2,335 research outputs found
Gaze Distribution Analysis and Saliency Prediction Across Age Groups
Knowledge of the human visual system helps to develop better computational
models of visual attention. State-of-the-art models have been developed to
mimic the visual attention system of young adults that, however, largely ignore
the variations that occur with age. In this paper, we investigated how visual
scene processing changes with age and we propose an age-adapted framework that
helps to develop a computational model that can predict saliency across
different age groups. Our analysis uncovers how the explorativeness of an
observer varies with age, how well saliency maps of an age group agree with
fixation points of observers from the same or different age groups, and how age
influences the center bias. We analyzed the eye movement behavior of 82
observers belonging to four age groups while they explored visual scenes.
Explorativeness was quantified in terms of the entropy of a saliency map, and
area under the curve (AUC) metrics was used to quantify the agreement analysis
and the center bias. These results were used to develop age adapted saliency
models. Our results suggest that the proposed age-adapted saliency model
outperforms existing saliency models in predicting the regions of interest
across age groups
Reinforcement learning approaches to the analysis of the emergence of goal-directed behaviour
Over recent decades, theoretical neuroscience, helped by computational methods
such as Reinforcement Learning (RL), has provided detailed descriptions of the
psychology and neurobiology of decision-making. RL has provided many insights
into the mechanisms underlying decision-making processes from neuronal to behavioral
levels. In this work, we attempt to demonstrate the effectiveness of RL
methods in explaining behavior in a normative setting through three main case
studies.
Evidence from literature shows that, apart from the commonly discussed cognitive
search process, that governs the solution procedure of a planning task, there
is an online perceptual process that directs the action selection towards moves that
appear more ‘natural’ at a given configuration of a task. These two processes can
be partially dissociated through developmental studies, with perceptual processes
apparently more dominant in the planning of younger children, prior to the maturation
of executive functions required for the control of search. Therefore, we
present a formalization of planning processes to account for perceptual features of
the task, and relate it to human data.
Although young children are able to demonstrate their preferences by using
physical actions, infants are restricted because of their as-yet-undeveloped motor
skills. Eye-tracking methods have been employed to tackle this difficulty. Exploring
different model-free RL algorithms and their possible cognitive realizations in
decision making, in a second case study, we demonstrate behavioral signatures of
decision making processes in eye-movement data and provide a potential framework
for integrating eye-movement patterns with behavioral patterns.
Finally, in a third project we examine how uncertainty in choices might guide exploration
in 10-year-olds, using an abstract RL-based mathematical model. Throughout,
aspects of action selection are seen as emerging from the RL computational
framework. We, thus, conclude that computational descriptions of the developing
decision making functions provide one plausible avenue by which to normatively characterize and define the functions that control action selection
Understanding Vehicular Traffic Behavior from Video: A Survey of Unsupervised Approaches
Recent emerging trends for automatic behavior analysis and understanding from infrastructure video are reviewed. Research has shifted from high-resolution estimation of vehicle state and instead, pushed machine learning approaches to extract meaningful patterns in aggregates in an unsupervised fashion. These patterns represent priors on observable motion, which can be utilized to describe a scene, answer behavior questions such as where is a vehicle going, how many vehicles are performing the same action, and to detect an abnormal event. The review focuses on two main methods for scene description, trajectory clustering and topic modeling. Example applications that utilize the behavioral modeling techniques are also presented. In addition, the most popular public datasets for behavioral analysis are presented. Discussion and comment on future directions in the field are also provide
Discovering a Domain Knowledge Representation for Image Grouping: Multimodal Data Modeling, Fusion, and Interactive Learning
In visually-oriented specialized medical domains such as dermatology and radiology, physicians explore interesting image cases from medical image repositories for comparative case studies to aid clinical diagnoses, educate medical trainees, and support medical research. However, general image classification and retrieval approaches fail in grouping medical images from the physicians\u27 viewpoint. This is because fully-automated learning techniques cannot yet bridge the gap between image features and domain-specific content for the absence of expert knowledge. Understanding how experts get information from medical images is therefore an important research topic.
As a prior study, we conducted data elicitation experiments, where physicians were instructed to inspect each medical image towards a diagnosis while describing image content to a student seated nearby. Experts\u27 eye movements and their verbal descriptions of the image content were recorded to capture various aspects of expert image understanding. This dissertation aims at an intuitive approach to extracting expert knowledge, which is to find patterns in expert data elicited from image-based diagnoses. These patterns are useful to understand both the characteristics of the medical images and the experts\u27 cognitive reasoning processes.
The transformation from the viewed raw image features to interpretation as domain-specific concepts requires experts\u27 domain knowledge and cognitive reasoning. This dissertation also approximates this transformation using a matrix factorization-based framework, which helps project multiple expert-derived data modalities to high-level abstractions.
To combine additional expert interventions with computational processing capabilities, an interactive machine learning paradigm is developed to treat experts as an integral part of the learning process. Specifically, experts refine medical image groups presented by the learned model locally, to incrementally re-learn the model globally. This paradigm avoids the onerous expert annotations for model training, while aligning the learned model with experts\u27 sense-making
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