scholarly journals Predicting human performance in fundamental visual tasks with natural stimuli

2016 ◽  
Vol 16 (12) ◽  
pp. 4
Author(s):  
Johannes Burge
Keyword(s):  
Human Performance ◽  
Natural Stimuli ◽  
Visual Tasks

scholarly journals STABILITY AND INSTABILITY OF AN OUTDOOR ILLUMINATION INSTALLATION

2004 ◽  
Vol 12 (4) ◽  
pp. 115-119
Author(s):  
Klaus-Dieter Fröhner ◽  
Ze Li
Keyword(s):  
Human Performance ◽  
Night Shift ◽  
Lighting Design ◽  
Work Efficiency ◽  
Safety And Health ◽  
Stability And Instability ◽  
Visual Tasks ◽  
And Performance ◽  
The Stability

Stability and instability are very important for the layout of real world processes concerning safety and health esp. when planned by scientists. The long‐term investigation of stability was carried out for the last ten years on the basis of the installation and the evaluation of an ergonomically designed outdoor illumination. In the depicted dynamic situation the lighting design influences directly visual discomfort and human performance and in the end stability and instability. The improvement of the adaptation of luminance and its influence on the visual tasks after the rearrangement are presented and discussed. The effective factors on the visual capability and performance of workers, work efficiency and potential accidents in the night shift, and furthermore the accelerators and barriers for the stability of the project are analysed and discussed.

Eyetracking-based assessment of affect-related decay of human performance in visual tasks

2019 ◽  
Vol 92 ◽  
pp. 504-515 ◽  
Author(s):  
Jaromir Przybyło ◽  
Eliasz Kańtoch ◽  
Piotr Augustyniak
Keyword(s):  
Human Performance ◽  
Visual Tasks

scholarly journals The lawful imprecision of human surface tilt estimation in natural scenes

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Seha Kim ◽  
Johannes Burge
Keyword(s):  
Human Performance ◽  
Three Dimensional ◽  
Model Performance ◽  
Surface Orientation ◽  
Dimensional Structure ◽  
Natural Scenes ◽  
Natural Scene Statistics ◽  
Three Dimensional Structure ◽  
Natural Stimuli ◽  
Performance Patterns

Estimating local surface orientation (slant and tilt) is fundamental to recovering the three-dimensional structure of the environment. It is unknown how well humans perform this task in natural scenes. Here, with a database of natural stereo-images having groundtruth surface orientation at each pixel, we find dramatic differences in human tilt estimation with natural and artificial stimuli. Estimates are precise and unbiased with artificial stimuli and imprecise and strongly biased with natural stimuli. An image-computable Bayes optimal model grounded in natural scene statistics predicts human bias, precision, and trial-by-trial errors without fitting parameters to the human data. The similarities between human and model performance suggest that the complex human performance patterns with natural stimuli are lawful, and that human visual systems have internalized local image and scene statistics to optimally infer the three-dimensional structure of the environment. These results generalize our understanding of vision from the lab to the real world.

scholarly journals The lawful imprecision of human surface tilt estimation in natural scenes

2017 ◽  
Author(s):  
Seha Kim ◽  
Johannes Burge
Keyword(s):  
Human Performance ◽  
Three Dimensional ◽  
Surface Orientation ◽  
Human Vision ◽  
Dimensional Structure ◽  
Natural Scenes ◽  
Natural Scene Statistics ◽  
Three Dimensional Structure ◽  
Natural Stimuli ◽  
Performance Patterns

AbstractEstimating local surface orientation (slant and tilt) is fundamental to recovering the three-dimensional structure of the environment, but it is unknown how well humans perform this task in natural scenes. Here, with a high-fidelity database of natural stereo-images with groundtruth surface orientation at each pixel, we find dramatic differences in human tilt estimation with natural and artificial stimuli. With artificial stimuli, estimates are precise and unbiased. With natural stimuli, estimates are imprecise and strongly biased. An image-computable normative model grounded in natural scene statistics predicts human bias, precision, and trial-by-trial errors without fitting parameters to the human data. These similarities suggest that the complex human performance patterns with natural stimuli are lawful, and that human visual systems have internalized local image and scene statistics to optimally infer the three-dimensional structure of the environment. The current results help generalize our understanding of human vision from the lab to the real world.

scholarly journals Contour statistics in natural images: Grouping across occlusions

2009 ◽  
Vol 26 (1) ◽  
pp. 109-121 ◽  
Author(s):  
WILSON S. GEISLER ◽  
JEFFREY S. PERRY
Keyword(s):  
Human Performance ◽  
Statistical Properties ◽  
Ideal Observer ◽  
Neural Mechanisms ◽  
Natural Images ◽  
Natural Image ◽  
Edge Element ◽  
Visual Tasks ◽  
Contour Grouping ◽  
The Ideal

AbstractCorrectly interpreting a natural image requires dealing properly with the effects of occlusion, and hence, contour grouping across occlusions is a major component of many natural visual tasks. To better understand the mechanisms of contour grouping across occlusions, we (a) measured the pair-wise statistics of edge elements from contours in natural images, as a function of edge element geometry and contrast polarity, (b) derived the ideal Bayesian observer for a contour occlusion task where the stimuli were extracted directly from natural images, and then (c) measured human performance in the same contour occlusion task. In addition to discovering new statistical properties of natural contours, we found that naïve human observers closely parallel ideal performance in our contour occlusion task. In fact, there was no region of the four-dimensional stimulus space (three geometry dimensions and one contrast dimension) where humans did not closely parallel the performance of the ideal observer (i.e., efficiency was approximately constant over the entire space). These results reject many other contour grouping hypotheses and strongly suggest that the neural mechanisms of contour grouping are tightly related to the statistical properties of contours in natural images.

UNDERSTANDING IN THE SHAPE UNDERSTANDING SYSTEM

2004 ◽  
Vol 18 (04) ◽  
pp. 727-745 ◽  
Author(s):  
ZBIGNIEW LES ◽  
MAGDALENA LES
Keyword(s):  
Human Performance ◽  
Intelligence Tests ◽  
Visual Object ◽  
Visual Thinking ◽  
Visual Concept ◽  
Visual Tasks ◽  
Visual Understanding ◽  
And Performance

Understanding is based on a large number of highly varied abilities called intelligence that can be measured. In this paper understanding abilities of the shape understanding system (SUS) are tested based on the adoption of the intelligence tests. The SUS tests are formulated as the tasks given to the system and performance of SUS is compared with the human performance of these tasks. The main novelty of the presented method is that the process of understanding is related to the visual concept represented as a symbolic name of the possible classes of shape. The visual concept is one of the ingredients of the concept of the visual object (the phantom concept) that makes it possible to perform different tasks that are characteristic for the visual understanding. The presented results are part of the research aimed at developing the shape understanding method able to perform the complex visual tasks connected with visual thinking. The shape understanding method is implemented as the shape understanding system (SUS).

scholarly journals Model neuron response statistics to natural images

2018 ◽  
Author(s):  
Arvind Iyer ◽  
Johannes Burge
Keyword(s):  
Receptive Field ◽  
Signal To Noise Ratio ◽  
Model Neuron ◽  
Natural Images ◽  
Natural Image ◽  
Signal To Noise ◽  
Scale Invariant ◽  
Natural Stimuli ◽  
Visual Tasks ◽  
Noise Ratio

AbstractTo model the responses of neurons in the early visual system, at least three basic components are required: a receptive field, a normalization term, and a specification of encoding noise. Here, we examine how the receptive field, the normalization factor, and the encoding noise impact the model neuron responses to natural images and the signal-to-noise ratio for natural image discrimination. We show that when these components are modeled appropriately, the model neuron responses to natural stimuli are Gaussian distributed, scale-invariant, and very nearly maximize the signal-to-noise ratio for stimulus discrimination. We discuss the statistical models of natural stimuli that can account for these response statistics, and we show how some commonly used modeling practices may distort these results. Finally, we show that normalization can equalize important properties of neural response across different stimulus types. Specifically, narrowband (stimulus- and feature-specific) normalization causes model neurons to yield Gaussian-distributed responses to natural stimuli, 1/f noise stimuli, and white noise stimuli. The current work makes recommendations for best practices and it lays a foundation, grounded in the response statistics to natural stimuli, upon which principled models of more complex visual tasks can be built.

scholarly journals Linking V1 Activity to Behavior

2018 ◽  
Vol 4 (1) ◽  
pp. 287-310
Author(s):  
Eyal Seidemann ◽  
Wilson S. Geisler
Keyword(s):  
Visual Cortex ◽  
Human Performance ◽  
Neural Responses ◽  
Conceptual Approach ◽  
Visual Neuroscience ◽  
V1 Neurons ◽  
Visual Tasks ◽  
Early Visual Cortex ◽  
The Moment ◽  
And Behavior

A long-term goal of visual neuroscience is to develop and test quantitative models that account for the moment-by-moment relationship between neural responses in early visual cortex and human performance in natural visual tasks. This review focuses on efforts to address this goal by measuring and perturbing the activity of primary visual cortex (V1) neurons while nonhuman primates perform demanding, well-controlled visual tasks. We start by describing a conceptual approach—the decoder linking model (DLM) framework—in which candidate decoding models take neural responses as input and generate predicted behavior as output. The ultimate goal in this framework is to find the actual decoder—the model that best predicts behavior from neural responses. We discuss key relevant properties of primate V1 and review current literature from the DLM perspective. We conclude by discussing major technological and theoretical advances that are likely to accelerate our understanding of the link between V1 activity and behavior.

Human Performance in Interpreting Robot-Generated 2d and 3d Maps

2018 ◽  
Vol 62 (1) ◽  
pp. 1272-1276
Author(s):  
A. N. Foots ◽  
J. M. Gregory ◽  
A. W. Evans ◽  
D. G. Baran ◽  
B. S. Perelman
Keyword(s):  
Autonomous Navigation ◽  
Human Performance ◽  
Cloud Data ◽  
Critical Technology ◽  
Visual Tasks ◽  
Within Subjects ◽  
Map Data ◽  
Computational Resources ◽  
2D And 3D ◽  
Story Building

Robust mapping capabilities are a critical technology for intelligent robotic systems. They can (1) provide valuable information to human and robot teammates without requiring prior knowledge or experience and (2) enable other, higher-level behaviors, such as autonomous navigation and exploration. To maximize interpretability, a map must be coherent, accurate, and displayed in an intuitive fashion. However, maps inherently require a large amount of computational resources. Therefore, it is beneficial to determine the minimum amount of information that must be provided to a user to meet the specific mission requirements. The purpose of this study is to evaluate human performance on visual tasks using 2D and 3D maps generated from laser point cloud data. In a within-subjects study, 20 participants were tasked with locating and identifying objects, doorways, and windows in a two-story building. The characterizations made herein could ultimately influence how map data from robotic assets are shared and displayed.

Sign in / Sign up

Export Citation Format

Share Document