Home
Resource
Computer vision methods for large-scale online clustering and quantitative dermatology
PDF
PDF format is widely accepted and good for printing.
Plug-in required
PDF-1(9.62 MB)
Citation & Export
View Usage Statistics
Staff View

Citation & Export
Hide

Simple citation

Madan, Siddharth K.. Computer vision methods for large-scale online clustering and quantitative dermatology. Retrieved from https://doi.org/doi:10.7282/T3XW4HDJ

Export

Click here for information about Citation Management Tools at Rutgers.

Statistics
Hide

Description

TitleComputer vision methods for large-scale online clustering and quantitative dermatology
NameMadan, Siddharth K. (author); Dana, Kristin J (chair); Orfanidis, Sophocles (internal member); Marsic, Ivan (internal member); Meer, Peter (internal member); Cula, Oana (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2013
Other Date2013-05 (degree)
SubjectElectrical and Computer Engineering, Computer vision, Dermatology--Data processing, Database management
Extentxvii, 92 p. : ill.
DescriptionIn modern computer vision applications where datasets are large and updates with new data may be ongoing, methods of online clustering are extremely important. Online clustering algorithms incrementally cluster the data points, use a fraction of the dataset memory, and update the clustering decisions when new data comes in. In this thesis we adapt a classic online clustering algorithm called Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) to incrementally cluster large datasets of features commonly used in computer vision, e.g., 840K color SIFT descriptors, 1.09 million color patches, 60K outlier corrupted grayscale patches, and 700K grayscale SIFT descriptors. We use the algorithm to cluster datasets consisting of non-convex clusters, e.g., Hopkins 155 3-D motion segmentation dataset. We call the adapted version modified-BIRCH (m-BIRCH). BIRCH was originally developed by the database management community. Modifications made in m-BIRCH enable data driven parameter selection and effectively handle varying density regions in the feature space. Data driven parameter selection automatically controls the level of coarseness of the data summarization. Effective handling of varying density regions is necessary to well represent the different density regions in the data summarization. Our implementation of the algorithm provides a useful clustering tool and is made publicly available. In the second part of the thesis, we present a micro-level feature based approach to register time-lapse skin images acquired over an extended period of time and multimodal skin images acquired in quick succession. Misregistration between the images makes it difficult to perform quantitative analysis and track the progression of skin disease. We demonstrate the utility of both types of registration, by employing the results for two quantitative dermatology tasks: 1) automatic detection of acne-like regions, and 2) separation of surface and subsurface reflection. Additionally, we have created a time-lapse video showing the registered time-lapse images, which clearly brings out the evolution of acne lesions with time.
NotePh.D.
NoteIncludes bibliographical references
Noteby Siddharth K. Madan
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3XW4HDJ
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
Version 8.5.5
Rutgers University Libraries - Copyright ©2024