中国科学院机构知识库网格系统: 检索

SWISTA-Nets: Subband-adaptive wavelet iterative shrinkage thresholding networks for image reconstruction 期刊论文 OAI收割
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2024, 卷号: 113, 页码: 12
作者: Lu, Binchun; Fu, Lidan; Pan, Yixuan; Dong, Yonggui

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收藏 | 浏览/下载：8/0 | 提交时间：2024/05/30

Image reconstruction Inverse problem Deep learning Iterative shrinkage-thresholding algorithm Electromagnetic tomography Sparse-view CT

Scene- and pixel-level analysis of Landsat cloud coverage and image acquisition probability in South and Southeast Asia 期刊论文 OAI收割
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 卷号: 123, 页码: 13
作者: Yang, Yin; Li, Peng

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收藏 | 浏览/下载：14/0 | 提交时间：2024/03/25

Landsat Cloud coverage Image acquisition probability Coarse -and -fine thresholding Scene and pixel -level South and Southeast Asia

Segmentation of liver cyst in ultrasound image based on adaptive threshold algorithm and particle swarm optimization 期刊论文 OAI收割
MULTIMEDIA TOOLS AND APPLICATIONS, 2017, 卷号: 76, 期号: 6, 页码: 8951-8968
作者: Zhu, Haijiang; Zhuang, Zhanhong; Zhou, Jinglin; Zhang, Fan; Wang, Xuejing

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收藏 | 浏览/下载：31/0 | 提交时间：2017/07/18

Ultrasound Image Wellner's Thresholding Algorithm Particle Swarm Optimization Segmentation Of Liver Cyst

LS-SVM-based image segmentation using pixel color-texture descriptors 期刊论文 OAI收割
PATTERN ANALYSIS AND APPLICATIONS, 2014, 卷号: 17, 期号: 2, 页码: 341-359
Yang, Hong-Ying; Zhang, Xian-Jin; Wang, Xiang-Yang

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收藏 | 浏览/下载：23/0 | 提交时间：2014/12/16

Image segmentation Least squares support vector machine Human visual attention Local texture content Arimoto entropy thresholding

Color image segmentation using multilevel Thresholding-Hybrid particle swarm optimization 会议论文 OAI收割
2nd International Conference on Mechatronics and Automatic Control Systems, Beijing, China, September 20-21, 2014.
作者: Liu Y(刘洋); Hu KY(胡琨元); Zhu YL(朱云龙); Chen HN(陈瀚宁)

收藏 | 浏览/下载：45/0 | 提交时间：2015/08/23

Image segmentation Multilevel thresholding Particle swarm optimization (PSO) Hybrid particle swarm optimization (HPSO)

Electro-optical imaging system identification using pseudo-random binary pattern (EI CONFERENCE) 会议论文 OAI收割
2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China
Le Y.; Jian W.; Jianzhong Z.; Qiang S.; Jianzhuo L.

收藏 | 浏览/下载：23/0 | 提交时间：2013/03/25

A method using pseudo-random binary pattern is developed for electro-optical imaging system identification. The imaging system is taken as a stable linear time-invariant and causal filter and its transfer function is measured through the pseudo-random binary sequence impulse responses identification. A digital mirror device (DMD) light projector is developed as the target generator and wavelet thresholding is used to denoise the captured image. Pre-filtering spectral estimation algorithm with adaptive parameter selection is also proposed for the identification process overcoming the challenge brought by the size limit derived from the optical isoplantic region. Simulations and experiments are presented to show the effectiveness of the proposed method.0-64. 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.

High-accuracy real-time automatic thresholding for centroid tracker (EI CONFERENCE) 会议论文 OAI收割
ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China
作者: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Zhang Y.

收藏 | 浏览/下载：37/0 | 提交时间：2013/03/25

Many of the video image trackers today use the centroid as the tracking point. In engineering we can get several key pairs of peaks which can include the target and the background around it and use the method of Otsu to get intensity thresholds from them. According to the thresholds it give a great help for us to get a glancing size a target's centroid is computed from a binary image to reduce the processing time. Hence thresholding of gray level image to binary image is a decisive step in centroid tracking. How to choose the feat thresholds in clutter is still an intractability problem unsolved today. This paper introduces a high-accuracy real-time automatic thresholding method for centroid tracker. It works well for variety types of target tracking in clutter. The core of this method is to get the entire information contained in the histogram we can gain the binary image and get the centroid from it. To track the target so that we can compare the size of the object in the current frame with the former. If the change is little such as the number of the peaks the paper also suggests subjoining an eyeshot-window we consider the object has been tracked well. Otherwise their height just like our eyes focus on a target if the change is bigger than usual position and other properties in the histogram. Combine with this histogram analysis we will not miss it unless it is out of our eyeshot we should analyze the inflection in the histogram to find out what happened to the object. In general the impression will help us to extract the target in clutter and track it and we will wait its emergence since it has been covered. To obtain the impression what we have to do is turning the analysis into codes for the tracker to determine a feat threshold. The paper will show the steps in detail. The paper also discusses the hardware architecture which can meet the speed requirement. the paper offers a idea comes from the method of Snakes

Adaptive Image Segmentation based on Fast Thresholding and Image Merging (EI CONFERENCE) 会议论文 OAI收割
16th International Conference on Artificial Reality and Telexistence - Workshops, ICAT 2006, November 29, 2006 - December 1, 2006, Hangzhou, China
作者: Zhang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y.

收藏 | 浏览/下载：14/0 | 提交时间：2013/03/25

Image segmentation is the first essential and important step of low level vision. This paper proposes a novel algorithm for adaptive image segmentation it can be applied in many conditions based on thresholding technique and segments merging according to their characteristics combine with spatial position. Our earlier work of getting the entire information of the histogram could help choose the multiple thresholds. However including complex target segmented. We describe the algorithm in detail and perform simulation experiments. The computation based on pixels can fully parallel processing to save time. 2006 IEEE. not all the peaks of the histogram correspond to obvious structural unit in the image. Spatial information must be involved. This paper also suggests subjoining segments matching for video image tracking. They will give great help to image segmentation. The proposed algorithm can meet the real-time requirement and lead to higher segmentation accuracy some types of texture can also be segmented well

Thresholding technique with adaptive window selection for uneven lighting image 期刊论文 iSwitch采集
Pattern recognition letters, 2005, 卷号: 26, 期号: 6, 页码: 801-808
作者: Huang, QM; Gao, W; Cai, WJ

收藏 | 浏览/下载：24/0 | 提交时间：2019/05/10

Image segmentation Thresholding Adaptive window selection Lorentz information measure