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Monitoring of dynamic industrial process has been increasingly important due to more and more strict safety and reliability requirements. Popular methods like time lagged arrangement-based and subspace-based approaches exhibit good performance in fault detection, however, they suffer from difficulty in accurately isolating faulty variables and diagnosing fault types. To alleviate this difficulty,...
This paper proposes a classification method based on principal component reconstruction (PCR) for target recognition in synthetic aperture radar (SAR) image. To characterize the SAR image and alleviate the influence of different intensity of the same targets on target recognition, the SAR image is mapped into the principal component space by the principal component analysis with zero mean. In the...
Deep learning has greatly improved visual recognition in recent years. However, recent research has shown that there exist many adversarial examples that can negatively impact the performance of such an architecture. This paper focuses on detecting those adversarial examples by analyzing whether they come from the same distribution as the normal examples. Instead of directly training a deep neural...
Leveraging class semantic descriptions and examples of known objects, zero-shot learning makes it possible to train a recognition model for an object class whose examples are not available. In this paper, we propose a novel zero-shot learning model that takes advantage of clustering structures in the semantic embedding space. The key idea is to impose the structural constraint that semantic representations...
Unsupervised learning from visual data is one of the most difficult challenges in computer vision. It is essential for understanding how visual recognition works. Learning from unsupervised input has an immense practical value, as huge quantities of unlabeled videos can be collected at low cost. Here we address the task of unsupervised learning to detect and segment foreground objects in single images...
We present detection of various fdters using neural networks usable for our Long wave infrared (LWIR) hyperspectral detection system (HDES). Some reduction techniques are shown, for our aim of the small neural network with small computing requirements. In addition, the filter measurement is usable for calibration and verification of the HDES properties.
Face recognition systems as a biometrie system for human identification and verification has indeed shown a lot of progress over the years, illustrated through the various applications it has been used for. These applications include Human Computer Interaction systems, law, terrorist attacks, health and multimedia indexing. Furthermore, the installation of face recognition systems in public and private...
Nowadays face recognition plays a central role in surveillance, biometrics and security. In this paper a Field-Programmable Gate Array (FPGA) based low-cost real-time architecture for face recognition is presented. The face recognition module receives the detected faces from a video stream and processes the data with the widely used Eigenfaces, also known as the Principal Component Analysis (PCA)...
This paper proposes an effective fusion scheme for extracting more discriminative information from bimodal biometrics at data, feature and decision levels. In all these three levels of fusion, information from both face andfingerprint image of a single subject are fused to effectively represent it in a more discriminative ways. For all these three approaches, a combination of wavelet and principal...
Some of the best current face recognition approaches use feature extraction techniques based on either Principle Component Analysis (PCA), Local Binary Patterns (LBP), Autoencoder (non-linear PCA), etc. While each of these feature techniques works fairly well, we propose to combine multiple feature extractors with deep learning in a system so that the overall face recognition accuracy can be improved...
Anaerobic ammonium oxidation (anammox) process has been recognized as efficient biological nitrogen removal process, which has the advantages of cost-effective and low energy compared to the conventional nitrification and denitrification processes. However, the efficient operation and control is limited due to the complexity of nonlinear and biochemical phenomena involved. This paper proposes an appropriate...
Gender classification play a significant role in recognition performance. For the purpose of visual surveillance, gender is considered as an important factor. In this paper a hybrid approach is proposed by fusing Gait Energy Image (GEI) with spatio temporal parameters for the gender classification. The dataset used is CASIA B which comprises of 118 subjects (89 males and 29 females). The proposed...
The application of various statistical machine learning methods for the identification of bi-heterocyclic drugs that are based on the THz spectra is presented. A comparison of classification efficiency with six algorithms (LDA, QDA, SVM, Naive Bayes, KNN with Euclidean metrics and the cosine similarity) is shown and a complete THz system allowing for the identification of drugs with an efficiency...
Incremental learning allows incorporating new data in a classifier model without full retraining for computational efficiency. In this paper, we present two ways of performing incremental learning on Grassmann manifolds. In a Grassmann kernel learning framework, data are embedded on subspaces and kernels are constructed to map data subspaces to a projection space for classification. As new data samples...
The goals of this paper were twofold: to continue and refine previous research in the topic of tree cover type classification by harnessing modern machine learning models, and to extend the conclusions of that work to demonstrate that results gained from such models can be used to assist U.S. land management agencies in current challenges they face. Using the same dataset as the past study, an artificial...
Recent work in the recognition of naturalistic expressions, which is also known as spontaneous facial expressions recognition, has attracted researchers' attention due to its importance in different behavioural and clinical applications. The main design challenges in the area of emotion computing for automatic recognition of spontaneous facial expression are the face pose, capture distance, illumination...
With the evolution of large computer data, every corner of the society is filled with a variety of text information. Indeed, large data information that need manage by people has been unable to meet the rapid development of society. Therefore, the technology of efficient management and accurate positioning of vast quantities of text information has become a hot topic in the research community. In...
Parametrisation of the shape of deformable objects is of paramount importance in many computer vision applications. Many state-of-the-art statistical deformable models perform landmark localisation via optimising an objective function over a certain parametrisation of the object's shape. Arguably, the most popular way is by employing statistical techniques. The points of shape samples of an object...
Component Analysis (CA) consists of a set of statistical techniques that decompose data to appropriate latent components that are relevant to the task-at-hand (e.g., clustering, segmentation, classification, alignment). During the past few years, an explosion of research in probabilistic CA has been witnessed, with the introduction of several novel methods (e.g., Probabilistic Principal Component...
In this paper, we propose a method to classify K-pop dance based on motion data obtained from Kinect V2 for research of motion classification and development of anti-plagiarism system. To do this, 200-point dances of K-pop are acquired. Dance motions from 40 amateur dancers are acquired to construct a total of 400 data. The proposed classification method consists of three steps. First, we obtain 13...
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