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雾中成像实现的一个思路 被引量:1
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作者 陈为忠 庄其仁 +1 位作者 张文珍 戴再平 《华侨大学学报(自然科学版)》 CAS 2001年第3期257-260,共4页
在子弹光成像理论的基础上 ,应用 CCD器件 ,结合自适应滤波理由 ,提出一种雾中成像系统的设计思路 .原始输入信号和参考信号经自适应抵消器滤波后 ,消除蛇行光信号和噪声信号 ,得到子弹光信号 。
关键词 雾中成像 CCD器件 子弹光成像理论 自适应滤波 物像提取 数字图像处理
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Automatic cell object extraction of red tide algae in microscopic images
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作者 于堃 姬光荣 郑海永 《Chinese Journal of Oceanology and Limnology》 SCIE CAS CSCD 2017年第2期275-293,共19页
Extracting the cell objects of red tide algae is the most important step in the construction of an automatic microscopic image recognition system for harmful algal blooms.This paper describes a set of composite method... Extracting the cell objects of red tide algae is the most important step in the construction of an automatic microscopic image recognition system for harmful algal blooms.This paper describes a set of composite methods for the automatic segmentation of cells of red tide algae from microscopic images.Depending on the existence of setae,we classify the common marine red tide algae into non-setae algae species and Chaetoceros,and design segmentation strategies for these two categories according to their morphological characteristics.In view of the varied forms and fuzzy edges of non-setae algae,we propose a new multi-scale detection algorithm for algal cell regions based on border-correlation,and further combine this with morphological operations and an improved GrabCut algorithm to segment single-cell and multicell objects.In this process,similarity detection is introduced to eliminate the pseudo cellular regions.For Chaetoceros,owing to the weak grayscale information of their setae and the low contrast between the setae and background,we propose a cell extraction method based on a gray surface orientation angle model.This method constructs a gray surface vector model,and executes the gray mapping of the orientation angles.The obtained gray values are then reconstructed and linearly stretched.Finally,appropriate morphological processing is conducted to preserve the orientation information and tiny features of the setae.Experimental results demonstrate that the proposed methods can effectively remove noise and accurately extract both categories of algae cell objects possessing a complete shape,regular contour,and clear edge.Compared with other advanced segmentation techniques,our methods are more robust when considering images with different appearances and achieve more satisfactory segmentation effects. 展开更多
关键词 non-setae algae CHAETOCEROS cell extraction border-correlation non-interactive GrabCut
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Building Extraction from DSM Acquired by Airborne 3D Image
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作者 YOUHongjian LIShukai 《Geo-Spatial Information Science》 2003年第3期25-31,共7页
Segmentation and edge regulation are studied deeply to extract buildings fromDSM data produced in this paper. Building segmentation is the first step to extract buildings, anda new segmentation method-adaptive iterati... Segmentation and edge regulation are studied deeply to extract buildings fromDSM data produced in this paper. Building segmentation is the first step to extract buildings, anda new segmentation method-adaptive iterative segmentation considering rati-o mean square-is proposedto extract the contour of buildings effectively. A sub-image (such as 50X50 pixels) of the image isprocessed in sequence, the average gray level and its ratio mean square are calculated first, thenthreshold of the sub-image is selected by using iterative threshold segmentation. The current pixelis segmented according to the threshold, the average gray level and the ratio mean square of thesub-image. The edge points of the building are grouped according to the azimuth of neighbor points,and then the optimal azimuth of the points that belong to the same group can be calculated by usingline interpolation. 展开更多
关键词 building extraction digital surface model SEGMENTATION REGULATION
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A Dense Feature Iterative Fusion Network for Extracting Building Contours from Remote Sensing Imagery
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作者 WU Jiangyan WANG Tong 《Journal of Donghua University(English Edition)》 CAS 2024年第6期654-661,共8页
Extracting building contours from aerial images is a fundamental task in remote sensing.Current building extraction methods cannot accurately extract building contour information and have errors in extracting small-sc... Extracting building contours from aerial images is a fundamental task in remote sensing.Current building extraction methods cannot accurately extract building contour information and have errors in extracting small-scale buildings.This paper introduces a novel dense feature iterative(DFI)fusion network,denoted as DFINet,for extracting building contours.The network uses a DFI decoder to fuse semantic information at different scales and learns the building contour knowledge,producing the last features through iterative fusion.The dense feature fusion(DFF)module combines features at multiple scales.We employ the contour reconstruction(CR)module to access the final predictions.Extensive experiments validate the effectiveness of the DFINet on two different remote sensing datasets,INRIA aerial image dataset and Wuhan University(WHU)building dataset.On the INRIA aerial image dataset,our method achieves the highest intersection over union(IoU),overall accuracy(OA)and F 1 scores compared to other state-of-the-art methods. 展开更多
关键词 remote sensing image building contour extraction feature iteration
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