scholarly journals Fast Detection of Multiple Objects in Traffic Scenes with a Common Detection Framework

2021 ◽  
Vol 9 (VIII) ◽  
pp. 642-648
Author(s):  
Aaryan Srivastava
Keyword(s):  
Image Processing ◽  
Object Recognition ◽  
Visual Detection ◽  
Multiple Objects ◽  
Traffic Signs ◽  
Fast Detection ◽  
Street Traffic ◽  
Three Stages

Object visual detection (OVD) intends to extract precise ongoing on-street traffic signs, which includes three stages: discovery of objects of interest, acknowledgment of recognized items, and following of items moving. Here OpenCV instruments give the calculation backing to various item identification. Item discovery is a PC innovation that is associated with picture handling and PC vision that manage recognizing occasion objects of certain class in computerized pictures and recordings. This paper describes how object recognition is a difficult work in image processing based PC applications, here CNN and RCNN algorithm is used to recognize objects. It is accustomed to distinguishing whether a scene or picture object has been there or not. In this paper, we will introduce procedures and techniques for distinguishing or perceiving objects with different advantages like effectiveness, precision, power and so forth.

Implementation of Object Recognition Based on Local Texture Operators in FPGA Technology

2014 ◽  
Vol 573 ◽  
pp. 453-458
Author(s):  
M. Monica Dhana Ranjini ◽  
P. Gnana Skanda Parthiban ◽  
G. Prabhakar ◽  
A. Mohamed Rajithkhan ◽  
M. Paul Jeyaraj
Keyword(s):  
Image Processing ◽  
Object Recognition ◽  
Local Binary Pattern ◽  
Verilog Hdl ◽  
Multiple Objects ◽  
Local Ternary Pattern ◽  
Complex Image ◽  
Extraction Algorithm ◽  
Object Feature ◽  
High Processing

In the arrival of today’s highly integrated multimedia device and fast emerging applications, image processing have become more important than any others. These devices require complex image processing tasks lead to a very challenge design process as it demands more efficient and high processing systems. The scope of the project is to extract locations and features of multi objects in an image for object recognition. For low power consumption and better performance we design a proposed system in FPGA. In existing system an cell based multi object feature extraction algorithm is used to extract simultaneously autocorrelation feature of objects. It is calculated using zeroth order and first order moments to obtain the size and location of multiple objects. To reduce computational complexity and memory consumption, Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) is used to extract the feature of multiple objects are proposed. In the local binary pattern, the LBP value is computed by comparing a gray level value of centre pixel in an image with its neighbors. The local ternary pattern is extended from LBP to three-valued code in which gray values are quantized to zero,+1,-1. The proposed architecture is designed using verilog HDL, simulated using Modelsim software and synthesized using Xilinx project navigator.

scholarly journals A Robot Object Recognition Method Based on Scene Text Reading in Home Environments

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1919
Author(s):  
Shuhua Liu ◽  
Huixin Xu ◽  
Qi Li ◽  
Fei Zhang ◽  
Kun Hou
Keyword(s):  
Object Recognition ◽  
Recognition Accuracy ◽  
Multiple Objects ◽  
Recognition Method ◽  
Home Environments ◽  
Complex Scenes ◽  
Scene Text ◽  
Text Reading ◽  
Text Images ◽  
Chinese And English

With the aim to solve issues of robot object recognition in complex scenes, this paper proposes an object recognition method based on scene text reading. The proposed method simulates human-like behavior and accurately identifies objects with texts through careful reading. First, deep learning models with high accuracy are adopted to detect and recognize text in multi-view. Second, datasets including 102,000 Chinese and English scene text images and their inverse are generated. The F-measure of text detection is improved by 0.4% and the recognition accuracy is improved by 1.26% because the model is trained by these two datasets. Finally, a robot object recognition method is proposed based on the scene text reading. The robot detects and recognizes texts in the image and then stores the recognition results in a text file. When the user gives the robot a fetching instruction, the robot searches for corresponding keywords from the text files and achieves the confidence of multiple objects in the scene image. Then, the object with the maximum confidence is selected as the target. The results show that the robot can accurately distinguish objects with arbitrary shape and category, and it can effectively solve the problem of object recognition in home environments.

GPU processing for parallel image processing and real-time object recognition

2014 ◽  
Author(s):  
Kevin Vincent ◽  
Damien Nguyen ◽  
Brian Walker ◽  
Thomas Lu ◽  
Tien-Hsin Chao
Keyword(s):  
Image Processing ◽  
Object Recognition ◽  
Real Time ◽  
Parallel Image Processing ◽  
Parallel Image ◽  
Gpu Processing

scholarly journals Implementasi Metode Super Resolusi Untuk Meningkatkan Kualitas Citra Hasil Screenshot

2020 ◽  
Vol 7 (3) ◽  
pp. 432
Author(s):  
Windi Astuti
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Median Filter ◽  
Super Resolution ◽  
Linear Interpolation ◽  
Resolution Method ◽  
Image Zooming ◽  
Image Enlargement ◽  
Three Stages

Various types of image processing that can be done by computers, such as improving image quality is one of the fields that is quite popular until now. Improving the quality of an image is necessary so that someone can observe the image clearly and in detail without any disturbance. An image can experience major disturbances or errors in an image such as the image of the screenshot is used as a sample. The results of the image from the screenshot have the smallest sharpness and smoothness of the image, so to get a better image is usually done enlargement of the image. After the screenshot results are obtained then, the next process is cropping the image and the image looks like there are disturbances such as visible blur and cracked. To get an enlarged image (Zooming image) by adding new pixels or points. This is done by the super resolution method, super resolution has three stages of completion, first Registration, Interpolation, and Reconstruction. For magnification done by linear interpolation and reconstruction using a median filter for image refinement. This method is expected to be able to solve the problem of improving image quality in image enlargement applications. This study discusses that the process carried out to implement image enlargement based on the super resolution method is then built by using R2013a matlab as an editor to edit programs

scholarly journals 2 Generación del mapa de cobertura y uso del suelo de la provincia del Azuay

2017 ◽  
pp. 23-37
Author(s):  
Carlos Tenesaca Pacheco ◽  
Toa Quinde Pomavilla ◽  
Gabriela Delgado Orellana ◽  
Edgar Toledo López ◽  
Omar Delgado Inga
Keyword(s):  
Image Processing ◽  
Land Use ◽  
Digital Image Processing ◽  
Digital Image ◽  
Vegetation Cover ◽  
Provincial Government ◽  
Thematic Cartography ◽  
Three Stages ◽  
Definition Of ◽  
Sistemas De Informacion

La Universidad del Azuay, a través del Instituto de Estudios de Régimen Seccional del Ecuador (IERSE), suscribió un convenio de cooperación interinstitucional con el Gobierno Provincial del Azuay para la generación del “Mapa de Cobertura Vegetal y Uso de Suelo de la provincia del Azuay, a escala 1:5.000”, con base en las ortofotogra fías del año 2010, generadas por el proyecto SIGTIERRAS del Ministerio de Agricultura, Ganadería, Acuacultura y Pesca (MAGAP). La generación de la cartografía temática se realizó en tres etapas: a) recopilación de información de ortofotografías suministradas por la SENPLADES; b) definición de la leyenda de trabajo realizada en base a la información del mapa de cobertura y uso del suelo, generado por el MAGAP-MAE 2015; y c) digitalización de elementos geográficos y tratamiento digital de imágenes, la cual se realizó mediante el uso de sistemas de información geográfica (SIG) obteniendo como resultado 33 capas de información de elementos geográficos naturales y antrópicos.Palabras clave:Cobertura vegetal, uso del suelo, ortofotografía, provincia del Azuay. AbstractThe University of Azuay, through the Institute of Studies of Sectional Regime of Ecuador (IERSE), signed an agreement of interinstitutional cooperation with the Provincial Government of Azuay for the generation of the “Map of Vegetation Cover and Land Use of the province of Azuay, scale 1: 5,000” Based on the 2010 orthophotographs generated by the SIGTIERRAS project of the Ministry of Agriculture, Livestock, Aquaculture and Fisheries (MAGAP). The generation of thematic cartography was carried out in three stages: A) compilation of orthophoto information provided by SENPLADES; B) definition of the work legend based on the information of the map of coverage and land use generated by MAGAP-MAE 2015; and c) digitalization of geographic elements and digital image processing which was done through the use of geographic information systems (GIS) Resulting in 33 layers of information from natural and man-made geographic elements.Keywords:Vegetal cover, land use, orthophotography, province of Azuay.

scholarly journals Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework

2016 ◽  
Vol 17 (4) ◽  
pp. 1002-1014 ◽  
Author(s):  
Qichang Hu ◽  
Sakrapee Paisitkriangkrai ◽  
Chunhua Shen ◽  
Anton van den Hengel ◽  
Fatih Porikli
Keyword(s):  
Multiple Objects ◽  
Fast Detection

scholarly journals Raw data based image processing algorithm for fast detection of surface breaking cracks

2017 ◽  
Author(s):  
Sruthi Krishna K. P. ◽  
Nithin Puthiyaveetil ◽  
Renil Kidangan ◽  
Sreedhar Unnikrishnakurup ◽  
Mathias Zeigler ◽  
...  
Keyword(s):  
Image Processing ◽  
Processing Algorithm ◽  
Image Processing Algorithm ◽  
Raw Data ◽  
Fast Detection

The Error Analysis about Gauge Visual Detection System between High Signals and Contact Net

2012 ◽  
Vol 522 ◽  
pp. 347-350
Author(s):  
Xi Lin Zhu ◽  
Yong Yu ◽  
Qiang Wei ◽  
Xiang Zou ◽  
Chen Jun Huang
Keyword(s):  
Image Processing ◽  
Error Analysis ◽  
System Design ◽  
Visual Detection ◽  
Imaging System ◽  
Detection System ◽  
Systematic Errors ◽  
Lighting Conditions ◽  
Specific Impact

t is need to experiment to verify the correctness and validity of various stages in system design after gauge visual detection system designing between high signals and contact net. Then it does error analysis from lighting conditions, camera resolution, binocular imaging system installation structure, camera out of synchronized, noise and subsequent image processing operations, etc. It analysis the systematic errors principle and specific impact, then identifies specific improvements.

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