Comparative Study of Data Matrix Codes Localization and Recognition Methods

We provide a comprehensive and in-depth overview of the various approaches applicable to the recognition of Data Matrix codes in arbitrary images. All presented methods use the typical “L” shaped Finder Pattern to locate the Data Matrix code in the image. Well-known image processing techniques such as edge detection, adaptive thresholding, or connected component labeling are used to identify the Finder Pattern. The recognition rate of the compared methods was tested on a set of images with Data Matrix codes, which is published together with the article. The experimental results show that methods based on adaptive thresholding achieved a better recognition rate than methods based on edge detection.

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Staff Detection and Removal

Visual Perception of Music Notation ◽

10.4018/978-1-59140-298-5.ch001 ◽

2011 ◽

pp. 1-39 ◽

Cited By ~ 35

Author(s):

Ichiro Fujinaga

Keyword(s):

Image Processing ◽

Connected Component ◽

Optical Music Recognition ◽

Run Length ◽

Image Processing Techniques ◽

Run Length Coding ◽

Musical Scores ◽

Music Recognition ◽

Recognition Systems ◽

Processing Techniques

This chapter describes the issues involved in the detection and removal of stavelines of musical scores. This removal process is an important step for many Optical Music Recognition systems and facilitates the segmentation and recognition of musical symbols. The process is complicated by the fact that most music symbols are placed on top of stavelines and these lines are often neither straight nor parallel to each other. The challenge here is to remove as much of stavelines as possible while preserving the shapes of the musical symbols, which are superimposed on stavelines. Various problematic examples are illustrated and a detailed explanation of an algorithm is presented. Image processing techniques used in the algorithm include: run-length coding, connected-component analysis, and projections.

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APLICAÇÃO DE REDES NEURAIS CONVOLUCIONAIS NO RECONHECIMENTO DE CARACTERES EM PLACAS INFORMATIVAS JAPONESAS

Colloquium Exactarum ◽

10.5747/ce.2021.v13.n2.e355 ◽

2021 ◽

Vol 13 (2) ◽

pp. 12-24

Author(s):

Rafael Yuji Hirata Furusho ◽

Francisco Assis da Silva ◽

Leandro Luiz de Almeida ◽

Danillo Roberto Pereira ◽

Mário Augusto Pazoti ◽

...

Keyword(s):

Neural Network ◽

Image Processing ◽

Edge Detection ◽

Convolutional Neural Network ◽

Mathematical Morphology ◽

Digital Image Processing ◽

Digital Image ◽

Image Processing Techniques ◽

Processing Techniques

Unlike most Western countries, which have a Latin-derived base alphabet, Japan has two syllabic alphabets called Hiragana and Katakana, and a Chinese alphabet, called Kanji. The vast differences in the writing of these Eastern alphabets to Western alphabets, Western alphabet-based OCR algorithms tend not to efficiently detect Japanese characters. This work contributes to a methodology applying digital image processing techniques, such as color range-based segmentation, edge detection and mathematical morphology techniques, to detect Japanese traffic informationalplates correctly the perspective and segment the characters contained in it. A convolutional neural network wasused to perform the classification of Hiragana characters contained in the segmented plates, withaccuracyof 94.37%.

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Penerapan Metode Sobel Edge Detection dan Image Processing Untuk Mengetahui Diameter Apel Fuji Menggunakan Aplikasi Matlab

JURIKOM (Jurnal Riset Komputer) ◽

10.30865/jurikom.v7i3.2261 ◽

2020 ◽

Vol 7 (3) ◽

pp. 450

Author(s):

Alima Hakkon Hasibuan ◽

Taronisokhi Zebua ◽

Rivalri Kristianto Hondro

Keyword(s):

Image Processing ◽

Edge Detection ◽

Detection Method ◽

Selling Price ◽

Image Processing Techniques ◽

Sobel Edge Detection ◽

Yellow Line ◽

Edge Detection Method ◽

Processing Techniques

Fuji apples (Malus Domestika) are fruits that contain a lot of antioxidants. Besides the fruit flesh, the apple also contains pectin. Fuji apples are red and have a yellow line. The size of apples can affect the selling price of apples, the determination of the size of apples can be seen from the size of the diameter, measuring the diameter of apples is usually done visually by comparing apples. Based on these problems, a research is needed to develop a system to determine the diameter of fuji apples by using image processing techniques to find the diameter. This measurement process uses the matlab application and tests with the sobel edge detection method and image processing to see the more visible edges of the lines. The results showed that the developed system was able to obtain images and identify the diameter of fuji apples

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Modified Hybrid Edge Detection in MR image via Image Processing Techniques

2020 International Conference on Information Science and Communication Technology (ICISCT) ◽

10.1109/icisct49550.2020.9079945 ◽

2020 ◽

Author(s):

Hafsa Habib ◽

Muhammad Haseeb ◽

Qutub Khan

Keyword(s):

Image Processing ◽

Edge Detection ◽

Mr Image ◽

Image Processing Techniques ◽

Processing Techniques

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Three-Dimensional Structure of Rotavirus-Fab Complex

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100179944 ◽

1990 ◽

Vol 48 (1) ◽

pp. 238-239

Author(s):

B.V.V. Prasad ◽

E. Marietta ◽

J.W. Burns ◽

M.K. Estes ◽

W. Chiu

Keyword(s):

Image Processing ◽

Smooth Surface ◽

Three Dimensional ◽

Outer Shell ◽

Dimensional Structure ◽

Structural Studies ◽

Cell Penetration ◽

Electron Cryomicroscopy ◽

Image Processing Techniques ◽

Processing Techniques

Rotaviruses are spherical, double-shelled particles. They have been identified as a major cause of infantile gastroenteritis worldwide. In our earlier studies we determined the three-dimensional structures of double-and single-shelled simian rotavirus embedded in vitreous ice using electron cryomicroscopy and image processing techniques to a resolution of 40Å. A distinctive feature of the rotavirus structure is the presence of 132 large channels spanning across both the shells at all 5- and 6-coordinated positions of a T=13ℓ icosahedral lattice. The outer shell has 60 spikes emanating from its relatively smooth surface. The inner shell, in contrast, exhibits a bristly surface made of 260 morphological units at all local and strict 3-fold axes (Fig.l).The outer shell of rotavirus is made up of two proteins, VP4 and VP7. VP7, a glycoprotein and a neutralization antigen, is the major component. VP4 has been implicated in several important functions such as cell penetration, hemagglutination, neutralization and virulence. From our earlier studies we had proposed that the spikes correspond to VP4 and the rest of the surface is composed of VP7. Our recent structural studies, using the same techniques, with monoclonal antibodies specific to VP4 have established that surface spikes are made up of VP4.

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A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit2063211 ◽

2020 ◽

pp. 967-973

Author(s):

V. Deepika ◽

T. Rajasenbagam

Keyword(s):

Image Processing ◽

Brain Tumor ◽

Soft Tissues ◽

Tumor Detection ◽

Tumor Classification ◽

Normal Brain ◽

Image Processing Techniques ◽

Normal Brain Function ◽

Processing Techniques ◽

The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

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