Abstract
Image-based recognition systems commonly use an extracted image from the target object using texture analysis. However, some of the proposed and implemented recognitionues systems of wood types up to this time have not been achieving adequatue accuracy, efficiency and feasable execution speed with respect to practicality. This paper discussed a new method of image-based recognition system for wood type identification by dividing the wood image into several blocks, each of which is extracted using gray image and edge detection techniques. The wood feature analysis concentrates on three parameters entropy, standard deviation, and correlation. Our experiment results showed that our method can increase the recognition accuracy up to 95%, which is faster and better than the previous existing method with 85% recognition accuracy. Moreover, our method needs only to analyze three feature parameters compared to the previous existing method needs to analyze seven feature parameters, ang thus implying a simpler and faster recognition process.
Highlights
The identification of wood types becomes very important when it related to illegal logging, taxes, and the suitability of the product
An experiment on the identification of types of wood that has been done in this research, has given better results than researches conducted previously authors
In This research has been done on the image the block method, with a combination of image blocks that is divides the image into four equal parts
Summary
The identification of wood types becomes very important when it related to illegal logging, taxes, and the suitability of the product. This activity is constrained, because the experts in identification of wood are very limited in terms of amount, power, and time. If there is still doubt, the expert will observe the microscopic elements in the cross-sectional area, radial cross-section, and cross tangent. This activity uses a magnifying glass (10x). Its unique features can identify Wood of a particular species These features include strength, density, hardness, odor, texture and color. Reliable wood identification usually requires the ability to recognize basic differences in cellular structure and wood anatomy
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