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This paper presents an exemplar-based image completion via a new quality measure based on phaseless texture features. The proposed method derives a new quality measure obtained by monitoring errors caused in power spectra, i.e., errors of phaseless texture features, converged through phase retrieval. Even if a target patch includes missing pixels, this measure enables selection of the best matched...
This paper proposes a hybrid approach, integrating Decision Trees (DT) and Artificial Neural Networks (ANN) for energy price classification in deregulated electricity market. The proposed model does not aim to predict future values of energy prices, but classify and explain the negative Locational Marginal Price (LMP) that are observed in the grid. The negative LMPs are grouped by the K-means technique...
The conventional approach of generating clinical opinions from general blood test (GBT) results uses the deep neural network (DNN) comprised of fully-connected layers. The large number of input neurons and output neurons result in the complex DNN structure, which causes overfitting problem. However, the dimension of the input vector and the output vector cannot be reduced arbitrarily, as all GBT results...
Synapse crossbar is an elementary structure in neuromorphic computing systems (NCS). However, the limited size of crossbars and heavy routing congestion impede the NCS implementation of large neural networks. In this paper, we propose a two-step framework (namely, group scissor) to scale NCS designs to large neural networks. The first step rank clipping integrates low-rank approximation into the training...
This paper proposes a new scheme for hyperspectral image classification through k-means clustering. The scheme includes three steps. Firstly, principal component analysis (PCA) is utilized for dimension reduction of the hyperspectral image. Secondly, the reduced features are clustered using k-means clustering algorithm and subsequently the clusters are trained separately by multi-class support vector...
A large number of face recognition algorithms have been developed in last decades. Over the past four decades, performance of Face Recognition on frontal faces in controlled environment has improved significantly but frontal faces with uncontrolled environment and expression remains a challenge. The GBU Based Face Recognition Techniques focuses on the attention of the fundamental problem of comparing...
Causal relationship between physical activity and prevention of several diseases has been known for some time. Recently, attempts to quantify dose-response relationship between physical activity and health show that automatic tracking and quantification of the exercise efforts not only help in motivating people but improve health conditions as well. However, no commercial devices are available for...
Identification of colorants of artworks is of paramount importance in the context of museums and art galleries. We present a technique to discriminate the fiber dyes into natural or synthetic class using principal component analysis (PCA). Spectral imaging is used to measure the reflectance spectra of a variety of dyed wools in visible to near infrared (Vis/NIR): 400–1000 nm and short wave infrared...
Face recognition has been gaining popularity for long time in various fields of human computer interaction. Moreover face recognition technique is widely used for automatic biometric security control, document verification, criminal investigation etc. In this paper we propose a new approach of using PCA based face recognition method for human verification. PCA based method seems to be interested due...
This paper addresses the problem of damage detection technique of structural health monitoring (SHM). Kernel principal components analysis (KPCA)-based generalized likelihood ratio (GLR) technique is developed to enhance the damage detection of SHM processes. The data are collected from the complex three degree of freedom spring-mass-dashpot system in order to calculate the KPCA model. The developed...
In this paper, we study the performance of different classifiers on the CIFAR-10 dataset, and build an ensemble of classifiers to reach a better performance. We show that, on CIFAR-10, K-Nearest Neighbors (KNN) and Convolutional Neural Network (CNN), on some classes, are mutually exclusive, thus yield in higher accuracy when combined. We reduce KNN overfitting using Principal Component Analysis (PCA),...
In this work, we discuss utility of Restricted Boltzmann Machine (RBM) in face-deidentification challenge. GRBM is a generative modeling technique and its unsupervised learning provides vantage of using raw faces data. Faces are deidentified by reconstructed face images from the trained GRBM model. The reconstructed image uses random information from the stochastic units which makes it hard to re-identify...
Machine Learning has a wide array of applications in the healthcare domain and has been used extensively for analyzing data. Apnea of Prematurity is a breathing disorder commonly observed in preterm infants. This paper compares the usage of Support Vector Machines and Random Forests, which are supervised learning algorithms, to predict Apnea of Prematurity at the end of the first week of the child's...
Developing fields such as Brain Computer Interface, Virtual Reality are now a day's in research are using brain signal as an equipment for a good start to differentiate tasks. It created new break points in aiding wellness training, rehabilitation, games, education, entertainment etc. Here, the content has been summarized about the technology, which had been developed for acquisition of brain signal,...
With the proliferation of smartphones, the security threats have correspondingly increased. Although some form of security mechanisms like authentication and encryption have been provided on platforms such as Android, these alone cannot mitigate all the forms of threats. Thus, the need for an intrusion detection system for smartphones has become immensely important. In this project, we capitalize...
Regarding texture features, Local-based methods such as Local Binary Pattern (LBP) and its variants are computationally efficient high-performing but sensitive to noise, and suffering global structure information loss. By contrast, filter-based counterparts, the Scattering Transform for instance, are tolerant to noise and translation but often lack of small local structure information. In this paper...
Human Epithelial type-2 (HEp-2) cells are used as substrates for the detection of Anti Nuclear Antibodies (ANA) in the Indirect Immunofluorescence (IIF) test to diagnose autoimmune diseases. Pathologists in the laboratory examine the IIF slides to detect and recognize theHEp-2 cell patterns to generate the report. So, the IIF test is subjective and requires objective analysis. This paper introduces...
Orientation Field (OF) is one of the most significant characters to distinguish fingerprint images from non-fingerprint images. An effective definition of fingerprint OF pattern will not only benefit fingerprint enhancement, but also contribute to latent fingerprint detection and segmentation. The existing fingerprint OF models either require pre-knowledge of singular points, or cannot be generalized...
Finding pre-image is crucial for kernel principal component analysis (KPCA) based pattern de-noising. This paper proposes to learn the systematic error of some classical methods of pre-image finding, and to refine the obtained pre-image via error compensation. Experiments based on simulated data as well as real-world data demonstrate that the proposed approach can improve effectively the results from...
Recently, hash algorithms catch amounts of sights in the field of machine learning. Most existing hash methods directly utilize a vector, which can be piped by the column of image matrix, as a unit and adopt some feature extraction functions to project the original data into generally shorter fixed-length values or characters. Then each of these projected real values is quantized or hashed into zero-one...
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