Many studies for computer-based chromosome analysis using artificial neural network (ANN) have shown that it is possible to classify chromosomes into 24 subgroups. It is important to select optimum features for training the ANN. Our purpose was to select features that had the low classification error and the best ability for human chromosome classification. We applied the medial axis transformation for the medial line, extended the line to the boundary and obtained relative length, relative area and centromeric index. The Giemsa-stained human chromosome has a sequence of banding pattern that is perpendicular to the medial axis of the chromosome. Density profile is a one-dimensional graph of the banding pattern property of the chromosome computed at a sequence of points along the possibly curved chromosome medial axis. Some studied used relative length, centromeric index and 62 density profile as features, but we prepared two data sets as features that one set was relative length, centromeric index and 80 density profile considered No. 1 chromosome's length and the other was relative length, centromeric index, the 80 density profile and relative area and compared classification error of each set. We found that the classification error showed to be decreased by adding relative area to the other features.
The assembled PCB is made to insert the electric components manually or automatically. After inserting some electric elements on a PCB, the PCB is processed in the soldering process. However, there are some insertion or mounting errors in the PCB after the inserting or soldering process. The machine vision has been used to detect these errors. When a CCD camera and an X-Y positioning table in a machine vision use, it takes a high cost and long inspection time because it should move on some inspecting spots. In case workers inspect a PCB with eyes to find the errors, a result is different depending on worker's physical and mental condition. To solve these problems, we used a PC, universal serial bus (USB) hub to use several USB devices and several USB cameras that were located over the PCB and inspected whether the errors were found or not. In this study we found the fact that our system's error was lower than worker's and it took less cost than other machine vision system using the CCD camera and X-Y table.