scholarly journals A Plane Extraction Approach in Inverse Depth Images Based on Region-Growing

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1141
Author(s):  
Xiaoning Han ◽  
Xiaohui Wang ◽  
Yuquan Leng ◽  
Weijia Zhou
Keyword(s):  
Computer Vision ◽  
Region Growing ◽  
Extraction Methods ◽  
Vital Role ◽  
Practical Applications ◽  
Depth Images ◽  
Indoor Scenes ◽  
Plane Extraction ◽  
Public Datasets ◽  
And Robotics

Planar surfaces are prevalent components of man-made indoor scenes, and plane extraction plays a vital role in practical applications of computer vision and robotics, such as scene understanding, and mobile manipulation. Nowadays, most plane extraction methods are based on reconstruction of the scene. In this paper, plane representation is formulated in inverse-depth images. Based on this representation, we explored the potential to extract planes in images directly. A fast plane extraction approach, which employs the region growing algorithm in inverse-depth images, is presented. This approach consists of two main components: seeding, and region growing. In the seeding component, seeds are carefully selected locally in grid cells to improve exploration efficiency. After seeding, each seed begins to grow into a continuous plane in succession. Both greedy policy and a normal coherence check are employed to find boundaries accurately. During growth, neighbor coplanar planes are checked and merged to overcome the over-segmentation problem. Through experiments on public datasets and generated saw-tooth images, the proposed approach achieves 80.2% CDR (Correct Detection Rate) on the ABW SegComp Dataset, which has proven that it has comparable performance with the state-of-the-art. The proposed approach runs at 5 Hz on typical 680 × 480 images, which has shown its potential in real-time practical applications in computer vision and robotics with further improvement.

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

Computer Vision and robotics in postal automation

1999 ◽  
Vol 18 (3-4) ◽  
pp. 265-273
Author(s):  
Giovanni B. Garibotto
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Material Handling ◽  
State Of The Art ◽  
Short Description ◽  
The Other ◽  
Functional Requirements ◽  
Postal Automation ◽  
And Robotics

The paper is intended to provide an overview of advanced robotic technologies within the context of Postal Automation services. The main functional requirements of the application are briefly referred, as well as the state of the art and new emerging solutions. Image Processing and Pattern Recognition have always played a fundamental role in Address Interpretation and Mail sorting and the new challenging objective is now off-line handwritten cursive recognition, in order to be able to handle all kind of addresses in a uniform way. On the other hand, advanced electromechanical and robotic solutions are extremely important to solve the problems of mail storage, transportation and distribution, as well as for material handling and logistics. Finally a short description of new services of Postal Automation is referred, by considering new emerging services of hybrid mail and paper to electronic conversion.

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199691
Author(s):  
Omar AlShorman ◽  
Fahad Alkahatni ◽  
Mahmoud Masadeh ◽  
Muhammad Irfan ◽  
Adam Glowacz ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Induction Motor ◽  
Vital Role ◽  
Remaining Useful Life ◽  
Useful Life ◽  
Review Study ◽  
Public Datasets ◽  
Fatigue Detection ◽  
Types Of Faults

Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating machinery (RM) has a vital role in the modern industrial world. However, the remaining useful life (RUL) of machinery is crucial for continuous monitoring and timely maintenance. Moreover, reduced maintenance costs, enhanced safety, efficiency, reliability, and availability are the main important industrial issues to maintain valuable and high-cost machinery. Undoubtedly, induction motor (IM) is considered to be a pivotal component in industrial machines. Recently, acoustic emission (AE) becomes a very accurate and efficient method for fault, leaks and fatigue detection and monitoring techniques. Moreover, CM and FD based on the AE of IM have been growing over recent years. The proposed research study aims to review condition monitoring (CM) and fault diagnosis (FD) studies based on sound and AE for four types of faults: bearings, rotor, stator, and compound. The study also points out the advantages and limitations of using sound and AE analysis in CM and FD. Existing public datasets for AE based analysis for CM and FD of IM are also mentioned. Finally, challenges facing AE based CM and FD for RM, especially for IM, and possible future works are addressed in this study.

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 Implementing Bloom’s Taxonomy Tool for Better Learning Outcomes of PLC and Robotics Course

2020 ◽  
Vol 15 (05) ◽  
pp. 184
Author(s):  
Madhuri Gummineni
Keyword(s):  
Learning Outcomes ◽  
Classroom Environment ◽  
Bloom's Taxonomy ◽  
Practical Experience ◽  
Vital Role ◽  
Real World Application ◽  
Bloom’S Taxonomy ◽  
Prerequisite Knowledge ◽  
Signal Interface ◽  
And Robotics

Instrumentation subject has a major and vital role in the industrial field. The concepts of sensors, actuators, signal interface, and conditioning, programming the microprocessor and microcontroller are the most important requisites to comprehend and contribute to the real-world application. The application of these concepts is PLC and Robotics course where the students can apply and practical experience the output. To design a project and to implement we need multidisciplinary concepts and sequence of steps viz., defining an idea, requirements and the fabricating parts to bring out a visual structure in order to perform an intended function. To inculcate this culture it’s much more important to follow and implement the standard and well-known methodology called Bloom’s taxonomy in the classroom environment for a better outcome of the course. Current leading technology PLC and Robotics course, (which require prerequisite knowledge of courses like Instrumentation, Microprocessors, Mechatronics) are very well connected for applying the gained concepts to continue the stream of the learning process. The paper presents how to bring better learning Outcomes and also create interest in the course PLC and Robotics by implementing Bloom’s taxonomy by conducting activities in the classroom.

scholarly journals Vector Autoregressive Weighting Reversion Strategy for Online Portfolio Selection

2020 ◽  
Author(s):  
Xia Cai
Keyword(s):  
Portfolio Selection ◽  
Moving Average ◽  
Time Series Prediction ◽  
Vector Autoregressive ◽  
Practical Applications ◽  
Public Datasets ◽  
Dynamic Relationships ◽  
Online Portfolio ◽  
Online Portfolio Selection ◽  
Sharp Ratio

Aiming to improve the performance of existing reversion based online portfolio selection strategies, we propose a novel multi-period strategy named “Vector Autoregressive Weighting Reversion” (VAWR). Firstly, vector autoregressive moving-average algorithm used in time series prediction is transformed into exploring the dynamic relationships between different assets for more accurate price prediction. Secondly, we design the modified online passive aggressive technique and advance a scheme to weigh investment risk and cumulative experience to update the closed-form of portfolio. Theoretical analysis and experimental results confirm the effectiveness and robustness of our strategy. Compared with the state-of-the-art strategies, VAWR greatly increases cumulative wealth, and it obtains the highest annualized percentage yield and sharp ratio on various public datasets. These improvements and easy implementation support the practical applications of VAWR.

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