Representation of human vision in the brain: How does human perception recognize images?

2001 ◽  
Vol 10 (1) ◽  
pp. 123 ◽  
Author(s):  
Lawrence W. Stark
Keyword(s):  
Human Perception ◽  
Human Vision ◽  
The Brain

Eye Anatomy, Eye Movements and Vision

2012 ◽  
pp. 10-20 ◽  
Author(s):  
Fiona Mulvey
Keyword(s):  
Eye Movements ◽  
Human Computer Interaction ◽  
Eye Movement ◽  
Human Perception ◽  
Human Vision ◽  
Emotional States ◽  
Visual World ◽  
Potential Measurement ◽  
Movement Data ◽  
The Brain

This chapter introduces the basics of eye anatomy, eye movements and vision. It will explain the concepts behind human vision sufficiently for the reader to understand later chapters in the book on human perception and attention, and their relationship to (and potential measurement with) eye movements. We will first describe the path of light from the environment through the structures of the eye and on to the brain, as an introduction to the physiology of vision. We will then describe the image registered by the eye, and the types of movements the eye makes in order to perceive the environment as a cogent whole. This chapter explains how eye movements can be thought of as the interface between the visual world and the brain, and why eye movement data can be analysed not only in terms of the environment, or what is looked at, but also in terms of the brain, or subjective cognitive and emotional states. These two aspects broadly define the scope and applicability of eye movements technology in research and in human computer interaction in later sections of the book.

Temporal Dynamics of Figure-Ground Segregation in Human Vision

2007 ◽  
Vol 97 (1) ◽  
pp. 951-957 ◽  
Author(s):  
Peter Neri ◽  
Dennis M. Levi
Keyword(s):  
Visual Stimulus ◽  
Temporal Dynamics ◽  
Human Perception ◽  
Human Vision ◽  
Reverse Correlation ◽  
Single Neurons ◽  
Neural Signals ◽  
Temporal Window ◽  
Electrophysiological Studies ◽  
The Brain

The segregation of figure from ground is arguably one of the most fundamental operations in human vision. Neural signals reflecting this operation appear in cortex as early as 50 ms and as late as 300 ms after presentation of a visual stimulus, but it is not known when these signals are used by the brain to construct the percepts of figure and ground. We used psychophysical reverse correlation to identify the temporal window for figure-ground signals in human perception and found it to lie within the range of 100–160 ms. Figure enhancement within this narrow temporal window was transient rather than sustained as may be expected from measurements in single neurons. These psychophysical results prompt and guide further electrophysiological studies.

scholarly journals Dense Associative Memory Is Robust to Adversarial Inputs

2018 ◽  
Vol 30 (12) ◽  
pp. 3151-3167 ◽  
Author(s):  
Dmitry Krotov ◽  
John Hopfield
Keyword(s):  
Objective Function ◽  
Associative Memory ◽  
Energy Function ◽  
Human Subjects ◽  
Human Perception ◽  
Higher Order ◽  
Human Vision ◽  
Training Data ◽  
Decision Boundary ◽  
Interaction Vertex

Deep neural networks (DNNs) trained in a supervised way suffer from two known problems. First, the minima of the objective function used in learning correspond to data points (also known as rubbish examples or fooling images) that lack semantic similarity with the training data. Second, a clean input can be changed by a small, and often imperceptible for human vision, perturbation so that the resulting deformed input is misclassified by the network. These findings emphasize the differences between the ways DNNs and humans classify patterns and raise a question of designing learning algorithms that more accurately mimic human perception compared to the existing methods. Our article examines these questions within the framework of dense associative memory (DAM) models. These models are defined by the energy function, with higher-order (higher than quadratic) interactions between the neurons. We show that in the limit when the power of the interaction vertex in the energy function is sufficiently large, these models have the following three properties. First, the minima of the objective function are free from rubbish images, so that each minimum is a semantically meaningful pattern. Second, artificial patterns poised precisely at the decision boundary look ambiguous to human subjects and share aspects of both classes that are separated by that decision boundary. Third, adversarial images constructed by models with small power of the interaction vertex, which are equivalent to DNN with rectified linear units, fail to transfer to and fool the models with higher-order interactions. This opens up the possibility of using higher-order models for detecting and stopping malicious adversarial attacks. The results we present suggest that DAMs with higher-order energy functions are more robust to adversarial and rubbish inputs than DNNs with rectified linear units.

scholarly journals Visual Impairment Caused by Monovision Surgical Design

2021 ◽  
Author(s):  
Humberto Dória Silva ◽  
Rostan Silvestre da Silva ◽  
Eduardo Dória Silva ◽  
Maria Tamires Dória Silva ◽  
Cristiana Pereira Dória ◽  
...  
Keyword(s):  
Visual Field ◽  
Binocular Vision ◽  
Trabecular Meshwork ◽  
Human Vision ◽  
Implantation Technique ◽  
Refractive Errors ◽  
Mobile Mass ◽  
Surgical Implantation ◽  
The Brain ◽  
Continuous Progression

Neurophysiological anatomy of natural binocular vision shows the need to focus with both eyes to jointly produce the two corneas accommodation, correcting, in a compensatory way, the divergences inherent in the two different images, of the same visual field projected in the two distinct spaces, the two retinas. Corneal accommodation is part of the forced convection mechanism for the transfer of mobile mass in the cornea, trabecular meshwork and retina, to inhibit the accumulation of dehydrated intraocular metabolic residue, which can cause refractive errors in the cornea, obstruction of the trabecular meshwork and reduction of the amplitude of the signals produced by the phototransducers and sent to the brain. The IOL monovision surgical implantation technique differs from the physiology of natural binocular vision, which can cause after surgery disorders, described in this chapter, in that it imposes a different adaptation from the neurophysiological anatomy of human vision in addition to favoring the continuous progression of residue accumulation dehydrated intraocular metabolic and stimulate ocular.

scholarly journals IMPLEMENTASI JARINGAN SYARAF TIRUAN (JST) DAN PENGOLAHAN CITRA UNTUK KlASIFIKASI KEMATANGAN TBS KELAPA SAWIT

2018 ◽  
Vol 15 (1) ◽  
pp. 36
Author(s):  
Minarni Minarni ◽  
Roni Salumbae ◽  
Zilhan Hasbi
Keyword(s):  
Oil Palm ◽  
Color Space ◽  
Human Perception ◽  
Image Data ◽  
Cost Effective ◽  
Fresh Fruit ◽  
Human Vision ◽  
Palm Fruit ◽  
Color Parameters ◽  
Fresh Fruit Bunches

The clasification of ripeness stages of oil palm fresh fruit bunches (FFBs) can be done using color parameters. These parameters are often evaluated by human vision, whose degree of accuracy is subjective which can cause doubt in judgement. Automatic clasifications offreshfruit bunches (FFBs) based on color parameters can be done using computer vision. This method is known as a nondestructive, fast and cost effective method. In this research, a MATLAB computer program has been developed which consists of RGB and HSV GUI which is used to record, display, and process FFB image data. The backpropagation artificial neural network (ANN) program is also developed which is used to classify the oil palm fruit fresh bunches (FFBs). Samples are fresh fruit bunches (FFB) of oil palm varieties of Tenera which comprise of Topaz, Marihat, and Lonsum clones. Each clone composed of three levels of ripeness represented by five fractions. The measurements were started by capturing images of oil palm, extracting RGB and HSV values, calculating weight values from the image database to make anANN program, preparing grid programs for oil palm FFBs, and comparing grading levels of oil palm FFBs using program and by harvester. This program successfully classified oil palm (FFBs) into three categories of ripeness which are unripe (F0 and F1), ripe (F1 and F1) and over ripe (F4 and F5). The RGB and HSV programs successfully classified 79 out of 216 FFBs or 36.57% and 106 out of 216 TBS or 49.07%. Respectively the HSV program is better than RGB program because the representation of HSV color space are more understood by human perception hence can be used in calibration and color comparison.

scholarly journals Spontaneous perception: a framework for task-free, self-paced perception

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Shira Baror ◽  
Biyu J He
Keyword(s):  
Time Course ◽  
Visual Experience ◽  
Brain Activity ◽  
Human Perception ◽  
Future Research ◽  
Task Constraints ◽  
Spontaneous Brain Activity ◽  
The Brain ◽  
Coarse To Fine ◽  
Organizing Principles

Abstract Flipping through social media feeds, viewing exhibitions in a museum, or walking through the botanical gardens, people consistently choose to engage with and disengage from visual content. Yet, in most laboratory settings, the visual stimuli, their presentation duration, and the task at hand are all controlled by the researcher. Such settings largely overlook the spontaneous nature of human visual experience, in which perception takes place independently from specific task constraints and its time course is determined by the observer as a self-governing agent. Currently, much remains unknown about how spontaneous perceptual experiences unfold in the brain. Are all perceptual categories extracted during spontaneous perception? Does spontaneous perception inherently involve volition? Is spontaneous perception segmented into discrete episodes? How do different neural networks interact over time during spontaneous perception? These questions are imperative to understand our conscious visual experience in daily life. In this article we propose a framework for spontaneous perception. We first define spontaneous perception as a task-free and self-paced experience. We propose that spontaneous perception is guided by four organizing principles that grant it temporal and spatial structures. These principles include coarse-to-fine processing, continuity and segmentation, agency and volition, and associative processing. We provide key suggestions illustrating how these principles may interact with one another in guiding the multifaceted experience of spontaneous perception. We point to testable predictions derived from this framework, including (but not limited to) the roles of the default-mode network and slow cortical potentials in underlying spontaneous perception. We conclude by suggesting several outstanding questions for future research, extending the relevance of this framework to consciousness and spontaneous brain activity. In conclusion, the spontaneous perception framework proposed herein integrates components in human perception and cognition, which have been traditionally studied in isolation, and opens the door to understand how visual perception unfolds in its most natural context.

Human Stereopsis, Fusion, and Stereoscopic Virtual Environments

1995 ◽  
Author(s):  
Elizabeth Thorpe Davis ◽  
Larry F. Hodges
Keyword(s):  
Virtual Environments ◽  
Visual Cues ◽  
Spatial Information ◽  
Spatial Perception ◽  
Human Perception ◽  
Stereoscopic Vision ◽  
Human Vision ◽  
Spatial Factors ◽  
Stereoscopic Displays ◽  
And Performance

Two fundamental purposes of human spatial perception, in either a real or virtual 3D environment, are to determine where objects are located in the environment and to distinguish one object from another. Although various sensory inputs, such as haptic and auditory inputs, can provide this spatial information, vision usually provides the most accurate, salient, and useful information (Welch and Warren, 1986). Moreover, of the visual cues available to humans, stereopsis provides an enhanced perception of depth and of three-dimensionality for a visual scene (Yeh and Silverstein, 1992). (Stereopsis or stereoscopic vision results from the fusion of the two slightly different views of the external world that our laterally displaced eyes receive (Schor, 1987; Tyler, 1983).) In fact, users often prefer using 3D stereoscopic displays (Spain and Holzhausen, 1991) and find that such displays provide more fun and excitement than do simpler monoscopic displays (Wichanski, 1991). Thus, in creating 3D virtual environments or 3D simulated displays, much attention recently has been devoted to visual 3D stereoscopic displays. Yet, given the costs and technical requirements of such displays, we should consider several issues. First, we should consider in what conditions and situations these stereoscopic displays enhance perception and performance. Second, we should consider how binocular geometry and various spatial factors can affect human stereoscopic vision and, thus, constrain the design and use of stereoscopic displays. Finally, we should consider the modeling geometry of the software, the display geometry of the hardware, and some technological limitations that constrain the design and use of stereoscopic displays by humans. In the following section we consider when 3D stereoscopic displays are useful and why they are useful in some conditions but not others. In the section after that we review some basic concepts about human stereopsis and fusion that are of interest to those who design or use 3D stereoscopic displays. Also in that section we point out some spatial factors that limit stereopsis and fusion in human vision as well as some potential problems that should be considered in designing and using 3D stereoscopic displays. Following that we discuss some software and hardware issues, such as modelling geometry and display geometry as well as geometric distortions and other artifacts that can affect human perception.

5 Models of the world: from perception to hallucination

2020 ◽  
Vol 91 (8) ◽  
pp. e2.3-e2
Author(s):  
Paul Fletcher
Keyword(s):  
Model Building ◽  
Processing System ◽  
Human Perception ◽  
Predictive Processing ◽  
External Reality ◽  
Consultant Psychiatrist ◽  
University Of Cambridge ◽  
The World ◽  
The University ◽  
The Brain

Paul Fletcher is Wellcome Investigator and Bernard Wolfe Professor of Health Neuroscience at the University of Cambridge. He is also Director of Studies for Preclinical Medicine at Clare College and Honorary Consultant Psychiatrist with the Cambridgeshire and Peterborough NHS Foundation Trust. He studied Medicine, before carrying out specialist training in Psychiatry and taking a PhD in cognitive neuroscience. He researches human perception, learning and decision-making in health and mental illness.We do not have direct contact with external reality. We must rely on messages from the sense organs, conveying information about the state of the world and our bodies. These messages are not easy to decipher, being noisy and ambiguous, but from them we have to construct models of the world. I will discuss this challenge and how we are very adept at creating a model of reality based on achieving a balance between what our senses are telling us and our expectations of what should be the case. This is often referred to as the predictive processing framework.Relying on this balance comes at a cost, rendering us vulnerable to illusions and biases and, in more extreme cases, to creating a reality that diverges from that experienced by others. This can arise for a variety of reasons but, at the root, I suggest, lies the nature of the brain as a model-building organ. Though this divergence from reality – psychosis – often seems inexplicable and incomprehensible, I suggest that a few core principles can help us to understand it and offers ways of thinking about how phenomena like hallucinations can be understood. Interestingly, the framework suggests ways in which apparently similar phenomena like hallucinations can arise from distinct alterations to the function of a predictive processing system.

Perceptual Image Representations for Support Vector Machine Image Coding

2011 ◽  
pp. 303-324
Author(s):  
Juan Gutiérrez ◽  
Gabriel Gómez-Perez ◽  
Jesús Malo ◽  
Gustavo Camps-Valls
Keyword(s):  
Support Vector Machine ◽  
Image Coding ◽  
General Procedure ◽  
Image Representation ◽  
Rate Distortion ◽  
Human Perception ◽  
Human Vision ◽  
Support Vector ◽  
Perception Model ◽  
Image Representations

Support vector machine (SVM) image coding relies on the ability of SVMs for function approximation. The size and the profile of the e-insensitivity zone of the support vector regression (SVR) at some specific image representation determines (a) the amount of selected support vectors (the compression ratio), and (b) the nature of the introduced error (the compression distortion). However, the selection of an appropriate image representation is a key issue for a meaningful design of the e-insensitivity profile. For example, in image coding applications, taking human perception into account is of paramount relevance to obtain a good rate-distortion performance. However, depending on the accuracy of the considered perception model, certain image representations are not suitable for SVR training. In this chapter, we analyze the general procedure to take human vision models into account in SVR-based image coding. Specifically, we derive the condition for image representation selection and the associated e-insensitivity profiles.

Target Tracking by PPI Display or by Automatic Computer

1964 ◽  
Vol 17 (4) ◽  
pp. 414-418
Author(s):  
L. Gérardin
Keyword(s):  
Target Tracking ◽  
General Purpose ◽  
Human Vision ◽  
Human Operator ◽  
Human Observer ◽  
Modes Of Operation ◽  
Automatic Computer ◽  
The Brain

The observation of a radar display by a human operator leads to the establishment of aircraft tracks. These tracks are subsequently used by the controller. More and more often, it is proposed to replace both PPI display and human observer by an automatic computer, either special or general purpose, to perform tracking.In the present paper the basic performances of these two modes of operation are examined, taking into account the psychological and physiological features of human vision and hence the mental association of the viewer. The computer is more precise, but more costly and, when saturated, the drop in performance is abrupt. The number of tracks handled by a human operator is small, but the brain is very versatile and works very well in confused situations, with a slower drop in efficiency than the computer.

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