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Trajectories obtained from low level tracking algorithm provide an opportunity for us to analyze meaningful behaviors and monitor adverse or malicious events. How to abstract meaningful features from the raw data of trajectories is a challenge due to the high dimensionality and noise. In this paper, a novel approach, stacked denoising autoencoder(SDA) is applied to address this problem. This method...
A radar signal recognition can be accomplished by exploiting the particular features of a radar signal observed in presence of noise. The features are the result of slight radar component variations and acts as an individual signature. The paper describes radar signal recognition algorithm based on time frequency analysis, noise reduction and statistical classification procedures. The proposed method...
Effective use of feature set and selection of a suitable classification method are significant for improving classification accuracy. However, mammogram images classification is affected by many factors such as additive gaussian noise, low contrast and artifacts. Therefore, the aim of this paper is to observe the impact of presence /absence of noise on the quality and classification accuracy of mammogram...
Enhancement of text information from the images captured by mobile camera is a very challenging task due to the high variation between the background and the foreground that contains shadows, poor contrast and non uniform illumination. In this paper, denoising along with binarization algorithm that uses phase congruency features is proposed to extract the text information from the document images...
The performance of a speaker verification system is damagingly affected by large amount of noise. In order to compensate the mismatch between enrollment and test acoustic conditions, this paper presents a novel approach based on Gaussian Mixture Model-Universal Background Model (GMM-UBM) algorithm. Noisy background adaption is proposed to make speaker models more close to the one in real-world scenarios...
In this paper, a technique to classify seven different forearm movements using surface electromyography (sEMG) data which were received from 8 able bodied subjects was proposed. A 2-channel sEMG system was used for data acquisition and recording, then this raw electromyography (EMG) signals were applied to the wavelet denoising. In the next step, time-frequency feature is extracted calculating wavelet...
In this paper, a fully automated system for source detection of the partial discharges (PD) as an online diagnosis test in rotating machineries is proposed. This technique uses a modified version of the Expectation Maximization-based (EM) clustering technique to separate the multi source Phase-Resolved Partial Discharge (PRPD) measurements into multiple single-source clusters. Afterwards, the fuzzy...
Preprocessing and post processing steps significantly improve the performance of binarization methods, especially in the case of severely-degraded historical documents. In this paper, an unsupervised post processing method is introduced based on the phase-preserved denoised image and also phase congruency features extracted from the input image. The core of the method consists of two robust mask images...
In order to overcome the shortcoming of the soft-threshold denosing method in curvelet domain, which may cause edges blurred, a new denoising method based on the edge features of the given image is proposed. In this method, we make full use of the anisotropie advantages of curvelet, extract the detail and edge information from the low-frequency domain and restore it, which can prevent the destruction...
This paper explains the issues of study that was designed to evaluate the effect of denoising algorithm to detect emotional expression through Electroencephalogram (EEG). This research led to classify the EEG features due to emotion which was induced by the facial expression stimulus include of happy and sad and neutral cases. Event-related potential (ERP) method was selected to probe the ability...
Widespread current cameras are part of multisensory systems with an integrated computer (smartphones). Computer vision thus starts evolving to cross-modal sensing, where vision and other sensors cooperate. This exists in humans and animals, reflecting nature, where visual events are often accompanied with sounds. Can vision assist in denoising another modality? As a case study, we demonstrate this...
This paper presents the ways we explored until now for detecting and dealing with the class noise found in large annotated datasets used for training the classifiers that we have previously designed for industrial-scale malware identification. First we established a number of distance-based filtering rules that allow us to identify different "levels'' of potential noise in the training data,...
Wavelet packet analysis provides a more subtle approach for signal analysis, in the signal processing, through multiple levels division of the signal band, which made the high-frequency part a deeper level decomposition. There is also according to the nature of the signal itself choose the corresponding band adaptively, so as to consistent with the signal spectrum, and thus make the time — frequency...
A Power quality Classification system can easily extract features from the second detail signal obtained after Discrete Wavelet Transform and using these features to construct a Rule Based Algorithm for identifying types of disturbances that exist in the captured power signal. Unfortunately, the signal under investigation is often polluted by noises, rendering the extraction of features a difficult...
Wavelet-based denoising has comprehensive functionalities including feature extraction and low-pass filtering, while keeping characteristics such as low entropy, multi-resolution, irrelevance, etc. Wavelet-based denoising methods have been successfully applied for image processing in varieties. However, one of the main factors degrading underwater imaging is the backward scattered light, which performs...
According to the characteristic of X-ray images of weld, defect extraction techniques were studied. Firstly, on the basis of wavelet analysis, a new wavelet adaptive threshold de-noising method based on genetic algorithm optimization is proposed. Secondly, an algorithm of multi-scale morphological to local contrast enhancement is designed. Finally, background is simulated, and the defect regions were...
Signal is polluted by broadband noise in mechanical fault diagnosis, which makes it difficult to extract the fault feature. A de-noising method based on Singular Value Decomposition (SVD) of attractor track matrix by time series is presented. We put emphasis on determination of time delay and embedding dimension of SVD appropriately, and the effectiveness of fault feature extraction before and after...
In this paper, a novel approach is proposed for unsupervised change detection of multitemporal remote sensing images. The proposed method is able to produce the change detection result on the difference image without a priori assumptions .Firstly, the difference image which is acquired from multitemporal images. Mean shift algorithm is used to reduce noise of difference image and fake change. Then...
In this paper we present a novel noise reduction method using Coordinate Logic (CL) filters applied to printed text and handwriting images. CL Filters and their associated Coordinate Logic Operations (CLOs) are widely used in common practical image process applications like noise removing, magnification, opening, closing, skeletonization, coding, edge detection, feature extraction, and fractal modeling...
After a simple introduction of intelligent monitoring system, monitor video processing methods based on wavelet transform, including denoising, enhancement, feature extraction and compression, are proposed in this paper. Results relating to the series of methods are given by Matlab software system. At the end of this paper, the advantage of using of wavelet in video processing is discussed.
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