IMAGE MOSAICS ALGORITHM BASED ON FEATURE BLOCK MATCHING (CANNY EDGE) AND HARRIS CORNER DETECTION
DESIGN DETAILS
Image mosaic techniques can be mainly divided into two categories: feature-based methods, and featureless methods. Feature-based methods assume that feature correspondences between image pairs are available and utilize these correspondences to find transforms which register the image pairs. A major difficulty of these methods is the acquisition and tracking of image features. In this Matlab design, two methods are used for image mosaic.
IMAGE MOSAIC METHOD BASED ON HARRIS CORNER FEATURE
The sequence of photographs taken around 360 degree is projected to cylinder coordinate system, and then the Harris corners are detected by a Harris corner detecting algorithm. After the NCC (Normalized Cross Correlation) corner matching algorithm performed a rough match, an improved RANSAC (Random Sample Consensus) algorithm is applied twice to remove the mismatched points as far as possible to improve the correct matching ratio. Finally, the image mosaic and fusion are accomplished according to the registration result.
IMAGE MOSAIC METHOD BASED ON FEATURE-BLOCK MATCHING
First through the edge detection of splicing image automatically extract the image feature block; consider on of the feature blocks in overlapping region as the basic template then find the corresponding block on another image. Edge detection(canny) is used to pick up base feature-block automatically; in the problem of searching matching block, a hierarchical search strategy is employed to improve the speed and precision of image stitching.
REFERENCES
Reference Paper-1: Image Mosaics Algorithm Based on Feature-Block Matching
Author’s Name: Xiong Shi and Jun Chen
Source: IEEE
Year: 2011
Reference Paper-2: A Point Feature-based Cylindrical Image Mosaic Method
Author’s Name: Xi Li and Ping Gong
Source: IEEE
Year: 2011
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