Object identification in images acquired through underwater turbulent media

2018 ◽  
Author(s):  
Md Hasan Furhad ◽  
Murat Tahtali ◽  
Andrew Lambert
Keyword(s):  
Object Identification

Editorial

2017 ◽  
Author(s):  
Vu Chi Kien ◽  
Do Ngoc Minh ◽  
Nguyen Hoang Ha ◽  
Nguyen Linh Trung
Keyword(s):  
Good Practice ◽  
Scientific Publication ◽  
Associate Editor ◽  
Open Access Journal ◽  
Object Identification ◽  
Online Journal ◽  
International Visibility ◽  
National University ◽  
Standard Practices

Dear readers,The year 2017 marks the 55th anniversary of the Journal of Information & Communications of the Ministry of Information and Communications, and the 18th anniversary of its scientific publication – the Research and Development on Information and Communication Technology (RD-ICT) journal. Again, the purpose of RD-ICT is to provide a forum for researchers and professionals to disseminate original and innovative ideas in the fields of information technology, communications and electronics in Vietnam and worldwide.Without kind support and invaluable contribution of readers and authors, and hard work of the anonymous reviewers and editors under the former editorship of Prof. Nguyễn Thúc Hải, Prof. Trần Văn Lộc and Prof. Nguyễn Cảnh Tuấn, RD-ICT would not be what it is today – a total of 37 issues in Vietnamese and 14 issues in English.To contribute to the development of research in Vietnam, toward standard practices, high quality and international visibility, RD-ICT has been taking measures by following current practices of prestigious international research journals. In this editorial, we would like to inform you some of the things we have been doing lately.Since June 2014, RD-ICT has applied online journal management and publishing, thanks to the well-known open-source Open Journal System of the Public Knowledge Project, which is used by thousands of online scientific journals worldwide. The editorial board of RD-ICT is currently being extended to include international prominent scientists, thus forming a team of international associate editors, under the complementary technical editorship of Prof. Đỗ Ngọc Minh (University of Illinois at Urbana-Champaign, United States), Prof. Nguyễn Hoàng Hà (University of Saskatchewan, Canada) and Prof. Nguyễn Linh Trung (Vietnam National University, Hanoi). Each submission is now assigned to an associate editor who then coordinates the review process and makes editorial decision.For improved paper quality in terms of organization and presentation, authors are guided to good practice of technical paper writing. In addition, accepted submissions are now copy-edited, by the corresponding associate editors, and laid out using LATEX.Apart from already being an open-access journal, RD-ICT is also looking into other measures to increase its visibility, such as all-English publishing, digital object identification, Google Scholar citation, and SCOPUS indexing.Taking the opportunity of informing the above changes, we would like to, again, express our sincere gratitude and appreciation to the readers, authors, reviewers and editors of RD-ICT, and to the leadership of the Ministry of Information and Communications and its predecessors – the Directorate General of Posts and Telecommunications, the Ministry of Post and Telecommunications – for their continued support and contribution to RD-ICT.We look forward to your comments and feedback for better developing the RD-ICT journal for Vietnam.Sincerely,Vũ Chí Kiên, Editor-in-ChiefĐỗ Ngọc Minh, Nguyễn Hoàng Hà, Nguyễn Linh Trung, Technical Editors-in-Chief

ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Feature Detection ◽  
Video Stream ◽  
Object Identification ◽  
Vision Systems ◽  
Modern Computer ◽  
Computer Vision Systems ◽  
Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

Symbolic Representation Based Approach for Object Identification in Infrared Images

2017 ◽  
Vol 9 (3) ◽  
pp. 235-240
Author(s):  
Shimoga N. B. Bhushan ◽  
. Harisha ◽  
Arti Pawar ◽  
. Vidyalakshmi
Keyword(s):  
Symbolic Representation ◽  
Object Identification ◽  
Infrared Images

scholarly journals VAGADRONE: Intelligent and Fully Automatic Drone Based on Raspberry Pi and Android

2021 ◽  
Vol 11 (7) ◽  
pp. 3153
Author(s):  
Saifeddine Benhadhria ◽  
Mohamed Mansouri ◽  
Ameni Benkhlifa ◽  
Imed Gharbi ◽  
Nadhem Jlili
Keyword(s):  
Remote Control ◽  
Autonomous Navigation ◽  
Object Identification ◽  
External Control ◽  
Raspberry Pi ◽  
Optimal Trajectories ◽  
Live Streaming ◽  
Wide Range ◽  
Fully Automatic ◽  
Direct Use

Multirotor drones are widely used currently in several areas of life. Their suitable size and the tasks that they can perform are their main advantages. However, to the best of our knowledge, they must be controlled via remote control to fly from one point to another, and they can only be used for a specific mission (tracking, searching, computing, and so on). In this paper, we intend to present an autonomous UAV based on Raspberry Pi and Android. Android offers a wide range of applications for direct use by the UAV depending on the context of the assigned mission. The applications cover a large number of areas such as object identification, facial recognition, and counting objects such as panels, people, and so on. In addition, the proposed UAV calculates optimal trajectories, provides autonomous navigation without external control, detects obstacles, and ensures live streaming during the mission. Experiments are carried out to test the above-mentioned criteria.

scholarly journals A Methodology for Utilizing Vector Space to Improve the Performance of a Dog Face Identification Model

2021 ◽  
Vol 11 (5) ◽  
pp. 2074
Author(s):  
Bohan Yoon ◽  
Hyeonji So ◽  
Jongtae Rhee
Keyword(s):  
Vector Space ◽  
Object Identification ◽  
Face Identification ◽  
Learning Method ◽  
Open Set ◽  
L2 Norm ◽  
The Face ◽  
Triplet Loss ◽  
Animal Faces ◽  
Identification Model

Recent improvements in the performance of the human face recognition model have led to the development of relevant products and services. However, research in the similar field of animal face identification has remained relatively limited due to the greater diversity and complexity in shape and the lack of relevant data for animal faces such as dogs. In the face identification model using triplet loss, the length of the embedding vector is normalized by adding an L2-normalization (L2-norm) layer for using cosine-similarity-based learning. As a result, object identification depends only on the angle, and the distribution of the embedding vector is limited to the surface of a sphere with a radius of 1. This study proposes training the model from which the L2-norm layer is removed by using the triplet loss to utilize a wide vector space beyond the surface of a sphere with a radius of 1, for which a novel loss function and its two-stage learning method. The proposed method classifies the embedding vector within a space rather than on the surface, and the model’s performance is also increased. The accuracy, one-shot identification performance, and distribution of the embedding vectors are compared between the existing learning method and the proposed learning method for verification. The verification was conducted using an open-set. The resulting accuracy of 97.33% for the proposed learning method is approximately 4% greater than that of the existing learning method.

scholarly journals Traceable Air Baggage Handling System Based on RFID Tags in the Airport

2008 ◽  
Vol 3 (1) ◽  
pp. 106-115 ◽  
Author(s):  
Ting Zhang ◽  
Yuanxin Ouyang ◽  
Yang He
Keyword(s):  
Supply Chain ◽  
Real Time ◽  
Recognition Rate ◽  
Cost Effective ◽  
Object Identification ◽  
Rfid Tags ◽  
Handling System ◽  
International Airport ◽  
Management Efficiency ◽  
Baggage Handling

The RFID is not only a feasible, novel, and cost-effective candidate for daily object identification but it is also considered as a significant tool to provide traceable visibility along different stages of the aviation supply chain. In the air baggage handing application, the RFID tags are used to enhance the ability for baggage tracking, dispatching and conveyance so as to improve the management efficiency and the users’ satisfaction. We surveyed current related work and introduce the IATA RP1740c protocol used for the standard to recognize the baggage tags. One distributed aviation baggage traceable application is designed based on the RFID networks. We describe the RFID-based baggage tracking experiment in the BCIA (Beijing Capital International Airport). In this experiment the tags are sealed in the printed baggage label and the RFID readers are fixed in the certain interested positions of the BHS in the Terminal 2. We measure the accurate recognition rate and monitor the baggage’s real-time situation on the monitor’s screen. Through the analysis of the measured results within two months we emphasize the advantage of the adoption of RFID tags in this high noisy BHS environment. The economical benefits achieved by the extensive deployment of RFID in the baggage handing system are also outlined.

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