A survey of different image processing methods for the design and development of an efficient traffic sign board recognition system

2017 ◽  
Author(s):  
Abhinav V. Deshpande ◽  
M. Monica Subashini
Keyword(s):  
Image Processing ◽  
Recognition System ◽  
Design And Development ◽  
Traffic Sign ◽  
Processing Methods

Recognition of Traffic Signs and Korean Texts on Traffic Signs Using Japanese Puzzle

2008 ◽  
Vol 12 (6) ◽  
pp. 509-515
Author(s):  
Youngsun Sohn ◽  
◽  
Ilsik Shin ◽  
Keyword(s):  
Image Processing ◽  
Processing Speed ◽  
Recognition Rate ◽  
Recognition System ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Black And White ◽  
Intended Object

This paper embodies a recognition system that recognizes the traffic signs and the Korean characters on the traffic signs through reverse application of a Japanese puzzle. The Japanese puzzle used in this system reveals the shape of the intended object when marked onto the mesh grids according to the (x,y) coordinate information provided by the puzzle creator. When the puzzle described above is applied to the color and the shape of the traffic sign after the separating the traffic sign image from the inputted image, the system outputs the traffic sign and its contents as text if the image is recognized as a traffic sign. With the black-and-white image processing and unneeded penciling procedure, the proposed system outperformed the existing systems at a faster processing speed and higher recognition rate.

scholarly journals Fast and Accurate Recognition for Codes on Complex Backgrounds for Real-Life Industrial Applications

2021 ◽  
Author(s):  
Qiaokang Liang ◽  
◽  
Qiao Ge ◽  
Wei Sun ◽  
Dan Zhang ◽  
...  
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Character Recognition ◽  
Real Life ◽  
Recognition System ◽  
Industrial Applications ◽  
Processing Methods ◽  
Data Generator ◽  
Model Training ◽  
Traditional Image

In the food and beverage industry, the existing recognition of code characters on the surface of complex packaging usually suffers from low accuracy and low speed. This work presents an efficient and accurate inkjet code recognition system based on the combination of the deep learning and traditional image processing methods. The proposed system mainly consists of three sequential modules, i.e., the characters region extraction by modified YOLOv3-tiny network, the character processing by the traditional image processing methods such as binarization and the modified character projection segmentation, and the character recognition by a Convolutional recurrent neural network (CRNN) model based on a modified version of MobileNetV3. In this system, only a small amount of tag data has been made and an effective character data generator is designed to randomly generate different experimental data for the CRNN model training. To the best of our knowledge, this report for the first time describes that deep learning has been applied to the recognition of codes on complex background for the real-life industrial application. Experimental results have been provided to verify the accuracy and effectiveness of the proposed model, demonstrating a recognition accuracy of 0.986 and a processing speed of 100 ms per bottle in the end-to-end character recognition system.

scholarly journals Detecting Change between Urban Road Environments along a Route Based on Static Road Object Occurrences

2021 ◽  
Vol 11 (8) ◽  
pp. 3666
Author(s):  
Zoltán Fazekas ◽  
László Gerencsér ◽  
Péter Gáspár
Keyword(s):  
Change Detection ◽  
Traffic Safety ◽  
Recognition System ◽  
Driver Assistance Systems ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Safety Risk ◽  
Urban Road ◽  
Environment Type ◽  
Cusum Method

For over a decade, urban road environment detection has been a target of intensive research. The topic is relevant for the design and implementation of advanced driver assistance systems. Typically, embedded systems are deployed in these for the operation. The environments can be categorized into road environment-types. Abrupt transitions between these pose a traffic safety risk. Road environment-type transitions along a route manifest themselves also in changes in the distribution of traffic signs and other road objects. Can the placement and the detection of traffic signs be modelled jointly with an easy-to-handle stochastic point process, e.g., an inhomogeneous marked Poisson process? Does this model lend itself for real-time application, e.g., via analysis of a log generated by a traffic sign detection and recognition system? How can the chosen change detector help in mitigating the traffic safety risk? A change detection method frequently used for Poisson processes is the cumulative sum (CUSUM) method. Herein, this method is tailored to the specific stochastic model and tested on realistic logs. The use of several change detectors is also considered. Results indicate that a traffic sign-based road environment-type change detection is feasible, though it is not suitable for an immediate intervention.

Comparison of Different Image Processing Methods with Spatial Information in Clinical Brain MRI

2020 ◽  
Author(s):  
Iza Sazanita Isa ◽  
Mohamad Khairul Faizi Mat Saad ◽  
Muhammad Haris Khusairi Mohmad Kadir ◽  
Ahmad Afifi Ahmad Afandi ◽  
Noor Khairiah A. Karim ◽  
...  
Keyword(s):  
Image Processing ◽  
Spatial Information ◽  
Brain Mri ◽  
Processing Methods

scholarly journals Image Processing Methods of LANDSAT TM Data for Lineament Analysis

1989 ◽  
Vol 1989 (14B) ◽  
pp. 25-39
Author(s):  
Katsuaki KOIKE ◽  
Hiroyuki ITOH ◽  
Michito OHMI
Keyword(s):  
Image Processing ◽  
Landsat Tm ◽  
Processing Methods ◽  
Lineament Analysis

scholarly journals Fast Transforms in Image Processing: Compression, Restoration, and Resampling

2014 ◽  
Vol 2014 ◽  
pp. 1-23 ◽  
Author(s):  
Leonid P. Yaroslavsky
Keyword(s):  
Image Processing ◽  
Image Compression ◽  
Image Restoration ◽  
Compressive Sensing ◽  
Adaptive Filters ◽  
Processing Methods ◽  
Fast Transforms ◽  
Image Transforms

Transform image processing methods are methods that work in domains of image transforms, such as Discrete Fourier, Discrete Cosine, Wavelet, and alike. They proved to be very efficient in image compression, in image restoration, in image resampling, and in geometrical transformations and can be traced back to early 1970s. The paper reviews these methods, with emphasis on their comparison and relationships, from the very first steps of transform image compression methods to adaptive and local adaptive filters for image restoration and up to “compressive sensing” methods that gained popularity in last few years. References are made to both first publications of the corresponding results and more recent and more easily available ones. The review has a tutorial character and purpose.

Sign in / Sign up

Export Citation Format

Share Document