Real-Time Video Stitching for Mine Surveillance Using a Hybrid Image Registration Method

Bai, Zongwen; Li, Ying; Chen, Xiaohuan; Yi, Tingting; Wei, Wei; Woźniak, Marcin; Damaševičius, Robertas

doi:10.3390/electronics9091336

Cited by 24 publications

(20 citation statements)

References 66 publications

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“…Most of them combine multiple features to enhance the effects of the feature extraction. Interested application areas in [8], it integrated the Moravec corner point detection and the scale-invariant feature transform (SIFT) feature extractor. Deep learning and spiking neural networks are excellent methods in machine learning fields and so on.…”

Section: Figurementioning

confidence: 99%

“…In modern intelligent video surveillance systems, the researches mainly focus on: abnormal detection, virtual reality and video stitching [8,100,102], in which the abnormal detection has an important application scenarios and it has raised much attention within the past twenty years [9,82]. Abnormal detection in video is vital to ensure security in both internal spaces with the outside (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Abnormal Human Behavior Detection in Videos: A Review

Sun

Yuan

et al. 2021

ITC

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Modeling human behavior patterns for detecting the abnormal event has become an important domain in recentyears. A lot of efforts have been made for building smart video surveillance systems with the purpose ofscene analysis and making correct semantic inference from the video moving target. Current approaches havetransferred from rule-based to statistical-based methods with the need of efficient recognition of high-levelactivities. This paper presented not only an update expanding previous related researches, but also a study coveredthe behavior representation and the event modeling. Especially, we provided a new perspective for eventmodeling which divided the methods into the following subcategories: modeling normal event, predictionmodel, query model and deep hybrid model. Finally, we exhibited the available datasets and popular evaluationschemes used for abnormal behavior detection in intelligent video surveillance. More researches will promotethe development of abnormal human behavior detection, e.g. deep generative network, weakly-supervised. It isobviously encouraged and dictated by applications of supervising and monitoring in private and public space.The main purpose of this paper is to widely recognize recent available methods and represent the literature ina way of that brings key challenges into notice.

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Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Abnormal Human Behavior Detection in Videos: A Review

Sun

Yuan

et al. 2021

ITC

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“…The proposed technique uses the observation that the number of distinct colors in outdoor haze-free images is much less than the number pixels [46,47]. The said assumption has been extensively used in color-palette selection [47], segmentation [28], stitching [36], compression [1], color matting [68] and tone mapping [55] of images. Hence we propose natural images can be approximated by a set of distant colors and their shadings, which can be learned beforehand over a collection of haze-free images.…”

Section: Learning the Natural Color-palettementioning

confidence: 99%

Visibility Restoration Using Generalized Haze-Lines

Riaz¹,

Anwar

Riaz³

et al. 2021

ITC

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Haze reduces the perceived scene radiance and limits the visibility in outdoor images. The visibility is different for each scene point and is proportional to haze thickness, and distance from the camera. Transmission map represents percentage of scene radiance captured by the camera and is unknown for every pixel. This work generalizes the concept of haze-lines, and presents an algorithm to estimate transmission map and restore scene radiance accurately. The proposed technique depends on the perception that the colors of haze-free natural images can be well approximated by a set of distinct colors and their shades (natural color-palette) that can be learned beforehand. In presence of haze, the pixels forming a cluster in haze-free image, make a line (haze-line) in RGB color space. The two endpoints of this haze-line are the haze-free color and the airlight. We propose that these haze-lines can be generalized, with one end as learned color-palette of natural images and the other as airlight. Hence the scene radiance end can be made independent of underlying image. The algorithm recovers the transmission map, by determining membership of each pixel to a given haze-line and finding how far-off it is from its learned color-palette. The algorithm is linear to the size of image, and requires just a collection of haze-free natural images for training. The results obtained on a diverse range of images demonstrate the efficiency of proposed algorithm.

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“…Nowadays, medical imaging technology is an essential part of medicine [ 8 , 9 , 10 ]. Surgeons, pathologists, and other medical groups can observe symptoms of the diseases directly.…”

Section: Introductionmentioning

confidence: 99%

A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

Pourasad

Cavallaro

2021

IJERPH

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At present, there is an increase in the capacity of data generated and stored in the medical area. Thus, for the efficient handling of these extensive data, the compression methods need to be re-explored by considering the algorithm’s complexity. To reduce the redundancy of the contents of the image, thus increasing the ability to store or transfer information in optimal form, an image processing approach needs to be considered. So, in this study, two compression techniques, namely lossless compression and lossy compression, were applied for image compression, which preserves the image quality. Moreover, some enhancing techniques to increase the quality of a compressed image were employed. These methods were investigated, and several comparison results are demonstrated. Finally, the performance metrics were extracted and analyzed based on state-of-the-art methods. PSNR, MSE, and SSIM are three performance metrics that were used for the sample medical images. Detailed analysis of the measurement metrics demonstrates better efficiency than the other image processing techniques. This study helps to better understand these strategies and assists researchers in selecting a more appropriate technique for a given use case.

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Real-Time Video Stitching for Mine Surveillance Using a Hybrid Image Registration Method

Cited by 24 publications

References 66 publications

Abnormal Human Behavior Detection in Videos: A Review

Abnormal Human Behavior Detection in Videos: A Review

Visibility Restoration Using Generalized Haze-Lines

A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

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