[ { "id": "Groeller_2016_P7", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Depth functions as a quality measure and for steering multidimensional projections", "date": "2016-11", "abstract": "The analysis of multidimensional data has been a topic of continuous research for many years.This type of data can be found inseveral different areas ofscience. \nThe analysis of multidimensional data has been a topic of continuous research for many years. This type of data can be found in several different areas of science. A common task while analyzing such data is to investigate patterns by interacting with spatializations of the data in a visual domain. Understanding the relation between the underlying dataset characteristics and the technique used to provide its visual representation is of fundamental importance since it can provide a better intuition on what to expect from the spatialization. In this paper, we propose the usage of concepts from non-parametric statistics, namely depth functions, as a quality measure for spatializations. We evaluate the action of multi-dimensional projection techniques on such estimates. We apply both qualitative and quantitative ana-lyses on four different multidimensional techniques selected according to the properties they aim to preserve. We evaluate them with datasets of different characteristics: synthetic, real world, high dimensional; and contaminated with outliers. As a straightforward application, we propose to use depth information to guide multidimensional projection techniques which rely on interaction through control point selection and positioning. Even for techniques which do not intend to preserve any centrality measure, interesting results can be achieved by separating regions possibly contaminated with outliers.\n", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 453, "image_height": 362, "name": "Groeller_2016_P7-image.PNG", "type": "image/png", "size": 28144, "path": "Publication:Groeller_2016_P7", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/Groeller_2016_P7/Groeller_2016_P7-image.PNG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/Groeller_2016_P7/Groeller_2016_P7-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1408, 1195, 1413, 166, 1414, 1415 ], "issn": "doi: 10.1016/j.cag.2016.08.008", "journal": "Computers & Graphics (Special Section on SIBGRAPI 2016)", "lecturer": [ 1416 ], "pages_from": "93", "pages_to": "106", "volume": "60", "research_areas": [ "Perception" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 453, "image_height": 362, "name": "Groeller_2016_P7-image.PNG", "type": "image/png", "size": 28144, "path": "Publication:Groeller_2016_P7", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/Groeller_2016_P7/Groeller_2016_P7-image.PNG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/Groeller_2016_P7/Groeller_2016_P7-image:thumb{{size}}.png" }, { "description": null, "filetitle": "Paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "Groeller_2016_P7-Paper.pdf", "type": "application/pdf", "size": 2203763, "path": "Publication:Groeller_2016_P7", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/Groeller_2016_P7/Groeller_2016_P7-Paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/Groeller_2016_P7/Groeller_2016_P7-Paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2016/Groeller_2016_P7/", "__class": "Publication" }, { "id": "vad-2016-bre", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Generalized box-plot for root growth ensembles", "date": "2016", "abstract": "Background In the field of root biology there has been a remarkable progress in root phenotyping, which is the efficient acquisition and quantitative description of root morphology. What is currently missing are means to efficiently explore, exchange and present the massive amount of acquired, and often time dependent root phenotypes. \nResults In this work, we present visual summaries of root ensembles by aggregating root images with identical genetic characteristics. We use the generalized box plot concept with a new formulation of data depth. In addition to spatial distributions, we created a visual representation to encode temporal distributions associated with the development of root individuals.\nConclusions The new formulation of data depth allows for much faster implementation close to interactive frame rates. This allows us to present the statistics from bootstrapping that characterize the root sample set quality. As a positive side effect of the new data-depth formulation we are able to define the geometric median for the curve ensemble, which was well received by the domain experts.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1195, 1408, 1409, 962, 171 ], "journal": "BMC Bioinformatics", "lecturer": [ 1195 ], "research_areas": [ "BioVis" ], "keywords": [], "weblinks": [ { "href": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5333180/", "caption": null, "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "preview_image_width": 1200, "preview_image_height": 900, "name": "vad-2016-bre-paper.pdf", "type": "application/pdf", "size": 2416818, "path": "Publication:vad-2016-bre", "preview_name": "vad-2016-bre-paper:preview.png", "preview_type": "image/png", "preview_size": 174658, "url": "https://www.cg.tuwien.ac.at/research/publications/2016/vad-2016-bre/vad-2016-bre-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/vad-2016-bre/vad-2016-bre-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "Video", "main_file": false, "use_in_gallery": false, "access": "public", "name": "vad-2016-bre-Video.mp4", "type": "video/mp4", "size": 7077690, "path": "Publication:vad-2016-bre", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/vad-2016-bre/vad-2016-bre-Video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/vad-2016-bre/vad-2016-bre-Video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2016/vad-2016-bre/vad-2016-bre-Video:video.mp4" } ], "projects_workgroups": [ "illvisation" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2016/vad-2016-bre/", "__class": "Publication" } ]