scholarly journals Computer Vision

2021 ◽  
Vol 9 (VII) ◽  
pp. 1237-1239
Author(s):  
Omkar Madhukar Deshmukh
Keyword(s):  
Computer Vision ◽  
Sensory System ◽  
Image Understanding ◽  
Scientific Field ◽  
High Dimensional ◽  
Thought Processes ◽  
Knowledge Domain ◽  
Nursing Knowledge ◽  
Pure Mathematics ◽  
High Level

Computer vision may be a field of computer science that trains computers to interpret and perceive the visual world. exploitation digital pictures from cameras and videos and deep learning models, machines will accurately determine and classify objects — and so react to what they "see.”. Computer vision is Associate in Nursing knowledge domain scientific field that deals with however computers will gain high-level understanding from digital pictures or videos. From the angle of engineering, it seeks to grasp and alter tasks that the human sensory system will do. Computer vision tasks embrace strategies for exploit, processing, analyzing and understanding digital pictures, and extraction of high-dimensional knowledge from the important world so as to supply numerical or symbolic info, e.g. within the styles of selections. Understanding during this context suggests that the transformation of visual pictures (the input of the retina) into descriptions of the planet that be to thought processes and might elicit acceptable action. This image understanding will be seen because the disentangling of symbolic info from image knowledge mistreatment models created with the help of pure mathematics, physics, statistics, and learning theory.

Digital Imaging Light Energy Saving Lamp Based On A Single Board Computer

2020 ◽  
Vol 4 (4) ◽  
pp. 751-756
Author(s):  
Hadid Tunas Bangsawan ◽  
Lukman Hanafi ◽  
Deny Suryana
Keyword(s):  
Computer Vision ◽  
Energy Saving ◽  
Direct Observation ◽  
Digital Imaging ◽  
Digital Images ◽  
Scientific Field ◽  
Single Board Computer ◽  
The Difference ◽  
High Level ◽  
Threshold Setting

Computer Vision (CV) is an interdisciplinary scientific field that discusses how computers can gain a high-level understanding of digital images or video. A system has been created that is capable of detecting a compact fluoresence lamp (CFL) light. However, in previous research there is no justification that the lamp is only a part that can glow on the lamp alone and has not been done in multi-lamp testing. This study aims to compare the lamp segmentation when it goes OFF and ON so that it could be justified the accuracy of this system and does multi-lamp testing. The method used is an experiment with collecting data by direct observation of the results of the system made. The system consists of a single board computer and a common webcam. The result is the difference between the lamp segmentation when it goes OFF and ON is small with the appropriate threshold setting. So that lamp light imaging had been made could function with good reability.  

scholarly journals Visual Behavior Based Bio-Inspired Polarization Techniques in Computer Vision and Robotics

2012 ◽  
pp. 243-272 ◽  
Author(s):  
Abd El Rahman Shabayek ◽  
Olivier Morel ◽  
David Fofi
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
Visual Perception ◽  
Polarization Vision ◽  
Visual Behavior ◽  
Biologically Inspired ◽  
Long Time ◽  
Comprehensive Survey ◽  
High Level ◽  
And Robotics

For long time, it was thought that the sensing of polarization by animals is invariably related to their behavior, such as navigation and orientation. Recently, it was found that polarization can be part of a high-level visual perception, permitting a wide area of vision applications. Polarization vision can be used for most tasks of color vision including object recognition, contrast enhancement, camouflage breaking, and signal detection and discrimination. The polarization based visual behavior found in the animal kingdom is briefly covered. Then, the authors go in depth with the bio-inspired applications based on polarization in computer vision and robotics. The aim is to have a comprehensive survey highlighting the key principles of polarization based techniques and how they are biologically inspired.

scholarly journals Distinction between the concepts of mathematical and logical modeling

2019 ◽  
Vol 19 (3) ◽  
pp. 262-267 ◽  
Author(s):  
E. N. Kolybenko
Keyword(s):  
Mathematical Modeling ◽  
Problem Solving ◽  
Practical Significance ◽  
Material Objects ◽  
Knowledge Domain ◽  
Organic Unity ◽  
Control Processes ◽  
Management Processes ◽  
Logical Modeling ◽  
High Level

Introduction. Technologies of mathematical and logical modeling of problem solving according to the existing practice of their distribution are divided into two areas: widespread mathematical modeling and infological modeling which is currently underdeveloped, especially for sophisticated systems. Fundamental differences between these technologies, in particular for the machining preproduction, are that logical modeling is informationally and logically related to organization systems, and mathematical modeling is associated with control processes in the organization systems. Logical modeling is used to operate with geometric objects in the technological schemes of their interaction through basing methods, geometric shaping in a static (ideal) setting of the corresponding schemes. Mathematical simulation is used to operate material objects in the control processes of their transformations through cutting methods, i.e. imperfectly, considering heterogeneous errors. Between the organization systems under study and management processes in them, there are information and logical links of their organic unity, which deny their separate consideration. In the information deterministic technology for solving problems of a high-level automation, the distinction between the concepts of “mathematical” and “logical” modeling is relevant; it has scientific novelty and practical significance.Materials and Methods. To characterize the properties of the concepts of “mathematical modeling”, “logical modeling” and the knowledge functions resulting from the formulation of these concepts, fundamentally different methods and appropriate tools are used. The differentiation of the concepts under consideration is based on the differentiation of technologies (methods, appropriate tools, algorithms, operations) for solving applied problems of any knowledge domain.Research Results. The ideas of “logical modeling” and “mathematical modeling” are conceptual general-theoretical notions with invariant properties required for solving practical problems of any application domain. In accordance with the distinction between these concepts, the problem solving technologies are divided into two types: system engineering technology – in the organization of information object systems, and system science – in the management processes of transformation of the corresponding material objects. These areas should exist in the information and logical link of their organic unity.Discussion and Conclusions. The author distinguishes between the concepts of “logical modeling” and “mathematical modeling”, which is a key condition for a successful transition to the deterministic information technology of a high-level automation in solving practical problems of any knowledge domain, for example, of the production design machining

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.

Object Recognition with a Limited Database Using Shape Space Theory

2012 ◽  
pp. 128-147
Author(s):  
Yuexing Han ◽  
Bing Wang ◽  
Hideki Koike ◽  
Masanori Idesawa
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
Shape Space ◽  
Image Understanding ◽  
Data Models ◽  
Hard Work ◽  
Space Theory ◽  
Single Object ◽  
Intermediate Data ◽  
Computer Vision Applications

One of the main goals of image understanding and computer vision applications is to recognize an object from various images. Object recognition has been deeply developed for the last three decades, and a lot of approaches have been proposed. Generally, these methods of object recognition can successfully achieve their goal by relying on a large quantity of data. However, if the observed objects are shown to diverse configurations, it is difficult to recognize them with a limited database. One has to prepare enough data to exactly recognize one object with multi-configurations, and it is hard work to collect enough data only for a single object. In this chapter, the authors will introduce an approach to recognize objects with multi-configurations using the shape space theory. Firstly, two sets of landmarks are obtained from two objects in two-dimensional images. Secondly, the landmarks represented as two points are projected into a pre-shape space. Then, a series of new intermediate data can be obtained from data models in the pre-shape space. Finally, object recognition can be achieved in the shape space with the shape space theory.

scholarly journals The polar sea ice topography reconstruction system

2020 ◽  
Vol 61 (82) ◽  
pp. 127-138
Author(s):  
Scott Sorensen ◽  
Vinit Veerendraveer ◽  
Wayne Treible ◽  
Andrew R. Mahoney ◽  
Chandra Kambhamettu
Keyword(s):  
Computer Vision ◽  
Sea Ice ◽  
The Arctic ◽  
Polar Regions ◽  
Camera System ◽  
Melt Pond ◽  
Camera Systems ◽  
Ice Concentration ◽  
Level Information ◽  
High Level

AbstractThe Polar Sea Ice Topography REconstruction System, or PSITRES, is a 3D camera system designed to continuously monitor an area of ice and water adjacent to an ice-going vessel. Camera systems aboard ships in the polar regions are common; however, the application of computer vision techniques to extract high-level information from the imagery is infrequent. Many of the existing systems are built for human involvement throughout the process and lack automation necessary for round the clock use. The PSITRES was designed with computer vision in mind. It can capture images continuously for days on end with limited oversight. We have applied the system in different ice observing scenarios. The PSITRES was deployed on three research expeditions in the Arctic and Subarctic, and we present applications in measuring ice concentration, melt pond fraction and presence of algae. Systems like PSITRES and the computer vision algorithms applied represent steps toward automatically observing, evaluating and analyzing ice and the environment around ships in ice-covered waters.

Design by a CBIR System Supporting High Level Concepts

2011 ◽  
pp. 259-268
Author(s):  
M. V. Ramakrishna ◽  
S. Nepal ◽  
S. Sumanasekara ◽  
S. M.M. Tahaghoghi
Keyword(s):  
Similarity Measures ◽  
Linear Mapping ◽  
Content Based Image Retrieval ◽  
High Dimensional ◽  
Test Bed ◽  
Development Activity ◽  
Indexing Structure ◽  
Level Data ◽  
High Dimensional Indexing ◽  
High Level

Content Based Image Retrieval (CBIR) systems that are able to “retrieve images of Clinton with Lewinsky” are unrealistic at present. However, this area has seen much research and development activity since IBM’s QBIC announcement in 1994. The CHITRA CBIR system under development at the RMIT and Monash Universities, addresses the need for a test bed system. Users can dynamically incorporate new features and similarity measures in to the system, enabling it to act as a testbed for CBIR research. The system uses a 4-level data model we have developed and supports definition and querying of high level concepts such as MOUNTAIN and SUNSET. These advanced capabilities are supported by a powerful graphical query mechanism and a high-dimensional indexing structure based on linear mapping. In this paper we describe the design of the system, our contributions to the state of the art and provide some implementation details.

scholarly journals Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ritaban Dutta ◽  
Cherry Chen ◽  
David Renshaw ◽  
Daniel Liang
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Shape Memory ◽  
Video Data ◽  
The Body ◽  
Restricted Boltzmann Machine ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Boltzmann Machine ◽  
High Level

AbstractExtraordinary shape recovery capabilities of shape memory alloys (SMAs) have made them a crucial building block for the development of next-generation soft robotic systems and associated cognitive robotic controllers. In this study we desired to determine whether combining video data analysis techniques with machine learning techniques could develop a computer vision based predictive system to accurately predict force generated by the movement of a SMA body that is capable of a multi-point actuation performance. We identified that rapid video capture of the bending movements of a SMA body while undergoing external electrical excitements and adapting that characterisation using computer vision approach into a machine learning model, can accurately predict the amount of actuation force generated by the body. This is a fundamental area for achieving a superior control of the actuation of SMA bodies. We demonstrate that a supervised machine learning framework trained with Restricted Boltzmann Machine (RBM) inspired features extracted from 45,000 digital thermal infrared video frames captured during excitement of various SMA shapes, is capable to estimate and predict force and stress with 93% global accuracy with very low false negatives and high level of predictive generalisation.

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