中国科学院机构知识库网格系统: 检索

浏览/检索结果: 共4条，第1-4条

帮助

条数/页：排序方式：
	基于变换域分析的噪声鲁棒声源定位方法研究及无人车应用学位论文 OAI收割工学博士, 中国科学院自动化研究所: 中国科学院大学, 2015 作者: 雪巍收藏 \| 浏览/下载：87/0 \| 提交时间：2015/09/02 麦克风阵列声源定位空间声学变换域分析噪声无人驾驶汽车 Directional of Arrival Microphone Arrays Spatial Audio Transform Domain Analysis Noise Intelligent Vehicle
	Model Research of Electric Coal Calorific Value Based on Near Infrared Frequency Domain Self-Adaption Analysis Method 期刊论文 OAI收割 SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 卷号: 34, 期号: 10, 页码: 2792-2798 作者: Li Zhi; Wang Shenghao; Zhao Yong; Wang Xiangfeng; Li Yaozheng \| 收藏 \| 浏览/下载：19/0 \| 提交时间：2021/02/26 SPECTROSCOPY Near infrared spectra Fast Fourier transform Frequency domain self-adaption analysis method Calorific value of electric coal Quantitative analysis model
	Directional multiscale edge detection using the contourlet transform (EI CONFERENCE) 会议论文 OAI收割 2010 IEEE International Conference on Advanced Computer Control, ICACC 2010, March 27, 2010 - March 29, 2010, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States 作者: Jin L.-X.; Han S.-L.; Zhang R.-F. 收藏 \| 浏览/下载：35/0 \| 提交时间：2013/03/25 Wavelet multiresolution analysis allows us to detect edges at different scales also to obtain other important aspects of the extracted edges. However due to the usual two-dimensional tensor product wavelet transform is not optimal for representing images. The main problem in edge detection using wavelet transform is that it can only capture point-singularities and the extracted edges are not continuous. In order to solve that problem we propose a new image edge detection method based on the contourlet transform. The directional multiresolution representation Contourlet takes advantages of the intrinsic geometrical structure of images and is appropriate for the analysis of the image edges. Using the modulus maxima detection an image edge detection method based on contourlet transform is proposed. To suppress the image noise effect on edge detection the scale multiplication in contourlet domain is also proposed. Through real images experiments the proposed edge detection method's performance for the extracted edges is analyzed and compared with other two edge detection methods. The experiment result proves that the proposed edge detection method improves over wavelet-based techniques and Canny detector and also works well for noisy images. 2010 IEEE.
	A new approach for the removal of mixed noise based on wavelet transform (EI CONFERENCE) 会议论文 OAI收割 ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China 作者: Li Y.; Li Y.; Li Y.; Li Y. 收藏 \| 浏览/下载：38/0 \| 提交时间：2013/03/25 This paper proposed a new approach for the removal of mixed noise. There are many different ways in image denoising. Donoho et al have proposed a method for image de-noising by thresholding ambiguity is often resulted in determining the correspondence of a modulus maximum to a singularity. In the light and indeed we combine the merits of the two techniques to form a new approach for the removal of mixed noise. At first the application of their method to image denoising has been extremely successful. But the method of Donoho is based on the assumption that the type of noise is only additive Gaussian noise we used wavelet singularity detection (WSD) technique to analyze singularities of signal and noise. According to the characteristic that wavelet transform modulus maxima of impulse noise rapidly decreases as the scale increases in wavelet domain which is not successful for impulse noise. Mallat has also presented a method for signal denoising by discriminating the noise and the signal singularities through an analysis of their wavelet transform modulus maxima (WTMM). Nevertheless it can be accurately located with multiscale space by going through dyadic orthogonal wavelet transform and removed. Furthermore the Gaussian noise is also removed through a level-dependent thresholding algorithm the tracing of WTMM is not just tedious procedure computationally algorithm. The experimental results demonstrate that the proposed method can effectively detect impulse noise and remove almost all of the noise while preserve image details very well.