You are here
Bioinformatics-inspired binary image correlation
| Title: | Bioinformatics-inspired binary image correlation: application to bio-/medical-images, microsarrays, finger-prints and signature classifications. |
 746 views 635 downloads |
|---|---|---|
| Name(s): |
Pappusetty, Deepti College of Engineering and Computer Science Department of Computer and Electrical Engineering and Computer Science |
|
| Type of Resource: | text | |
| Genre: | Electronic Thesis Or Dissertation | |
| Issuance: | monographic | |
| Date Issued: | 2011 | |
| Publisher: | Florida Atlantic University | |
| Physical Form: | electronic | |
| Extent: | xii, 122 p. : ill. (some col.) | |
| Language(s): | English | |
| Summary: | The efforts addressed in this thesis refer to assaying the extent of local features in 2D-images for the purpose of recognition and classification. It is based on comparing a test-image against a template in binary format. It is a bioinformatics-inspired approach pursued and presented as deliverables of this thesis as summarized below: 1. By applying the so-called 'Smith-Waterman (SW) local alignment' and 'Needleman-Wunsch (NW) global alignment' approaches of bioinformatics, a test 2D-image in binary format is compared against a reference image so as to recognize the differential features that reside locally in the images being compared 2. SW and NW algorithms based binary comparison involves conversion of one-dimensional sequence alignment procedure (indicated traditionally for molecular sequence comparison adopted in bioinformatics) to 2D-image matrix 3. Relevant algorithms specific to computations are implemented as MatLabTM codes 4. Test-images considered are: Real-world bio-/medical-images, synthetic images, microarrays, biometric finger prints (thumb-impressions) and handwritten signatures. Based on the results, conclusions are enumerated and inferences are made with directions for future studies. | |
| Identifier: | 777953747 (oclc), 3333052 (digitool), FADT3333052 (IID), fau:3795 (fedora) | |
| Note(s): |
by Deepti Pappusetty. Thesis (M.S.C.S.)--Florida Atlantic University, 2011. Includes bibliography. Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web. |
|
| Subject(s): |
Bioinformatics -- Statistical methods Diagnostic imaging -- Digital techniques Image processing -- Digital techniques Pattern perception -- Data processing DNA microarrays |
|
| Held by: | FBoU FAUER | |
| Persistent Link to This Record: | http://purl.flvc.org/FAU/3333052 | |
| Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
| Host Institution: | FAU |
