Image synthesis based on a model of human vision

Modern computer graphics systems are able to construct renderings of such high quality that viewers are deceived into regarding the images as coming from a photographic source. Large amounts of computing resources are expended in this rendering process, using complex mathematical models of lighting and shading. However, psychophysical experiments have revealed that viewers only regard certain informative regions within a presented image. Furthermore, it has been shown that these visually important regions contain low-level visual feature differences that attract the attention of the viewer. This thesis will present a new approach to image synthesis that exploits these experimental findings by modulating the spatial quality of image regions by their visual importance. Efficiency gains are therefore reaped, without sacrificing much of the perceived quality of the image. Two tasks must be undertaken to achieve this goal. Firstly, the design of an appropriate region-based model of visual importance, and secondly, the modification of progressive rendering techniques to effect an importance-based rendering approach. A rule-based fuzzy logic model is presented that computes, using spatial feature differences, the relative visual importance of regions in an image. This model improves upon previous work by incorporating threshold effects induced by global feature difference distributions and by using texture concentration measures. A modified approach to progressive ray-tracing is also presented. This new approach uses the visual importance model to guide the progressive refinement of an image. In addition, this concept of visual importance has been incorporated into supersampling, texture mapping and computer animation techniques. Experimental results are presented, illustrating the efficiency gains reaped from using this method of progressive rendering. This visual importance-based rendering approach is expected to have applications in the entertainment industry, where image fidelity may be sacrificed for efficiency purposes, as long as the overall visual impression of the scene is maintained. Different aspects of the approach should find many other applications in image compression, image retrieval, progressive data transmission and active robotic vision

Proceedings of the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

This book is a collection of 15 reviewed technical reports summarizing the presentations at the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. The covered topics include image processing, optical signal processing, visual inspection, pattern recognition and classification, human-machine interaction, world and situation modeling, autonomous system localization and mapping, information fusion, and trust propagation in sensor networks

Functional Organization of the Human Brain: How We See, Feel, and Decide.

The human brain is responsible for constructing how we perceive, think, and act in the world around us. The organization of these functions is intricately distributed throughout the brain. Here, I discuss how functional magnetic resonance imaging (fMRI) was employed to understand three broad questions: how do we see, feel, and decide? First, high-resolution fMRI was used to measure the polar angle representation of saccadic eye movements in the superior colliculus. We found that eye movements along the superior-inferior visual field are mapped across the medial-lateral anatomy of a subcortical midbrain structure, the superior colliculus (SC). This result is consistent with the topography in monkey SC. Second, we measured the empathic responses of the brain as people watched a hand get painfully stabbed with a needle. We found that if the hand was labeled as belonging to the same religion as the observer, the empathic neural response was heightened, creating a strong ingroup bias that could not be readily manipulated. Third, we measured brain activity in individuals as they made free decisions (i.e., choosing randomly which of two buttons to press) and found the activity within fronto-thalamic networks to be significantly decreased compared to being instructed (forced) to press a particular button. I also summarize findings from several other projects ranging from addiction therapies to decoding visual imagination to how corporations are represented as people. Together, these approaches illustrate how functional neuroimaging can be used to understand the organization of the human brain

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Functional Organization of the Human Brain: How We See, Feel, and Decide.

The human brain is responsible for constructing how we perceive, think, and act in the world around us. The organization of these functions is intricately distributed throughout the brain. Here, I discuss how functional magnetic resonance imaging (fMRI) was employed to understand three broad questions: how do we see, feel, and decide? First, high-resolution fMRI was used to measure the polar angle representation of saccadic eye movements in the superior colliculus. We found that eye movements along the superior-inferior visual field are mapped across the medial-lateral anatomy of a subcortical midbrain structure, the superior colliculus (SC). This result is consistent with the topography in monkey SC. Second, we measured the empathic responses of the brain as people watched a hand get painfully stabbed with a needle. We found that if the hand was labeled as belonging to the same religion as the observer, the empathic neural response was heightened, creating a strong ingroup bias that could not be readily manipulated. Third, we measured brain activity in individuals as they made free decisions (i.e., choosing randomly which of two buttons to press) and found the activity within fronto-thalamic networks to be significantly decreased compared to being instructed (forced) to press a particular button. I also summarize findings from several other projects ranging from addiction therapies to decoding visual imagination to how corporations are represented as people. Together, these approaches illustrate how functional neuroimaging can be used to understand the organization of the human brain

Imaging the subthalamic nucleus in Parkinson’s disease

This thesis is comprised of a set of work that aims to visualize and quantify the anatomy, structural variability, and connectivity of the subthalamic nucleus (STN) with optimized neuroimaging methods. The study populations include both healthy cohorts and individuals living with Parkinson's disease (PD). PD was chosen specifically due to the involvement of the STN in the pathophysiology of the disease. Optimized neuroimaging methods were primarily obtained using ultra-high field (UHF) magnetic resonance imaging (MRI). An additional component of this thesis was to determine to what extent UHF-MRI can be used in a clinical setting, specifically for pre-operative planning of deep brain stimulation (DBS) of the STN for patients with advanced PD. The thesis collectively demonstrates that i, MRI research, and clinical applications must account for the different anatomical and structural changes that occur in the STN with both age and PD. ii, Anatomical connections involved in preparatory motor control, response inhibition, and decision-making may be compromised in PD. iii. The accuracy of visualizing and quantifying the STN strongly depends on the type of MR contrast and voxel size. iv, MRI at a field strength of 3 Tesla (T) can under certain circumstances be optimized to produce results similar to that of 7 T at the expense of increased acquisition time

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered