Shoulder lines are the most important landform demarcations for geographical analysis,soil erosion modeling and land use planning in the Loess Plateau area of China.This paper proposes an automatic,effective and accur...Shoulder lines are the most important landform demarcations for geographical analysis,soil erosion modeling and land use planning in the Loess Plateau area of China.This paper proposes an automatic,effective and accurate method of determining loess shoulder line from DEMs by integrating a hydrological D8 algorithm and a snake model.The watershed boundary line is adopted as the initial contour which evolves to identify the exact position of loess shoulder-line by the guidance of an external force of snake model from DEMs.Experiments show that the method overcomes the difficulties in both threshold selection for edge detection and the disconnecting issues in former extraction approaches.The accuracy evaluation of shoulder-line maps from the two test sites of the loess plateau area show obvious improvements in the extraction.The average contour matching distance of the new method is 12.0 m on 5 m resolution DEM,and shows improvement in the accuracy and continuity.The comparisons of accuracy evaluations of the two test sites show that the snake model method performs better in the loess plain area than in the area with high gully density.展开更多
Active Contour Model or Snake model is an efficient method by which the users can extract the object contour of Region Of Interest （ROI）. In this paper, we present an improved method combining Hermite splines curve ...Active Contour Model or Snake model is an efficient method by which the users can extract the object contour of Region Of Interest （ROI）. In this paper, we present an improved method combining Hermite splines curve and Snake model that can be used as a tool for fast and intuitive contour extraction. We choose Hermite splines curve as a basic function of Snake contour curve and present its energy function. The optimization of energy minimization is performed hy Dynamic Programming technique. The validation results are presented, comparing the traditional Snake model and the HSCM, showing the similar performance of the latter. We can find that HSCM can overcome the non-convex constraints efficiently. Several medical images applications illustrate that Hermite Splines Contour Model （HSCM） is more efficient than traditional Snake model.展开更多
A novel Snake model with region information is proposed to detect and track moving objects. Generally, the region-information-based approach is sensitive to illumination changes and small movement in the background, w...A novel Snake model with region information is proposed to detect and track moving objects. Generally, the region-information-based approach is sensitive to illumination changes and small movement in the background, while the edge-information-based approach often obtains incorrect results for ambiguous images. The two types of information are introduced in computing the image force. Edge-information-based features make the algorithm fast and robust, and region information makes the active confour energy function obtains correct results for ambiguous images. Furthermore, an automatic contour initialization method using double difference images is given to meet the requirement of video sequence tracking. Meanwhile, a simple forecast section is added to estimate the position of the contour in the algorithm so that it can improve the convergence speed of the active contour. Experimental results show that the computation time of the algorithm is less than 0.1 s/frame. And it can be applied to a real-time system.展开更多
Automatic extraction features and buildings in particular from digital images is one of the most complex and challenging task faced by computer vision and photogrammetric communities. Extracted buildings are required ...Automatic extraction features and buildings in particular from digital images is one of the most complex and challenging task faced by computer vision and photogrammetric communities. Extracted buildings are required for varieties of applications including urban planning, creation of GIS databases and development of urban city models for taxation. For decades, extraction of features has been done by photogrammetric methods using stereo plotters and digital work stations. Photogrammetric methods are tedious, manually operated and require well-trained personnel. In recent years, there has been emergence of high-resolution space borne images, which have disclosed a large number of new opportunities for medium and large-scale topographic mapping. In this paper, a semi-automatic method is introduced to extract buildings in planned and informal settlements in urban areas from high resolution imagery. The proposed method uses modified snakes model and radial casting algorithm to initialize snakes contours and refinement of building outlines. The extraction rate is 91 percent as demonstrated by examples over selected test areas. The potential, limitations and future work is discussed.展开更多
Objective To propose two novel methods based on deep learning for computer-aided tongue diagnosis,including tongue image segmentation and tongue color classification,improving their diagnostic accuracy.Methods LabelMe...Objective To propose two novel methods based on deep learning for computer-aided tongue diagnosis,including tongue image segmentation and tongue color classification,improving their diagnostic accuracy.Methods LabelMe was used to label the tongue mask and Snake model to optimize the labeling results.A new dataset was constructed for tongue image segmentation.Tongue color was marked to build a classified dataset for network training.In this research,the Inception+Atrous Spatial Pyramid Pooling(ASPP)+UNet(IAUNet)method was proposed for tongue image segmentation,based on the existing UNet,Inception,and atrous convolution.Moreover,the Tongue Color Classification Net(TCCNet)was constructed with reference to ResNet,Inception,and Triple-Loss.Several important measurement indexes were selected to evaluate and compare the effects of the novel and existing methods for tongue segmentation and tongue color classification.IAUNet was compared with existing mainstream methods such as UNet and DeepLabV3+for tongue segmentation.TCCNet for tongue color classification was compared with VGG16 and GoogLeNet.Results IAUNet can accurately segment the tongue from original images.The results showed that the Mean Intersection over Union(MIoU)of IAUNet reached 96.30%,and its Mean Pixel Accuracy(MPA),mean Average Precision(mAP),F1-Score,G-Score,and Area Under Curve(AUC)reached 97.86%,99.18%,96.71%,96.82%,and 99.71%,respectively,suggesting IAUNet produced better segmentation than other methods,with fewer parameters.Triplet-Loss was applied in the proposed TCCNet to separate different embedded colors.The experiment yielded ideal results,with F1-Score and mAP of the TCCNet reached 88.86% and 93.49%,respectively.Conclusion IAUNet based on deep learning for tongue segmentation is better than traditional ones.IAUNet can not only produce ideal tongue segmentation,but have better effects than those of PSPNet,SegNet,UNet,and DeepLabV3+,the traditional networks.As for tongue color classification,the proposed network,TCCNet,had better F1-Score and mAP values as compared with other neural networks such as VGG16 and GoogLeNet.展开更多
An adaptive object tracking algorithm based on particle filtering and a modified Gradient Vector Flow (GVF) Snake is proposed for tracking moving and deforming objects. The original contours of objects are obtained by...An adaptive object tracking algorithm based on particle filtering and a modified Gradient Vector Flow (GVF) Snake is proposed for tracking moving and deforming objects. The original contours of objects are obtained by using the background differencing method,and the true contours of objects can be converged by means of the powerful searching ability of a modified GVF-Snake. Finally,an Energetic Particle Filtering (EPF) algorithm is obtained by combining particle filtering and a modified GVF-Snake,and by using K-means and the EPF algorithm,multiple objects can be tracked. The proposed tracking tactic for partially occluded objects can effectively improve its anti-occlusion ability. Experiments show that this algorithm can obtain better tracking effect even though the tracked object is occluded.展开更多
The traditional Snake algorithm cannot effectively detect the object edge of an image with non-convex shapes or low SNR.This paper studies the characteristics of this type of image with complex shape target or noise a...The traditional Snake algorithm cannot effectively detect the object edge of an image with non-convex shapes or low SNR.This paper studies the characteristics of this type of image with complex shape target or noise and presents an improved Snake algorithm.The traditional Snake function model and operation strategy are improved by increasing new control energy functions,and the influencing weight of these energy factors is discussed.At the same time,a dynamic arrangement for the Snake points is used to adapt different target shapes.The simulation results indicate that the new Snake model greatly decreases the dependence on the Snake point’s initial position and effectively overcomes noise influence.This method enhances the Snake algorithm’s ability of detecting object edge.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos. 40930531, 41001294, 41301422)the Open Project Foundation of State Key Laboratory of Resources and Environmental Information System in China (Grant No. 2010KF0002SA)
文摘Shoulder lines are the most important landform demarcations for geographical analysis,soil erosion modeling and land use planning in the Loess Plateau area of China.This paper proposes an automatic,effective and accurate method of determining loess shoulder line from DEMs by integrating a hydrological D8 algorithm and a snake model.The watershed boundary line is adopted as the initial contour which evolves to identify the exact position of loess shoulder-line by the guidance of an external force of snake model from DEMs.Experiments show that the method overcomes the difficulties in both threshold selection for edge detection and the disconnecting issues in former extraction approaches.The accuracy evaluation of shoulder-line maps from the two test sites of the loess plateau area show obvious improvements in the extraction.The average contour matching distance of the new method is 12.0 m on 5 m resolution DEM,and shows improvement in the accuracy and continuity.The comparisons of accuracy evaluations of the two test sites show that the snake model method performs better in the loess plain area than in the area with high gully density.
文摘Active Contour Model or Snake model is an efficient method by which the users can extract the object contour of Region Of Interest （ROI）. In this paper, we present an improved method combining Hermite splines curve and Snake model that can be used as a tool for fast and intuitive contour extraction. We choose Hermite splines curve as a basic function of Snake contour curve and present its energy function. The optimization of energy minimization is performed hy Dynamic Programming technique. The validation results are presented, comparing the traditional Snake model and the HSCM, showing the similar performance of the latter. We can find that HSCM can overcome the non-convex constraints efficiently. Several medical images applications illustrate that Hermite Splines Contour Model （HSCM） is more efficient than traditional Snake model.
文摘A novel Snake model with region information is proposed to detect and track moving objects. Generally, the region-information-based approach is sensitive to illumination changes and small movement in the background, while the edge-information-based approach often obtains incorrect results for ambiguous images. The two types of information are introduced in computing the image force. Edge-information-based features make the algorithm fast and robust, and region information makes the active confour energy function obtains correct results for ambiguous images. Furthermore, an automatic contour initialization method using double difference images is given to meet the requirement of video sequence tracking. Meanwhile, a simple forecast section is added to estimate the position of the contour in the algorithm so that it can improve the convergence speed of the active contour. Experimental results show that the computation time of the algorithm is less than 0.1 s/frame. And it can be applied to a real-time system.
文摘Automatic extraction features and buildings in particular from digital images is one of the most complex and challenging task faced by computer vision and photogrammetric communities. Extracted buildings are required for varieties of applications including urban planning, creation of GIS databases and development of urban city models for taxation. For decades, extraction of features has been done by photogrammetric methods using stereo plotters and digital work stations. Photogrammetric methods are tedious, manually operated and require well-trained personnel. In recent years, there has been emergence of high-resolution space borne images, which have disclosed a large number of new opportunities for medium and large-scale topographic mapping. In this paper, a semi-automatic method is introduced to extract buildings in planned and informal settlements in urban areas from high resolution imagery. The proposed method uses modified snakes model and radial casting algorithm to initialize snakes contours and refinement of building outlines. The extraction rate is 91 percent as demonstrated by examples over selected test areas. The potential, limitations and future work is discussed.
基金Scientific Research Project of the Education Department of Hunan Province(20C1435)Open Fund Project for Computer Science and Technology of Hunan University of Chinese Medicine(2018JK05).
文摘Objective To propose two novel methods based on deep learning for computer-aided tongue diagnosis,including tongue image segmentation and tongue color classification,improving their diagnostic accuracy.Methods LabelMe was used to label the tongue mask and Snake model to optimize the labeling results.A new dataset was constructed for tongue image segmentation.Tongue color was marked to build a classified dataset for network training.In this research,the Inception+Atrous Spatial Pyramid Pooling(ASPP)+UNet(IAUNet)method was proposed for tongue image segmentation,based on the existing UNet,Inception,and atrous convolution.Moreover,the Tongue Color Classification Net(TCCNet)was constructed with reference to ResNet,Inception,and Triple-Loss.Several important measurement indexes were selected to evaluate and compare the effects of the novel and existing methods for tongue segmentation and tongue color classification.IAUNet was compared with existing mainstream methods such as UNet and DeepLabV3+for tongue segmentation.TCCNet for tongue color classification was compared with VGG16 and GoogLeNet.Results IAUNet can accurately segment the tongue from original images.The results showed that the Mean Intersection over Union(MIoU)of IAUNet reached 96.30%,and its Mean Pixel Accuracy(MPA),mean Average Precision(mAP),F1-Score,G-Score,and Area Under Curve(AUC)reached 97.86%,99.18%,96.71%,96.82%,and 99.71%,respectively,suggesting IAUNet produced better segmentation than other methods,with fewer parameters.Triplet-Loss was applied in the proposed TCCNet to separate different embedded colors.The experiment yielded ideal results,with F1-Score and mAP of the TCCNet reached 88.86% and 93.49%,respectively.Conclusion IAUNet based on deep learning for tongue segmentation is better than traditional ones.IAUNet can not only produce ideal tongue segmentation,but have better effects than those of PSPNet,SegNet,UNet,and DeepLabV3+,the traditional networks.As for tongue color classification,the proposed network,TCCNet,had better F1-Score and mAP values as compared with other neural networks such as VGG16 and GoogLeNet.
基金Supported by the Significant Term of Science and Technology Research in Ministry of Education (No. 205060)Open Research Fund of National Mobile Communications Research Laboratory,Southeast University (N200911)+2 种基金Significant Basic Research of Jiangsu Province Colleges and Universities Natural Science Projects (07 KJA51006)Research Fund of Nanjing College of Traffic Vocational Technology (JY0903)Huawei Science and Technology Fund
文摘An adaptive object tracking algorithm based on particle filtering and a modified Gradient Vector Flow (GVF) Snake is proposed for tracking moving and deforming objects. The original contours of objects are obtained by using the background differencing method,and the true contours of objects can be converged by means of the powerful searching ability of a modified GVF-Snake. Finally,an Energetic Particle Filtering (EPF) algorithm is obtained by combining particle filtering and a modified GVF-Snake,and by using K-means and the EPF algorithm,multiple objects can be tracked. The proposed tracking tactic for partially occluded objects can effectively improve its anti-occlusion ability. Experiments show that this algorithm can obtain better tracking effect even though the tracked object is occluded.
基金supported by The National High Technology Research and Development Program of China(863 Program,2002AA813032).
文摘The traditional Snake algorithm cannot effectively detect the object edge of an image with non-convex shapes or low SNR.This paper studies the characteristics of this type of image with complex shape target or noise and presents an improved Snake algorithm.The traditional Snake function model and operation strategy are improved by increasing new control energy functions,and the influencing weight of these energy factors is discussed.At the same time,a dynamic arrangement for the Snake points is used to adapt different target shapes.The simulation results indicate that the new Snake model greatly decreases the dependence on the Snake point’s initial position and effectively overcomes noise influence.This method enhances the Snake algorithm’s ability of detecting object edge.
