Free Online Productivity Tools
i2Speak
i2Symbol
i2OCR
iTex2Img
iWeb2Print
iWeb2Shot
i2Type
iPdf2Split
iPdf2Merge
i2Bopomofo
i2Arabic
i2Style
i2Image
i2PDF
iLatex2Rtf
Sci2ools
336
click to vote
Tutorial
Correlation based similarity measures-Summary
Correlation based matching typically produces dense depth maps by calculating the disparity at each pixel within a neighborhood. This is achieved by taking a square window of certain size around the pixel of interest in the reference image and finding the homologous pixel within the window in the target image, while moving along the corresponding scanline. The goal is to find the corresponding (correlated) pixel within a certain disparity range d (d E [0,….,dmax]) that minimizes the associated error and maximizes the similarity.
In brief, the matching process involves computation of the similarity measure for each disparity value, followed by an aggregation and optimization step. Since these steps consume a lot of processing power, there are significant speed-performance advantages to be had in optimizing the matching algorithm.
Real-time TrafficCorrelation | LSAD | LSSD | NCC | Normalized Cross Correlation | SAD | SHD | Similarity Measure | SSD | Sum Of Squared Differences | ZNCC | ZSAD | ZSSD |
| Added | 01 Jul 2010 |
| Updated | 09 Dec 2010 |
| Authors | siddhant ahuja |
Comments (0)
Researcher Info
|
From Researcher
