Scene illumination classification based on histogram quartering of CIE-Y component

Despite the rapidly expanding research into various aspects of illumination estimation methods, there are limited number of studies addressing illumination classification for different purposes. The increasing demand for color constancy process, wide application of it and high dependency of color constancy to illumination estimation makes this research topic challenging. Definitely, an accurate estimation of illumination in the image will provide a better platform for doing correction and finally will lead in better color constancy performance. The main purpose of any illumination estimation algorithm from any type and class is to estimate an accurate number as illumination. In scene illumination estimation dealing with large range of illumination and small variation of it is critical. Those algorithms which performed estimation carrying out lots of calculation that leads in expensive methods in terms of computing resources. There are several technical limitations in estimating an accurate number as illumination. In addition using light temperature in all previous studies leads to have complicated and computationally expensive methods. On the other hand classification is appropriate for applications like photography when most of the images have been captured in a small set of illuminants like scene illuminant. This study aims to develop a framework of image illumination classifier that is capable of classifying images under different illumination levels with an acceptable accuracy. The method will be tested on real scene images captured with illumination level is measured. This method is a combination of physic based methods and data driven (statistical) methods that categorize the images based on statistical features extracted from illumination histogram of image. The result of categorization will be validated using inherent illumination data of scene. Applying the improving algorithm for characterizing histograms (histogram quartering) handed out the advantages of high accuracy. A trained neural network which is the parameters are tuned for this specific application has taken into account in order to sort out the image into predefined groups. Finally, for performance and accuracy evaluation misclassification error percentages, Mean Square Error (MSE), regression analysis and response time are used. This developed method finally will result in a high accuracy and straightforward classification system especially for illumination concept. The results of this study strongly demonstrate that light intensity with the help of a perfectly tuned neural network can be used as the light property to establish a scene illumination classification system

Lung Nodule Detection, Classification and Segmentation via Deep Learning

University of Technology Sydney. Faculty of Engineering and Information Technology.Accurately detecting and segmenting lung nodules from CT images play a critical role in the earlier diagnosis of lung cancer, staging and evaluating patients’ response to cancer therapy. Thus, this research area has attracted much interest from the research community. Meanwhile, deep learning based image segmentation has by now been firmly established as a robust tool in image segmentation. In the first part of this thesis, an advanced deep learning solution is proposed to segment lung nodules from CT images by employing a deep residual network structure with Atrous convolution. The second part of this thesis presents a highly effective and robust solution to this problem by innovatively utilising the changes of nodule shapes over continuous slices (inter-slice changes), where a deep learning based end-to-end system is developed. To explore the inter-slice features, we propose to create a novel synthetic image to depict the unique changing pattern of nodules between slices in distinctive colour patterns. To further improve the detection and segmentation accuracy, in the third part of this research, a two-stage segmentation approach is developed which is capable of accurately detecting and segmenting lung nodules from 2D CT images

Performance of various training algorithms on scene illumination classification

The increasing number of training algorithms along with their convincing results will make this question that which algorithm will be more efficient. This study aims to perform some widespread tests on some well-known training algorithms (Levenberg-Marquardt, Resilient back propagation and Scaled conjugate gradient) to evaluate their performance for scene illumination classification. The results presented by this research can provide a reliable guide line for choosing the most appropriate training algorithm depends on the problem specification. The results of this study select the LM training method with the accuracy of 94.41% as the most accurate and RP as the most quick method with response time of 0.426 s

Malaysia solar energy experience: intelligent fault location algorithm for unbalanced radial distribution network including PV systems

Due to environmental issues and the upward trend of fossil fuel prices, the study of renewable energy (RE) based generation and their effects on the electrical system has become an important part of the government's energy policies and university projects. In RE generation, as solar photovoltaic (PV) systems are modular, silent, and transportable and demonstrate ease of installation, they have attracted a greater amount of attention specifically in those areas which receive considerable average solar radiation per day such as Malaysia. However, connecting solar PV farms to the grid like any other distributed generation (DG) units poses serious issues which arise in the distribution network. This paper presents a novel fault location algorithm based on the recording of short circuit power values at the primary substation of unbalanced radial distribution networks including PV systems. The recorded values are evaluated by a designed and tuned multi-layer feed forward neural network and the fault distances from the source are estimated accordingly. In order to highlight the accuracy of the presented method, the scenario is also repeated by recording the peak values of short circuit current which have been mostly used in the published intelligent fault location studies and the obtained results via two different values are compared with each other. The results reveal that the presented algorithm using a small scale input set is able to precisely locate different fault types in the unbalanced distribution networks including DG units

Designing the network topology of feedforward neural network for scene illumination classification

Determining the topology of network as one of the objectives of ANN systems, is not following any certain rules or algorithms but still there are several hints which help us to restrict the neural network architecture set. Hence, the process of testing structures will be the solution of finding most effective one among a limited set. This study aims to apply testing method on scene illumination classification system to find out the appropriate ANN structure. The results of this study apply on similar classification systems to avoid redoing the testing process

Scene illumination classification using illumination histogram analysis and neural network

This study proposed a classification method to classify the considered image in the most similar illumination cluster rather than estimating an illumination value. This method categorizes the images based on inherent illumination data of scene and statistical features extracted from illumination histogram of image. It has advantages of high accuracy and flexibility of defining the classes. A trained neural network is taken into account in order to classify the image into predefined groups. Finally, for performance and accuracy evaluation we use misclassification error percentages and Mean Square Error (MSE)

On the fault location algorithm for distribution networks in presence of DG

Connecting distributed generation (DG) units to the distribute networks impose several impacts on it which have not been considered in conventional fault location algorithms. This paper presents an accurate fault location technique for unbalanced radial distribution networks based on evaluating measured values of short Circuit Current (S/C.C) at the source bus with a designed Multi-Layer Feed Forwarded Neural Network (ML-FFNN). The estimated locations of different fault types are compared with the actual distances and Average Difference Percentage (ADP) is calculated for each fault type. The designed neural network is able to work with small scale datasets. Hence the proposed method can be implemented in the real distribution networks

Short circuit power based fault location algorithm in distribution networks

This paper presents a novel accurate fault location technique for the radial unbalanced distribution systems, based on measurement of the Short Circuit Power (S/C.P) peak values at the substation. To evaluate the gathered dataset, a Multi-Layer Feed Forward Neural Network (ML-FFNN) with the tuned parameters is designed and the locations of faults are estimated in low, medium and far distances from the source. The estimated distances are compared with the real fault locations to show the accuracy of estimations. The proposed method can work with the small scale datasets and it is capable of being implemented in distribution systems with several laterals