of the mammalian head

While much of modern developmental biology has focussed upon molecularly defined cell populations, relatively little is understood about how clonal groups within these broad cell populations organise complex tissues. In this thesis, I explore the clonal architecture of the jaws and teeth, and of the dermal bones of the calvaria, revealing cryptic modules as novel developmental features in both. I combine Confetti multicolour genetic lineage labelling with novel analytical techniques in order to map clonal populations in 3D and provide quantitative parameters for clonal expansions. Tooth identity within the mandible is thought to be encoded by the an initial proximodistal position within the branchial arch, which implies that cells do not undergo migration. I observe distal Hand2-Cre labelled cells in the proximal territory, which necessitates migration. These distal cells give rise to the mandible, alveolar bone and a small proportion of odontoblasts, while unlabelled proximal cells were found in the distal territory of the incisors in different proportions. The clonal composition of teeth and jaw bones is dissected by novel analysis of mixed cell populations. I find odontogenic and alveolar bone populations to share a common lineage, comprising a cryptic developmental unit distinct from the mandible, a feature that I can also verify in another transgenic for the upper jaw. I also find that the initial tooth composition radically changes in ontogenetic time. Starting from similar compositions of distal and proximal cells I find that in incisors the distal population expands while the proximal wanes, while in molars the opposite occurs. This is the first evidence for a temporally changing cell population structure underlying the well defined heterodonty between incisors and molars and allows a reinterpretation of early tooth specification events. The dermal bones of the calvaria are thought to grow in thickness by static osteoblasts depositing matrix appositionally and growth is supposed to occur exclusively at sutures. Whole calvaria single-cell clonal lineage analysis of cranial neural crest cells with Wnt1-Cre and Confetti labelling reveals an extensively dynamic program of invasive growth distributed throughout all parts of the dermal bone. Cryptic clonal modules grow laterally, with invasion through and into the bone primarily organised around the centres of these `patches'. The process of bone maturation is revealed to consist of a series of invasions between the three layers of the bone, with the innermost compacta layer driving initial thickness growth by invasion into the middle spongy layer, and the outermost (dermis-adjacent) compacta layer driving later growth. Conversely there is no evidence to suggest that the sutures are principal generative regions, as they do not share a common clonal lineage with adjacent bone. I also investigate muscle attachment regions and find that the same clones traverse tissue boundaries from bone into muscle connective tissues, thus a clonal model predicated on joint 'attachment point precursor cells' can now explain patterns of skeletomuscular connectivity previously found at the population level by my supervisor. A novel generative model of bone growth from cryptic clonal patch modules is proposed, allowing us for the first time to understand thickness growth and the evolutionary transition from a micromeric to a macromeric dermal bone condition, events first visible in crown gnathostomes (placoderms)

Molecular Imaging

The present book gives an exceptional overview of molecular imaging. Practical approach represents the red thread through the whole book, covering at the same time detailed background information that goes very deep into molecular as well as cellular level. Ideas how molecular imaging will develop in the near future present a special delicacy. This should be of special interest as the contributors are members of leading research groups from all over the world

Colour depth-from-defocus incorporating experimental point spread function measurements

Depth-From-Defocus (DFD) is a monocular computer vision technique for creating depth maps from two images taken on the same optical axis with different intrinsic camera parameters. A pre-processing stage for optimally converting colour images to monochrome using a linear combination of the colour planes has been shown to improve the accuracy of the depth map. It was found that the first component formed using Principal Component Analysis (PCA) and a technique to maximise the signal-to-noise ratio (SNR) performed better than using an equal weighting of the colour planes with an additive noise model. When the noise is non-isotropic the Mean Square Error (MSE) of the depth map by maximising the SNR was improved by 7.8 times compared to an equal weighting and 1.9 compared to PCA. The fractal dimension (FD) of a monochrome image gives a measure of its roughness and an algorithm was devised to maximise its FD through colour mixing. The formulation using a fractional Brownian motion (mm) model reduced the SNR and thus produced depth maps that were less accurate than using PCA or an equal weighting. An active DFD algorithm to reduce the image overlap problem has been developed, called Localisation through Colour Mixing (LCM), that uses a projected colour pattern. Simulation results showed that LCM produces a MSE 9.4 times lower than equal weighting and 2.2 times lower than PCA. The Point Spread Function (PSF) of a camera system models how a point source of light is imaged. For depth maps to be accurately created using DFD a high-precision PSF must be known. Improvements to a sub-sampled, knife-edge based technique are presented that account for non-uniform illumination of the light box and this reduced the MSE by 25%. The Generalised Gaussian is presented as a model of the PSF and shown to be up to 16 times better than the conventional models of the Gaussian and pillbox

Proceedings, MSVSCC 2014

Proceedings of the 8th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 17, 2014 at VMASC in Suffolk, Virginia