In the present study, a generalized active contour model of gradient vector flow is combined with the video techniques of Argus system to delineate and track sequential nearshore wave crest profiles in the shoaling pr...In the present study, a generalized active contour model of gradient vector flow is combined with the video techniques of Argus system to delineate and track sequential nearshore wave crest profiles in the shoaling process, up to their breaking on the shoreline. Previous applications of active contour models to water wave problems are limited to controllable wave tank experiments. By contrast, our application in this study is in a nearshore field environment where oblique images obtained under natural and varying condition of ambient light are employed. Existing Argus techniques produce plane image data or time series data from a selected small subset of discrete pixels. By contrast, the active contour model produces line image data along continuous visible curves such as wave crest profiles. The combination of these two existing techniques, the active contour model and Argus methodologies, facilitates the estimates of the direction wave field and phase speeds within the whole area covered by camera. These estimates are useful for the purpose of inverse calculation of the water depth. Applications of the present techniques to Hsi-tzu bay where a beach restoration program is currently undertaken are illustrated. This extension of Argus video techniques provides new application of optical remote sensing to study the hydrodynamics and morphology of a nearshore environment.展开更多
While executing tasks such as ocean pollution monitoring,maritime rescue,geographic mapping,and automatic navigation utilizing remote sensing images,the coastline feature should be determined.Traditional methods are n...While executing tasks such as ocean pollution monitoring,maritime rescue,geographic mapping,and automatic navigation utilizing remote sensing images,the coastline feature should be determined.Traditional methods are not satisfactory to extract coastline in high-resolution panchromatic remote sensing image.Active contour model,also called snakes,have proven useful for interactive specification of image contours,so it is used as an effective coastlines extraction technique.Firstly,coastlines are detected by water segmentation and boundary tracking,which are considered initial contours to be optimized through active contour model.As better energy functions are developed,the power assist of snakes becomes effective.New internal energy has been done to reduce problems caused by convergence to local minima,and new external energy can greatly enlarge the capture region around features of interest.After normalization processing,energies are iterated using greedy algorithm to accelerate convergence rate.The experimental results encompassed examples in images and demonstrated the capabilities and efficiencies of the improvement.展开更多
In many practical applications of image segmentation problems,employing prior information can greatly improve segmentation results.This paper continues to study one kind of prior information,called prior distribution....In many practical applications of image segmentation problems,employing prior information can greatly improve segmentation results.This paper continues to study one kind of prior information,called prior distribution.Within this research,there is no exact template of the object;instead only several samples are given.The proposed method,called the parametric distribution prior model,extends our previous model by adding the training procedure to learn the prior distribution of the objects.Then this paper establishes the energy function of the active contour model(ACM)with consideration of this parametric form of prior distribution.Therefore,during the process of segmenting,the template can update itself while the contour evolves.Experiments are performed on the airplane data set.Experimental results demonstrate the potential of the proposed method that with the information of prior distribution,the segmentation effect and speed can be both improved efficaciously.展开更多
A new edge detection method combining the scanning window central edge (SWCE) detector and an improved active contour model is proposed. The method first emploies the SWCE detector based on the difference of area pi...A new edge detection method combining the scanning window central edge (SWCE) detector and an improved active contour model is proposed. The method first emploies the SWCE detector based on the difference of area pixel value means to perform an optimal edge detection, and then proposes an improved active contour model with modified energy functions to refine the location of the edges. The initial nodes of the improved active contour model are automatically found from the vectorised results of the SWCE detector. Tests on simulated speckled images and real airborne SAR images show that the combined method can benefit from the advantages of the both techniques and get satisfactory edge detection and localization abilities at the same time.展开更多
This paper proposes a computer-aided diagnosis system which can automatically detect thyroid nodules (TNs)and discriminate them as benign or malignant. The system firstly uses variational level set active contour with...This paper proposes a computer-aided diagnosis system which can automatically detect thyroid nodules (TNs)and discriminate them as benign or malignant. The system firstly uses variational level set active contour withgradients and phase information to complete automatic extraction of the boundaries of thyroid nodules images.Then according to thyroid ultrasound images and clinical diagnostic criteria, a new feature extraction methodbased on the fusion of shape, gray and texture is explored. Due to the imbalance of thyroid sample classes, thispaper introduces a weight factor to improve support vector machine, offering different classes of samples withdifferent weights. Finally, thyroid nodules are classified and discriminated by the improved support vector machine.Experiments show that the efficiency of discrimination on benign and malignant thyroid nodules is improved.展开更多
A multiscale foreground detection method was developed to segment moving objects from a sta- tionary background. The algorithm is based on a fixed-mesh-based contour model, which starts at the bounding box of the di...A multiscale foreground detection method was developed to segment moving objects from a sta- tionary background. The algorithm is based on a fixed-mesh-based contour model, which starts at the bounding box of the difference map between an input image and its background and ends at a final contour. An adaptive algorithm was developed to calculate an appropriate energy threshold to control the contours to identify the foreground silhouettes. Experiments show that this method more successfully ignores the nega- tive influence of image noise to obtain an accurate foreground map than other foreground detection algo- rithms. Most shadow pixels are also eliminated by this method.展开更多
Analysis of cellular behavior is significant for studying cell cycle and detecting anti-cancer drugs. It is a very difficult task for image processing to isolate individual cells in confocal microscopic images of non-...Analysis of cellular behavior is significant for studying cell cycle and detecting anti-cancer drugs. It is a very difficult task for image processing to isolate individual cells in confocal microscopic images of non-stained live cell cultures. Because these images do not have adequate textural variations. Manual cell segmentation requires massive labor and is a time consuming process. This paper describes an automated cell segmentation method for localizing the cells of Chinese hamster ovary cell culture. Several kinds of high-dimensional feature descriptors, K-means clustering method and Chan-Vese model-based level set are used to extract the cellular regions. The region extracted are used to classify phases in cell cycle. The segmentation results were experimentally assessed. As a result, the proposed method proved to be significant for cell isolation. In the evaluation experiments, we constructed a database of Chinese Hamster Ovary Cell’s microscopic images which includes various photographing environments under the guidance of a biologist.展开更多
The subsurface of urban cities is becoming increasingly congested.In-time records of subsur-face structures are of vital importance for the maintenance and management of urban infrastructure beneath or above the groun...The subsurface of urban cities is becoming increasingly congested.In-time records of subsur-face structures are of vital importance for the maintenance and management of urban infrastructure beneath or above the ground.Ground-penetrating radar(GPR)is a nondestructive testing method that can survey and image the subsurface without excava-tion.However,the interpretation of GPR relies on the operator’s experience.An automatic workflow was proposed for recognizing and classifying subsurface structures with GPR using computer vision and machine learning techniques.The workflow comprises three stages:first,full-cover GPR measurements are processed to form the C-scans;second,the abnormal areas are extracted from the full-cover C-scans with coefficient of variation-active contour model(CV-ACM);finally,the extracted segments are recognized and classified from the corresponding B-scans with aggregate channel feature(ACF)to produce a semantic map.The selected computer vision methods were validated by a controlled test in the laboratory,and the entire workflow was evaluated with a real,on-site case study.The results of the controlled and on-site case were both promising.This study establishes the necessity of a full-cover 3D GPR survey,illustrating the feasibility of integrating advanced computer vision techniques to analyze a large amount of 3D GPR survey data,and paves the way for automating subsurface modeling with GPR.展开更多
基金supported by the Science Council,Taiwan,under Grant No.NSC95-2221-E-006-475-MY2
文摘In the present study, a generalized active contour model of gradient vector flow is combined with the video techniques of Argus system to delineate and track sequential nearshore wave crest profiles in the shoaling process, up to their breaking on the shoreline. Previous applications of active contour models to water wave problems are limited to controllable wave tank experiments. By contrast, our application in this study is in a nearshore field environment where oblique images obtained under natural and varying condition of ambient light are employed. Existing Argus techniques produce plane image data or time series data from a selected small subset of discrete pixels. By contrast, the active contour model produces line image data along continuous visible curves such as wave crest profiles. The combination of these two existing techniques, the active contour model and Argus methodologies, facilitates the estimates of the direction wave field and phase speeds within the whole area covered by camera. These estimates are useful for the purpose of inverse calculation of the water depth. Applications of the present techniques to Hsi-tzu bay where a beach restoration program is currently undertaken are illustrated. This extension of Argus video techniques provides new application of optical remote sensing to study the hydrodynamics and morphology of a nearshore environment.
基金Sponsoreds by the National Natural Science Foundation of China (Grant No. 60575016)
文摘While executing tasks such as ocean pollution monitoring,maritime rescue,geographic mapping,and automatic navigation utilizing remote sensing images,the coastline feature should be determined.Traditional methods are not satisfactory to extract coastline in high-resolution panchromatic remote sensing image.Active contour model,also called snakes,have proven useful for interactive specification of image contours,so it is used as an effective coastlines extraction technique.Firstly,coastlines are detected by water segmentation and boundary tracking,which are considered initial contours to be optimized through active contour model.As better energy functions are developed,the power assist of snakes becomes effective.New internal energy has been done to reduce problems caused by convergence to local minima,and new external energy can greatly enlarge the capture region around features of interest.After normalization processing,energies are iterated using greedy algorithm to accelerate convergence rate.The experimental results encompassed examples in images and demonstrated the capabilities and efficiencies of the improvement.
基金supported by the National Key R&D Program of China(2018YFC0309400)the National Natural Science Foundation of China(61871188)
文摘In many practical applications of image segmentation problems,employing prior information can greatly improve segmentation results.This paper continues to study one kind of prior information,called prior distribution.Within this research,there is no exact template of the object;instead only several samples are given.The proposed method,called the parametric distribution prior model,extends our previous model by adding the training procedure to learn the prior distribution of the objects.Then this paper establishes the energy function of the active contour model(ACM)with consideration of this parametric form of prior distribution.Therefore,during the process of segmenting,the template can update itself while the contour evolves.Experiments are performed on the airplane data set.Experimental results demonstrate the potential of the proposed method that with the information of prior distribution,the segmentation effect and speed can be both improved efficaciously.
文摘A new edge detection method combining the scanning window central edge (SWCE) detector and an improved active contour model is proposed. The method first emploies the SWCE detector based on the difference of area pixel value means to perform an optimal edge detection, and then proposes an improved active contour model with modified energy functions to refine the location of the edges. The initial nodes of the improved active contour model are automatically found from the vectorised results of the SWCE detector. Tests on simulated speckled images and real airborne SAR images show that the combined method can benefit from the advantages of the both techniques and get satisfactory edge detection and localization abilities at the same time.
基金This work was supported in part by National Natural Science Foundation of China under Grant Nos.61572063 and 61401308Natural Science Foundation of Hebei Province under Grant Nos.F2016201142,F2018210148,F2019201151 and F2020201025+3 种基金Science Research Project of Hebei Province under Grant Nos.BJ2020030,QN2016085 and QN2017306Foundation of President of Hebei University under Grant No.XZJJ201909Opening Foundation of Machine Vision Technology Innovation Center of Hebei Province under Grant Nos.2018HBMV01 and 2018HBMV02Natural Science Foundation of Hebei University under Grant Nos.2014-303 and 8012605.
文摘This paper proposes a computer-aided diagnosis system which can automatically detect thyroid nodules (TNs)and discriminate them as benign or malignant. The system firstly uses variational level set active contour withgradients and phase information to complete automatic extraction of the boundaries of thyroid nodules images.Then according to thyroid ultrasound images and clinical diagnostic criteria, a new feature extraction methodbased on the fusion of shape, gray and texture is explored. Due to the imbalance of thyroid sample classes, thispaper introduces a weight factor to improve support vector machine, offering different classes of samples withdifferent weights. Finally, thyroid nodules are classified and discriminated by the improved support vector machine.Experiments show that the efficiency of discrimination on benign and malignant thyroid nodules is improved.
文摘A multiscale foreground detection method was developed to segment moving objects from a sta- tionary background. The algorithm is based on a fixed-mesh-based contour model, which starts at the bounding box of the difference map between an input image and its background and ends at a final contour. An adaptive algorithm was developed to calculate an appropriate energy threshold to control the contours to identify the foreground silhouettes. Experiments show that this method more successfully ignores the nega- tive influence of image noise to obtain an accurate foreground map than other foreground detection algo- rithms. Most shadow pixels are also eliminated by this method.
文摘Analysis of cellular behavior is significant for studying cell cycle and detecting anti-cancer drugs. It is a very difficult task for image processing to isolate individual cells in confocal microscopic images of non-stained live cell cultures. Because these images do not have adequate textural variations. Manual cell segmentation requires massive labor and is a time consuming process. This paper describes an automated cell segmentation method for localizing the cells of Chinese hamster ovary cell culture. Several kinds of high-dimensional feature descriptors, K-means clustering method and Chan-Vese model-based level set are used to extract the cellular regions. The region extracted are used to classify phases in cell cycle. The segmentation results were experimentally assessed. As a result, the proposed method proved to be significant for cell isolation. In the evaluation experiments, we constructed a database of Chinese Hamster Ovary Cell’s microscopic images which includes various photographing environments under the guidance of a biologist.
基金supported by the Shenzhen University[860-000002111308].
文摘The subsurface of urban cities is becoming increasingly congested.In-time records of subsur-face structures are of vital importance for the maintenance and management of urban infrastructure beneath or above the ground.Ground-penetrating radar(GPR)is a nondestructive testing method that can survey and image the subsurface without excava-tion.However,the interpretation of GPR relies on the operator’s experience.An automatic workflow was proposed for recognizing and classifying subsurface structures with GPR using computer vision and machine learning techniques.The workflow comprises three stages:first,full-cover GPR measurements are processed to form the C-scans;second,the abnormal areas are extracted from the full-cover C-scans with coefficient of variation-active contour model(CV-ACM);finally,the extracted segments are recognized and classified from the corresponding B-scans with aggregate channel feature(ACF)to produce a semantic map.The selected computer vision methods were validated by a controlled test in the laboratory,and the entire workflow was evaluated with a real,on-site case study.The results of the controlled and on-site case were both promising.This study establishes the necessity of a full-cover 3D GPR survey,illustrating the feasibility of integrating advanced computer vision techniques to analyze a large amount of 3D GPR survey data,and paves the way for automating subsurface modeling with GPR.