IC Assembly Product Inspection Using Image Processing Techniques

2011 ◽  
Vol 145 ◽  
pp. 209-213
Author(s):  
Hsi Chieh Lee ◽  
Shao Hsuan Chang
Keyword(s):  
Image Processing ◽  
Success Rate ◽  
Measurement System ◽  
Process Time ◽  
Automatic Identification ◽  
Circuit Boards ◽  
Image Identification ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Product Specifications

In this study, we present an image identification and measurement system for examining and testing the packaged semiconductor specifications. Most semiconductor processes rely on measurement gauges or inspectors to examine the finished product specifications visually. Measurement gauges are costly; inspectors have to be trained professionally and their performance depends on the maturity of their skills, which requires enormous costs, time, and efforts. Therefore, an automatic identification and measurement system will not only reduce costs, but minimize the complexity of measurement, thereby upgrading the effectiveness of manpower significantly. Experiments were conducted using 1657 BMP images with 640 * 480 pixels taken with a semiconductor machine. These images are snapshots randomly taken from of a variety of the 7.2 cm * 4.8 cm Substrate circuit boards each includes 180 pieces of 30 * 6 IC packaged products. Promising results were derived where 1492 out of 1657 product images were successfully detected and measured. In addition to the 90.04% success rate for inspection, the process time is reduced significantly to about 1/6 as compared to human professionals.

Image processing techniques in 3-D foot shape measurement system

2008 ◽  
Author(s):  
Guozhong Liu ◽  
Ping Li ◽  
Boxiong Wang ◽  
Hui Shi ◽  
Xiuzhi Luo
Keyword(s):  
Image Processing ◽  
Measurement System ◽  
Shape Measurement ◽  
Image Processing Techniques ◽  
Processing Techniques

A New Measurement System of Yarn Appearance Based on Machine Vision

2014 ◽  
Vol 945-949 ◽  
pp. 1810-1814
Author(s):  
Jun Juan Li ◽  
Chen Wang ◽  
Wen Xiao Tu ◽  
Bao Qi Zuo
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Measurement System ◽  
Coefficient Of Variation ◽  
Suitable Method ◽  
Main Body ◽  
Image Processing Techniques ◽  
Image Acquisition System ◽  
Processing Techniques ◽  
Morphological Opening

In this paper, a new yarn appearance measurement system based on machine vision is introduced. The yarn images are continuously captured by image acquisition system. To extract the main body of the yarn accurately, the yarn images are processed sequentially with threshold segmentation and morphological opening operation. Then the coefficient of variation (CV value) of diameter is calculated to characterize the yarn evenness. The measurement process achieves result (CV value) which can be compared with USTER evenness tester by image processing techniques. By comparing different methods which use different algorithms, a suitable method is chosen to be used in this new measurement system. Then a more accurate, more efficient and faster measurement system will meet requirements in the future.

scholarly journals Klasifikasi Topeng Cirebon menggunakan Metode Convolutional Neural Network

2021 ◽  
Vol 8 (1) ◽  
pp. 163-169
Author(s):  
Felix Indra Kurniadi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Cultural Heritage ◽  
Convolutional Neural Network ◽  
Automatic Identification ◽  
Intangible Cultural Heritage ◽  
Image Processing Techniques ◽  
The Third ◽  
The Government ◽  
Processing Techniques

Cirebon mask is one of the intangible cultural heritage in Indonesia. It is one of the prominent cultural assets from Cirebon and becoming one of the identity Cirebon culture. However, the current condition people tend to forget the cultural asset and lack of help from the government makes the Cirebon mask become the third-rate assets. Our concern lays on the extinction of this Mask. We want to implement digitation and automatic identification using image processing techniques. In this paper, we applied the Convolutional Neural Network for Cirebon Mask classification.

Three-Dimensional Structure of Rotavirus-Fab Complex

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.

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

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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