scholarly journals Context-Aware Superpixel and Bilateral Entropy—Image Coherence Induces Less Entropy

Entropy ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 20
Author(s):  
Feihong Liu ◽  
Xiao Zhang ◽  
Hongyu Wang ◽  
Jun Feng
Keyword(s):  
Computer Vision ◽  
Context Aware ◽  
Conditional Intensity ◽  
Top Down ◽  
Superpixel Segmentation ◽  
Image Partition ◽  
Gestalt Grouping ◽  
Underlying Mechanisms ◽  
Encoding Efficiency ◽  
Local Image

Superpixel clustering is one of the most popular computer vision techniques that aggregates coherent pixels into perceptually meaningful groups, taking inspiration from Gestalt grouping rules. However, due to brain complexity, the underlying mechanisms of such perceptual rules are unclear. Thus, conventional superpixel methods do not completely follow them and merely generate a flat image partition rather than hierarchical ones like a human does. In addition, those methods need to initialize the total number of superpixels, which may not suit diverse images. In this paper, we first propose context-aware superpixel (CASP) that follows both Gestalt grouping rules and the top-down hierarchical principle. Thus, CASP enables to adapt the total number of superpixels to specific images automatically. Next, we propose bilateral entropy, with two aspects conditional intensity entropy and spatial occupation entropy, to evaluate the encoding efficiency of image coherence. Extensive experiments demonstrate CASP achieves better superpixel segmentation performance and less entropy than baseline methods. More than that, using Pearson’s correlation coefficient, a collection of data with a total of 120 samples demonstrates a strong correlation between local image coherence and superpixel segmentation performance. Our results inversely support the reliability of above-mentioned perceptual rules, and eventually, we suggest designing novel entropy criteria to test the encoding efficiency of more complex patterns.

Learning human poses in natural scenes

2018 ◽  
Author(s):  
◽  
Guanghan Ning
Keyword(s):  
Computer Vision ◽  
Pose Estimation ◽  
The Body ◽  
Human Pose Estimation ◽  
Natural Scenes ◽  
Top Down ◽  
University Of Missouri ◽  
Single Person ◽  
Human Pose ◽  
High Level

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The task of human pose estimation in natural scenes is to determine the precise pixel locations of body keypoints. It is very important for many high-level computer vision tasks, including action and activity recognition, human-computer interaction, motion capture, and animation. We cover two different approaches for this task: top-down approach and bottom-up approach. In the top-down approach, we propose a human tracking method called ROLO that localizes each person. We then propose a state-of-the-art single-person human pose estimator that predicts the body keypoints of each individual. In the bottomup approach, we propose an efficient multi-person pose estimator with which we participated in a PoseTrack challenge [11]. On top of these, we propose to employ adversarial training to further boost the performance of single-person human pose estimator while generating synthetic images. We also propose a novel PoSeg network that jointly estimates the multi-person human poses and semantically segment the portraits of these persons at pixel-level. Lastly, we extend some of the proposed methods on human pose estimation and portrait segmentation to the task of human parsing, a more finegrained computer vision perception of humans.

Locative-Media Artists in the Contested-Aware City

Leonardo ◽  
2006 ◽  
Vol 39 (4) ◽  
pp. 345-347 ◽  
Author(s):  
Anthony Townsend
Keyword(s):  
Collective Action ◽  
Mobile Devices ◽  
21St Century ◽  
Command And Control ◽  
Public Spaces ◽  
Context Aware ◽  
Top Down ◽  
Locative Media ◽  
Context Aware Computing ◽  
And Control

The adoption of mobile devices as the computers of the 21st century marks a shift away from the fixed terminals that dominated the first 50 years of computing. Associated with this shift will be a new emphasis on context-aware computing. This article examines design approaches to context-aware computing and argues that the evolution of this technology will be characterized by an interplay between top-down systems for command and control and bottom-up systems for collective action. This process will lead to the emergence of “contested-aware cities,” in which power struggles are waged in public spaces with the assistance of context-aware systems.

Sensor fusion and computer vision for context-aware control of a multi degree-of-freedom prosthesis

2015 ◽  
Vol 12 (6) ◽  
pp. 066022 ◽  
Author(s):  
Marko Markovic ◽  
Strahinja Dosen ◽  
Dejan Popovic ◽  
Bernhard Graimann ◽  
Dario Farina
Keyword(s):  
Computer Vision ◽  
Sensor Fusion ◽  
Degree Of Freedom ◽  
Context Aware

scholarly journals Biologically Inspired Visual System Architecture for Object Recognition in Autonomous Systems

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 167 ◽  
Author(s):  
Dan Malowany ◽  
Hugo Guterman
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Object Recognition ◽  
Visual System ◽  
Human Visual System ◽  
State Of The Art ◽  
Top Down ◽  
Feed Forward ◽  
Recognition Process ◽  
The World

Computer vision is currently one of the most exciting and rapidly evolving fields of science, which affects numerous industries. Research and development breakthroughs, mainly in the field of convolutional neural networks (CNNs), opened the way to unprecedented sensitivity and precision in object detection and recognition tasks. Nevertheless, the findings in recent years on the sensitivity of neural networks to additive noise, light conditions, and to the wholeness of the training dataset, indicate that this technology still lacks the robustness needed for the autonomous robotic industry. In an attempt to bring computer vision algorithms closer to the capabilities of a human operator, the mechanisms of the human visual system was analyzed in this work. Recent studies show that the mechanisms behind the recognition process in the human brain include continuous generation of predictions based on prior knowledge of the world. These predictions enable rapid generation of contextual hypotheses that bias the outcome of the recognition process. This mechanism is especially advantageous in situations of uncertainty, when visual input is ambiguous. In addition, the human visual system continuously updates its knowledge about the world based on the gaps between its prediction and the visual feedback. CNNs are feed forward in nature and lack such top-down contextual attenuation mechanisms. As a result, although they process massive amounts of visual information during their operation, the information is not transformed into knowledge that can be used to generate contextual predictions and improve their performance. In this work, an architecture was designed that aims to integrate the concepts behind the top-down prediction and learning processes of the human visual system with the state-of-the-art bottom-up object recognition models, e.g., deep CNNs. The work focuses on two mechanisms of the human visual system: anticipation-driven perception and reinforcement-driven learning. Imitating these top-down mechanisms, together with the state-of-the-art bottom-up feed-forward algorithms, resulted in an accurate, robust, and continuously improving target recognition model.

Team coordination and organizational routines: bottoms up – and top down

2016 ◽  
Vol 54 (5) ◽  
pp. 1059-1072 ◽  
Author(s):  
George Christopher Banks ◽  
Jeffrey M. Pollack ◽  
Anson Seers
Keyword(s):  
Point Of View ◽  
Theory And Practice ◽  
Top Down ◽  
Bottom Up ◽  
Team Coordination ◽  
Content Type ◽  
Relational Processes ◽  
Underlying Mechanisms ◽  
Work Coordination ◽  
Explanatory Mechanism

Purpose – Conceptualizations of work coordination historically assumed that work systems are put into place and that these systems shape the ability of workers to accomplish tasks. Formalization has thus long been invoked as an explanatory mechanism for work coordination. Recent studies have extended interest in emergent implicit and relational coordination, yet their underlying mechanisms of bottom-up coordination have yet to be explicated such that formal top-down coordination can be approached as a complementary mechanism rather than an alternative substitute. The purpose of this paper is to integrate the literatures related to coordination and routines, and extend analysis of bottom-up coordination toward an understanding of how it can be complemented by top-down formalized coordination of routines within organizations. Implications of this work, for both theory and practice, are discussed. Design/methodology/approach – A conceptual review was conducted. Findings – By integrating the literatures related to coordination and routines, the authors extend analysis of bottom-up coordination toward an understanding of how it can be complemented by top-down formalized coordination of routines within organizations. Research limitations/implications – From a theory-based point of view, in the present work, the authors integrated the literatures related to coordination and routines and arrived at the conclusion that bottom-up coordination can be complemented by top-down formalized coordination of routines within organizations. Practical implications – The authors suggest that there is a need in the contemporary workplace for implicit, relational processes to enable individuals to continuously assess what changes are needed and adapt coordinated routines to accomplish the task at hand. This propensity will continue to increase as technology facilitates even more seamless communication among employees, organizations, and external partners. Originality/value – For the first time the authors integrate the literatures related to coordination and routines, in order to extend analysis of bottom-up coordination toward an understanding of how it can be complemented by top-down formalized coordination of routines within organizations.

scholarly journals Assessment of locomotion in chlorine exposed mice by computer vision and neural networks

2012 ◽  
Vol 112 (6) ◽  
pp. 1064-1072 ◽  
Author(s):  
Aristotelis S. Filippidis ◽  
Sotirios G. Zarogiannis ◽  
Alan Randich ◽  
Timothy J. Ness ◽  
Sadis Matalon
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Two Dimensions ◽  
Analgesic Agent ◽  
Network Algorithms ◽  
Novel Method ◽  
Noxious Stimuli ◽  
Underlying Mechanisms

Assessment of locomotion following exposure of animals to noxious or painful stimuli can offer significant insights into underlying mechanisms of injury and the effectiveness of various treatments. We developed a novel method to track the movement of mice in two dimensions using computer vision and neural network algorithms. By using this system we demonstrated that mice exposed to chlorine (Cl2) gas developed impaired locomotion and increased immobility for up to 9 h postexposure. Postexposure administration of buprenorphine, a common analgesic agent, increased locomotion and decreased immobility times in Cl2- but not air-exposed mice, most likely by decreasing Cl2-induced pain. This method can be adapted to assess the effectiveness of various therapies following exposure to a variety of chemical and behavioral noxious stimuli.

scholarly journals COMPARATIVE ANALYSIS OF SUPERPIXEL SEGMENTATION METHODS

2020 ◽  
Vol 5 (3) ◽  
pp. 1-9
Author(s):  
Sumit Kaur ◽  
R.K Bansal
Keyword(s):  
Computer Vision ◽  
Comparative Analysis ◽  
Computational Complexity ◽  
Point Of View ◽  
Computational Time ◽  
Time Consumption ◽  
Superpixel Segmentation ◽  
Segmentation Methods ◽  
Computer Vision Applications ◽  
High Computational Complexity

Superpixel segmentation showed to be a useful preprocessing step in many computer vision applications. Superpixel’s purpose is to reduce the redundancy in the image and increase efficiency from the point of view of the next processing task. This led to a variety of algorithms to compute superpixel segmentations, each with individual strengths and weaknesses. Many methods for the computation of superpixels were already presented. A drawback of most of these methods is their high computational complexity and hence high computational time consumption. K mean based SLIC method shows better performance as compare to other while evaluating on the bases of under segmentation error and boundary recall, etc parameters.

scholarly journals A Search Advantage for Horizontal Targets in Dynamic Displays

i-Perception ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 204166952110046
Author(s):  
Ian M. Thornton ◽  
Quoc C. Vuong ◽  
Karin S. Pilz
Keyword(s):  
Visual Search ◽  
Change Detection ◽  
Visual Processing ◽  
Display Size ◽  
Top Down ◽  
Future Studies ◽  
Strong Orientation ◽  
Dynamic Target ◽  
Underlying Mechanisms ◽  
Lines Of Evidence

Several lines of evidence point to the existence of a visual processing advantage for horizontal over vertical orientations. We investigated whether such a horizontal advantage exists in the context of top-down visual search. Inspired by change detection studies, we created displays where a dynamic target -- a horizontal or a vertical group of five dots that changed contrast synchronously -- was embedded within a randomly flickering grid of dots. The display size (total dots) varied across trials, and the orientation of the target was constant within interleaved blocks. As expected, search was slow and inefficient. Importantly, participants were almost a second faster finding horizontal compared to vertical targets. They were also more efficient and more accurate during horizontal search. Such findings establish that the attentional templates thought to guide search for known targets can exhibit strong orientation anisotropies. We discuss possible underlying mechanisms and how these might be explored in future studies.

Design and Development of a Prototype Heads-Up Display: Supporting Context-Aware, Semi-Automated, Hands-Free Medical Documentation

2021 ◽  
Vol 10 (1) ◽  
pp. 18-22
Author(s):  
Nicolette M. McGeorge ◽  
Susan Latiff ◽  
Christopher Muller Lucas Dong ◽  
Ceara Chewning ◽  
Daniela Friedson-Trujillo ◽  
...  
Keyword(s):  
Computer Vision ◽  
User Interfaces ◽  
Medical Information ◽  
Critical Role ◽  
Data Capture ◽  
Multimodal Interfaces ◽  
Medical Documentation ◽  
Future Research ◽  
Context Aware ◽  
Coordinated Care

Military and civilian medical personnel across all echelons of medical care play a critical role in evaluating, caring for, and treating casualties. Accurate medical documentation is critical to effective, coordinated care and positive patient outcomes. We describe our prototype, Context-Aware Procedure Support Tools and User Interfaces for Rapid and Effective Workflows (CAPTURE). Leveraging human factors and usercentered design methods, and advanced artificial intelligence and computer vision capabilities, CAPTURE was designed to enable Tactical Combat Causality Care (TCCC) providers to more efficiently and effectively input critical medical information through hands-free interaction techniques and semiautomated data capture methods. We designed and prototyped a heads-up display that incorporates: multimodal interfaces, including augmented reality-based methods for input and information display to support visual image capture and heads-up interaction; post-care documentation support (e.g., artifacts to support post-care review and documentation); context-aware active and passive data capture methods, specifically natural language interpretation using systemic functional grammars; and computer vision technologies for semi-automated data capture capabilities. During the course of this project we encountered challenges towards effective design which fall into three main categories: (1) challenges related to designing novel multimodal interfaces; (2) technical challenges related to software and hardware development to meet design needs; and (3) challenges as a result of domain characteristics and operational constraints. We discuss how we addressed some of these challenges and provide additional considerations necessary for future research regarding next generation technology design for medical documentation in the field.

scholarly journals Facilitating animacy perception by manipulating relative visual attention

2022 ◽  
Author(s):  
Toshiki Saito ◽  
Kosuke Motoki ◽  
Rui Nouchi ◽  
Motoaki Sugiura
Keyword(s):  
Visual Attention ◽  
Active Role ◽  
The Other ◽  
Potential Danger ◽  
Top Down ◽  
The Face ◽  
Reverse Relationship ◽  
Underlying Mechanisms ◽  
Study Participants

Animacy perception—discriminating between animate and inanimate visual stimuli—is the basis for engaging in social cognition and for our survival (e.g. avoiding potential danger). Previous studies indicate that bottom-up factors, such as the features or motion of a target, enhance animacy perception. However, top-down factors such as elements in perceivers have received little attention. Research on judgment, decision-making, and neuroeconomics indicate the active role of visual attention in constructing decisions. This study examined the role of visual attention in the perception of animacy by manipulating the relative visual attention to targets. Among Studies 1a to 1c conducted in this study, participants saw two face illustrations alternately; one of the faces was shown to be longer than the other. The participants chose the face that they considered more animated and rounder. Consequently, longer visual attention towards targets facilitated animacy perception and preference rather than the perception of roundness. Furthermore, pre-registered Study 2 examined the underlying mechanisms. The results suggest that mere exposure, rather than orienting behaviour, might play a key role in the perception of animacy. These results suggest that in the reverse relationship between attention and animacy perception, animate objects capture attention, and attention results in the perception of animacy.

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