To control the mobile robot with the surrounding information is the essential method to realize the intelligence and automatic moving. The vision information is the most important way to perceive the environment for t...
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To control the mobile robot with the surrounding information is the essential method to realize the intelligence and automatic moving. The vision information is the most important way to perceive the environment for the mobile robot. This paper presents an essential camera calibration technique for mobile robot, which is based on Pioneer II experiment platform. The technique includes transformation of coordinates system for vision system, the model and principle of image formation, camera distortion calibration. Because of non-linear distortion of camera, algorithm with optimizing operators is presented to improve calibration precision. We verify the validity and feasibility of the algorithm through experiment.
Aero-optic effects cause distortions, including blurring, vibration, deformation and spatial shifting, of the objects in the image obtained by the infra-red sensor. Contributions of this paper are in the following two...
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Several neutrosophic combination rules based on the Dempster-Shafer theory (DST) and Dezert-Smarandache theory (DSmT) are presented in this study. The new information fusing approaches proposed the neutrosophic belief...
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B-scan ultrasound is the primary means for the diagnosis of fatty liver. However, due to use of various ultrasound equipments, poor quality of ultrasonic images and physical differences of patients, fatty liver diagno...
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ISBN:
(纸本)9781424418145
B-scan ultrasound is the primary means for the diagnosis of fatty liver. However, due to use of various ultrasound equipments, poor quality of ultrasonic images and physical differences of patients, fatty liver diagnosis is mainly qualitative, and often depends on the subjective judgment of technicians and doctors. Therefore, computer-aided feature extraction and quantitative analysis of liver B-scan ultrasonic images will help to improve clinical diagnostic accuracy, repeatability and efficiency, and could provide a measure for severity of hepatic steatosis. This paper proposed a novel method of fatty liver diagnosis based on liver B-mode ultrasonic images using support vector machine (SVM). Fatty liver diagnosis was transformed into a pattern recognition problem of liver ultrasound image features. According to the different characteristics of fatty liver and healthy liver, important image features were extracted and selected to distinguish between the two categories. These features could be represented by near-field light-spot density, near-far-field grayscale ratio, grayscale co-occurrence matrix, and neighborhood gray-tone difference matrix (NGTDM). A SVM classifier was modeled and trained using the clinical ultrasound images of both fatty liver and normal liver. It was then exploited to classify normal and fatty livers, achieving a high recognition rate. The diagnostic results are satisfactorily consistent with those made by doctors. This method could be used for computer-aided diagnosis of fatty liver, and help doctors identify the fatty liver ultrasonic images rapidly, objectively and accurately.
Multi-robot tracking of mobile target is studied in the paper, which is based on the communication and sensors. For an independent tracking robot, the processes are separated into three layers and four tasks, and allo...
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Multi-robot tracking of mobile target is studied in the paper, which is based on the communication and sensors. For an independent tracking robot, the processes are separated into three layers and four tasks, and allocated to different robots for distinct roles in tracking, which is named the Distributed Decision control System (DDCS). After that, two tracking models, centralized and distributed models, are designed for multi-robot tracking. Furthermore, a Proportional Navigation Guidance Law (PNGL) and l-ϕ formation control algorithm are mentioned to realize the robot motion control. At last the simulation has shown the feasibility and validity of both models.
In this paper, a variant of support vector novelty detection (SVND) with dot product kernels is presented for non-spherical distributed data. Firstly we map the data in input space into a reproducing kernel Hilbert sp...
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In this paper, a variant of support vector novelty detection (SVND) with dot product kernels is presented for non-spherical distributed data. Firstly we map the data in input space into a reproducing kernel Hilbert space (RKHS) by using kernel trick. Secondly we perform whitening process on the mapped data using kernel principal component analysis (KPCA). Finally, we adopt SVND method to train and test whitened data. Experiments were performed on artificial and real-world data.
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.
In this paper, we presented a ringing metric to evaluate the quality of images restored using iterative image restoration algorithms. A ringing metrics is used to assessment the restored images based on the Gabor filt...
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In this paper, we presented a ringing metric to evaluate the quality of images restored using iterative image restoration algorithms. A ringing metrics is used to assessment the restored images based on the Gabor filter. The experimental results validate the proposed method perform well over a wide range of restoration image ringing levels assessment. And the proposed model has given good agreement with observer ratings obtained in subjective experiments.
Liquid State Machine (LSM) is a newly developed computational model with many interesting properties. It has great advantages of dealing with biologic computing when compared to the traditional computational model. In...
Liquid State Machine (LSM) is a newly developed computational model with many interesting properties. It has great advantages of dealing with biologic computing when compared to the traditional computational model. In this paper, the LSM was used to deal with the direction classification problem of the spike series which were distilled from the neurons in motor cortex of a monkey. In the output layer, a linear regression and back-propagation are employed as the training algorithms. Compare to outcomes of the two algorithms, it is showed that ideal classification results were derived when using BP as the training algorithm.
In this paper, based on Baldwin effect, an improved clonal selection algorithm, Baldwin clonal selection algorithm, termed as BCSA, is proposed to deal with complex multimodal optimization problems. BCSA evolves and i...
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In this paper, based on Baldwin effect, an improved clonal selection algorithm, Baldwin clonal selection algorithm, termed as BCSA, is proposed to deal with complex multimodal optimization problems. BCSA evolves and improves antibody population by three operations: clonal proliferation operation, Baldwinian learning operation and clonal selection operation. By introducing Baldwin effect, BCSA can make the most of experience of antibodies, accelerate the convergence, and obtain the global optimization quickly. In experiments, BCSA is tested on four types of functions and compared with the clonal selection algorithm and other optimization methods. Experimental results indicate that BCSA achieves a good performance, and is also an effective and robust technique for optimization.
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