Representing and Inferring Visual Perceptual Skills in Dermatological Image Understanding

Experts have a remarkable capability of locating, perceptually organizing, identifying, and categorizing objects in images specific to their domains of expertise. Eliciting and representing their visual strategies and some aspects of domain knowledge will benefit a wide range of studies and applications. For example, image understanding may be improved through active learning frameworks by transferring human domain knowledge into image-based computational procedures, intelligent user interfaces enhanced by inferring dynamic informational needs in real time, and cognitive processing analyzed via unveiling the engaged underlying cognitive processes. An eye tracking experiment was conducted to collect both eye movement and verbal narrative data from three groups of subjects with different medical training levels or no medical training in order to study perceptual skill. Each subject examined and described 50 photographical dermatological images. One group comprised 11 board-certified dermatologists (attendings), another group was 4 dermatologists in training (residents), and the third group 13 novices (undergraduate students with no medical training). We develop a novel hierarchical probabilistic framework to discover the stereotypical and idiosyncratic viewing behaviors exhibited by the three expertise-specific groups. A hidden Markov model is used to describe each subject\u27s eye movement sequence combined with hierarchical stochastic processes to capture and differentiate the discovered eye movement patterns shared by multiple subjects\u27 eye movement sequences within and among the three expertise-specific groups. Through these patterned eye movement behaviors we are able to elicit some aspects of the domain-specific knowledge and perceptual skill from the subjects whose eye movements are recorded during diagnostic reasoning processes on medical images. Analyzing experts\u27 eye movement patterns provides us insight into cognitive strategies exploited to solve complex perceptual reasoning tasks. Independent experts\u27 annotations of diagnostic conceptual units of thought in the transcribed verbal narratives are time-aligned with discovered eye movement patterns to help interpret the patterns\u27 meanings. By mapping eye movement patterns to thought units, we uncover the relationships between visual and linguistic elements of their reasoning and perceptual processes, and show the manner in which these subjects varied their behaviors while parsing the images

Deep Learning of Representations: Looking Forward

Deep learning research aims at discovering learning algorithms that discover multiple levels of distributed representations, with higher levels representing more abstract concepts. Although the study of deep learning has already led to impressive theoretical results, learning algorithms and breakthrough experiments, several challenges lie ahead. This paper proposes to examine some of these challenges, centering on the questions of scaling deep learning algorithms to much larger models and datasets, reducing optimization difficulties due to ill-conditioning or local minima, designing more efficient and powerful inference and sampling procedures, and learning to disentangle the factors of variation underlying the observed data. It also proposes a few forward-looking research directions aimed at overcoming these challenges

Multiple-Target Tracking in Complex Scenarios

In this dissertation, we develop computationally efficient algorithms for multiple-target tracking: MTT) in complex scenarios. For each of these scenarios, we develop measurement and state-space models, and then exploit the structure in these models to propose efficient tracking algorithms. In addition, we address design issues such as sensor selection and resource allocation. First, we consider MTT when the targets themselves are moving in a time-varying multipath environment. We develop a sparse-measurement model that allows us to exploit the inherent joint delay-Doppler diversity offered by the environment. We then reformulate the problem of MTT as a block-support recovery problem using the sparse measurement model. We exploit the structure of the dictionary matrix to develop a computationally efficient block support recovery algorithm: and thereby a multiple-target tracking algorithm) under the assumption that the channel state describing the time-varying multipath environment is known. Further, we also derive an upper bound on the overall error probability of wrongly identifying the support of the sparse signal. We then relax the assumption that the channel state is known. We develop a new particle filter called the Multiple Rao-Blackwellized Particle Filter: MRBPF) to jointly estimate both the target and the channel states. We also compute the posterior Cramér-Rao bound: PCRB) on the estimates of the target and the channel states and use the PCRB to find a suitable subset of antennas to be used for transmission in each tracking interval, as well as the power transmitted by these antennas. Second, we consider the problem of tracking an unknown number and types of targets using a multi-modal sensor network. In a multi-modal sensor network, different quantities associated with the same state are measured using sensors of different kinds. Hence, an efficient method that can suitably combine the diverse information measured by each sensor is required. We first develop a Hierarchical Particle Filter: HPF) to estimate the unknown state from the multi-modal measurements for a special class of problems which can be modeled hierarchically. We then model our problem of tracking using a hierarchical model and then use the proposed HPF for joint initiation, termination and tracking of multiple targets. The multi-modal data consists of the measurements collected from a radar, an infrared camera and a human scout. We also propose a unified framework for multi-modal sensor management that comprises sensor selection: SS), resource allocation: RA) and data fusion: DF). Our approach is inspired by the trading behavior of economic agents in commercial markets. We model the sensors and the sensor manager as economic agents, and the interaction among them as a double sided market with both consumers and producers. We propose an iterative double auction mechanism for computing the equilibrium of such a market. We relate the equilibrium point to the solutions of SS, RA and DF. Third, we address MTT problem in the presence of data association ambiguity that arises due to clutter. Data association corresponds to the problem of assigning a measurement to each target. We treat the data association and state estimation as separate subproblems. We develop a game-theoretic framework to solve the data association, in which we model each tracker as a player and the set of measurements as strategies. We develop utility functions for each player, and then use a regret-based learning algorithm to find the correlated equilibrium of this game. The game-theoretic approach allows us to associate measurements to all the targets simultaneously. We then use particle filtering on the reduced dimensional state of each target, independently

SEGMENTATION, RECOGNITION, AND ALIGNMENT OF COLLABORATIVE GROUP MOTION

Modeling and recognition of human motion in videos has broad applications in behavioral biometrics, content-based visual data analysis, security and surveillance, as well as designing interactive environments. Significant progress has been made in the past two decades by way of new models, methods, and implementations. In this dissertation, we focus our attention on a relatively less investigated sub-area called collaborative group motion analysis. Collaborative group motions are those that typically involve multiple objects, wherein the motion patterns of individual objects may vary significantly in both space and time, but the collective motion pattern of the ensemble allows characterization in terms of geometry and statistics. Therefore, the motions or activities of an individual object constitute local information. A framework to synthesize all local information into a holistic view, and to explicitly characterize interactions among objects, involves large scale global reasoning, and is of significant complexity. In this dissertation, we first review relevant previous contributions on human motion/activity modeling and recognition, and then propose several approaches to answer a sequence of traditional vision questions including 1) which of the motion elements among all are the ones relevant to a group motion pattern of interest (Segmentation); 2) what is the underlying motion pattern (Recognition); and 3) how two motion ensembles are similar and how we can 'optimally' transform one to match the other (Alignment). Our primary practical scenario is American football play, where the corresponding problems are 1) who are offensive players; 2) what are the offensive strategy they are using; and 3) whether two plays are using the same strategy and how we can remove the spatio-temporal misalignment between them due to internal or external factors. The proposed approaches discard traditional modeling paradigm but explore either concise descriptors, hierarchies, stochastic mechanism, or compact generative model to achieve both effectiveness and efficiency. In particular, the intrinsic geometry of the spaces of the involved features/descriptors/quantities is exploited and statistical tools are established on these nonlinear manifolds. These initial attempts have identified new challenging problems in complex motion analysis, as well as in more general tasks in video dynamics. The insights gained from nonlinear geometric modeling and analysis in this dissertation may hopefully be useful toward a broader class of computer vision applications

동적 멀티모달 데이터 학습을 위한 심층 하이퍼네트워크

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. 장병탁.Recent advancements in information communication technology has led the explosive increase of data. Dissimilar to traditional data which are structured and unimodal, in particular, the characteristics of recent data generated from dynamic environments are summarized as high-dimensionality, multimodality, and structurelessness as well as huge-scale size. The learning from non-stationary multimodal data is essential for solving many difficult problems in artificial intelligence. However, despite many successful reports, existing machine learning methods have mainly focused on solving practical problems represented by large-scaled but static databases, such as image classification, tagging, and retrieval. Hypernetworks are a probabilistic graphical model representing empirical distribution, using a hypergraph structure that is a large collection of many hyperedges encoding the associations among variables. This representation allows the model to be suitable for characterizing the complex relationships between features with a population of building blocks. However, since a hypernetwork is represented by a huge combinatorial feature space, the model requires a large number of hyperedges for handling the multimodal large-scale data and thus faces the scalability problem. In this dissertation, we propose a deep architecture of hypernetworks for dealing with the scalability issue for learning from multimodal data with non-stationary properties such as videos, i.e., deep hypernetworks. Deep hypernetworks handle the issues through the abstraction at multiple levels using a hierarchy of multiple hypergraphs. We use a stochastic method based on Monte-Carlo simulation, a graph MC, for efficiently constructing hypergraphs representing the empirical distribution of the observed data. The structure of a deep hypernetwork continuously changes as the learning proceeds, and this flexibility is contrasted to other deep learning models. The proposed model incrementally learns from the data, thus handling the nonstationary properties such as concept drift. The abstract representations in the learned models play roles of multimodal knowledge on data, which are used for the content-aware crossmodal transformation including vision-language conversion. We view the vision-language conversion as a machine translation, and thus formulate the vision-language translation in terms of the statistical machine translation. Since the knowledge on the video stories are used for translation, we call this story-aware vision-language translation. We evaluate deep hypernetworks on large-scale vision-language multimodal data including benmarking datasets and cartoon video series. The experimental results show the deep hypernetworks effectively represent visual-linguistic information abstracted at multiple levels of the data contents as well as the associations between vision and language. We explain how the introduction of a hierarchy deals with the scalability and non-stationary properties. In addition, we present the story-aware vision-language translation on cartoon videos by generating scene images from sentences and descriptive subtitles from scene images. Furthermore, we discuss the meaning of our model for lifelong learning and the improvement direction for achieving human-level artificial intelligence.1 Introduction 1.1 Background and Motivation 1.2 Problems to be Addressed 1.3 The Proposed Approach and its Contribution 1.4 Organization of the Dissertation 2 RelatedWork 2.1 Multimodal Leanring 2.2 Models for Learning from Multimodal Data 2.2.1 Topic Model-Based Multimodal Leanring 2.2.2 Deep Network-based Multimodal Leanring 2.3 Higher-Order Graphical Models 2.3.1 Hypernetwork Models 2.3.2 Bayesian Evolutionary Learning of Hypernetworks 3 Multimodal Hypernetworks for Text-to-Image Retrievals 3.1 Overview 3.2 Hypernetworks for Multimodal Associations 3.2.1 Multimodal Hypernetworks 3.2.2 Incremental Learning of Multimodal Hypernetworks 3.3 Text-to-Image Crossmodal Inference 3.3.1 Representatation of Textual-Visual Data 3.3.2 Text-to-Image Query Expansion 3.4 Text-to-Image Retrieval via Multimodal Hypernetworks 3.4.1 Data and Experimental Settings 3.4.2 Text-to-Image Retrieval Performance 3.4.3 Incremental Learning for Text-to-Image Retrieval 3.5 Summary 4 Deep Hypernetworks for Multimodal Cocnept Learning from Cartoon Videos 4.1 Overview 4.2 Visual-Linguistic Concept Representation of Catoon Videos 4.3 Deep Hypernetworks for Modeling Visual-Linguistic Concepts 4.3.1 Sparse Population Coding 4.3.2 Deep Hypernetworks for Concept Hierarchies 4.3.3 Implication of Deep Hypernetworks on Cognitive Modeling 4.4 Learning of Deep Hypernetworks 4.4.1 Problem Space of Deep Hypernetworks 4.4.2 Graph Monte-Carlo Simulation 4.4.3 Learning of Concept Layers 4.4.4 Incremental Concept Construction 4.5 Incremental Concept Construction from Catoon Videos 4.5.1 Data Description and Parameter Setup 4.5.2 Concept Representation and Development 4.5.3 Character Classification via Concept Learning 4.5.4 Vision-Language Conversion via Concept Learning 4.6 Summary 5 Story-awareVision-LanguageTranslation usingDeepConcept Hiearachies 5.1 Overview 5.2 Vision-Language Conversion as a Machine Translation 5.2.1 Statistical Machine Translation 5.2.2 Vision-Language Translation 5.3 Story-aware Vision-Language Translation using Deep Concept Hierarchies 5.3.1 Story-aware Vision-Language Translation 5.3.2 Vision-to-Language Translation 5.3.3 Language-to-Vision Translation 5.4 Story-aware Vision-Language Translation on Catoon Videos 5.4.1 Data and Experimental Setting 5.4.2 Scene-to-Sentence Generation 5.4.3 Sentence-to-Scene Generation 5.4.4 Visual-Linguistic Story Summarization of Cartoon Videos 5.5 Summary 6 Concluding Remarks 6.1 Summary of the Dissertation 6.2 Directions for Further Research Bibliography 한글초록Docto

Aircraft state estimation using cameras and passive radar

Multiple target tracking (MTT) is a fundamental task in many application domains. It is a difficult problem to solve in general, so applications make use of domain specific and problem-specific knowledge to approach the problem by solving subtasks separately. This work puts forward a MTT framework (MTTF) which is based on the Bayesian recursive estimator (BRE). The MTTF extends a particle filter (PF) to handle the multiple targets and adds a probabilistic graphical model (PGM) data association stage to compute the mapping from detections to trackers. The MTTF was applied to the problem of passively monitoring airspace. Two applications were built: a passive radar MTT module and a comprehensive visual object tracking (VOT) system. Both applications require a solution to the MTT problem, for which the MTTF was utilized. The VOT system performed well on real data recorded at the University of Cape Town (UCT) as part of this investigation. The system was able to detect and track aircraft flying within the region of interest (ROI). The VOT system consisted of a single camera, an image processing module, the MTTF module and an evaluation module. The world coordinate frame target localization was within ±3.2 km and these results are presented on Google Earth. The image plane target localization has an average reprojection error of ±17.3 pixels. The VOT system achieved an average area under the curve value of 0.77 for all receiver operating characteristic curves. These performance figures are typical over the ±1 hr of video recordings taken from the UCT site. The passive radar application was tested on simulated data. The MTTF module was designed to connect to an existing passive radar system developed by Peralex Electronics Pty Ltd. The MTTF module estimated the number of targets in the scene and localized them within a 2D local world Cartesian coordinate system. The investigations encompass numerous areas of research as well as practical aspects of software engineering and systems design

Multi-SLAM Systems for Fault-Tolerant Simultaneous Localization and Mapping

Mobile robots need accurate, high fidelity models of their operating environments in order to complete their tasks safely and efficiently. Generating these models is most often done via Simultaneous Localization and Mapping (SLAM), a paradigm where the robot alternatively estimates the most up-to-date model of the environment and its position relative to this model as it acquires new information from its sensors over time. Because robots operate in many different environments with different compute, memory, sensing, and form constraints, the nature and quality of information available to individual instances of different SLAM systems varies substantially. `One-size-fits-all\u27 solutions are thus exceedingly difficult to engineer, and highly specialized systems, which represent the state-of-the-art for most types of deployments, are not robust to operating conditions in which their assumptions are not met. This thesis seeks to investigate an alternative approach to these robustness and universality problems by incorporating existing SLAM solutions within a larger framework supported by planning and learning. The central idea is to combine learned models that estimate SLAM algorithm performance under a variety of sensory conditions, in this case neural networks, with planners designed for planning under uncertainty and partial observability, in this case partially observable Markov decision problems (POMDPs). Models of existing SLAM algorithms can be learned, and these models can then be used online to estimate the performance of a range of solutions to the SLAM problem at hand. The POMDP policy then selects the appropriate algorithm, given the estimated performance, cost of switching methods, and other information. This general approach may also be applicable to many other robotics problems that rely on data-fusion, such as grasp planning, motion planning, or object identification

Learning Behavior Models for Interpreting and Predicting Traffic Situations

In this thesis, we present Bayesian state estimation and machine learning methods for predicting traffic situations. The cognitive ability to assess situations and behaviors of traffic participants, and to anticipate possible developments is an essential requirement for several applications in the traffic domain, especially for self-driving cars. We present a method for learning behavior models from unlabeled traffic observations and develop improved learning methods for decision trees

WATCHING PEOPLE: ALGORITHMS TO STUDY HUMAN MOTION AND ACTIVITIES

Nowadays human motion analysis is one of the most active research topics in Computer Vision and it is receiving an increasing attention from both the industrial and scientific communities. The growing interest in human motion analysis is motivated by the increasing number of promising applications, ranging from surveillance, human–computer interaction, virtual reality to healthcare, sports, computer games and video conferencing, just to name a few. The aim of this thesis is to give an overview of the various tasks involved in visual motion analysis of the human body and to present the issues and possible solutions related to it. In this thesis, visual motion analysis is categorized into three major areas related to the interpretation of human motion: tracking of human motion using virtual pan-tilt-zoom (vPTZ) camera, recognition of human motions and human behaviors segmentation. In the field of human motion tracking, a virtual environment for PTZ cameras (vPTZ) is presented to overcame the mechanical limitations of PTZ cameras. The vPTZ is built on equirectangular images acquired by 360° cameras and it allows not only the development of pedestrian tracking algorithms but also the comparison of their performances. On the basis of this virtual environment, three novel pedestrian tracking algorithms for 360° cameras were developed, two of which adopt a tracking-by-detection approach while the last adopts a Bayesian approach. The action recognition problem is addressed by an algorithm that represents actions in terms of multinomial distributions of frequent sequential patterns of different length. Frequent sequential patterns are series of data descriptors that occur many times in the data. The proposed method learns a codebook of frequent sequential patterns by means of an apriori-like algorithm. An action is then represented with a Bag-of-Frequent-Sequential-Patterns approach. In the last part of this thesis a methodology to semi-automatically annotate behavioral data given a small set of manually annotated data is presented. The resulting methodology is not only effective in the semi-automated annotation task but can also be used in presence of abnormal behaviors, as demonstrated empirically by testing the system on data collected from children affected by neuro-developmental disorders