scholarly journals A Turing test for crowds

2020 ◽  
Vol 7 (7) ◽  
pp. 200307
Author(s):  
Jamie Webster ◽  
Martyn Amos
Keyword(s):  
Urban Design ◽  
Human Perception ◽  
Simulation Models ◽  
Turing Test ◽  
Classification Performance ◽  
Minimal Set ◽  
Crowd Simulations ◽  
Student Volunteers ◽  
Human Crowd ◽  
Random Guessing

The accuracy and believability of crowd simulations underpins computational studies of human collective behaviour, with implications for urban design, policing, security and many other areas. Accuracy concerns the closeness of the fit between a simulation and observed data, and believability concerns the human perception of plausibility. In this paper, we address both issues via a so-called ‘Turing test’ for crowds, using movies generated from both accurate simulations and observations of real crowds. The fundamental question we ask is ‘Can human observers distinguish between real and simulated crowds?’ In two studies with student volunteers ( n = 384 and n = 156), we find that non-specialist individuals are able to reliably distinguish between real and simulated crowds when they are presented side-by-side, but they are unable to accurately classify them. Classification performance improves slightly when crowds are presented individually, but not enough to out-perform random guessing. We find that untrained individuals have an idealized view of human crowd behaviour which is inconsistent with observations of real crowds. Our results suggest a possible framework for establishing a minimal set of collective behaviours that should be integrated into the next generation of crowd simulation models.

scholarly journals Preparatory Experiments Regarding Human Brain Perception and Reasoning of Image Complexity for Synthetic Color Fractal and Natural Texture Images via EEG

2020 ◽  
Vol 11 (1) ◽  
pp. 164
Author(s):  
Irina E. Nicolae ◽  
Mihai Ivanovici
Keyword(s):  
Human Perception ◽  
Event Related Potentials ◽  
Classification Performance ◽  
Subjective Perception ◽  
Image Perception ◽  
Image Complexity ◽  
Temporal Area ◽  
Practical Applications ◽  
Related Potentials ◽  
Texture Complexity

Texture plays an important role in computer vision in expressing the characteristics of a surface. Texture complexity evaluation is important for relying not only on the mathematical properties of the digital image, but also on human perception. Human subjective perception verbally expressed is relative in time, since it can be influenced by a variety of internal or external factors, such as: Mood, tiredness, stress, noise surroundings, and so on, while closely capturing the thought processes would be more straightforward to human reasoning and perception. With the long-term goal of designing more reliable measures of perception which relate to the internal human neural processes taking place when an image is perceived, we firstly performed an electroencephalography experiment with eight healthy participants during color textural perception of natural and fractal images followed by reasoning on their complexity degree, against single color reference images. Aiming at more practical applications for easy use, we tested this entire setting with a WiFi 6 channels electroencephalography (EEG) system. The EEG responses are investigated in the temporal, spectral and spatial domains in order to assess human texture complexity perception, in comparison with both textural types. As an objective reference, the properties of the color textural images are expressed by two common image complexity metrics: Color entropy and color fractal dimension. We observed in the temporal domain, higher Event Related Potentials (ERPs) for fractal image perception, followed by the natural and one color images perception. We report good discriminations between perceptions in the parietal area over time and differences in the temporal area regarding the frequency domain, having good classification performance.

scholarly journals Subjectively Measured Streetscape Perceptions to Inform Urban Design Strategies for Shanghai

2021 ◽  
Vol 10 (8) ◽  
pp. 493
Author(s):  
Waishan Qiu ◽  
Wenjing Li ◽  
Xun Liu ◽  
Xiaokai Huang
Keyword(s):  
Urban Design ◽  
Design Theory ◽  
Human Perception ◽  
Image Features ◽  
Design Strategies ◽  
Subjective Measures ◽  
Explanatory Variables ◽  
Visual Elements ◽  
Urban Amenities ◽  
Human Perceptions

Recently, many new studies applying computer vision (CV) to street view imagery (SVI) datasets to objectively extract the view indices of various streetscape features such as trees to proxy urban scene qualities have emerged. However, human perception (e.g., imageability) have a subtle relationship to visual elements that cannot be fully captured using view indices. Conversely, subjective measures using survey and interview data explain human behaviors more. However, the effectiveness of integrating subjective measures with SVI datasets has been less discussed. To address this, we integrated crowdsourcing, CV, and machine learning (ML) to subjectively measure four important perceptions suggested by classical urban design theory. We first collected ratings from experts on sample SVIs regarding these four qualities, which became the training labels. CV segmentation was applied to SVI samples extracting streetscape view indices as the explanatory variables. We then trained ML models and achieved high accuracy in predicting scores. We found a strong correlation between the predicted complexity score and the density of urban amenities and services points of interest (POI), which validates the effectiveness of subjective measures. In addition, to test the generalizability of the proposed framework as well as to inform urban renewal strategies, we compared the measured qualities in Pudong to other five urban cores that are renowned worldwide. Rather than predicting perceptual scores directly from generic image features using a convolution neural network, our approach follows what urban design theory has suggested and confirmed as various streetscape features affecting multi-dimensional human perceptions. Therefore, the results provide more interpretable and actionable implications for policymakers and city planners.

HIGH ORDER STATISTICS FOR IMAGE CLASSIFICATION

2001 ◽  
Vol 11 (04) ◽  
pp. 371-377 ◽  
Author(s):  
ABDERRAHIM LABBI ◽  
HOLGER BOSCH ◽  
CHRISTIAN PELLEGRINI
Keyword(s):  
Image Classification ◽  
Principal Component ◽  
Classification Performance ◽  
Local Information ◽  
Minimal Set ◽  
Global Representation ◽  
Invariant Object Recognition ◽  
Band Pass ◽  
Independent Features ◽  
High Order Statistics

This paper addresses the problem of image classification using local information which is aggregated to provide global representation of different image classes. Local information is adaptively extracted from an image database using Independent Component Analysis (ICA) which provides a set of localized, oriented, and band-pass filters selective to independent features of the images. Local representation using ICA techniques has been previously investigated by several researchers. However, very little work has been done on further use of these representations to provide more complex and global description of images. In this paper, we present an algorithm which uses the energy of a minimal set of ICA filters to provide class-specific signatures which are shown to be strongly discriminant. Computer simulations are carried on two image databases, one consisting of five classes -referred to as categories- (buildings, rooms, mountains, forests and beaches) and one consisting of a set of 30 objects from multiple views for viewpoint invariant object recognition. The classification performance of the algorithm using both Independent and Principal Component Analyses are reported and discussed.

scholarly journals Usability Evaluation of Randomly Generated Fonts for Bubble Captcha

MENDEL ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 143-150
Author(s):  
Ondrej Bostik ◽  
Karel Horak ◽  
Jan Klecka
Keyword(s):  
Web Services ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Human Perception ◽  
Turing Test ◽  
Usability Evaluation ◽  
Wide Spread ◽  
Optical Character ◽  
Ongoing Work ◽  
The Web

A Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), is the wide-spread concept of systems suited to secure the web services from automated SPAM scripts. The most common CAPTCHA systems benefit from imperfections of Optical Character Recognition algorithms. This paper presents our ongoing work focused on the development of a new CAPTCHA scheme based on a human perception. The goal of this work is to evaluate the usability of randomly generated fonts used in Bubble Captcha scheme with both humans and OCR classifiers.

HUMAN PERCEPTION IN THE LIBYAN BUILT ENVIRONMENT: AL- KHUMS AND BANI WALID CITIES AS CASE STUDIES

2017 ◽  
Vol 11 (2) ◽  
pp. 157
Author(s):  
Fawzi Mohamed Agael ◽  
Özlem Özer
Keyword(s):  
Built Environment ◽  
Urban Design ◽  
Psychological Impact ◽  
Space Syntax ◽  
Human Perception ◽  
Clear Understanding ◽  
High Quality ◽  
Mental Map ◽  
The Impact ◽  
The City

This paper is concerned with the identification of different influences on the built environment, and those which have a physical and psychological impact on people. The aim of this study is to analyze the impact of the built environment on the lives of people. The interrelationship between people and built environment is based on human perception. This research will explore this relationship further in order to develop a clear understanding of the ways in which architecture may influence peoples’ perceptions and experiences. Additionally, the research entails a comparison between two important theories: the first is an Image of the city derived using the Mental Map Theory; the second is related to Space Syntax Theory. The two theories will be applied in two different cities in Libya with the aim of assessing the importance of their interrelationship and how it may be understood more clearly. This paper will also provide guidelines for improving urban design and planning standards with the end goal of producing a high quality perception by those who actually use the space. Moreover, it concludes with a number of research avenues that should be pursued to answer how the properties of built environment affect human perception.

scholarly journals Estimating Visibility of Annotations for View Management in Spatial Augmented Reality Based on Machine-Learning Techniques

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 939 ◽  
Author(s):  
Keita Ichihashi ◽  
Kaori Fujinami
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Mental Workload ◽  
Spatial Relationship ◽  
Human Perception ◽  
Physical Object ◽  
Classification Performance ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
View Management

Augmented Reality (AR) is a class of “mediated reality” that artificially modifies the human perception by superimposing virtual objects on the real world, which is expected to supplement reality. In visual-based augmentation, text and graphics, i.e., label, are often associated with a physical object or a place to describe it. View management in AR is to maintain the visibility of the associated information and plays an important role on communicating the information. Various view management techniques have been investigated so far; however, most of them have been designed for two dimensional see-through displays, and few have been investigated for projector-based AR called spatial AR. In this article, we propose a view management method for spatial AR, VisLP, that places labels and linkage lines based on the estimation of the visibility. Since the information is directly projected on objects, the nature of optics such as reflection and refraction constrains the visibility in addition to the spatial relationship between the information, the objects, and the user. VisLP employs machine-learning techniques to estimate the visibility that reflects human’s subjective mental workload in reading information and objective measures of reading correctness in various projection conditions. Four classes are defined for a label, while the visibility of a linkage line has three classes. After 88 and 28 classification features for label and linkage line visibility estimators are designed, respectively, subsets of features with 15 and 14 features are chosen to improve the processing speed of feature calculation up to 170%, with slight degradation of classification performance. An online experiment with new users and objects showed that 76.0% of the system’s judgments were matched with the users’ evaluations, while 73% of the linkage line visibility estimations were matched.

scholarly journals ELEMENTS OF URBAN DESIGN IN MODERN CITY ARCHITECTURAL PARTERRE FORMATION

World Science ◽  
2019 ◽  
Vol 1 (3(43)) ◽  
pp. 26-29
Author(s):  
Dubinskiy V. P. ◽  
Skorobohatko O. V.
Keyword(s):  
Urban Design ◽  
Human Perception ◽  
Urban Density ◽  
Design Elements ◽  
Modern City ◽  
Environmental Object

The article deals with the problem of architectural parterre as an environmental object in urban realm structure. Tendencies in urban building are described, resulting in architectural parterre formation. The rates of urban density and number of floors, where architectural parterre is formed, are provided. The description of 3-D elements, that are included in architectural parterre structure, is indicated. This article gives the analysis of architectural parterre human perception peculiarities. On the grounds of conducted analysis, the article presents urban design elements, by means of which the architectural-artistic appearance of architectural parterre is formed. The article makes a description of the urban design revealed elements and systematizes them.

Correlation of Pore Structure and Permeability by SEM-Image Analysis

1990 ◽  
Vol 48 (1) ◽  
pp. 428-429
Author(s):  
C. A. Callender ◽  
Wm. C. Dawson ◽  
J. J. Funk
Keyword(s):  
Image Analysis ◽  
Pore Structure ◽  
Imaging System ◽  
Pore Space ◽  
Simulation Models ◽  
Image Analyzer ◽  
Imaging Data ◽  
Sem Images ◽  
Thin Sections ◽  
Rock Types

The geometric structure of pore space in some carbonate rocks can be correlated with petrophysical measurements by quantitatively analyzing binaries generated from SEM images. Reservoirs with similar porosities can have markedly different permeabilities. Image analysis identifies which characteristics of a rock are responsible for the permeability differences. Imaging data can explain unusual fluid flow patterns which, in turn, can improve production simulation models.Analytical SchemeOur sample suite consists of 30 Middle East carbonates having porosities ranging from 21 to 28% and permeabilities from 92 to 2153 md. Engineering tests reveal the lack of a consistent (predictable) relationship between porosity and permeability (Fig. 1). Finely polished thin sections were studied petrographically to determine rock texture. The studied thin sections represent four petrographically distinct carbonate rock types ranging from compacted, poorly-sorted, dolomitized, intraclastic grainstones to well-sorted, foraminiferal,ooid, peloidal grainstones. The samples were analyzed for pore structure by a Tracor Northern 5500 IPP 5B/80 image analyzer and a 80386 microprocessor-based imaging system. Between 30 and 50 SEM-generated backscattered electron images (frames) were collected per thin section. Binaries were created from the gray level that represents the pore space. Calculated values were averaged and the data analyzed to determine which geological pore structure characteristics actually affect permeability.

Communication Recovery Groups for Persons with Aphasia: A Replicable Program for Medical and University Settings

2014 ◽  
Vol 24 (3) ◽  
pp. 106-113 ◽  
Author(s):  
Candace Vickers ◽  
Darla Hagge
Keyword(s):  
Medical Setting ◽  
Group Program ◽  
Intake Process ◽  
Student Volunteers ◽  
Group Members ◽  
University Settings ◽  
University Setting ◽  
Recovery Groups

This article describes Communication Recovery Groups (CRG), an aphasia group program that is sponsored by a medical setting and more recently a university setting. CRG's history and approach and its model of service in light of current healthcare challenges are summarized. The article also provides a detailed discussion regarding the logistics of offering conversation groups to persons with aphasia which are sponsored by medical and/or university settings, the intake process for new group members, and the training of student volunteers to help lead conversation groups.

The Sound of Enemies and Friends in the Neighborhood

2011 ◽  
Vol 58 (6) ◽  
pp. 454-463 ◽  
Author(s):  
Diane Pecher ◽  
Inge Boot ◽  
Saskia van Dantzig ◽  
Carol J. Madden ◽  
David E. Huber ◽  
...  
Keyword(s):  
Word Recognition ◽  
Visual Word Recognition ◽  
Neighborhood Effects ◽  
Classification Performance ◽  
Visual Word ◽  
Orthographic Neighborhood ◽  
Semantic Classification ◽  
Semantic Class ◽  
Orthographic Neighbors ◽  
Target Words

Previous studies (e.g., Pecher, Zeelenberg, & Wagenmakers, 2005) found that semantic classification performance is better for target words with orthographic neighbors that are mostly from the same semantic class (e.g., living) compared to target words with orthographic neighbors that are mostly from the opposite semantic class (e.g., nonliving). In the present study we investigated the contribution of phonology to orthographic neighborhood effects by comparing effects of phonologically congruent orthographic neighbors (book-hook) to phonologically incongruent orthographic neighbors (sand-wand). The prior presentation of a semantically congruent word produced larger effects on subsequent animacy decisions when the previously presented word was a phonologically congruent neighbor than when it was a phonologically incongruent neighbor. In a second experiment, performance differences between target words with versus without semantically congruent orthographic neighbors were larger if the orthographic neighbors were also phonologically congruent. These results support models of visual word recognition that assume an important role for phonology in cascaded access to meaning.

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