Performance analysis on color image mosaicing techniques on FPGA

Jayalaxmi, H; Ramachandran, S.

doi:10.11591/ijece.v9i3.pp1630-1636

Cited by 2 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, multiple images need to be spliced to fully reflect the target scene [3][4] . Image Mosaic technology is to splice multiple images with overlapping areas into a panoramic image with a large field of view, which is widely used in photogrammetry, remote sensing monitoring, military reconnaissance and other fields [5][6] [7] . How to establish the geometric transformation model of the image to be Mosaic is the key step of image Mosaic.…”

Section: Introductionmentioning

confidence: 99%

Parallax image stitching based on subdivision mesh and deviation correction

Liu,

Sun,

et al. 2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

View full text Add to dashboard Cite

In order to solve the problem of feature point registration and local alignment in parallax image stitching, we proposed an image stitching algorithm based on grid refinement and deviation correction, and optimizes feature points through random sample consensus (RANSAC) and normal distribution theory. Refine the grid according to the distribution of matching points, use the moving direct linear transformation (MDLT) to calculate the local homography matrix and combine with the global optimal similarity matrix to complete the grid distortion, and apply the thin plate spline (TPS) theory to correct the local projection deviation. To achieve better alignment of images in local overlapping areas. The experimental results show that the method in this paper has obvious advantages compared with other advanced algorithms in stitching quality and stitching speed, which proves that the algorithm in this paper has the feasibility of practical application in image mosaic work.

show abstract

Section: Introductionmentioning

confidence: 99%

Parallax image stitching based on subdivision mesh and deviation correction

Liu,

Sun,

et al. 2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

View full text Add to dashboard Cite

show abstract

“…These models are used for corner detection of two images and perform the automatic image registration whereas DCT based CIM aligns local and global alignment of images using phase correlation method. (18) addressed feature-based corner detection for mosaicing images. Here the feature techniques extract accurate corner positions and offer a high degree of pipelining and parallelism and hence produces high throughputs and offers better reconstructed image quality (19) .…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Feature based Multiple Descriptors with Histogram Equalization for Image Mosaicing

Shridhar,

Harakannanavar,

Kanabur

et al. 2023

IJST

View full text Add to dashboard Cite

Objectives:The main objective of the proposed work is to develop an image mosaicing model for combining the images of different individual images. In other way, the union of two images and to evaluate the performance of the model in terms of the number of run time in seconds and number of key features they use. Methods: In this work, the Histogram Equalization is a processing step required to make the mosaic invariant to intra image and inter image intensity variability. The detailed feature of the enhanced image is extracted using Scale-invariant feature transform (SIFT), Oriented Fast and Rotated Brief (ORB), Binary Robust invariant scalable key points (BRISK) feature descriptors techniques. The features including local and global are matched using K-Nearest Neighbor. Then, homography is performed using RANdom SAmple Consensus (RANSAC) algorithm to compute the camera motion. Finally, the image warping is performed using smoothing filter of size 40x40 to obtain the panorama image. Findings: The model is tested on various datasets using three different feature extractors popularly used in image mosaicing or image stitching algorithms SIFT, ORB and BRISK descriptors. It is observed that the ORB is the best feature extractor among the state-of-the-art feature extractors. The ORB with HE uses a minimum of 500 key features to match the images and generates panoramic images that are invariant to shift and rotation with the minimum run time of 0.0836 seconds. Novelty: The state-of-the-art models developed by the researchers suffer with good number of matching points of the input images to generate the image mosaicing. This issue is addressed in the proposed model using multiple descriptors SIFT, ORB and BRISK techniques. The ORB with HE will provide the minimum key features and enough good matches of features with the record of minimum runtime to obtain the panoramic images.https://www.indjst.org/

show abstract

Performance analysis on color image mosaicing techniques on FPGA

Cited by 2 publications

References 17 publications

Parallax image stitching based on subdivision mesh and deviation correction

Parallax image stitching based on subdivision mesh and deviation correction

Performance Analysis of Feature based Multiple Descriptors with Histogram Equalization for Image Mosaicing

Contact Info

Product

Resources

About