- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Very High Resolution Optical Image Classification Using Watershed Segmentation and a Region-Based Kernel
摘要： In this paper, the problem of the spatial-spectral classification of very high-resolution optical images is addressed using a kernel- and region-based approach. A novel method based on integrating region-based or object-based information into a kernel machine is developed. A Gaussian process model is used to characterize each segment in a segmentation map and to define a region-based admissible kernel accordingly. This kernel is combined with a marker-controlled watershed segmentation that incorporates scale adaptivity. Spatial-spectral fusion capabilities are also ensured by combining the resulting classification method with composite kernels.
关键词： watershed segmentation,region-based classification,Kernel machines,geospatial object-based image analysis (GEOBIA)
更新于2025-09-23 15:21:21
-
[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Configurable Cell Segmentation Solution Using Hough Circles Transform and Watershed Algorithm
摘要： There are many methods for obtaining microscopic images, they can be obtained for different types of cells in different environments, which makes it impossible to create universal recognition algorithms. Therefore, for each specific task, it is necessary to implement a separate algorithm that will be effective for the existing set of images, and take into account the peculiarities of these images. The task of this work is the development of a flexible and customizable algorithm that can be configured to segment cells on different types of images. As a result, a solution was developed that has many customizable parameters to optimize the result for a specific data set. Also, this it is resistant to a lot of noise and artifacts that can occur on images, such as uneven background, small debris, loss of focus when shooting.
关键词： watershed,segmentation,cells recognition,Hough transform
更新于2025-09-23 15:19:57
-
Tree crown segmentation based on a tree crown density model derived from Airborne Laser Scanning
摘要： This letter describes a new algorithm for automatic tree crown delineation based on a model of tree crown density, and its validation. The tree crown density model was first used to create a correlation surface, which was then input to a standard watershed segmentation algorithm for delineation of tree crowns. The use of a model in an early step of the algorithm neatly solves the problem of scale selection. In earlier studies, correlation surfaces have been used for tree crown segmentation, involving modelling tree crowns as solid geometric shapes. The new algorithm applies a density model of tree crowns, which improves the model’s suitability for segmentation of Airborne Laser Scanning (ALS) data because laser returns are located inside tree crowns. The algorithm was validated using data acquired for 36 circular (40 m radius) field plots in southern Sweden. The algorithm detected high proportions of field-measured trees (40–97% of live trees in the 36 field plots: 85% on average). The average proportion of detected basal area (cross-sectional area of tree stems, 1.3 m above ground) was 93% (range: 84–99%). The algorithm was used with discrete return ALS point data, but the computation principle also allows delineation of tree crowns in ALS waveform data.
关键词： Tree crown segmentation,tree crown density model,Airborne Laser Scanning,forest mapping,watershed segmentation
更新于2025-09-11 14:15:04
-
[IEEE 2018 Fourth International Conference on Biosignals, Images and Instrumentation (ICBSII) - Chennai, India (2018.3.22-2018.3.24)] 2018 Fourth International Conference on Biosignals, Images and Instrumentation (ICBSII) - Examination of Digital Mammogram Using Otsu's Function and Watershed Segmentation
摘要： Breast malignancy is one of dangerous illness among the women community and premature detection may facilitate to diminish/eliminate breast cancer. Digital Mammogram (DM) is a commonly approved imaging scheme to record and scrutinize the breast cancer. This paper implements a novel hybrid approach based on the combination Otsu’s multi-thresholding and Water Shed Segmentation (WSS) to mine the suspicious sections from the DM. Initially, the multi-level thresholding using the Bat Algorithm (BA) driven Otsu with a bi-, tri- and four-level thresholding is implemented to pre-process the DM. Afterward, a marker controlled WSS is implemented to mine the infected division of DM. The mined section is then evaluated using the Haralick texture feature in order to know the severity of the disease by examining its texture feature. In this paper, DM dataset with dense, medium, low and normal breast regions are analyzed independently with the proposed approach. The experimental result of this paper confirms that, proposed method is very proficient in extracting the breast malignancy from the considered DM database.
关键词： Digital mammogram,Watershed segmentation,Bat algorithm,Otsu,GLCM features
更新于2025-09-10 09:29:36