On Accuracy and Speed of Object Recognition Based on Local Arrangements of Feature Points
Language
Japanese
Type
Proceedings of Japanese Conferences
Authors
Masakazu Iwamura, Tomohiro Nakai, Koichi Kise
Title
On Accuracy and Speed of Object Recognition Based on Local Arrangements of Feature Points
Journal
IEICE Technical Report
Vol.
106
No.
PRMU-229
Presentation number
PRMU2006-67
Pages
pp.49-56
Reviewed
Not reviewed
Date
September 2006
Research fields
Document Image Analysis, Document Image Retrieval
Key
Iwamura2006_IEICEPRMU_Accuracy (id=945)
Abstract
The geometric hashing is a well-known object recognition technique
based on the arrangements of feature points.
We have proposed ``locally likely arrangement hashing (LLAH)''
which outperforms the geometric hashing
in both retrieval accuracy and processing time.
In this report, by comparing both methods,
we consider the major factors which bring the improvement.
We also consider the relationship between
a picture angle and the accuracy of the LLAH
because the accuracy of the LLAH depends on the picture angle.
Tab-separated. Paste into the sheet starting at column A. Required fields that cannot be inferred are left blank.
Format definition
Edits apply immediately and stay in effect until you pick a style again or reset. First line is the default; add lines like "book: …" to override per type.
How to write the definition
Insert a field with braces like {authors}. A part enclosed in [ ... ] is omitted entirely when any field inside it is empty. Available fields:
One macro per line as \name{field}{field}…. A field name in braces is replaced by its (LaTeX-escaped) value; other text is kept. Rename macros or add lines freely — they appear in the macro selector. A line like master_thesis: thesis fixes the macro for a type (automatic choice: journals use paperdoi with a DOI, paperno with a number, else paper; conferences conf with pages, else confpaper; books book). {pages} comes as 119--124; {journal} falls back to the book title. Fields: