A Hybrid Chebyshev-ICA Image Fusion Method Based on Regional Saliency

An image fusion method that performs robustly for image sets heavily corrupted by noise is presented in this paper. The approach combines the advantages of two state-of-the-art fusion techniques, namely Independent Component Analysis (ICA) and Chebyshev Poly-nomial Analysis (CPA) fusion. Fusion using ICA performs well in transferring the salient features of the input images into the composite output, but its performance deteriorates severely under mild to moderate noise conditions. CPA fusion is robust under severe noise conditions, but eliminates the high frequency information of the images involved. We pro-pose to use ICA fusion within high activity image areas, identified by edges and strong textured surfaces and CPA fusion in low activity areas identified by uniform background regions and weak texture. A binary image map is used for selecting the appropriate method, which is constructed by a standard edge detector followed by morphological operators. The results of the proposed approach are very encouraging as far as joint fusion and denoising is concerned. The works presented may prove beneficial for future image fusion tasks in real world applications such as surveillance, where noise is heavily present

Quantification and localisation of individual leaf disease lesion for grading severity of late blight

Detecting incidence and grading the severity of plant diseases caused by pathogens is among the essential activities in precision agriculture. This research proposes novel noetic integration between pathology and advanced yet straightforward image processing technique for grading the severity of vegetable late blight. Until recently, most of the presented image processing techniques had been, and some still are, grading severity based on the visual understanding of disease symptom as a single lesion colony. One of the most significant advantages of the proposed method is quantifying and localising disease symptom colonies into symptomatic and necrotic in accordance with pathological disease analogy for actual severity grading. In the 1st phase of the study, individual symptomatic (RS), necrotic (RN), and blurred (RB, in- between healthy and symptomatic) regions were identified and segmented. The isolated diseased lesions are then quantified and localised for correlationwith a standard area diagram which gives the accurate grading of disease severity. Results obtained indicated great potential for accurate grading by which pathological knowledge and advance computer network operate in proper synergy. It is also envisaged that this research method would provide meaningful insight into the critical current and future role pathological integration in machine learning for food security

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

Hadamard transform improvement for hevc using intel avx-512

High Efficiency Video Coding (HEVC) doubles the data compression ratio compared to previous generation compression technology, Moving Picture Expert Group-Advanced Video Codec (MPEG-AVC/H.264) without sacrificing the image quality. However, this superior compression comes at the cost of more computation payload resulting in longer time for encoding and decoding. This work proposes the vectorization on HEVC data heavy computation algorithm, Hadamard Transform or Sum of Absolute Transform Difference (SATD) and Sum of Absolute Difference (SAD) to achieve optimized compression performance. Single Instruction Multiple Data (SIMD) acceleration will be based on the Intel AVX-512 (Advanced Vector Extension) Instruction Set Architecture (ISA). Since HEVC supports more coding tree block (CTB) sizes, SATD and SAD algorithms eventually become more complex compared to AVC. As a result, SATD and SAD algorithms with various block sizes will be subjected to SIMD acceleration. We provide performance evaluation based on different SIMD ISA and without SIMD implementation on HEVC SATD and SAD and found that AVX-512 optimized implementation performed faster when compared to non- optimized SATD and SAD but showed signs of reduced performance when compared to SSE optimized SATD and SAD

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

Super resolution of car plate images using generative adversarial networks

Car plate recognition is used in traffic monitoring and control systems such as intelligent parking lot management, finding stolen vehicles, and automated highway toll. In practice, Low-Resolution (LR) images or videos are widely used in surveillance systems. In low resolution surveillance systems, the car plate text is often illegible. Super-Resolution (SR) techniques can be used to improve the car plate quality by processing a series of LR images into a single High-Resolution (HR) image. Recovering the HR image from a single LR is still an ill-conditioned problem for SR. Previous methods always minimize the mean square loss in order to improve the peak signal to noise ratio (PSNR). However, minimizing the mean square loss leads to overly smoothed reconstructed image. In this paper, Generative Adversarial Networks (GANs) based SR is proposed to reconstruct the LR images into HR images. Besides that, perceptual loss is proposed to solve the smoothing issue. The quality of the GAN based SR generated images is compared to existing techniques such as bicubic, nearest and Super-Resolution Convolution Neural Network (SRCNN). The results show that the reconstructed images using GANs based SR achieve better results in term of perceptual quality compared to previous methods

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

Time-frequency analysis of power signal : application to substation monitoring and management system

This paper presents a power disturbance detection and classification method based on Time-Frequency analysis. We will discuss how the choice the kernels affect the characteristic of the distribution function. Here we consider a non-stationary testing harmonic composed of a fundamental frequency of 50Hz and its second and third harmonic frequency components. The 50Hz signal is on for the entire record extending from 0 to 1second, while the 100Hz signal is from 100ms to 350 ms and 200Hz signal is from 200ms to 550ms. As for comparison, we will use the same testing signal for different type of kernels. This technique has been applied in a substation monitoring and management system which monitors the power quality at the substation as well as the street lighting panel

Improved roational matching of SIFT and SURF

Scale-Invariant Feature Transform(SIFT) and Speeded-Up Robust Feature(SURF) are common techniques used for extracting robust features that can be used to perform matching between different viewpoints of scenes. Both methods basically involve three main stages, which are feature extraction, orientation assignment and feature descriptor extraction for matching. SURF is computation efficient compared to SIFT because the integral image is used for the convolutions to reduce computation time. However, both methods also do not focus much on the technique of matching. This paper introduces a method which can help to improve the rotational matching performance in term of accuracy by establishing a decision matrix and an approximated rotational angle within two corresponding images. The proposed method generally improved the matching rate around 10% to 20% in terms of accuracy