Knots timber detection and classification with C-Support Vector Machine

Timber knots recognition is of prime importance to further determine the timber grade. The recognition is normally based on the human expert’s eyes in which can lead to some flaws based on human limitations and weaknesses. The use of X-ray can cause emits radiation and can be dangerous to the workers. This paper addresses the employment of computational methods for knot detection. A pre-processing and feature extraction methods include contrast stretching, median blur and thresholding, gray scale and local binary pattern were used. More than 400 datasets of knot images of the tropical timbers, namely Acacia and Hevea Brasiliensis have been tested using C-support vector machine as a knot classifier. The findings demonstrate different performances for three types of kernel. Linear kernel function outperformed both radial basis function and polynomial kernel functions for Acacia and Hevea Brasiliensis species. Both species classifications using linear kernel have managed to achieve a promising accuracy. Knots classification with the used of support vector machine has shown a promising result to improve the classifier and test with different types of tropical timbers

Application of integrated AHP and TOPSIS techniques for determining the best Fresh Fruit Bunches (FFB)

This study covers the importance of high quality of palm oil Fresh Fruit Bunches (FFB) to ensure high production in palm oil industry. The most important process to classify the palm oil FFB ripeness is the grading process. Usually, the grading process performed by some graders in each mill manually. However, this method takes time and may lead to errors in the classification process, especially if the graders have less experience. Analytical Hierarchy Process (AHP) and TOPSIS are the useful tools that can be employed to make decisions in classification process. The methodology in this study consists of five phases ie; data collection from expert grader and industries visited, identifying the most important criteria, analysis by AHP method, validation by TOPSIS technique and finally the ranking of the best criteria of high quality FFB. The Expert Choice Software and Microsoft Office Excel are tools used to analyze the data collected from expert graders in the AHP and TOPSIS techniques. The main objective of this study is to determine the best quality of FFB using AHP and TOPSIS techniques. The result found that the number of detached fruitlets is the most important criteria to determine the FFB ripeness with 0.560 priority vector followed by color with 0.219 priority vector compared to other criteria. The sensitivity analysis performed to ensure the results are consistent and reliable. It will help the graders to conduct a proper grading process at mills to increase the quality of OER

A linear model based on Kalman filter for improving neural network classification performance

Neural network has been applied in several classification problems such as in medical diagnosis, handwriting recognition, and product inspection, with a good classification performance. The performance of a neural network is characterized by the neural network's structure, transfer function, and learning algorithm. However, a neural network classifier tends to be weak if it uses an inappropriate structure. The neural network's structure depends on the complexity of the relationship between the input and the output. There are no exact rules that can be used to determine the neural network's structure. Therefore, studies in improving neural network classification performance without changing the neural network's structure is a challenging issue. This paper proposes a method to improve neural network classification performance by constructing a linear model based on the Kalman filter as a post processing. The linear model transforms the predicted output of the neural network to a value close to the desired output by using the linear combination of the object features and the predicted output. This simple transformation will reduce the error of neural network and improve classification performance. The Kalman filter iteration is used to estimate the parameters of the linear model. Five datasets from various domains with various characteristics, such as attribute types, the number of attributes, the number of samples, and the number of classes, were used for empirical validation. The validation results show that the linear model based on the Kalman filter can improve the performance of the original neural network

Application of Color and Size Measurement in Food Products Inspection

Color and size are external aspects considered by consumers in purchasing a food product and are used in food product inspection using computer vision. This paper reviews recent applications of color and size measurement in food product inspection using computer vision. RGB, HSI, HSL, HSV, La*b spaces and color index are widely used to measure color in food product inspection. Color features, including value, mean, variance, and standard deviation of each channel in a color space are widely used in food product inspection. The applications of color measurement in food product inspection are for grading, detection of anomaly or damage, detection of specific content and evaluation of color changes. Length, width, thickness, average radius, Feret’s diameter, area, perimeter, volume, and surface area are common size measurements in food product inspection. The applications of size measurement in food product inspection are for estimating size, sorting, grading, detect unwanted objects or defects, and measurement of physical properties