.png){kind=icon}
Return
Segmented Gray-Code Kernels for Fast Pattern Matching
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2012.2233484.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The gray-code kernels (GCK) family, which has Walsh Hadamard transform on sliding windows as a member, is a family of kernels that can perform image analysis efficiently using a fast algorithm, such as the GCK algorithm. The GCK has been successfully used for pattern matching. In this paper, we propose that the G4-GCK algorithm is more efficient than the previous algorithm in computing GCK. The G4-GCK algorithm requires four additions per pixel for three basis vectors independent of transform size and dimension. Based on the G4-GCK algorithm, we then propose the segmented GCK. By segmenting input data into L-s parts, the SegGCK requires only four additions per pixel for 3L(s) basis vectors. Experimental results show that the proposed algorithm can significantly accelerate the full-search equivalent pattern matching process and outperforms state-of-the-art methods.
Keywords:
Block matching
fast algorithm
feature extraction
pattern matching
template matching
Walsh Hadamard transform
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W
Organization
Cited Papers
ShareSaveGlobal bifurcations and chaos for a rotor-active magnetic bearing system with time-varying stiffness
PREAI
ShareSaveInvestigation of relation between singular points and number of limit cycles for a rotor–AMBs system
PREAI
ShareSaveFast Template Matching Based on Normalized Cross Correlation With Adaptive Multilevel Winner Update
PREAI
ShareSavePTEN suppresses axon outgrowth by down-regulating the level of detyrosinated microtubules.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAIShareSaveShareSaveShareSave
View more