An Improved Wood Identification Accuracy Using Gaussian Pyramid and Laplacian Edge Detection Based on Android Smartphone

Several studies have been carried out for the rapid wood identification process without eye observation of the wood anatomists. Computer vision is the first choice in this case so that the identification results are rapid and more accurate than the conventional method. Our previous research developed a method for wood identification using the Histogram of Oriented Gradient (HOG) feature extraction and Support Vector Machine (SVM) as a classifier on Android smartphones. This paper proposes an improved wood identification accuracy of the HOG method and SVM classifier by utilizing several methods on the image preprocessing i.e. the Gaussian pyramid and the Laplacian edge detection methods. The Gaussian pyramid is used to reduce the wood image into a smaller group of pixels to qualify size wood image in the extraction process without reducing the image quality. On the other hand, to clear and distinguish the pattern in the wood image, the Laplacian edge detection is used. In our experiments, wood images from five wood species were used i.e. Kembang Semangkok, Ketapang, Preparat Darat, Pinang, and Puspa. The result showed that each wood species have increased accuracy, precision, recall, and specificity. The lowest increment accuracy was for Pinang and Puspa species at 4.00% of accuracy and zero precision value is found in Puspa species. Furthermore, from five wood species, there was a significantly increased result so it is very useful for improving the result of identification using HOG descriptor and SVM Classifier.