Computer Motherboard Component Recognition Using Texture and Shape Features

In this modernized era, computer visualization is a powerful means to discover. It is used in translating from visual into digital information, analog to digital signal, and to name a few. A computer motherboard refers to a circuit board that holds the essential processing parts of a computer. Every part of the component has its own purpose, either to communicate with other component or to receive power. However, the components on a motherboard are difficult to detect due to the similarity and complexity of the design. Thus, a study of computer motherboard component recognition using a combination of texture and shape features is presented. The Gray Level Co-occurrence Matrices (GLCM) and Connected Region were implemented as the features extraction techniques. On the other hand, the classification is performed using a technique of Support Vector Machine (SVM). The computer motherboard components are classified into four components which are chipset, CPU socket, expansion slot, and memory slot. From the testing conducted, it is observed that 90% overall mean of recognition accuracy is achieved which proved that the proposed techniques are applicable for the computer motherboard component recognition.