We propose an unsupervised person search method for video surveillance. This method considers both the spatial features of persons within each frame and the temporal relationship of the same person among different fra...
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We present a novel fast method based on computer vision to identify microbe The proposed method is simple but absolutely effective It combines approximate parallel light source and industrial camera, to automatically ...
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ISBN:
(纸本)9781479920327
We present a novel fast method based on computer vision to identify microbe The proposed method is simple but absolutely effective It combines approximate parallel light source and industrial camera, to automatically accomplish the bacteria identification and monitor the growing states of bacteria during the progress of a drug sensitive test. Based on this method, the color information and turbidity information, which reflect the primary information of drug sensitive tests, can be obtained fast, while processing efficiency can be as high as hundreds of milliseconds per frame. The performance of our method is significantly accurate and robust.
Neuro-fuzzy(NF)networks are adaptive fuzzy inference systems(FIS)and have been applied to feature selection by some ***,their rule number will grow exponentially as the data dimension *** the other hand,feature select...
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Neuro-fuzzy(NF)networks are adaptive fuzzy inference systems(FIS)and have been applied to feature selection by some ***,their rule number will grow exponentially as the data dimension *** the other hand,feature selection algorithms with artificial neural networks(ANN)usually require normalization of input data,which will probably change some characteristics of original data that are important for *** overcome the problems mentioned above,this paper combines the fuzzification layer of the neuro-fuzzy system with the multi-layer perceptron(MLP)to form a new artificial neural ***,fuzzification strategy and feature measurement based on membership space are proposed for feature selection. Finally,experiments with both natural and artificial data are carried out to compare with other methods,and the results approve the validity of the algorithm.
Matching of appearance-based object representations using eigenimages is computationally very demanding. Most commonly, to recognize an object in an image, parts of the input image are projected onto the eigenspace an...
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In this paper, a universal full-reference (FR) image quality metric based on Edge structure similarity (QMESS) is proposed using spatial position displacement degree of wavelet transform modulus maxima between referen...
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In this paper, a universal full-reference (FR) image quality metric based on Edge structure similarity (QMESS) is proposed using spatial position displacement degree of wavelet transform modulus maxima between reference image and distorted image in multi-resolution domain. Firstly, we decompose images in wavelet domain. The structure error between reference images and distorted images is computed based on the statistics of spatial position error of local modulus maxima in wavelet domain. At the same time, peak signal to noise ratio (PSNR) is adopted to evaluate the stochastic noise in images. Finally, the low frequency resolution layer distortion is evaluated by means of the mutual information and the luminance distortion. The three components are combined for the whole visual distortion measurement. From the experiment results, the proposed metric is much better than conventional PSNR method and the state-of-the-art SSIM approach in terms of the performance relative to subjective judgment. Comparing to the excellent VIF method, the proposed method performs better in individual distortions and obtains similar results on cross-distortion type.
Tissue texture reflects the spatial distribution of contrasts of image voxel gray levels,i.e.,the tissue heterogeneity,and has been recognized as important biomarkers in various clinical *** computed tomography(CT)is ...
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Tissue texture reflects the spatial distribution of contrasts of image voxel gray levels,i.e.,the tissue heterogeneity,and has been recognized as important biomarkers in various clinical *** computed tomography(CT)is believed to be able to enrich tissue texture by providing different voxel contrast images using different X-ray ***,this paper aims to address two related issues for clinical usage of spectral CT,especially the photon counting CT(PCCT):(1)texture enhancement by spectral CT image reconstruction,and(2)spectral energy enriched tissue texture for improved lesion *** issue(1),we recently proposed a tissue-specific texture prior in addition to low rank prior for the individual energy-channel low-count image reconstruction problems in PCCT under the Bayesian *** results showed the proposed method outperforms existing methods of total variation(TV),low-rank TV and tensor dictionary learning in terms of not only preserving texture features but also suppressing image *** issue(2),this paper will investigate three models to incorporate the enriched texture by PCCT in accordance with three types of inputs:one is the spectral images,another is the cooccurrence matrices(CMs)extracted from the spectral images,and the third one is the Haralick features(HF)extracted from the *** were performed on simulated photon counting data by introducing attenuationenergy response curve to the traditional CT images from energy integration *** results showed the spectral CT enriched texture model can improve the area under the receiver operating characteristic curve(AUC)score by 7.3%,0.42%and 3.0%for the spectral images,CMs and HFs respectively on the five-energy spectral data over the original single energy data *** CM-and HF-inputs can achieve the best AUC of 0.934 and *** texture themed study shows the insight that incorporating clinical important prior information,e.g.,tiss
Curve matching is one of key issues in computer vision, image analysis and patternrecognition. Based on discrete V-transform, the distance is calculated between curves using the descriptor of V-system to find the mat...
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Curve matching is one of key issues in computer vision, image analysis and patternrecognition. Based on discrete V-transform, the distance is calculated between curves using the descriptor of V-system to find the matching curves, and then the matching parameters are evaluated in this article. The new approach can find efficiently the rough location of a short extracted image curve in a long reference curve. Different from the existing approaches, it needn't to extract feature points. Extensive tests show that it is efficient.
In view of the wide variety of plants on the earth, the plant species identification is particularly necessary to protect and preserve biodiversity. In this work, we propose a plant image classification method based o...
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ISBN:
(纸本)9781665426251
In view of the wide variety of plants on the earth, the plant species identification is particularly necessary to protect and preserve biodiversity. In this work, we propose a plant image classification method based on the encoder-decoder model with additive attention mechanism to extract plant image features and convert them into text descriptions related to plant features. In a well-trained network, it can successfully classify on the species of the generated plant texts. We show that, the proposed method not only equalizes the results of deep convolutional neural network on classification task, but also uses of the prior information of botanists in classification, and thus provide a significant prediction result.
Detecting multi-view faces is a challenging task, not only because of the face variations in scale, illumination, and expression, but also of the variations caused by multiple views. In this paper, we proposed a multi...
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Detecting multi-view faces is a challenging task, not only because of the face variations in scale, illumination, and expression, but also of the variations caused by multiple views. In this paper, we proposed a multi-layer cascaded architecture, which can focus attention on the promising regions of the image. The whole classifiers are only evaluated on the face like parts of the image, while the most amount of background blocks are excluded by the first few layers of the detector. Instead of using predefined priori knowledge about face view partition, we divide the sample space automatically by the branching competitive learning network at different discriminative resolutions. To maintain the high detection efficiency, we adopt the simplified Support Vector Machines (SVMs), called the mirror pair of points (MPP) classifiers, as the component of our detection system. Experimental results show that our system is competitive with other systems presented recently in the literature.
Level set method is convenient in image segmentation for the stabilization and *** filter is usually taken as a preprocess to reduce the influence of weak edges due to noises,but the disadvantage is obvious:blur fine ...
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Level set method is convenient in image segmentation for the stabilization and *** filter is usually taken as a preprocess to reduce the influence of weak edges due to noises,but the disadvantage is obvious:blur fine structures specially the important boundaries and lead to inaccurate segmentation *** paper introduces a robust method which filters the images with a Nonlinear Coherent Diffusion(NCD) to accelerate the evolution of level set in a spatially varying *** results show the performance of the proposed method in improving precision of segmentation.
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