Skip to Main Content
 

Global Search Box

 
 
 

ETD Abstract Container

Abstract Header

Recognition of Incomplete Objects based on Synthesis of Views Using a Geometric Based Local-Global Graphs

Robbeloth, Michael Christopher
http://rave.ohiolink.edu/etdc/view?acc_num=wright1557509373174391

Abstract Details

2019, Doctor of Philosophy (PhD), Wright State University, Computer Science and Engineering PhD.
The recognition of single objects is an old research field with many techniques and robust results. The probabilistic recognition of incomplete objects, however, remains an active field with challenging issues associated to shadows, illumination and other visual characteristics. With object incompleteness, we mean missing parts of a known object and not low-resolution images of that object. The employment of various single machine-learning methodologies for accurate classification of the incomplete objects did not provide a robust answer to the challenging problem. In this dissertation, we present a suite of high-level, model-based computer vision techniques encompassing both geometric and machine learning approaches to generate probabilistic matches of objects with varying degrees and forms of non-deformed incompleteness. The recognition of incomplete objects requires the formulation of a database of six sided views (e.g., model) of an object from which an identification can be made. The images are preprocessed (K-means segmentation, and region growing code to generate fully defined region and segment image information) from which local and global geometric and characteristic properties are generated in a process known as the Local-Global (L-G) Graph method. The characteristic properties are then stored into a database for processing against sample images featuring various types of missing features. The sample images are then characterized in the same manner. After this, a suite of methodologies is employed to match a sample against an exemplar image in a multithreaded manner. The approaches, which work with the multi-view model database characteristics in a parallel (e.g, multithreaded manner) determine probabilistically by application of weighted outcomes the application of various matching routines. These routines include treating segment border regions as chain codes which are then processed using various string matching algorithms, the matching by center of moments from global graph construction, the matching of chain code starting segment location, the differences in angles in the center of moments between the model and sample images to find the most similar graphs (e.g., image), and the use of Delaunay triangulations of the center of moments formed during global graph construction. The ability to find a most probable match is extensible in the future to adding additional detection methods with the appropriate weight adjustments. To enhance the detection of incomplete objects, separate investigations have been made into rotating the exemplars in standard increments and by object extraction of segment border regions’ chain codes and subsequent synthesis of objects from the multi-view database. This approach is novel and potentially extensible to compositing across multi-view segmented regions at the borders between views by either human aided input of border relations or a systematic, automated evaluation of common border objects between the views of an exemplar. The first results are promising and trigger again the field of recognition of incomplete objects in a different way that was not studied before.
Nikolaos G. Bourbakis, Ph.D. (Advisor)
Soon M. Chung, Ph.D. (Committee Member)
Yong Pei, Ph.D. (Committee Member)
Arnab K. Shaw, Ph.D. (Committee Member)
247 p.
Computer Science
incomplete objects; obstruction; Local-Global L-G Graph; synthesis of views; recognition; six-sided views; multi-view; chain code; geometric; segmentation; computer vision; image processing; graph algorithm; machine learning; Delaunay triangulations

Recommended Citations

Citations

  • Robbeloth, M. C. (2019). Recognition of Incomplete Objects based on Synthesis of Views Using a Geometric Based Local-Global Graphs [Doctoral dissertation, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1557509373174391

    APA Style (7th edition)

  • Robbeloth, Michael. Recognition of Incomplete Objects based on Synthesis of Views Using a Geometric Based Local-Global Graphs. 2019. Wright State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1557509373174391.

    MLA Style (8th edition)

  • Robbeloth, Michael. "Recognition of Incomplete Objects based on Synthesis of Views Using a Geometric Based Local-Global Graphs." Doctoral dissertation, Wright State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1557509373174391

    Chicago Manual of Style (17th edition)

wright1557509373174391
446
© 2019, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.