The security of maritime activity is enhanced by the detection of marine vessels. Satellite images are used to detect the marine vessels irrespective of extreme weather conditions. Marine vessels can be detected effic...
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The security of maritime activity is enhanced by the detection of marine vessels. Satellite images are used to detect the marine vessels irrespective of extreme weather conditions. Marine vessels can be detected efficiently using image segmentation algorithms. Many researchers have applied Haar-like classifier, convolution neural network, artificial neural network techniques to detect the marine vessels. In this work two different methodologies such as fuzzy C means (FCM) and marker-controlledwatershed segmentation algorithms are developed and demonstrated to detect the marine vessels from satellite images. The marker-controlled watershed algorithm can effectively visualize an image in three dimensions and easily segments three-dimensional images. On the other hand, the number of iterations needed to achieve a specific clustering exercise in FCM is very less. It calculates the distance between the pixels and the cluster centres in the spectral domain to calculate the membership function. Experiments are carried out using IKONOS image of 4-m resolution. The average users accuracy of FCM algorithm and marker-controlled watershed algorithm is 91.29% and 95.79%, respectively. The results obtained show that there is an increase in accuracy for marker-controlled watershed algorithm when compared to FCM algorithm.
Diabetic retinopathy (DR) is one of the most complications of diabetes. It is a progressive disease leading to significant vision loss in the patients. Abnormal capillary nonperfusion (CNP) regions are one of the impo...
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Diabetic retinopathy (DR) is one of the most complications of diabetes. It is a progressive disease leading to significant vision loss in the patients. Abnormal capillary nonperfusion (CNP) regions are one of the important characteristics of DR increasing with its progression. Therefore, automatic segmentation and quantification of abnormal CNP regions can be helpful to monitor the patient's treatment process. We propose an automatic method for segmentation of abnormal CNP regions on the superficial and deep capillary plexuses of optical coherence tomography angiography (OCTA) images using the marker-controlled watershed algorithm. The proposed method has three main steps. In the first step, original images are enhanced using the vesselness filter and then foreground and background marker images are computed. In the second step, abnormal CNP region candidates are segmented using the marker-controlled watershed algorithm, and in the third step, the candidates are modeled using an undirected weighted graph and finally, by applying merging and removing procedures correct abnormal CNP regions are identified. The proposed method was evaluated on a dataset with 36 normal and diabetic subjects using the ground truth obtained by two observers. The results show the proposed method outperformed some of the state-of-the-art methods on the superficial and deep capillary plexuses according to the most important metrics. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Detailed information about a building and its surrounding is generally contained in hi-resolution synthetic aperture radar (SAR) data. An approach to extract the information and to identify the building with linear fe...
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ISBN:
(纸本)9781479979295
Detailed information about a building and its surrounding is generally contained in hi-resolution synthetic aperture radar (SAR) data. An approach to extract the information and to identify the building with linear features from a single hi-resolution SAR image has been developed. With the strong radar backscattering and shape feature of a building in SAR image, external markers were derived by an OTSU algorithm and a morphology algorithm. Then the minima imposition technique was used to modify the edge-enhanced SAR image coupled with the markers. Finally, the linear building boundaries were obtained using the watershedalgorithm. Results of building extract from three types of developed areas were satisfactory.
Detailed information about a building and its surrounding is generally contained in hi-resolution synthetic aperture radar (SAR) data. An approach to extract the information and to identify the building with linear fe...
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ISBN:
(纸本)9781479979301
Detailed information about a building and its surrounding is generally contained in hi-resolution synthetic aperture radar (SAR) data. An approach to extract the information and to identify the building with linear features from a single hi-resolution SAR image has been developed. With the strong radar backscattering and shape feature of a building in SAR image, external markers were derived by an OTSU algorithm and a morphology algorithm. Then the minima imposition technique was used to modify the edge-enhanced SAR image coupled with the markers. Finally, the linear building boundaries were obtained using the watershedalgorithm. Results of building extract from three types of developed areas were satisfactory.
Comprehensive two-dimensional gas chromatography (GC x GC) can separate thousands of different compounds, and is used for many important applications such as petrochemical processing and environmental monitoring, etc....
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Comprehensive two-dimensional gas chromatography (GC x GC) can separate thousands of different compounds, and is used for many important applications such as petrochemical processing and environmental monitoring, etc. GC x GC generates rich and complex information, which requires automated processing for rapid chemical identification and classification. A challenge is to remove unwanted streaks that may affect the quantification and identification of analytes. It is difficult to detect streaks because of complex backgrounds, low-contrast data, and variable shapes, scales, and orientations of streaks in GC x GC data. This paper proposes a new approach to detect streaks effectively based on image analysis techniques. By adopting a pseudo-log function and preprocessing methods to compress the original data and enhance the low-contrast data, we employ steerable Gaussian filtering to delineate streak regions based on the specific orientations of streaks. A marker-controlled watershed algorithm is then used to segment the streaks, and highly discriminating characteristics are used to identify candidate regions and reject false streaks. In the end, with a diverse data set generated from gas chromatograph, experiments are carried out and the results demonstrate that our streak detection approach is effective and robust with respect to changes in streak patterns, even in variable chromatographic conditions. The proposed object detection method effective in complex backgrounds and low-contrast conditions is also helpful for object detection in other scenes.
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