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This paper combines three contributions to establish a new state-of-the-art in dynamic scene recognition. First, we present a novel ConvNet architecture based on temporal residual units that is fully convolutional in spacetime. Our model augments spatial ResNets with convolutions across time to hierarchically add temporal residuals as the depth of the network increases. Second, existing approaches...
This paper has proposed gait recognition approach for analyzing and classifying human identification under carrying a bag and wearing a clothing thus improving recognition performances. The proposed method is based on detail wavelet features extracted from the Haar-wavelet decomposition of dynamic areas in the Gait Energy Image (GEI). Spectral Regression Kernel Discriminant Analysis (SRKDA) is then...
The High Efficiency Video Coding (HEVC) standard significantly saves coding bit-rate over the proceeding H.264 standard, but at the expense of extremely high encoding complexity. In fact, the coding tree unit (CTU) partition consumes a large proportion of HEVC encoding complexity, due to the brute-force search for rate-distortion optimization (RDO). Therefore, we propose in this paper a complexity...
Nowadays the CNN is widely used in practical applications for image classification task. However the design of the CNN model is very professional work and which is very difficult for ordinary users. Besides, even for experts of CNN, to select an optimal model for specific task may still need a lot of time (to train many different models). In order to solve this problem, we proposed an automated CNN...
This paper proposes a novel kernel-based image subspace learning method for face recognition, by encoding an face image as a tensor of second order (matrix). First, we propose a kernel based discriminant tensor criterion, called kernel bilinear fisher criterion (KBFC), which is designed to simultaneously pursue two projection vectors to maximize the interclass scatter and at the same time minimize...
Facial expression recognition is a very active research topic due to its potential applications in the many fields such as human-robot interaction, human-machine interfaces, driving safety, and health-care. Despite of the significant improvements, facial expression recognition is still a challenging problem that wait for more and more accurate algorithms. This article presents a new model that is...
In this paper we utilize the first large-scale "in-the-wild" (Aff-Wild) database, which is annotated in terms of the valence-arousal dimensions, to train and test an end-to-end deep neural architecture for the estimation of continuous emotion dimensions based on visual cues. The proposed architecture is based on jointly training convolutional (CNN) and recurrent neural network (RNN) layers,...
Face recognition methods utilizing Sparse Representation based Classification (SRC) and Collaborative Representation based Classification (CRC) have recently attracted a great deal of attention due to inherent simplicity and efficiency. In this paper, we introduce the Large Margin Nearest Neighbor (LMNN), which learns a Mahalanobis distance metric that is applied, to SRC and CRC as the locality constraint...
In the field of civil engineering, Ground Penetrating Radar (GPR) is the most widely used method of Non-Destructive Testing (NDT). Using supervised learning methods or signal processing methods, it is possible to analyze the sub-surface defects in pavement. In this paper, we propose to use a supervised machine learning method called Support Vector Machines (SVM) to detect the presence of debondings...
This paper presents an approach called Gabor-feature-based Local Generic Representation (G-LGR), which take advantages of the sparse representation properties of face recognition in biometric applications. In this work, the main problem is that if only one training subject per class is available. One of the novelties of our new algorithm is to produce virtual samples of each subject; the new sample...
Ground Penetrating Radar (GPR) has been a precious tool for humanitarian demining. The GPR scans the ground and delivers a three-dimensional matrix representing three types of data: Ascan, Bscan and Cscan. The Ascan data represents the response from a reflection signal of a pulse emitted by the GPR at a given position. In the proposed landmine detection method, the Ascan data is normalized and then...
It is well recognized that the signal processing methods contributes in biology to the control of the DNA spatial structure. From the previous studies, it is inferred that the significant portion of the eukaryotic genomes is composed of transposable elements (TEs). The TEs play an important role as a driving force of genome evolution. An important sub class of ETs class II, Helitrons, have been revealed...
A robust kernel-based machine learning localization scheme using time of arrival (TOA) or time difference of arrival (TDOA) in none-line-of-sight (NLOS) environments is proposed. The scheme can provide accurate position estimation while the reference nodes are coarsely and randomly distributed in the area of interests. Moreover, the scheme is insensitive with respect to random TOA synchronization...
A weighted version of fusion strategy is introduced for multiple biometrics that compensates the limitations impinged on single biometrics. The least intrusive facial biometrics that combines the facial behaviometrics for person identification is a new trend that needs further investigation. The aim of this study is to apply this weighted fusion scheme for our proposed bi-modal framework that uses...
Local binary pattern (LBP) has limitation in extracting the edge and direction information, which is vital to infrared face recognition. A new infrared face recognition algorithm fusion of LBP and histogram of oriented gradients (HOG) is proposed. First, LBP operator is adopted to extract the texture feature of an infrared face, and then the edge features of the original infrared face are extracted...
Parkinson's disease (PD) is a neurological disorder associated with a progressive decline in motor skills, speech, and cognitive processes. Since the diagnosis of Parkinson's disease is difficult, researchers have worked to develop a support tool based on algorithms to separate healthy controls from PD patients. Online handwriting analysis is one of the methods that can be used to diagnose PD. The...
By passing of time, the size of data such as fMRI scans, speech signals and digital photographs becomes very high and it takes large amount of time for data processing. To overcome this problem, the dimensionality of data should be reduced. Whereas graph embedding introduces a successful framework for dimensionality reduction, we use it as the base of our proposed method. In this framework, similarity...
Detecting bugs with code mining has proven to be an effective approach. However, the existing methods suffer from reporting serious false positives and false negatives. In this paper, we developed an approach called AntMiner to improve the precision of code mining by carefully preprocessing the source code. Specifically, we employ the program slicing technique to decompose the original source repository...
In this paper, we propose a blind motion deblurring method based on sparse representation and structural self-similarity from a single image. The priors for sparse representation and structural self-similarity are explicitly added into the recovery of the latent image by means of sparse and multi-scale nonlocal regularizations, and the down-sampled version of the observed blurry image is used as training...
Developing cross-corpus, cross-domain, and cross-language emotion recognition algorithm has becoming more prevalent recently to ensure the wide applicability of robust emotion recognizer. In this work, we propose a computational framework on fusing multiple emotion perspectives by integrating cross-lingual emotion information. By assuming that each data is ‘perceived’ not only by a main perspective...
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