Relevant bibliographies by topics / Hand-drawn

Academic literature on the topic 'Hand-drawn'

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Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Hand-drawn"

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Swensen, Cole. "The Hand, Drawn." Chicago Review 47, no. 2 (2001): 45. http://dx.doi.org/10.2307/25304743.

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Xing, Jun, Li-Yi Wei, Takaaki Shiratori, and Koji Yatani. "Autocomplete hand-drawn animations." ACM Transactions on Graphics 34, no. 6 (November 4, 2015): 1–11. http://dx.doi.org/10.1145/2816795.2818079.

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Fitzgerald, Richard J. "Hand-drawn organic photovoltaics." Physics Today 71, no. 1 (January 2018): 21. http://dx.doi.org/10.1063/pt.3.3810.

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Dambha, Hajira. "Quentin Blake: Drawn By Hand." British Journal of General Practice 63, no. 609 (April 2013): 209.2–209. http://dx.doi.org/10.3399/bjgp13x665378.

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Rabbani, Mahdi, Reza Khoshkangini, H. S. Nagendraswamy, and Mauro Conti. "Hand Drawn Optical Circuit Recognition." Procedia Computer Science 84 (2016): 41–48. http://dx.doi.org/10.1016/j.procs.2016.04.064.

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Hollands, Charlotte. "A Hand-Drawn Conference Review." American Anthropologist 120, no. 2 (June 2018): 348–52. http://dx.doi.org/10.1111/aman.13039.

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Aaltonen, Tarja, and Sanna Raudaskoski. "Storyworld evoked by hand-drawn maps." Social Semiotics 21, no. 2 (April 2011): 317–36. http://dx.doi.org/10.1080/10350330.2011.548652.

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CERRI, ANDREA, MASSIMO FERRI, PATRIZIO FROSINI, and DANIELA GIORGI. "KEYPICS: FREE–HAND DRAWN ICONIC KEYWORDS." International Journal of Shape Modeling 13, no. 02 (December 2007): 125–37. http://dx.doi.org/10.1142/s0218654307001020.

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Sam Lemonick. "Translating hand-drawn molecules for computers." C&EN Global Enterprise 99, no. 9 (March 15, 2021): 9. http://dx.doi.org/10.1021/cen-09909-scicon8.

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Dewangan, Akanksha. "KNN based Hand Drawn Electrical Circuit Recognition." International Journal for Research in Applied Science and Engineering Technology 6, no. 6 (June 30, 2018): 1111–15. http://dx.doi.org/10.22214/ijraset.2018.6164.

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Dissertations / Theses on the topic "Hand-drawn"

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Ouyang, Tom Yu. "Recognition of hand drawn chemical diagrams." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41546.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 51-52).
Chemists often use hand-drawn structural diagrams to capture and communicate ideas about organic compounds. However, the software available today for specifying these structures to a computer relies on a traditional mouse and keyboard interface, and as a result lacks the ease of use, naturalness, and speed of drawing on paper. In response, we have developed a novel sketch-based system capable of interpreting hand-drawn organic chemistry diagrams, allowing users to draw molecules with a penbased input device in much the same way that they would on paper. The system's ability to interpret a sketch is based on knowledge about both chemistry and chemical drawing conventions. The system employs a trainable symbol recognizer incorporating both feature-based and image-based methods to locate and identify symbols in the sketch. Analysis of the spatial context around each symbol allows the system to choose among competing interpretations and determine an initial structure for the molecule. Finally, knowledge of chemistry (in particular atomic valence) enables the system to check the validity of its interpretation and, when necessary, refine it to recover from inconsistencies. We demonstrate that the system is capable of recognizing diagrams of common organic molecules and show that using domain knowledge produces a noticeable improvement in recognition accuracy.
by Tom Yu Ouyang.
S.M.
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Mikkelson, Joanne M. (Joanne Marie). "Recognition of hand-drawn circuit diagrams." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/42734.

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Dreijer, Janto F. "Interactive recognition of hand-drawn circuit diagrams." Thesis, Stellenbosch : University of Stellenbosch, 2006. http://hdl.handle.net/10019.1/2477.

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Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2006.
When designing electronic circuits, engineers frequently make hand-drawn sketches of circuits. These are then captured with a computerised design. This study aims to create an alternative to the common schematic capture process through the use of an interactive pen-based interface to the capturing software. Sketches are interpreted through a process of vectorising the user’s strokes into primitive shapes, extracting information on intersections between primitives and using a naive Bayesian classifier to identify symbol components. Various alternative approaches were also considered. It is concluded that it is feasible to use a pen-based interface and underlying recognition engine to capture circuit diagrams. It is hoped that this would provide an attractive early design environment for the engineer and enhance productivity.
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Daly, Katharine M. "Hand-drawn graph problems in online education." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100303.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 104-106).
Machine-gradable assessments in online education platforms are currently limited to questions that require only keyboard or mouse input, and grading efforts generally focus only on final answers. Some types of problems in the science, technology, engineering, and math (STEM) domain, however, are most naturally answered through sketches drawn with a pen. We introduce a simple graph problem type that accepts solutions drawn using a stylus as a proof-of-concept extension to online education platforms. Simple graphs have a small number of components (vertices, arrows, and edges only), and we describe a three-step recognition process consisting of segmentation, symbol classication, and domain interpretation for converting users' pen strokes into a simple graph object representation. An experiment run on Mechanical Turk demonstrates the usability of our trained, recognition-driven drawing interface, and examples of simple graph problems illustrate how course developers can not only check students' final answers but also provide students with intermediate feedback.
by Katharine M. Daly.
M. Eng.
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Sabiston, Walter Roberts. "Extracting 3D motion from hand-drawn animated figures." Thesis, Massachusetts Institute of Technology, 1991. http://hdl.handle.net/1721.1/13913.

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Bailey, Craig. "A sketch interface for understanding hand-drawn route maps /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p1418003.

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Branda, Ewan E. (Ewan Edward) 1964. "Drawing interfaces : building geometric models with hand-drawn sketches." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/64901.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1998.
Includes bibliographical references (p. 49-51).
Architects work on drawings and models, not buildings. Today, in many architectural practices, drawings and models are produced in digital format using Computer-aided Design (CAD) tools. Unquestionably, digital media have changed the way in which many architects perform their day to day activities. But these changes have been limited to the more prosaic aspects of practice. To be sure, CAD systems have made the daily operations of many design offices more efficient; nevertheless, they have been of little use - and indeed are often a hindrance - in situations where the task at hand is more conjectural and speculative in nature, as it is during the early stages of a project. Well-intentioned efforts to insinuate CAD into these aspects of practice have only served to reveal the incongruities between the demands of designer and the configuration of the available tools. One of the chief attributes of design practice is that it is action performed at a distance through the agency of representations. This fundamental trait implies that we have to understand how computers help architects describe buildings if we are to understand how they might help architects design buildings. As obvious as this claim might seem, CAD programs can be almost universally characterized by a tacit denigration of visual representation. In this thesis, I examine properties of design drawings that make them useful to architects. I go on to describe a computer program that I have written that allows a designer to build geometric models using freehand sketches. This program illustrates that it is possible to design a software tool in a way that profits from, rather than negates, the power of visual representations.
by Ewan E. Branda.
M.S.
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Sundbom, Oskar. "Putting things into context: segmenting photographs based on hand-drawn lines." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-169655.

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This report presents a method for finding areas of interest in an image, based on lines drawn in that image. The method is designed to work with photographic images of whiteboards, where the information on the whiteboard can be categorized based on the structure of what is drawn on it. Structurally, the method is divided into two main phases. The first phase processes a bitmap image and outputs a set of vectorized features representing strokes of a pen. The second phase filters and categorizes these features and matches them against pre-defined contextual models. The output from the second phase is a set of matching contextual models, each containing a set of area outlines representing contextually important areas of the image. The method proves robust both to variations in the quality of input – such as lighting, angles and signal-to-noise ratio - as well as to the choice of parameters used by the algorithms internally.
I den här rapporten presenteras en metod för att identifiera intressanta områden i en bild utifrån streck ritade i bilden. Metoden har designats för att hantera foton, specifikt av whiteboards, där informationen på whiteboarden kan delas in utifrån de streck som dragits på den. Strukturellt sett är metoden uppdelad i två faser. I den första fasen behandlas en bitmapbild och resultatet blir en mängd vektoriserade representationer av handritade linjer. Dessa behandlas sedan i den andra fasen, där de kategoriseras, filtreras och slutligen matchas mot fördefinierade kontextuella modeller. Resultatet av den andra fasen är den uppsättning kontextuella modeller som passar in, vardera med information om de kontextuellt intressanta områden i bilden som modellen identifierat. Metoden visar sig robust både vad gäller kvalitén på indata – såsom ljusförhållanden, vinklar och signal-till-brus-förhållande – som valet av de parametrar som används av algoritmerna internt.
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Parekh, Gaurav. "Scene matching between a quantitative map and a qualitative hand drawn sketch." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/5048.

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Thesis (M.S.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on November 12, 2008) Includes bibliographical references.
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Bozas, Konstantinos. "Scalable image retrieval based on hand drawn sketches and their semantic information." Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8828.

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The research presented in this thesis aims to extend the capabilities of traditional content-based image retrieval systems, towards more expressive and scalable interactions. The study focuses on machine sketch understanding and its applications. In particular, sketch based image retrieval (SBIR), a form of image search where the query is a user drawn picture (sketch), and freehand sketch recognition. SBIR provides a platform for the user to express image search queries that otherwise would be di cult to describe with text. The research builds upon two main axes: extension of the state-of-the art and scalability. Three novel approaches for sketch recognition and retrieval are presented. Notably, a patch hashing algorithm for scalable SBIR is introduced, along with a manifold learning technique for sketch recognition and a horizontal ip-invariant sketch matching method to further enhance recognition accuracy. The patch hashing algorithm extracts several overlapping patches of an image. Similarities between a hand drawn sketch and the images in a database are ranked through a voting process where patches with similar shape and structure con guration arbitrate for the result. Patch similarity is e ciently estimated with a hashing algorithm. A spatially aware index structure built on the hashing keys ensures the scalability of the scheme and allows for real time re-ranking upon query updates. Sketch recognition is achieved through a discriminant manifold learning method named Discriminant Pairwise Local Embeddings (DPLE). DPLE is a supervised dimensionality reduction technique that generates structure preserving discriminant subspaces. This objective is achieved through a convex optimization formulation where Euclidean distances between data pairs that belong to the same class are minimized, while those of pairs belonging to di erent classes are maximized. A scalable one-to-one sketch matching technique invariant to horizontal mirror re ections further improves recognition accuracy without high computational cost. The matching is based on structured feature correspondences and produces a dissimilarity score between two sketches. Extensive experimental evaluation of our methods demonstrates the improvements over the state-of-the-art in SBIR and sketch recognition.
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Books on the topic "Hand-drawn"

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Hand drawn jokes for smart attractive people. New York: Scribner, 2015.

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Perry, Michael. Over and over: A catalog of hand drawn patterns. New York: Princeton Architectural Press, 2008.

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Over and over: A catalog of hand drawn patterns. New York: Princeton Architectural Press, 2008.

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Stein, Gertrude. Five short war poems: Five hand-drawn images by Matthew Tyson. London: Imprints, 1989.

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Association, Hand Drawn Map, ed. From here to there: A curious collection from the hand drawn map association. New York: Princeton Architectural Press, 2009.

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Steven, Heller. Letterforms, bawdy, bad & beautiful: The evolution of hand-drawn, humorous, vernacular, and experimental type. New York: Watson-Guptill, 2000.

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Parsons, Jackie. Open - the allure of the hand-drawn: Letterforms into the digital realm : MA Communication Design thesis 2000. London: Central Saint Martins College of Art & Design, 2000.

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Stipe, Margo. Frank Lloyd Wright: The interactive portfolio : rare removable treasures, hand-drawn sketches, original letters, and more from the official archives. Philadelphia, Pa: Running Press, 2004.

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1867-1959, Wright Frank Lloyd, ed. Frank Lloyd Wright: The interactive portfolio : rare removable treasures, hand-drawn sketches, original letters, and more from the official archives. Philadelphia, Pa: Running Press, 2004.

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Hand Drawn Lettering. Book House, 2016.

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Book chapters on the topic "Hand-drawn"

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Szwoch, Wioleta, and Michał Mucha. "Recognition of Hand Drawn Flowcharts." In Advances in Intelligent Systems and Computing, 65–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32384-3_9.

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Sun, Zhenbang, Changhu Wang, Liqing Zhang, and Lei Zhang. "Free Hand-Drawn Sketch Segmentation." In Computer Vision – ECCV 2012, 626–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33718-5_45.

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Simhon, Saul, and Gregory Dudek. "On the Elaboration of Hand-Drawn Sketches." In Active Media Technology, 355–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45336-9_41.

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Koile, Kimberle, and Andee Rubin. "Machine Interpretation of Students’ Hand-Drawn Mathematical Representations." In Human–Computer Interaction Series, 49–56. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15594-4_5.

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Refaat, Khaled S., and Amir F. Atiya. "Hand-Drawn Shape Recognition Using the SVM’ed Kernel." In Artificial Neural Networks – ICANN 2009, 275–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04277-5_28.

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Essa, Ehab, Xianghua Xie, Richard Turner, Matthew Stevens, and Daniel Power. "Extracting Lineage Information from Hand-Drawn Ancient Maps." In Lecture Notes in Computer Science, 268–75. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41501-7_30.

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Schäfer, Bernhard, Han van der Aa, Henrik Leopold, and Heiner Stuckenschmidt. "Sketch2BPMN: Automatic Recognition of Hand-Drawn BPMN Models." In Advanced Information Systems Engineering, 344–60. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79382-1_21.

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Banfi, Folco, and Rolf Ingold. "Computing Dissimilarity between Hand-Drawn Sketches and Digitized Images." In Visual Information and Information Systems, 625–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48762-x_77.

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Viswanath, Pulabaigari, T. Gokaramaiah, and Gouripeddi V. Prabhakar Rao. "A Shape Representation Scheme for Hand-Drawn Symbol Recognition." In Lecture Notes in Computer Science, 213–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25725-4_19.

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Ly, Nam Tuan, Lili Liu, Ching Y. Suen, and Masaki Nakagawa. "Hand-Drawn Object Detection for Scoring Wartegg Zeichen Test." In Pattern Recognition and Artificial Intelligence, 109–14. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59830-3_9.

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Conference papers on the topic "Hand-drawn"

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Kaiser, Paul. "Hand-drawn spaces." In ACM SIGGRAPH 98 Electronic art and animation catalog. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/281388.281884.

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Ono, Yutaka, Bing-Yu Chen, and Tomoyuki Nishita. "Animating hand-drawn sketches." In ACM SIGGRAPH 2004 Posters. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1186415.1186448.

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Buck, Ian, Adam Finkelstein, Charles Jacobs, Allison Klein, David H. Salesin, Joshua Seims, Richard Szeliski, and Kentaro Toyama. "Performance-driven hand-drawn animation." In the first international symposium. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/340916.340929.

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Buck, Ian, Adam Finkelstein, Charles Jacobs, Allison Klein, David H. Salesiny, Joshua Seims, Richard Szeliski, and Kentaro Toyama. "Performance-driven hand-drawn animation." In ACM SIGGRAPH 2006 Courses. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1185657.1185865.

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Huang, Zhengyu, Haoran Xie, and Kazunori Miyata. "Manifold Learning for Hand Drawn Sketches." In 2020 Nicograph International (NicoInt). IEEE, 2020. http://dx.doi.org/10.1109/nicoint50878.2020.00032.

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Kiss, Márton L. "Hand-Drawn Characters Sensing with Accelerometer." In MultiScience - XXXIII. microCAD International Multidisciplinary Scientific Conference. University of Miskolc, 2019. http://dx.doi.org/10.26649/musci.2019.045.

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Zheng, Qingyuan, Zhuoru Li, and Adam Bargteil. "Learning to Shadow Hand-Drawn Sketches." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00746.

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Boniardi, Federico, Bahram Behzadian, Wolfram Burgard, and Gian Diego Tipaldi. "Robot navigation in hand-drawn sketched maps." In 2015 European Conference on Mobile Robots (ECMR). IEEE, 2015. http://dx.doi.org/10.1109/ecmr.2015.7324188.

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Ge, Xiaofei. "Android GUI Search Using Hand-Drawn Sketches." In 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). IEEE, 2019. http://dx.doi.org/10.1109/icse-companion.2019.00060.

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Behzadian, Bahram, Pratik Agarwal, Wolfram Burgard, and Gian Diego Tipaldi. "Monte Carlo localization in hand-drawn maps." In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015. http://dx.doi.org/10.1109/iros.2015.7353985.

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Reports on the topic "Hand-drawn"

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Gries, Daniel. Area from Hand-Drawn Functions. Washington, DC: The MAA Mathematical Sciences Digital Library, July 2008. http://dx.doi.org/10.4169/loci002640.

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Gries, Daniel. Average Value of a Hand-Drawn Function. Washington, DC: The MAA Mathematical Sciences Digital Library, July 2008. http://dx.doi.org/10.4169/loci002639.

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