Suchart Yammen

An Advanced Vision System for the Automatic Inspection of Corrosions on Pole Tips in Hard Disk Drives

Pole tips of hard disk drives are made of FeCo film, which is prone to corrosion due to the corrosive nature of the environment and wide range of pH experienced during the production. A machine vision system is utilized for the automatic auditing of corroded pole tips. This comprises three steps: 1) extraction of the top shield region; 2) fusion of extracted features; and 3) decision making. This model is capable of detecting corrosion in the critical part of the pole tips. The experiments show that the proposed system detects corrosion with high accuracy, whereas the overall processing time meets industrial requirements.

Pole Tip Corrosion Detection Using Various Image Processing Techniques

In this paper, three features are proposed for detecting the pole tip corrosion in the hard disk drives by using various techniques of image processing. The proposed method is divided into three parts. The first part involves with preparing the template image of the pole tip. The second part involves with selecting a region of interest. The third part involves with constructing the three features. The first feature is the area around the top shield of the pole tip. The second and third features are the row coordinate and the length along the lower edge around the top shield, respectively. Finally, the last part involves with measuring the detection efficiency. From experimental results with the 647 tested pole tip images, this shows that the method using the combination of the first feature and the second feature gives the better detection efficiency than that using the combination of the others in term of specification, precision and accuracy, respectively.

An Effective Method for Classification of White Rice Grains Using Various Image Processing Techniques

This chapter presents an algorithm for classifying grains of white rice by using image processing. Each image size is acquired via a digital camera. The resolution is 720 × 480 pixels. The algorithm begins with improving grain images, converting these images into binary images by using Otsu’s method, removing noise from the binary images by applying the morphological method with square structural elements, detecting each grain boundary by using the Canny operator, and determining the length of each grain by using the Euclidean method. Next, the grain length is used for classifying the rice grains according to the Rice Standards of Thailand. The testing results from processing 500 grain images; one grain per image, the algorithm provides good performance with the mean absolute error of 0.01 mm in length. For 300 grain images with some grains per image, the algorithm provides good classification with an average accuracy of 99.33 %.