scholarly journals On the Determination of the Chip Nozzle Recognition System by Using Machine Vision

2021 ◽  
Vol 1 (3) ◽  
pp. 1-7
Author(s):  
Jing Qiu ◽  
Yun Xu ◽  
Siyi Liu
Keyword(s):  
Machine Vision ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Analysis Method ◽  
Practical Application ◽  
Average Value ◽  
Blob Analysis ◽  
Sample Data ◽  
Semiconductor Chips

To solve the problem of chip damage caused by the using the wrong type of vacuum nozzle during the packaging of semiconductor chips. A recognition system of vacuum nozzle based on machine vision was proposed. In this research, 29 kinds of lifting nozzles are selected as test samples. The backlight intensity of two lifting nozzle images (one strong and one weak separately) is collected at the first beginning. Then, the Blob analysis method is using to analyze the weak backlighting image. The area of the lifting nozzle and the minimum outer rectangular feature can be obtained subsequently. To identify the shape of the liftin nozzle (round or square), the area ratio is calculated. At the same time, the minimum outer rectangular of the lifting nozzle is selected as the reference rectangle. Then, construct the measurement rectangle. The 2-dimensional size of the lifting nozzle is measured as well. Meanwhile, for the strong backlight image, the average value of the grayscale which located within the minimum outer rectangle is calculated. Therefore, the color (black, white, or beige) of the nozzle can be identified. Finally, the sample data is saved to the database as the sample database. During the recognition process, the shape, color, and size of the lifting nozzle being analyzing are using as the parameter to realize the condition inquire. The experimental results show that the recognition accuracy of this method is 98.85%, and the recognition time of one nozzle is around 1 second, which meets the requirements of practical application.

scholarly journals OCR with the Deep CNN Model for Ligature Script-Based Languages like Manchu

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Diandian Zhang ◽  
Yan Liu ◽  
Zhuowei Wang ◽  
Depei Wang
Keyword(s):  
Recognition Accuracy ◽  
Sliding Window ◽  
Recognition System ◽  
Text Retrieval ◽  
Text Recognition ◽  
Manual Segmentation ◽  
Low Resource ◽  
Sample Data ◽  
Deep Cnn

Manchu is a low-resource language that is rarely involved in text recognition technology. Because of the combination of typefaces, ordinary text recognition practice requires segmentation before recognition, which affects the recognition accuracy. In this paper, we propose a Manchu text recognition system divided into two parts: text recognition and text retrieval. First, a deep CNN model is used for text recognition, using a sliding window instead of manual segmentation. Second, text retrieval finds similarities within the image and locates the position of the recognized text in the database; this process is described in detail. We conducted comparative experiments on the FAST-NU dataset using different quantities of sample data, as well as comparisons with the latest model. The experiments revealed that the optimal results of the proposed deep CNN model reached 98.84%.

CLASSIFIER COMBINATION AND ITS APPLICATION IN IRIS RECOGNITION

2008 ◽  
Vol 22 (03) ◽  
pp. 617-638 ◽  
Author(s):  
XINHUA FENG ◽  
XIAOQING DING ◽  
YOUSHOU WU ◽  
PATRICK S. P. WANG
Keyword(s):  
Iris Recognition ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Recognition System ◽  
Classifier Combination ◽  
Practical Application ◽  
Finite Samples ◽  
Biometric Systems ◽  
Matching Score ◽  
Combined Classifier

Classifier combination is an effective method to improve the recognition accuracy of a biometric system. It has been applied to many practical biometric systems and achieved excellent performance. However, there is little literature involving theoretical analysis on the effectiveness of classifier combination. In this paper, we investigate classifiers combined with the max and min rules. In particular, we compute the recognition performance of each combined classifier, and illustrate the condition in which the combined classifier outperforms the original unimodal classifier. We focus our study on personal verification, where the input pattern is classified into one of two categories, the genuine or the impostor. For simplicity, we further assume that the matching score produced by the original classifier follows a normal distribution and the outputs of different classifiers are independent and identically distributed. Randomly-generated data are employed to test our conclusion. The influence of finite samples is explored at the same time. Moreover, an iris recognition system, which adopts multiple snapshots to identify a subject, is introduced as a practical application of the above discussions.

Development of a Modified Ear Recognition System for Personnel Identification

2020 ◽  
Vol 13 ◽  
Author(s):  
Mais Al-Sharqi ◽  
Haitham Sabah Hasan
Keyword(s):  
Recognition Accuracy ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Identification System ◽  
Segmentation Method ◽  
False Acceptance Rate ◽  
Ear Recognition ◽  
Human Ear ◽  
Algorithm Implementation

Aims: This study examined the development of a match region localization (MRL) ear recognition system (ERS). Background: The developed algorithm is called the match region localization (MRL) algorithm. MRL recognizes a human ear using only small visible portions of the ear while excluding covered or occluded portions. The MRL technique divides an ear image into segments of small blocks; these blocks are either regular (and equally sized) segments or irregularly shaped blocks depending on the adopted segmentation method. Objective: The recognition accuracy of the system is 97.07%, thereby implying that the system can perform efficiently as an identification system. Method: This research follows four major stages, namely, development of a PCA-based ear recognition algorithm, implementation of the developed algorithm, determination of the optimum ear segmentation method, and evaluation of the performance of the technique. Results: The False acceptance rate (FAR) of the developed ear recognition system (ERS) is 0.06. This result implies that six out of every 100 intruders will be falsely accepted. Conclusion: The developed ERS outperforms the existing ERS by approximately 24.61% in terms of system recognition accuracy; the developed ERS can be tested on other publicly available ear databases to check its performance on larger platforms. Other: The developed ERS can be tested on other publicly available ear databases to check its performance on larger platforms.

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

Practical Application

2018 ◽  
Author(s):  
Kubo Mačák
Keyword(s):  
Armed Conflict ◽  
Armed Conflicts ◽  
Practical Application ◽  
Temporal Perspective ◽  
Applicable Law ◽  
The Law ◽  
Geographical Scope

This chapter analyses the practical application of the law of belligerent occupation in internationalized armed conflicts in its temporal, geographical, and personal dimensions. Firstly, from a temporal perspective, the law is shown to apply once one of the conflict parties consolidates its control over the enemy territory and substitutes its own authority for that of the displaced enemy. Secondly, the chapter assesses the geographical scope of the applicable law and draws specific guidelines for the determination of the territory subject to the law of occupation in various types of internationalized armed conflicts. Thirdly, the chapter endorses the allegiance-based approach to the designation of protected persons under the law of occupation and applies it to the reality of internationalized armed conflict. Overall, the chapter presents a workable toolkit for the application of the law of occupation to internationalized armed conflicts.

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