2004 |
Petros Maragos, Anastasia Sofou, Giorgos B Stamou, Vassilis Tzouvaras, Efimia Papatheodorou, George P Stamou Image analysis of soil micromorphology: Feature extraction, segmentation, and quality inference Journal Article Eurasip Journal on Applied Signal Processing, 2004 (6), pp. 902–912, 2004, ISSN: 11108657. Abstract | BibTeX | Links: [PDF] @article{118, title = {Image analysis of soil micromorphology: Feature extraction, segmentation, and quality inference}, author = {Petros Maragos and Anastasia Sofou and Giorgos B Stamou and Vassilis Tzouvaras and Efimia Papatheodorou and George P Stamou}, url = {http://robotics.ntua.gr/wp-content/uploads/publications/2004_Maragos-et-al_ImageAnalSoilMicromorphologyFeatureExtractSegmentQualityInfer_jasp.pdf}, doi = {10.1155/S1110865704402054}, issn = {11108657}, year = {2004}, date = {2004-01-01}, journal = {Eurasip Journal on Applied Signal Processing}, volume = {2004}, number = {6}, pages = {902--912}, abstract = {We present an automated system that we have developed for estimation of the bioecological quality of soils using various image analysis methodologies. Its goal is to analyze soilsection images, extract features related to their micromorphology, and relate the visual features to various degrees of soil fertility inferred from biochemical characteristics of the soil. The image methodologies used range from low-level image processing tasks, such as nonlinear enhancement, multiscale analysis, geometric feature detection, and size distributions, to object-oriented analysis, such as segmentation, region texture, and shape analysis.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We present an automated system that we have developed for estimation of the bioecological quality of soils using various image analysis methodologies. Its goal is to analyze soilsection images, extract features related to their micromorphology, and relate the visual features to various degrees of soil fertility inferred from biochemical characteristics of the soil. The image methodologies used range from low-level image processing tasks, such as nonlinear enhancement, multiscale analysis, geometric feature detection, and size distributions, to object-oriented analysis, such as segmentation, region texture, and shape analysis. |
2002 |
Costas S Tzafestas, Petros Maragos Shape connectivity: Multiscale analysis and application to generalized granulometries Journal Article Journal of Mathematical Imaging and Vision, 17 (2), pp. 109–129, 2002, ISSN: 09249907. Abstract | BibTeX | Links: [PDF] @article{117, title = {Shape connectivity: Multiscale analysis and application to generalized granulometries}, author = {Costas S Tzafestas and Petros Maragos}, url = {http://robotics.ntua.gr/wp-content/uploads/publications/TzafestasMaragos_ShapeConnectMscale_JMIV2002.pdf}, doi = {10.1023/A:1020629402912}, issn = {09249907}, year = {2002}, date = {2002-01-01}, journal = {Journal of Mathematical Imaging and Vision}, volume = {17}, number = {2}, pages = {109--129}, abstract = {This paper develops a multiscale connectivity theory for shapes based on the axiomatic definition of new generalized connectivity measures, which are obtained using morphology-based nonlinear scale-space operators. The concept of connectivity-tree for hierarchical image representation is introduced and used to define generalized connected morphological operators. This theoretical framework is then applied to establish a class of generalized granulometries, implemented at a particular problem concerning soilsection image analysis and evaluation of morphological properties such as size distributions. Comparative results demonstrate the power and versatility of the proposed methodology with respect to the application of typical connected operators (such as reconstruction openings). This multiscale connectivity analysis framework aims at a more reliable evaluation of shape/size information within complex images, with particular applications to generalized granulometries, connected operators, and segmentation.}, keywords = {}, pubstate = {published}, tppubtype = {article} } This paper develops a multiscale connectivity theory for shapes based on the axiomatic definition of new generalized connectivity measures, which are obtained using morphology-based nonlinear scale-space operators. The concept of connectivity-tree for hierarchical image representation is introduced and used to define generalized connected morphological operators. This theoretical framework is then applied to establish a class of generalized granulometries, implemented at a particular problem concerning soilsection image analysis and evaluation of morphological properties such as size distributions. Comparative results demonstrate the power and versatility of the proposed methodology with respect to the application of typical connected operators (such as reconstruction openings). This multiscale connectivity analysis framework aims at a more reliable evaluation of shape/size information within complex images, with particular applications to generalized granulometries, connected operators, and segmentation. |
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