Starfruit classification based on linear hue computation

In this paper, a classification process to group starfruit into six maturity indices is proposed based on 1- dimensional color feature called hue, which is extracted from the starfruit image. As the original hue is quantified from the nonlinear transformation of the 3-dimensional Red, Green and Blue color, this paper proposes a linear hue transformation computation based on the 2 colors of Red and Green. The proposed hue computation leads to a reduced computational burden, less computational complexity and better class discriminant capability. The hue is then applied as the input for the maturity classification process. The classification process is based on the hypothesis that for each of the maturity index, certain area of the starfruit surface is supposed to have distinctive value of the hue. In this work, the said starfruit surface area is set as 70% of the total area and based on 600 samples, the proposed technique results in 93% classification accuracy

Depth estimation of inner wall defects by means of infrared thermography

There two common methods dealing with interpreting data from infrared thermography: qualitatively and quantitatively. On a certain condition, the first method would be sufficient, but for an accurate interpretation, one should undergo the second one. This report proposes a method to estimate the defect depth quantitatively at an inner wall of petrochemical furnace wall. Finite element method (FEM) is used to model multilayer walls and to simulate temperature distribution due to the existence of the defect. Five informative parameters are proposed for depth estimation purpose. These parameters are the maximum temperature over the defect area (Tmax-def), the average temperature at the right edge of the defect (Tavg-right), the average temperature at the left edge of the defect (Tavg-left), the average temperature at the top edge of the defect (Tavg-top), and the average temperature over the sound area (Tavg-so). Artificial Neural Network (ANN) was trained with these parameters for estimating the defect depth. Two ANN architectures, Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) network were trained for various defect depths. ANNs were used to estimate the controlled and testing data. The result shows that 100% accuracy of depth estimation was achieved for the controlled data. For the testing data, the accuracy was above 90% for the MLP network and above 80% for the RBF network. The results showed that the proposed informative parameters are useful for the estimation of defect depth and it is also clear that ANN can be used for quantitative interpretation of thermography data

Wavelet-based medical image fusion via a non-linear operator

Medical image fusion has been extensively used to aid medical diagnosis by combining images of various modalities such as Computed Tomography (CT) and Magnetic Resonance Image (MRI) into a single output image that contains salient features from both inputs. This paper proposes a novel fusion algorithm through the use of a non-linear fusion operator, based on the low sub-band coefficients of the Discrete Wavelet Transform (DWT). Rather than employing the conventional mean rule for approximation sub-bands, a modified approach is taken by the introduction of a non-linear fusion rule that exploits the multimodal nature of the image inputs by prioritizing the stronger coefficients. Performance evaluation of CT-MRI image fusion datasets based on a range of wavelet filter banks shows that the algorithm boasts improved scores of up to 92% as compared to established methods. Overall, the non-linear fusion rule holds strong potential to help improve image fusion applications in medicine and indeed other fields

Development of Automatic Mixing Process for Fertigation System in Rock Melon Cultivation

This work proposed an automatic mixing system of nutrient solution for rock melon fertigation according to the required electrical conductivity (EC) level. Compared to the manual practice, this automatic system will ensure continuous supply of mixed nutrient solution without the need to daily check and mix new nutrient. Thus, this easy to use and low cost automatic system will reduce the burden of the farmers. This system uses an EC sensor to automatically check the concentration level of the mixed nutrient solution. Other than that, the system only consists of electronic pumps for mixing process and an Arduino board as the controller. The controller will monitor the EC level and run the mixing process when the EC level is below the required level. By calibrating the EC sensors, the test shows that the automatic mixing system is able to accurately keep the mixed nutrient solution concentration in a 400 L mixing reservoir at several required levels

Enhanced rotational feature points matching using orientation correction

In matching between images, several techniques have been developed particularly for estimating orientation assignment in order to make feature points invariant to rotation. However, imperfect estimation of the orientation assignment may lead to feature mismatching and a low number of correctly matched points. Additionally, several possible candidates with high correlation values for one feature in the reference image may lead to matching confusion. In this paper, we propose a post-processing matching technique that will not only increase the number of correctly matched points but also manage to solve the above mentioned two issues. The key idea is to modify feature orientation based on the relative rotational degree between two images, obtained by taking the difference between the major correctly matched points in the first matching cycle. From the analysis, our proposed method shows that the number of detected points correctly matched with the reference image can be increased by up to 50%. In addition, some mismatched points due to similar correlation values in the first matching round can be corrected. Another advantage of the proposed algorithm it that it can be applied to other state-of-the-art orientation assignment techniques

Enhanced rotational feature points matching using orientation correction

In matching between images, several techniques have been developed particularly for estimating orientation assignment in order to make feature points invariant to rotation. However, imperfect estimation of the orientation assignment may lead to feature mismatching and a low number of correctly matched points. Additionally, several possible candidates with high correlation values for one feature in the reference image may lead to matching confusion. In this paper, we propose a post-processing matching technique that will not only increase the number of correctly matched points but also manage to solve the above mentioned two issues. The key idea is to modify feature orientation based on the relative rotational degree between two images, obtained by taking the difference between the major correctly matched points in the first matching cycle. From the analysis, our proposed method shows that the number of detected points correctly matched with the reference image can be increased by up to 50%. In addition, some mismatched points due to similar correlation values in the first matching round can be corrected. Another advantage of the proposed algorithm it that it can be applied to other state-of-the-art orientation assignment techniques

An image registration technique to enhance PCB inspection algorithms with real images

It is well known that real PCD image inspection based on referential approaches faces misalignment problems in detecting defects between a template image and a defective image. Hence, a reliable image registration technique is needed to align these two images perfectly. Hence, a registration technique which incorporates affine transformation and bi-cubic interpolation has been proposed. Experimental results have shown that this registration technique is suitable to be employed to obtain well-aligned defective images before detection algorithm takes place in PCD inspection

A study on the application of Gravitational Search Algorithm in optimizing Stereo Matching Algorithm's parameters for star fruit inspection system

This paper reports the result obtained by implementing Gravitational Search Algorithm for tuning Stereo Matching Algorithm's parameters for the application star fruit inspection system. The hardware for the inspection system is built by CvviP from Universiti Teknologi Malaysia using only single camera. The implemented Stereo Matching Algorithm used on the system comes from the built-in Matlab library. Each agent of Gravitational Search Algorithm in the search pace represents a set of candidate numerical value of the stereo matching's parameters. The sum of absolute error of the gray scale value of both images is used to indicate the fitness function. Benchmarking has done by comparing the result obtained with the previous literature that implements Particle Swarm Optimization. The result indicates that the application of Gravitational Search Algorithm as parameters tuner for stereo matching's parameters tuning is essentially on par with the Particle Swarm Optimization Algorithm

Brain Lesion Segmentation from Diffusion-Weighted MRI Based on Adaptive Thresholding and Gray Level Co-Occurence Matrix

This project presents brain lesion segmentation of diffusion-weighted magnetic resonance images (DWI) based on thresholding technique and gray level co-occurrence matrix (GLCM). The lesions are hyperintense lesion from tumour, acute infarction, haemorrhage and abscess, and hypointense lesion from chronic infarction and haemorrhage. Pre-processing is applied to the DWI for intensity normalization, background removal and intensity enhancement. Then, the lesions are segmented by using two different methods which are thresholding technique and GLCM. For the thresholding technique, image histogram is calculated at each region to find the maximum number of pixels for each intensity level. The optimal threshold is determined by comparing normal and lesion regions. Conversely, GLCM is computed to segment the lesions. Different peaks from the GLCM crosssection indicate the present of normal brain region, cerebral spinal fluid (CSF), hyperintense or hypointense lesions. Minimum and maximum threshold values are computed from the GLCM cross-section. Region and boundary information from the GLCM are introduced as the statistical features for segmentation of hyperintense and hypointense lesions. The proposed technique has been validated by using area overlap (AO), false positive rate (FPR), false negative rate (FNR), misclassified area (MA), mean absolute percentage error (MAPE) and pixels absolute error ratio (r err) . The results are demonstrated in three indexes MA, MAPE and r err , where 0.3167, 0.1440 and 0.0205 for GLCM, while 0.3211, 0.1524 and 0.0377 for thresholding technique. Overall, GLCM provides better segmentation performance compared to thresholding techniqu

Application of Particle Swarm Optimization in Optimizing Stereo Matching Algorithm’s Parameters for Star Fruit Inspection System

This paper reports the finding of the experimentation of the Particle Swarm Optimization in optimizing the stereo matching algorithm’s parameters for the star fruit inspection system. The star fruit inspection system is built by CvviP Universiti Teknologi Malaysia. While the stereo matching algorithm used in the experiment is taken from the Matlab library. Each particle of Particle Swarm Optimization in the search pace repsents a set of candidate numerical value of the stereo matching’s parameters. The fitness function for this application is the sum of absolute error of the gray scale value of both images. Based on this information, the particles will improve its position in the search space by moving towards its best record and the swarm best record. The process repeated until the maximum iteration met. The result indicates that there is potential application of Particle Swarm Optimization in stereo matching’s parameters tuning