The image stitching algorithm based on aggregated star groups

Qiu, Shi; Zhou, Dongmei; Du, Youwei

doi:10.1007/s11760-018-1349-y

Cited by 8 publications

(3 citation statements)

References 35 publications

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“…Most of the previous stitching methods were based on feature-based algorithms. For instance, in [25,26] images from multiple cameras were stitched together by extracting features from the images being stitched. These feature-based stitching algorithms have three phases: feature detection, image registration and image blending.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…Lowe proposed an invariant feature-based approach to fully automatic panoramic image stitching [30], while Xiaoyan et al created a large field of view for robot control and movement using dynamic image stitching when there was a moving object in the environment [31]. Qiu et al proposed an image-stitching algorithm based on aggregated star groups to obtain a complete star map [32]. is paper applies the image-stitching method in pavement detection to solve engineering application problems.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Pavement Distress Image Stitching Based on Fine-Grained Feature Matching

Weng

Liu

et al. 2020

Journal of Advanced Transportation

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Camera-based pavement distress detection plays an important role in pavement maintenance. Duplicate collections for the same distress and multiple overlaps of defects are both practical problems that greatly affect the detection results. In this paper, we propose a fine-grained feature-matching and image-stitching method for pavement distress detection to eliminate duplications and visually demonstrates local pavement distress. The original images are processed through a hierarchical structure, including rough data filtering, feature matching, and image stitching. The original data are firstly filtered based on the global position system (GPS) information, which can avoid full-dataset comparison and improve the calculating efficiency. A scale-invariant feature transform is introduced for feature matching based on the extracted key regions using spectral saliency mapping and bounding boxes. Two parameters: the mean Euclidean distance (MEuD) and the matching rate (MCR) are constructed to identify the duplication between two images. A support vector machine is then applied to determine the threshold of MEuD and MCR. This paper further discusses the correlation between the sampling frequency and the number of detection vehicles. The method provided can effectively solve the problem of duplications in pavement distress detection and enhances the feasibility of multivehicle pavement distress detection based on images.

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“…The differences of among aerial photography instruments, environments and target states, which lead to high information content, multiple heterogeneity and high dimensionality of aerial photography images or videos. Available image processing algorithms such as image denoising [ 4 ], image enhancement [ 5 ] and image mosaicking [ 6 ] can satisfy the real-time processing requirements of aerial image target recognition, but difficult problems and challenges remain in the realization of target tracking, including the following.…”

Section: Introductionmentioning

confidence: 99%

Aerial Video Trackers Review

Jia

Lai

Qian

et al. 2020

Entropy

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Target tracking technology that is based on aerial videos is widely used in many fields; however, this technology has challenges, such as image jitter, target blur, high data dimensionality, and large changes in the target scale. In this paper, the research status of aerial video tracking and the characteristics, background complexity and tracking diversity of aerial video targets are summarized. Based on the findings, the key technologies that are related to tracking are elaborated according to the target type, number of targets and applicable scene system. The tracking algorithms are classified according to the type of target, and the target tracking algorithms that are based on deep learning are classified according to the network structure. Commonly used aerial photography datasets are described, and the accuracies of commonly used target tracking methods are evaluated in an aerial photography dataset, namely, UAV123, and a long-video dataset, namely, UAV20L. Potential problems are discussed, and possible future research directions and corresponding development trends in this field are analyzed and summarized.

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The image stitching algorithm based on aggregated star groups

Cited by 8 publications

References 35 publications

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

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

Dynamic Pavement Distress Image Stitching Based on Fine-Grained Feature Matching

Aerial Video Trackers Review

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