Method for rectifying image deviation based on perspective transformation

Li, Xin; Li, Shengrong; Cui, Xiaoxiao; Yang, Guangsong; Zhou, Hao; Zhang, Chuanyou

doi:10.1088/1757-899x/231/1/012029

Cited by 15 publications

(4 citation statements)

References 2 publications

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“…To solve this issue, we use a perspective correction technique to transform the input image into a 2D top-view image. Furthermore, many studies applied the perspective correction technique [35][36][37][38] owing to its advantage of easy implementation. Figure 2 shows the process of this technique.…”

Section: Perspective Correction Techniquementioning

confidence: 99%

“…6. Lines (29)(30)(31)(32)(33)(34)(35)(36)(37)(38) show that if the coupled bounding boxes have a higher IoU or BTA value than the respective thresholds, they can be merged to create a new bounding box. list_ f inal_box adds the new bounding box, and the status of these coupled bounding boxes is set to false.…”

Section: Algorithm 2 Merging the Bounding Boxes Of Intermediate Resul...mentioning

confidence: 99%

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A Room Monitoring System Using Deep Learning and Perspective Correction Techniques

et al. 2020

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Studies on room monitoring have only focused on objects in a singular and uniform posture or low-density groups. Considering the wide use of convolutional neural networks for object detection, especially person detection, we use deep learning and perspective correction techniques to propose a room monitoring system that can detect persons with different motion states, high-density groups, and small-sized persons owing to the distance from the camera. This system uses consecutive frames from the monitoring camera as input images. Two approaches are used: perspective correction and person detection. First, perspective correction is used to transform an input image into a 2D top-view image. This allows users to observe the system more easily with different views (2D and 3D views). Second, the proposed person detection scheme combines the Mask region-based convolutional neural network (R-CNN) scheme and the tile technique for person detection, especially for detecting small-sized persons. All results are stored in a cloud database. Moreover, new person coordinates in 2D images are generated from the final bounding boxes and heat maps are created according to the 2D images; these enable users to examine the system quickly in different views. Additionally, a system prototype is developed to demonstrate the feasibility of the proposed system. Experimental results prove that our proposed system outperforms existing schemes in terms of accuracy, mean absolute error (MAE), and root mean squared error (RMSE).

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Section: Perspective Correction Techniquementioning

confidence: 99%

Section: Algorithm 2 Merging the Bounding Boxes Of Intermediate Resul...mentioning

confidence: 99%

A Room Monitoring System Using Deep Learning and Perspective Correction Techniques

et al. 2020

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“…Besides, the edge characteristics of skimming bowl have been studied [17,19]. Although many researchers have tried to obtain the ancient ceramic shape by using various image processing methods, due to the problems of two-dimensional image distortion, complex image background, and large difference in image quality, the extracted model error is large, and the restored 3D model deformation is serious [20]. ese problems have long been difficult to break through and become the shackles of the development of 3D printing technology of ancient ceramics [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Application of 3D Printing Reconstruction Algorithm in Ancient Ceramic Restoration

2022

Scientific Programming

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The study of the ancient ceramic is of great significance to the identification of authenticity, value recognition, cultural types, and dissemination channels of the ceramic. In this study, a series of technical problems such as ceramic contour extraction, image distortion correction, and nonlinear contour modeling for 3D printing of rotating body under complex background were solved and a restoration algorithm for the shape of ancient ceramics was proposed. Then, an accurate contour model is established by using the two-dimensional images of the rotating ancient ceramics to reconstruct the three-dimensional model of the ceramic shape. The experimental results show that the modeling algorithm for three-dimensional printed ceramics can accurately obtain the three-dimensional model of rotating ancient ceramics, which is of certain significance to explore a new direction of the research and development of three-dimensional printed ancient ceramics.

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“…This algorithm was based on a convolutional neural network to locate the gauge and adjust the image scale, and then the image distortion caused by non-parallelism between the gauge and the camera was eliminated through ellipse fitting followed by a perspective transform [16]. Finally, the Hough transform was used to obtain the pointer location, and get the gauge reading [2,3].…”

Section: Introductionmentioning

confidence: 99%

Pointer gauge adaptive reading method based on a double match

Xu¹,

Hou²,

Wang³

et al. 2020

Meas. Sci. Technol.

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In large-scale power transmission stations, more and more automated pointer gauge reading algorithms are being used to automatically and efficiently take gauge readings. However, most existing pointer gauge reading algorithms do not take into account image deformation, changes of illumination, and small object interference in one algorithm, so these algorithms have poor robustness and adaptability to complex situations in real environments, and cannot obtain correct gauge readings consistently. In this paper, we present a pointer gauge adaptive reading method based on a double match. Our doublematch gauge reading method is dedicated to reading the gauge adaptively by using some strong anti-interference methods after image registration. To achieve this, various gauge images should first be registered by detection and matching of feature points using perspective transformation, and then pointer center positioning and gauge reading calculations are performed, based on matching multiple image moments. In the experimental results, compared with the classical gauge reading algorithm based on saliency and Hough line detection, our method shows better precision and better anti-interference; furthermore, the adaptability of the pointer gauge reading algorithm is effectively improved.

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Method for rectifying image deviation based on perspective transformation

Cited by 15 publications

References 2 publications

A Room Monitoring System Using Deep Learning and Perspective Correction Techniques

A Room Monitoring System Using Deep Learning and Perspective Correction Techniques

Application of 3D Printing Reconstruction Algorithm in Ancient Ceramic Restoration

Pointer gauge adaptive reading method based on a double match

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