A novel panoramic image stitching algorithm based on ORB

Wang, Maosen; Niu, Shaozhang; Yang, Xuan

doi:10.1109/icasi.2017.7988559

Cited by 28 publications

(28 citation statements)

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“…Majority of the previously proposed(or existing) image-stitching mechanisms are primarily optimized to perform better in input scenarios, where the input stereo images are homogeneously paired in ideal conditions with slight exceptions in irregularities between them. Some image stitching methodologies [3], [5], [6], [8], [9], [11]- [16] have proposed custom feature extraction & pre + post-processing mechanisms, Some methodologies [17]- [20] have used DNNs to extract deep features from input stereo images to register them together for generating output panoramic-views, while some methodologies [1], [4], [7], [10] have used existing conventional feature extraction techniques and modified the matching/mapping methodologies to spike their proposed method's performance in stitching reliable panoramic outputs. For instance, Sampetoding et al [18], Proposed a novel framework for automatic personal photo improvement using photo collections without any 3D regeneration process.…”

Section: Related Workmentioning

confidence: 99%

l, r-Stitch Unit: Encoder-Decoder-CNN Based Image-Mosaicing Mechanism for Stitching Non-Homogeneous Image Sequences

Chilukuri

Padala

Padala³

et al. 2021

IEEE Access

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Image-stitching (or) mosaicing is considered an active research-topic with numerous use-cases in computer-vision, AR/VR, computer-graphics domains, but maintaining homogeneity among the input image sequences during the stitching/mosaicing process is considered as a primary-limitation & majordisadvantage. To tackle these limitations, this article has introduced a robust and reliable image stitching methodology (l,r-Stitch Unit), which considers multiple non-homogeneous image sequences as input to generate a reliable panoramically stitched wide view as the final output. The l,r-Stitch Unit further consists of a pre-processing, post-processing sub-modules & a l,r-PanoED-network, where each submodule is a robust ensemble of several deep-learning, computer-vision & image-handling techniques. This article has also introduced a novel convolutional-encoder-decoder deep-neural-network (l,r-PanoEDnetwork) with a unique split-encoding-network methodology, to stitch non-coherent input left, right stereo image pairs. The encoder-network of the proposed l,r-PanoED extracts semantically rich deep-featuremaps from the input to stitch/map them into a wide-panoramic domain, the feature-extraction & featuremapping operations are performed simultaneously in the l,r-PanoED's encoder-network based on the split-encoding-network methodology. The decoder-network of l,r-PanoED adaptively reconstructs the output panoramic-view from the encoder networks' bottle-neck feature-maps. The proposed l,r-Stitch Unit has been rigorously benchmarked with alternative image-stitching methodologies on our custom-built traffic dataset and several other public-datasets. Multiple evaluation metrics (SSIM, PSNR, MSE, L α,β,γ , FM-rate, Average-latency-time) & wild-Conditions (rotational/color/intensity variances, noise, etc) were considered during the benchmarking analysis, and based on the results, our proposed method has outperformed among other image-stitching methodologies and has proved to be effective even in wild non-homogeneous inputs.INDEX TERMS Deep feature extraction, encoder-decoder cnn, image mosaicing, multi-image registration, non-homogeneous image stitching.

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Section: Related Workmentioning

confidence: 99%

l, r-Stitch Unit: Encoder-Decoder-CNN Based Image-Mosaicing Mechanism for Stitching Non-Homogeneous Image Sequences

Chilukuri

Padala

Padala³

et al. 2021

IEEE Access

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“…By considering these assumptions, we proposed Oriented FAST and Rotated BRIEF (ORB) as a feature descriptor for feature extraction [47]. ORB is computationally efficient and fast compared to the SIFT descriptor mostly used for panorama generation [48,49].…”

Section: Feature Extractionmentioning

confidence: 99%

Automatic 360° Mono-Stereo Panorama Generation Using a Cost-Effective Multi-Camera System

Ullah

Zia

Kim³

et al. 2020

Sensors

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In recent years, 360° videos have gained the attention of researchers due to their versatility and applications in real-world problems. Also, easy access to different visual sensor kits and easily deployable image acquisition devices have played a vital role in the growth of interest in this area by the research community. Recently, several 360° panorama generation systems have demonstrated reasonable quality generated panoramas. However, these systems are equipped with expensive image sensor networks where multiple cameras are mounted in a circular rig with specific overlapping gaps. In this paper, we propose an economical 360° panorama generation system that generates both mono and stereo panoramas. For mono panorama generation, we present a drone-mounted image acquisition sensor kit that consists of six cameras placed in a circular fashion with optimal overlapping gap. The hardware of our proposed image acquisition system is configured in such way that no user input is required to stitch multiple images. For stereo panorama generation, we propose a lightweight, cost-effective visual sensor kit that uses only three cameras to cover 360° of the surroundings. We also developed stitching software that generates both mono and stereo panoramas using a single image stitching pipeline where the panorama generated by our proposed system is automatically straightened without visible seams. Furthermore, we compared our proposed system with existing mono and stereo contents generation systems in both qualitative and quantitative perspectives, and the comparative measurements obtained verified the effectiveness of our system compared to existing mono and stereo generation systems.

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“…Therefore, ORB extracts the BRIEF descriptor based on the direction performed by Equation 6. The random ORB uses a greedy algorithm to find the random pixel block with low correlation and vector length equal to a 256bit feature descriptor named BRIEF , [16], for which some previous research used a different type of test, such as the Gaussian distribution [3].…”

Section: Orbmentioning

confidence: 99%

“…In 1997, Szelinski and Shum defined creating a larger panorama image as the integration and overlapping the common contents of two or more images of the same scene by rotating the camera about its axis. In 2017, Wand et al defined panorama stitching as taking multiple images with an overlapping area and stitching them together into a single wide image [3] [4]. In 2015, Heekyeong Jeon et al classified the panorama stitching process as the three core steps of detecting features, matching them, and stitching [2].…”

Section: Introductionmentioning

confidence: 99%

Creating See-Around Scenes using Panorama Stitching

Alferidah

Alkhaldi

2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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Image stitching refers to the process of combining multiple images of the same scene to produce a single highresolution image, known as panorama stitching. The aim of this paper is to produce a high-quality stitched panorama image with less computation time. This is achieved by proposing four combinations of algorithms. First combination includes FAST corner detector, Brute Force K-Nearest Neighbor (KNN) and Random Sample Consensus (RANSAC). Second combination includes FAST, Brute Force (KNN) and Progressive Sample Consensus (PROSAC). Third combination includes ORB, Brute Force (KNN) and RANSAC. Fourth combination contains ORB, Brute Force (KNN) and PROSAC. Next, each combination involves a calculation of Transformation Matrix. The results demonstrated that the fourth combination produced a panoramic image with the highest performance and better quality compared to other combinations. The processing time is reduced by 67% for the third combination and by 68% for the fourth combination compared to stat-of-the-art.

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A novel panoramic image stitching algorithm based on ORB

Cited by 28 publications

References 10 publications

l, r-Stitch Unit: Encoder-Decoder-CNN Based Image-Mosaicing Mechanism for Stitching Non-Homogeneous Image Sequences

l, r-Stitch Unit: Encoder-Decoder-CNN Based Image-Mosaicing Mechanism for Stitching Non-Homogeneous Image Sequences

Automatic 360° Mono-Stereo Panorama Generation Using a Cost-Effective Multi-Camera System

Creating See-Around Scenes using Panorama Stitching

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