Contour-based algorithm for vectorization of satellite images

Kirsanov, Andrey; Vavilin, Andrey; Jo, K-H.

doi:10.1109/ifost.2010.5668109

Cited by 9 publications

(9 citation statements)

References 4 publications

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“…Comparing the proposed LAVM to the state-of-art in particular to [6,[28][29][30][31][32][33][34][35][36][37]. Many efforts achieved successful results but have some drawbacks; many researchers used vectorization for recognition proposes neglected some important factors.…”

Section: IVmentioning

confidence: 98%

“…Kirsanov (2010) presented a contour-based algorithm for vectorization of satellite images. Their system showed high performance and accuracy in comparison with other methods of processing satellite data [32]. Jun et al (2011) proposed the neighborhood filled algorithm for the interference information of the thematic map.…”

Section: IImentioning

confidence: 99%

“…The approach depends on Canny and using grayscale image [31]. Kirsanov (2010) presented a contour-based algorithm for vectorization of satellite images. Their system showed high performance and accuracy in comparison with other methods of processing satellite data [32].…”

Section: IImentioning

confidence: 99%

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A GIS vectorization model for quad-tree satellite images

Mesbah

Kholief

Mahdy

2012

2012 22nd International Conference on Computer Theory and Applications (ICCTA)

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Quad tree is a common raster data structure that uses recursive decomposition to divide a raster into a hierarchy of quadrants. This papers aims to develop a vectorization model for the conversion of quad tree satellite images to vector format that can be used and processed in geographic information systems (GIS) projects. The proposed model consists of four main phases: applying noise removal filter, segmenting images usimg edge detection methods, recognition of segments boundaries using a contour detection algorithm, and approximation of the sets of connected points by generalization and simplification procedures to create Shapefiles vector map data. The proposed vectorization model is implemented using Pascal. Results showed that the model is acceptable for mapping purposes. Future work may include developing a vector linker component, capable of linking different vectors at different blocks at the same layer.

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Section: IVmentioning

confidence: 98%

Section: IImentioning

confidence: 99%

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A GIS vectorization model for quad-tree satellite images

Mesbah

Kholief

Mahdy

2012

2012 22nd International Conference on Computer Theory and Applications (ICCTA)

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“…There are many vectorization approaches developed in various domains for different purposes (Wenyin & Dori 1999). These methods are roughly classified in six groups: (1) Hough Transform based methods (Duda & Hart 1971;Gates et al 2003;Jiaxin et al 2009;Xu et al 2011), (2) skeletonization based methods (Tamura 1978), (3) contour based methods (Martínez-Pérez et al 1987;Kirsanov et al 2010), (4) graph-structure based methods (Zenzo & Morelli 1989), (5) mesh pattern based methods (Lin et al 1985), and (6) pixel tracking based methods (Dori & Liu 1999). All these vectorization approaches have some common characteristics.…”

Section: Related Researchmentioning

confidence: 99%

An automated technique to determine spatio-temporal changes in satellite island images with vectorization and spatial queries

Eken

Sayar

2015

Sadhana

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For spatio-temporal and topologic analyses, vectorial information (carrying coordinate values defined as point sets) gives better information than its raster (grid of pixels) counterpart. The study presented in this paper is based on (i) recognition and extraction of an island object in a set of digital images captured by LandSat-7 satellite and (ii) modelling it as a polygon (vectorial) and making it easy to process and easy to understand for computers and information science applications. Polygon representations of island images then can be stored and manipulated through objectrelational spatial databases. Spatial databases have built-in functions and services for spatial objects defined with geometry types such as points, lines, and polygons. By this way we will be utilizing the rapidly changing and developing object-relational database communities' studies and discoveries in spatio-temporal and topological analysis for the investigation of digital satellite images. This approach also enables service qualities as well as a better performance. The efficiency and feasibility of the proposed system will be examined by various scenarios such as earthquake, erosion and accretion. Scenarios are based on measuring the effects of the natural phenomena on a selected island on satellite images.

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“…), facial feature extraction, medical imaging among many others [21,25,28,38,40,51]. Some of the applications of image segmentation in medical context consist of locating tumors, pulmonary nodules, locating Aberrant Crypt Foci (ACF), vessel segmentation or cervical vertebra segmentation, organs and bones segmentation [4,20,22,31,33,36,44].…”

Section: Introductionmentioning

confidence: 99%

Hybrid multi-GPU computing: accelerated kernels for segmentation and object detection with medical image processing applications

Graca

Falcão

Figueiredo

et al. 2015

J Real-Time Image Proc

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In the last two decades, we have seen an amazing development of image processing techniques targeted for medical applications. We propose multi-GPUbased parallel real-time algorithms for segmentation and shape-based object detection, aiming at accelerating two medical image processing methods: automated blood detection in wireless capsule endoscopy (WCE) images and automated bright lesion detection in retinal fundus images. In the former method we identified segmentation and object detection as being responsible for consuming most of the global processing time. While in the latter, as segmentation was not used, shape-based object detection was the compute-intensive task identified. Experimental results show that the accelerated method running on multi-GPU systems for blood detection in WCE images is on average 265 times faster than the original CPU version and is able to process 344 frames per second. By applying the multi-GPU framework for bright lesion detection in fundus images we are able to process 62 frames per second with a speedup average 667 times faster than the equivalent CPU version.

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Contour-based algorithm for vectorization of satellite images

Cited by 9 publications

References 4 publications

A GIS vectorization model for quad-tree satellite images

A GIS vectorization model for quad-tree satellite images

An automated technique to determine spatio-temporal changes in satellite island images with vectorization and spatial queries

Hybrid multi-GPU computing: accelerated kernels for segmentation and object detection with medical image processing applications

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