{"id":{"repo_id":"texas-state","oai_identifier":"oai:digital.library.txst.edu:10877/4988"},"canonical_url":"https://search.dev.ndltd.org/etd/texas-state/oai:digital.library.txst.edu:10877/4988","repository":{"repo_id":"texas-state","name":"Texas State University","base_url":"https://digital.library.txst.edu/server/oai/request"},"display":{"title":"3D Sketch Recognition Using The Microsoft Kinect","abstract":"The concept of sketch-based recognition has recently been used to enhance object categorization and speed up image retrieval. However, in each of the previous studies, the user was required to sketch on a two-dimensional plane. Currently there haven’t been any studies on the performance of incorporating depth information into a sketch. The motivation behind this project is to develop software that will allow a user to draw in a three-dimensional space, incorporating this information, as well as determining whether this depth information will result in higher accuracies for object categorization. First, utilizing the Microsoft Kinect, software was developed to establish a virtual drawing board for three-dimensional sketching. Second, a new learning-based approach is proposed to allow for model feature extraction and recognition. The experimental results demonstrate the validity of the study as well as the effectiveness of the proposed solution.","abstract_html":"The concept of sketch-based recognition has recently been used to enhance object categorization and speed up image retrieval. However, in each of the previous studies, the user was required to sketch on a two-dimensional plane. Currently there haven’t been any studies on the performance of incorporating depth information into a sketch. The motivation behind this project is to develop software that will allow a user to draw in a three-dimensional space, incorporating this information, as well as determining whether this depth information will result in higher accuracies for object categorization. First, utilizing the Microsoft Kinect, software was developed to establish a virtual drawing board for three-dimensional sketching. Second, a new learning-based approach is proposed to allow for model feature extraction and recognition. 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However, in each of the previous studies, the user was required to sketch on a two-dimensional plane. Currently there haven’t been any studies on the performance of incorporating depth information into a sketch. The motivation behind this project is to develop software that will allow a user to draw in a three-dimensional space, incorporating this information, as well as determining whether this depth information will result in higher accuracies for object categorization. First, utilizing the Microsoft Kinect, software was developed to establish a virtual drawing board for three-dimensional sketching. Second, a new learning-based approach is proposed to allow for model feature extraction and recognition. 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Currently there haven’t been any studies on the performance of incorporating depth information into a sketch. The motivation behind this project is to develop software that will allow a user to draw in a three-dimensional space, incorporating this information, as well as determining whether this depth information will result in higher accuracies for object categorization. First, utilizing the Microsoft Kinect, software was developed to establish a virtual drawing board for three-dimensional sketching. Second, a new learning-based approach is proposed to allow for model feature extraction and recognition. 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