301 research outputs found
Gaze Guidance, Task-Based Eye Movement Prediction, and Real-World Task Inference using Eye Tracking
The ability to predict and guide viewer attention has important applications in computer graphics, image understanding, object detection, visual search and training. Human eye movements provide insight into the cognitive processes involved in task performance and there has been extensive research on what factors guide viewer attention in a scene. It has been shown, for example, that saliency in the image, scene context, and task at hand play significant roles in guiding attention.
This dissertation presents and discusses research on visual attention with specific focus on the use of subtle visual cues to guide viewer gaze and the development of algorithms to predict the distribution of gaze about a scene. Specific contributions of this work include: a framework for gaze guidance to enable problem solving and spatial learning, a novel algorithm for task-based eye movement prediction, and a system for real-world task inference using eye tracking.
A gaze guidance approach is presented that combines eye tracking with subtle image-space modulations to guide viewer gaze about a scene. Several experiments were conducted using this approach to examine its impact on short-term spatial information recall, task sequencing, training, and password recollection. A model of human visual attention prediction that uses saliency maps, scene feature maps and task-based eye movements to predict regions of interest was also developed. This model was used to automatically select target regions for active gaze guidance to improve search task performance. Finally, we develop a framework for inferring real-world tasks using image features and eye movement data.
Overall, this dissertation naturally leads to an overarching framework, that combines all three contributions to provide a continuous feedback system to improve performance on repeated visual search tasks. This research has important applications in data visualization, problem solving, training, and online education
Towards assisting the decision-making process for content creators in cinematic virtual reality through the analysis of movie cuts and their influence on viewers' behavior
Virtual Reality (VR) is gaining popularity in recent years due to the commercialization of personal devices. VR is a new and exciting medium to tell stories, however, the development of Cinematic Virtual Reality (CVR) content is still in an exploratory phase. One of the main reasons is that in this medium the user has now total or partial control of the camera, therefore viewers create their own personal experiences by deciding what to see in every moment, which can potentially hinder the delivery of a pre-established narrative. In the particular case of transitions from one shot to another (movie cuts), viewers may not be aligned with the main elements of the scene placed by the content creator to convey the story. This can result in viewers missing key elements of the narrative. In this work, we explore recent studies that analyze viewers’ behavior during cinematic cuts in VR videos, and we discuss guidelines and methods which can help filmmakers with the decision-making process when filming and editing their movies
A Dual-Stream Neural Network Explains the Functional Segregation of Dorsal and Ventral Visual Pathways in Human Brains
The human visual system uses two parallel pathways for spatial processing and
object recognition. In contrast, computer vision systems tend to use a single
feedforward pathway, rendering them less robust, adaptive, or efficient than
human vision. To bridge this gap, we developed a dual-stream vision model
inspired by the human eyes and brain. At the input level, the model samples two
complementary visual patterns to mimic how the human eyes use magnocellular and
parvocellular retinal ganglion cells to separate retinal inputs to the brain.
At the backend, the model processes the separate input patterns through two
branches of convolutional neural networks (CNN) to mimic how the human brain
uses the dorsal and ventral cortical pathways for parallel visual processing.
The first branch (WhereCNN) samples a global view to learn spatial attention
and control eye movements. The second branch (WhatCNN) samples a local view to
represent the object around the fixation. Over time, the two branches interact
recurrently to build a scene representation from moving fixations. We compared
this model with the human brains processing the same movie and evaluated their
functional alignment by linear transformation. The WhereCNN and WhatCNN
branches were found to differentially match the dorsal and ventral pathways of
the visual cortex, respectively, primarily due to their different learning
objectives. These model-based results lead us to speculate that the distinct
responses and representations of the ventral and dorsal streams are more
influenced by their distinct goals in visual attention and object recognition
than by their specific bias or selectivity in retinal inputs. This dual-stream
model takes a further step in brain-inspired computer vision, enabling parallel
neural networks to actively explore and understand the visual surroundings
Predicting human behavior in smart environments: theory and application to gaze prediction
Predicting human behavior is desirable in many application scenarios in smart environments. The existing models for eye movements do not take contextual factors into account. This addressed in this thesis using a systematic machine-learning approach, where user profiles for eye movements behaviors are learned from data. In addition, a theoretical innovation is presented, which goes beyond pure data analysis. The thesis proposed the modeling of eye movements as a Markov Decision Processes. It uses Inverse Reinforcement Learning paradigm to infer the user eye movements behaviors
Recognition, Analysis, and Assessments of Human Skills using Wearable Sensors
One of the biggest social issues in mature societies such as Europe and Japan
is the aging population and declining birth rate. These societies have a serious
problem with the retirement of the expert workers, doctors, and engineers etc.
Especially in the sectors that require long time to make experts in fields like medicine and industry; the retirement and injuries of the experts, is a serious problem. The technology to support the training and assessment of skilled workers (like doctors, manufacturing
workers) is strongly required for the society. Although there are some solutions for
this problem, most of them are video-based which violates the privacy of the subjects.
Furthermore, they are not easy to deploy due to the need for large training data.
This thesis provides a novel framework to recognize, analyze, and assess human
skills with minimum customization cost. The presented framework tackles this problem
in two different domains, industrial setup and medical operations of catheter-based
cardiovascular interventions (CBCVI).
In particular, the contributions of this thesis are four-fold. First, it proposes an
easy-to-deploy framework for human activity recognition based on zero-shot learning
approach, which is based on learning basic actions and objects. The model recognizes
unseen activities by combinations of basic actions learned in a preliminary way and involved objects. Therefore, it is completely configurable by the user and can be used to detect completely new activities.
Second, a novel gaze-estimation model for attention driven object detection task is
presented. The key features of the model are: (i) usage of the deformable convolutional
layers to better incorporate spatial dependencies of different shapes of objects and
backgrounds, (ii) formulation of the gaze-estimation problem in two different way, as a
classification as well as a regression problem. We combine both formulations using a
joint loss that incorporates both the cross-entropy as well as the mean-squared error in
order to train our model. This enhanced the accuracy of the model from 6.8 by using only
the cross-entropy loss to 6.4 for the joint loss.
The third contribution of this thesis targets the area of quantification of quality of
i
actions using wearable sensor. To address the variety of scenarios, we have targeted two
possibilities: a) both expert and novice data is available , b) only expert data is available,
a quite common case in safety critical scenarios.
Both of the developed methods from these scenarios are deep learning based. In the
first one, we use autoencoders with OneClass SVM, and in the second one we use the
Siamese Networks. These methods allow us to encode the expert’s expertise and to learn
the differences between novice and expert workers. This enables quantification of the
performance of the novice in comparison to the expert worker.
The fourth contribution, explicitly targets medical practitioners and provides a
methodology for novel gaze-based temporal spatial analysis of CBCVI data. The developed
methodology allows continuous registration and analysis of gaze data for analysis
of the visual X-ray image processing (XRIP) strategies of expert operators in live-cases scenarios and may assist in transferring experts’ reading skills to novices
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