In real-world applications, selecting the appropriate hyper-parameters for support vector machines (SVM) is a difficult and vital step which impacts the generalization capability and classification performance of clas...
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A fast object detection method based on object region dissimilarity and 1-D AGADM(one dimensional average gray absolute difference maximum) between object and background isproposed for real-time defection of small off...
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A fast object detection method based on object region dissimilarity and 1-D AGADM(one dimensional average gray absolute difference maximum) between object and background isproposed for real-time defection of small offshore targets. Then computational complexity, antinoiseperformance, the signal-to-noise ratio (SNR) gain between original images and their results as afunction of SNR of original images and receiver operating characteristic (ROC) curve are analyzed andcompared with those existing methods of small target detection such as two dimensional average grayabsolute difference maximum (2-D AGADM), median contrast filter algorithm and multi-level filteralgorithm. Experimental results and theoretical analysis have shown that the proposed method hasfaster speed and more adaptability to small object shape and also yields improved SNR performance.
A magnetic sensor based on giant magnetoimpedance effect (GMI) was developed. The basic element of the sensor is a Fe-based nanocrystalline ribbon of composition Fe73.5Cu1Nb3Si13.5B9. A sensitivity of 0.6691 V/Oe for ...
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Diagnostic ultrasound is a useful and noninvasive method in clinical medicine. Although due to its qualitative, subjective and experience-based nature, ultrasound image interpretation can be influenced by image condit...
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Diagnostic ultrasound is a useful and noninvasive method in clinical medicine. Although due to its qualitative, subjective and experience-based nature, ultrasound image interpretation can be influenced by image conditions such as scanning frequency and machine settings. In this paper, a novel method is proposed to extract the liver features using the joint features of fractal dimension and the entropies of texture edge co-occurrence matrix based on ultrasound images, which is not sensitive to changes in emission frequency and gain. Then, Fisher linear classifier and support vector machine are employed to test a group of 99 in-vivo liver fibrosis images from 18 patients, as well as other 273 liver images from 18 normal human volunteers.
Owing to the weaknesses of existing correlation detection methods in digital fingerprint matching, such as difficult to determine the threshold and low matching accuracy rate, a method proposed in digital fingerprint ...
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Character information is hard to detect in billet scene images by CCD camera. In this paper, we present a method for detection of billet characters from measurements of recursive segmented image. This recursive segmen...
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We introduced a computer-aider tongue examination system, which can reduce the large variation between the diagnosis results of different doctors and quantize the tongue properties automatically in traditional Chinese...
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In this paper, a novel subspace learning method, semi-supervised marginal discriminant analysis (SMDA), is proposed for classification. SMDA aims at maintaining the intrinsic neighborhood relations between the data po...
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ISBN:
(纸本)9781424421749
In this paper, a novel subspace learning method, semi-supervised marginal discriminant analysis (SMDA), is proposed for classification. SMDA aims at maintaining the intrinsic neighborhood relations between the data points from the same class, while maximizing the margin between the neighboring data points with different class labels. Different from traditional dimensionality reduction algorithms like linear discriminant analysis (LDA) and maximum margin criterion (MMC) which seeks only the global Euclidean structure, SMDA takes local structure of the data into account. Moreover, it is designed for semi-supervised learning which incorporates both labeled and unlabeled data points and avoids suffering the small sample size (SSS) problem. QR decomposition is then employed to find the optimal transformation which makes the algorithm scalable and more efficient. Experiments on face recognition are presented to show the effectiveness of the method.
How to improve the probability of registration and precision of localization is a hard problem, which is desiderated to solve. The two basic approaches (normalized cross-correlation and phase correlation) for image re...
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How to improve the probability of registration and precision of localization is a hard problem, which is desiderated to solve. The two basic approaches (normalized cross-correlation and phase correlation) for image registration are analysed, two improved approaches based on spatial-temporal relationship are presented. This method adds the correlation matrix according to the displacements in x-direction and y-direction, and the registration pose is searched in the added matrix. The method overcomes the shortcoming that the probability of registration decreasing with area increasing owing to geometric distortion, improves the probability and the robustness of registration.
The goal of image coding is to reduce both the distortion and the bit rate to an acceptable *** are many ways to design the *** this paper several methods are presented,most of them are based on the Neural *** that in...
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The goal of image coding is to reduce both the distortion and the bit rate to an acceptable *** are many ways to design the *** this paper several methods are presented,most of them are based on the Neural *** that initial codebook is one of the deficiencies of LBG algorithm,the APEX algorithm is applied to generate the initial ***,a Neural Network way,the competitive learning neural network,is proposed to try to construct a new way of ***,the reduced dimention VQ system based on neural network is constructed to obtain an efficient way to compress image with low bit rale and distortion,especially the high dimention data compress problem.
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