Fast Radial and Bilateral Symmetry Detection Using Inverted Gradient Hash Maps
Journal Title: EAI Endorsed Transactions on Context-aware Systems and Applications - Year 2017, Vol 4, Issue 11
Abstract
This paper presents a fast and novel algorithm for both radial and bilateral symmetry detection based on inverted gradient hash maps (IGHMs). A hash map is an associative array that stores image gradient magnitudes and orientations in the form of an inverted index. This mapping of image gradients to their locations permits points of interest to be located very rapidly without needing to search through the image. Unlike other symmetry operators it is able to detect symmetries without needing the range of the symmetry to be known apriori. It can also easily detect large-scale symmetry. The method is described and experimentally evaluated against existing methods for both radial and bilateral symmetry detection.
Authors and Affiliations
R. Gonzalez, L. Lincoln
Keywords
Related Articles
- EP ID EP45790
- DOI http://dx.doi.org/10.4108/eai.6-3-2017.152336
- Views 251
- Downloads 0
Formal Modeling and Verification of Context-Aware Systems using Event-B
Context awareness is a computing paradigm that makes applications responsive and adaptive with their environment. Formal modeling and verification of context-aware systems are challenging issues in the development as the...
Toward Modeling and Reasoning with Words Based on Hedge Algebra
In this paper, we introduce a method for reasoning with words based on hedge algebra using linguistic cognitive map. Our computing method consists of static and dynamic reasoning. In static reasoning, inferring on causal...
Mobility Patterns Mining Algorithms with Fast Speed
In recent years, mobile networks and its applications are developing rapidly. Therefore, the issue to ensure quality of service (QoS) is a key issue for the service providers. The movement prediction of Mobile Users (MUs...
Context analysis approach for context aware applications deployed on pervasive environments
The increase of mobile and interconnected devices leads to the growth of demands for context aware applications. These applications deployed on top of pervasive environments must adapt themselves to context changes. Cont...
A Combination of Off-line and On-line Learning to Classifier Grids for Object Detection
We propose a new method for object detection by combining off-line and on-line boosting learning to classifier grids based on visual information without human intervention concerned to intelligent surveillance system. It...