The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Bingxuan Guo - One of the best experts on this subject based on the ideXlab platform.
-
application of a fast Linear Feature detector to road extraction from remotely sensed imagery
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011Co-Authors: Yuanzheng Shao, Bingxuan GuoAbstract:This paper presents a fast and effective algorithm for detecting ridge- or ribbon-like Linear Features from remote sensing imagery. To judge if a pixel is at the center of a Linear Feature, the first step is to find several biggest pixels by their grey values within orthogonal directional windows, and store them into an evaluation window. A simple evaluation method is then applied to make the yes or no decision on whether the pixel is a Linear Feature point. Aerial images were used to test the algorithm's ability to extract roads. This algorithm was compared with the Multiple Directional Non Maximum Suppression (MDNMS) algorithm. The experimental results indicate that with the proposed algorithm the processing of road details could improve and the processing time decrease.
Yuanzheng Shao - One of the best experts on this subject based on the ideXlab platform.
-
application of a fast Linear Feature detector to road extraction from remotely sensed imagery
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011Co-Authors: Yuanzheng Shao, Bingxuan GuoAbstract:This paper presents a fast and effective algorithm for detecting ridge- or ribbon-like Linear Features from remote sensing imagery. To judge if a pixel is at the center of a Linear Feature, the first step is to find several biggest pixels by their grey values within orthogonal directional windows, and store them into an evaluation window. A simple evaluation method is then applied to make the yes or no decision on whether the pixel is a Linear Feature point. Aerial images were used to test the algorithm's ability to extract roads. This algorithm was compared with the Multiple Directional Non Maximum Suppression (MDNMS) algorithm. The experimental results indicate that with the proposed algorithm the processing of road details could improve and the processing time decrease.
Yichen Wang - One of the best experts on this subject based on the ideXlab platform.
-
a simplified Linear Feature matching method using decision tree analysis weighted Linear directional mean and topological relationships
International Journal of Geographical Information Science, 2017Co-Authors: Ick Hoi Kim, Chenchieh Feng, Yichen WangAbstract:ABSTRACTLinear Feature matching is one of the crucial components for data conflation that sees its usefulness in updating existing data through the integration of newer data and in evaluating data accuracy. This article presents a simplified Linear Feature matching method to conflate historical and current road data. To measure the similarity, the shorter line median Hausdorff distance (SMHD), the absolute value of cosine similarity (aCS) of the weighted Linear directional mean values, and topological relationships are adopted. The decision tree analysis is employed to derive thresholds for the SMHD and the aCS. To demonstrate the usefulness of the simple Linear Feature matching method, four models with incremental configurations are designed and tested: (1) Model 1: one-to-one matching based on the SMHD; (2) Model 2: matching with only the SMHD threshold; (3) Model 3: matching with the SMHD and the aCS thresholds; and (4) Model 4: matching with the SMHD, the aCS, and topological relationships. These expe...
-
A simplified Linear Feature matching method using decision tree analysis, weighted Linear directional mean, and topological relationships
2016Co-Authors: Ick Hoi Kim, Chenchieh Feng, Yichen WangAbstract:Linear Feature matching is one of the crucial components for data conflation that sees its usefulness in updating existing data through the integration of newer data and in evaluating data accuracy. This article presents a simplified Linear Feature matching method to conflate historical and current road data. To measure the similarity, the shorter line median Hausdorff distance (SMHD), the absolute value of cosine similarity (aCS) of the weighted Linear directional mean values, and topological relationships are adopted. The decision tree analysis is employed to derive thresholds for the SMHD and the aCS. To demonstrate the usefulness of the simple Linear Feature matching method, four models with incremental configurations are designed and tested: (1) Model 1: one-to-one matching based on the SMHD; (2) Model 2: matching with only the SMHD threshold; (3) Model 3: matching with the SMHD and the aCS thresholds; and (4) Model 4: matching with the SMHD, the aCS, and topological relationships. These experiments suggest that Model 2, which considers only distance, does not provide stable results, while Models 3 and 4, which consider direction and topological relationships, produce stable results with levels of accuracy around 90% and 95%, respectively. The results suggest that the proposed method is simple yet robust for Linear Feature matching.
Xiangzhi Bai - One of the best experts on this subject based on the ideXlab platform.
-
Linear Feature detection based on the multi scale multi structuring element grey level hit or miss transform
Computers & Electrical Engineering, 2015Co-Authors: Xiangzhi Bai, Tao Wang, Fugen ZhouAbstract:Multi-scale extension of multi-structuring elements for hit-or-miss transform.Utilizing the grey-level hit-or-miss transform for Linear Feature detection.Performing Linear Feature detection without thresholding. Detecting the Linear Features in an image is a key technology for different applications. In this paper, a simple and effective algorithm based on the hit-or-miss transform is proposed. To detect Linear Features with different directions, multi-structuring elements corresponding to different directions are constructed. To detect Linear Features with different widths, a multi-scale extension of the constructed multi-structuring elements is used. Then, the grey-level hit-or-miss transform that utilizes the constructed multi-scales of multi-structuring elements could effectively extract all of the possible Linear Features without thresholding. Therefore, after refining the extracted Linear Feature regions using three simple steps, the final Linear Features could be effectively detected. Experimental results on different images from different applications show that the proposed algorithm performs well for detecting Linear Features with different widths, different grey distributions and noises.
-
Top-hat by reconstruction operators based multi-scale multi-structuring element method for multiple Linear Feature detection with simple post-processing
Optik, 2013Co-Authors: Xiangzhi BaiAbstract:Abstract To efficiently extract all the possible Linear Features in image, a multi-scale multi-structuring element top-hat by reconstruction operator based algorithm with simple post-processing is proposed in this paper. Multi-scale top-hat by reconstruction operator using multi-scale structuring elements is discussed, firstly. Also, through importing multi-structuring elements with Linear shapes at different directions, multi-scale multi-structuring element top-hat by reconstruction operator for Linear Feature extraction is shown. By using the multi-scales of multi-structuring elements, the method of extracting all the possible Linear Feature regions in an image is proposed. After extracting the Linear Feature regions, the final detected Linear Features, which are expressed as lines with different shapes and lengths, are obtained through image binarisation and refinement. Experimental results on different types of images show that, the proposed algorithm is efficient for Linear Feature detection and could be widely used in different applications related to multiple Linear Feature detection.
-
Multiple Linear Feature detection based on multiple-structuring-element center-surround top-hat transform.
Applied optics, 2012Co-Authors: Xiangzhi Bai, Fugen Zhou, Bindang XueAbstract:Linear Feature detection is an important technique in different applications of image processing. To detect Linear Features in different types of images, a simple but effective algorithm based on a multiple-structuring-element center-surround top-hat transform is proposed. The center-surround top-hat transform is discussed and analyzed. Based on the properties of this transform for image Feature detection, multiple structuring elements are constructed corresponding to the possible Linear Features at different directions. The whole algorithm is divided into four parts. First, the algorithm uses the center-surround top-hat transform to detect all the possible Linear Features at different directions through constructing multiple structuring elements. Second, the detected Linear Feature regions at each direction are processed by a closing operation to remove the possible holes or unconnected regions. Third, the processed results of the detected Linear Feature regions at all directions are combined to form all the possible detected Linear Feature regions. Fourth, the combined result is refined by using some simple operations to form the final result. Experimental results on different types of images from different applications verified the effective performance of the proposed algorithm. Moreover, the experimental results indicate that the proposed algorithm could be used in different applications.
-
Multiple Linear Feature detection through top-hat transform by using multi Linear structuring elements
Optik, 2012Co-Authors: Xiangzhi Bai, Fugen Zhou, Bindang XueAbstract:Abstract A multiple Linear Feature detection algorithm through top-hat transform using the constructed multiple Linear structuring elements is proposed in this paper. The desired Linear Features are treated as a set. And, the set is divided into different subsets. Multi Linear structuring elements corresponding to different subsets are constructed. After that, top-hat transform is performed by using the constructed Linear structuring elements, and the results are combined to reconstruct the desired Linear Features. Then, the extracted Linear Features are binarized and processed to form the final detected binary Linear Features. Because of the effective performance of the top-hat transform using the constructed multi Linear structuring elements, the Linear Features of different images from different applications could be well detected. The analysis and experimental results show that, the proposed algorithm could be well used for multiple Linear Feature detection in different applications.
Atr Key - One of the best experts on this subject based on the ideXlab platform.
-
Linear Feature extraction for sar image based on fused edge detector
Journal of Electronics Information & Technology, 2009Co-Authors: Atr KeyAbstract:A Linear Feature extraction algorithm for Synthetic Aperture Radar (SAR) image is proposed, which is based on the low signal-to-noise quality of SAR image. Firstly, a new edge detector, which fuses the Canny operator and Ratio Of Average (ROA) operator, is used to get the edge points. Then, radon transform is carried out to get the primitive line segments. Finally, the broken lines due to speckle noise are connected by means of the heuristic link idea. The experiment results which are based on the SAR images show, the proposed algorithm can describe the Linear characteristic of SAR images precisely, and it can be used for SAR auto target recognition and scene matching.