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Remotely sensed hyperspectral images exhibit very high dimensionality in the spectral domain. As opposed to band selection techniques, which extract a subset of the original spectral bands in the image, spectral partitioning (SP) techniques reassign the original bands into subgroups that are then processed separately. From a classification perspective, this strategy has the advantage that all the...
Deep learning models have showed great potential in classification and recognition over the last decade. Deep Belief Networks (DBNs) have been applied in visual, voice fields due to their great feature presentation capability. However, there are a vast number of time consuming calculations in the training of DBNs. Many researches have accelerated the training of DBNs with good speedups on CPU, GPU,...
Prosody is a kind of cues that are critical to human speech perception and comprehension, so it is plausible to integrate prosodic information into machine speech recognition. However, as a result of the supra-segmental nature, it is hard to integrate prosodic information with conventional acoustic features. Recently, RNNLMs have shown to be the state-of-the-art language model in many tasks. We thus...
As most electronic system structure is complex and uncertain, this paper presents a new efficiency method for spacecraft electrical characteristics identification. PCA (Principal Component Analysis) feature extraction, offline FCM (Fuzzy C-means) clustering and online SVM (Support Vector Machine) classifier is introduced into the registration model. At first step of the algorithm, get an expert training...
As most electronic system structure is complex and uncertain, this paper presents a new efficiency method for spacecraft electrical characteristics identification. Offline FCM clustering and online SVM classifier is introduced into the registration model. At first step of the algorithm, using FCM clustering method to get an expert training set. By get expert training set for SVM classifier make this...
This paper presents a healthcare application that tracks body parts movement in video recording persons who exercise, analyzes the motor performance (e.g., motion speed and space), and evaluates fitness status (e.g., motion accuracy and abnormality). We design a MapReduce video processing for collecting the data of body parts movement from a large number of video files and successfully shorten the...
Recently, sparse coding-based algorithms have achieved high performance on several popular scene classification benchmarks. Yet extensive efforts along this direction focus on strategies for coding and dictionary learning, few works have addressed the problem of optimal pooling regions selection. In this work, we show that the Viola-Jones algorithm, which is well-known in face detection, can be tailored...
In this paper, we propose an approach of multi-layered feature combination associated with support vector machine (SVM) for Chinese accent identification. The multi-layered features include both segmental and suprasegmental information, such as MFCC and pitch contour, to capture the diversity of variations in Chinese accented speech. The pitch contour is estimated using cubic polynomial method to...
Illumination variation is one of the most difficult problems for face recognition. In this paper, we represent a new ordinal feature based method for face recognition under varying illumination. We employ 2-D wavelet transform to compress face images and extract ordinal features from them. Here, the ordinal feature is extracted more easily than before and competent for face recognition, which is invariant...
Particle swarm optimization was applied in BP neural network training. It reasonably confirms threshold and connection weight of neural network, and improves capability of solving problems in realities. Meanwhile, PSO-BP neural network is applied into classification of fabric defect. The method of orthogonal wavelet transform was used to decompose monolayer from fabric image. And the sub-images of...
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