Camera self-calibration having the varying parameters and based on homography of the plane at infinity

Akkad, Nabil El; Merras, Mostafa; Baataoui, Aziz; Saaidi, Abderrahim; Satori, Khalid

doi:10.1007/s11042-017-5012-3

Cited by 24 publications

(14 citation statements)

References 25 publications

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“… SC is the subarea coefficient determined by equation ( 7),  PC is the partition coefficient presented in equation (8).…”

Section: A Fitness Functionmentioning

confidence: 99%

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An Efficient Image Clustering Technique based on Fuzzy C-means and Cuckoo Search Algorithm

Khrissi¹,

Akkad²,

Satori³

et al. 2021

IJACSA

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Clustering is a predominant technique used in image segmentation due to its simple, easy and efficient approach. It is very important for the analysis, extraction and interpretation of images; which makes it used in multiple applications and in various fields. In this article, we propose a different image segmentation technique based on the cooperation between an optimization algorithm which is the Cuckoo Search Algorithm (CSA) and a clustering technique which is the Fuzzy C-means (FCM). The clustering method we propose goes through two major steps. In the first step, CSA explores the entire search space of the specified data to find the optimal clustering centers. Subsequently, these centers are evaluated using a new objective function. The result of the first step is used to initialize the FCM algorithm in the second step. The efficiency of the suggested method is measured on several images selected from the BSD300 database and we compare it with other algorithms such as FCM optimized by genetic algorithms (FCM-GA) and FCM optimized by particle swarm optimization (FCM-PSO). The experimental results on the different algorithms used in this paper show that the proposed method improves the segmentation results, based on the analysis of the best values of fitness, MSE, PSNR, CC, RI, GCE, BDE and VOI.

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“… SC is the subarea coefficient determined by equation ( 7),  PC is the partition coefficient presented in equation (8).…”

Section: A Fitness Functionmentioning

confidence: 99%

“…The image processing system presented has a multidisciplinary aspect. Its applications can be found in various fields like medical imaging [7], video analysis [8], and remote sensing [9]. In the literature, we do not find a technique to generalize it to image segmentation.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Image Clustering Technique based on Fuzzy C-means and Cuckoo Search Algorithm

Khrissi¹,

Akkad²,

Satori³

et al. 2021

IJACSA

Self Cite

View full text Add to dashboard Cite

show abstract

“…Besides this, there are some other calibration methods which are calibrated by double planar mirrors [20], nonlinear optimization [21], feature Points Motions [22], the plane at infinity [23], invariance of cross-ratio [24], parallel circles [25], image information of different orientations from plane [26], coplanar camera calibration [27], planar mirrors from silhouettes [28], and turntable sequences from silhouettes [29].…”

Section: Introductionmentioning

confidence: 99%

A New Camera Calibration Technique for Serious Distortion

Huang

Zou

2022

Processes

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A new camera calibration technique based on serious distortion is proposed, which only requires the camera to observe the plane pattern in an arbitrary azimuth. It uses the geometrical imaging principle and radial distortion model to acquire radial lens distortion coefficient and the image coordinate (u0, v0), and then solves the linear equation aiming at the other parameters of the camera. This method has the following characteristics: Firstly, the position of the camera and the plane is arbitrary, and the technique needs only a single observation for plane pattern. Secondly, it is suitable for camera calibration with serious distortion. Thirdly, it does not need expensive ancillary equipment, accurate movement, or lots of photos observed from different orientations. Having been authenticated by computer emulation and actual experiment, the results of the proposed technique have proved to be satisfactory. The research has also paved a new way in camera calibration for further studies.

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“…Image processing is used in several areas of computer vision, in particular, image segmentation [1][2][3][4], 3D reconstruction [5,6], camera self-calibration [7,8] and cryptography [9,10]. In this paper we have treated our proposed method in the field of image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation Approach Based on Hybridization Between K-Means and Mask R-CNN

Moussaoui

Benslimane

Akkad

2021

Lecture Notes in Electrical Engineering

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In this article, we will introduce a hybrid method based on the combination of two image segmentation techniques. The first method adopted is the k-means algorithm which is an unsupervised machine learning technique used to group data points, the second is the Mask R-CNN which is a neural network architecture which combines two sub-problems: object detection and semantic segmentation. The main objective of this study is to approve the segmentation of the image using k-means. The first step is to apply Mask R-CNN on our original image to detect the objects that are present in the image, then, we will apply k-means clustering to have better segmentation. For our approach we used a set of metrics to evaluate our proposed approach such as the mean square error (MSE); the peak signal to noise ratio (PSNR) and many other measures such as the difference between two images. These measurements have shown satisfactory results and a performance of the proposed method.

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Camera self-calibration having the varying parameters and based on homography of the plane at infinity

Cited by 24 publications

References 25 publications

An Efficient Image Clustering Technique based on Fuzzy C-means and Cuckoo Search Algorithm

An Efficient Image Clustering Technique based on Fuzzy C-means and Cuckoo Search Algorithm

A New Camera Calibration Technique for Serious Distortion

Image Segmentation Approach Based on Hybridization Between K-Means and Mask R-CNN

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