To overcome the main drawbacks of global minimal for active contour models (L. D. Cohen and Ron Kimmel) that the contour is only extracted partially for low SNR images, Method of boundary extraction based on Schrö...
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To overcome the main drawbacks of global minimal for active contour models (L. D. Cohen and Ron Kimmel) that the contour is only extracted partially for low SNR images, Method of boundary extraction based on Schrödinger Equation is proposed. Our Method is based on computing the numerical solutions of initial value problem for second order nonlinear Schrödinger equation by using discrete Fourier Transformation. Schrödinger transformation of image is first given. We compute the probability P(b,a) that a particle moves from a point a to another point b according to I-Type Schrödinger transformation of image and obtain boundary of object by using quantum contour model.
The minirhizotron technique has provided agricultural scientists the opportunity of observing rhizosphere activities without destroying root structures. Nonetheless, the laborious analysis of the data still prohibits ...
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The minirhizotron technique has provided agricultural scientists the opportunity of observing rhizosphere activities without destroying root structures. Nonetheless, the laborious analysis of the data still prohibits its wide applications. Advanced image understanding techniques are needed to derive satisfactory descriptions of plant root networks in an efficient and robust way. The paper presents a plant root image analysis system designed as a blackboard architecture with a hierarchy of data abstractions. Important properties of plant roots are used throughout the processing and multiple sources of information are combined to resolve uncertainties in image interpretation. Experimental results from some stages of the research are given which support the overall processing scheme.< >
Wavelet image denoising has been well acknowledged as an important method of denoising in imageprocessing. This paper describers a new method for the suppression of noise in image by fusing the wavelet denoising tech...
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Wavelet image denoising has been well acknowledged as an important method of denoising in imageprocessing. This paper describers a new method for the suppression of noise in image by fusing the wavelet denoising technique with support vector regression (SVR). Based on the least squares support vector machine (LS-SVM), a new denoising operators used in the wavelet domain are obtained. Simulated noise images are used to evaluate the denoising performance of the proposed algorithm along with the other wavelet-based denoising algorithm. Experimental results show that the proposed denoising method outperforms standard wavelet denoising techniques in terms of the signal-to-noise ratio and the prevented edge information in most cases. It also achieves better performance than the median filter.
Pulsar candidate selection identifies prospective observations of modern radio pulsar surveys for further inspection in search of real pulsars. Typically, human experts visually select valuable candidates and eliminat...
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ISBN:
(纸本)9781509048410
Pulsar candidate selection identifies prospective observations of modern radio pulsar surveys for further inspection in search of real pulsars. Typically, human experts visually select valuable candidates and eliminate radio frequency interference or other noises. Recently, machine learning methods are adopted to automate this task, which saves human labor and makes it possible for processing millions of observations efficiently. Considering the number of positive training samples are relatively too small and the cost of incorrectly labeling a real pulsar candidate as negative is large, we propose a novel hierarchical candidate-sifting model by emphasizing the cost of incorrect prediction of positive samples and assembling multiple classifiers trained with different weighting parameters. Experiments on three pulsar selection datasets demonstrate our proposed method improves the pulsar-sifting performance a lot according to several standard evaluation metrics.
In classification of multi-source remote sensing image, it is usually difficult to obtain higher classification accuracy. In the previous work, the modeling technique for the remote sensing image classification based ...
In classification of multi-source remote sensing image, it is usually difficult to obtain higher classification accuracy. In the previous work, the modeling technique for the remote sensing image classification based on the minimum description length (MDL) principle with mixture model is analyzed theoretically. In this work, experimental studies are performed for investigating the modeling technique. With intensive experiments and sophisticated analysis, it is found that the developed modeling technique can build a robust classification system, which can avoid classifier over-fitting training data and make the learning process trade-off between bias and variance. Meanwhile, designed mixture model is more efficient to represent real multi-source remote sensing images compared to single model.
Shadow detection in high spatial resolution remote sensing image is very critical for locating geographical targets. In this paper, we proposed a new shadow detection method using Affinity Propagation (AP) algorithm i...
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Shadow detection in high spatial resolution remote sensing image is very critical for locating geographical targets. In this paper, we proposed a new shadow detection method using Affinity Propagation (AP) algorithm in the Hue-Saturation-Intensity (HSI) color space. Because the pixel matrix is a large-scale matrix, if we apply AP algorithm directly on the raw pixel space, it will be computation intensive to calculate the similarity matrix. To solve this problem, we propose to divide the matrix into several blocks and then applying AP to detect shadows in H, S and I components respectively. Then, three detected images are fused to obtain a final shadow detection result. Comparative experiments are performed for K-means and threshold segmentation methods. The experimental results show that higher detection accuracy of the proposed approach is obtained, and it can solve the problems of false dismissals of K-means and threshold segmentation method.
An image compression-encryption algorithm based on 2-D compressive sensing is proposed, which can accomplish encryption and compression simultaneously. The measurements are performed in two directi-ons and the measure...
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Object recognition from images is one of the essential problems in automatic imageprocessing. In this paper we focus specifically on nearest neighbor methods, which are widely used in many practical applications, not...
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Object recognition from images is one of the essential problems in automatic imageprocessing. In this paper we focus specifically on nearest neighbor methods, which are widely used in many practical applications, not necessarily related to image data. It has recently come to attention that high dimensional data also exhibit high hubness, which essentially means that some very influential data points appear and these points are referred to as hubs. Unsurprisingly, hubs play a very important role in the nearest neighbor classification. We examine the hubness of various image data sets, under several different feature representations. We also show that it is possible to exploit the observed hubness and improve the recognition accuracy.
As the development of CG industry and online games, the requirements of efficient texture synthesis methods are more and more exigent. It is one of the toughest problems while rendering the huge scenes efficiently and...
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As the development of CG industry and online games, the requirements of efficient texture synthesis methods are more and more exigent. It is one of the toughest problems while rendering the huge scenes efficiently and effectively. In this paper we propose an efficient texture synthesis algorithm by using wavelet technique. Much different from former texture synthesis methods, the method in this paper synthesizes high resolution texture by using lower resolution component decomposed by wavelet transform, which can improve the synthesis efficiency greatly for either stochastic texture or structural one. By using this method, we can also supply an effective control mechanism especially for the structural texture samples. The result shows that we make improvements both on efficiency and effect.
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