Camera orientation estimation using voting approach on the Gaussian sphere for in-vehicle camera

Jo, Youngran; Jang, Jinbeum; Shin, Minwoo; Paik, Joonki

doi:10.1364/oe.27.026600

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…In a different paper, the researchers propose a technique for determining the camera orientation in a vehicle by obtaining three orthogonal vanishing points [5]. The vanishing point along the driving direction is first estimated.…”

Section: A Brief Review Of the Relevant Literaturementioning

confidence: 99%

The Utilization of the Radon Transform for the Extraction of the Orientation of Linear Features in Binary Images

Matz¹

2021

MCS

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This paper considers the idea of using the Radon transform to extract the orientation features of lines in binary images. The Radon transform sends a line at a particular orientation in image space to a point in feature space. The ensuing set of points in feature space is called a sinogram. This computation is usually performed for a large group of angles (over the interval [0,179] degrees taken in integer increments in this paper). Therefore, linear features at specific orientations will be mapped to points having maximum value at particular angles. For angular spacing of at least 5 degrees, the peaks of the sinogram at the angles corresponding to the orientations of the lines will be clearly visible (in a bar plot of sinogram peaks) above sinogram values at other angles. The mapping from image space to feature space accomplished by the Radon transform which maps rectangular coordinates (x,y) to coordinates (range, angle) provides for the garnering of the orientation of the linear features in binary images. In particular, the coordinates (range, angle) in the sinogram allow for distinguishing between lines oriented at one angle versus lines oriented at another angle or angles. This particular property of the sinogram allows for the extraction of the orientation features of lines in an image.

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Section: A Brief Review Of the Relevant Literaturementioning

confidence: 99%

The Utilization of the Radon Transform for the Extraction of the Orientation of Linear Features in Binary Images

Matz¹

2021

MCS

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“…While some methods require knowledge of camera calibration, others operate in a non‐calibrated setting. Other methods on VP detection use a Hough transform (HT) of the line segments on the Gaussian sphere [16, 17]. In [15], on the other hand, line detection is computed with PClines [18] instead of HT, and VP estimation performed as in [19] with a posterior refinement.…”

Section: Introductionmentioning

confidence: 99%

Vanishing point detection using the teaching learning‐based optimisation algorithm

Lopez-Martinez

Cuevas

2020

IET image process

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Within the computer vision field, estimating image vanishing points has many applications regarding robotic navigation, camera calibration, image understanding, visual measurement, 3D reconstruction, among others. Different methods for detecting vanishing points relies on accumulator space techniques, while others employ a heuristic approach such as RANSAC. Nevertheless, these types of methods suffer from low accuracy or high computational cost. To explore a different technique, this paper focuses on improving the efficiency of the metaheuristic search for vanishing points by using a recently proposed population‐based method: The Teaching Learning Based Optimisation algorithm (TLBO). The TLBO algorithm is a metaheuristic technique inspired by the teaching–learning process. In our method, the TLBO algorithm is used after a line segment detection, to cluster line segments according to their more optimal vanishing point. Thus, our algorithm detects both orthogonal and nonorthogonal vanishing points in real images. To corroborate the performance of our proposed algorithm, different comparison and tests with other approaches were carried out. The results validate the accuracy and efficiency of our proposed method. Our approach had an average computational time of1.42 seconds and obtained a cumulative focal length error of 1 pixel, and cumulative angular error of 0.1°.

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Orientation and Line Thickness Determination in Binary Images

Matz

2021

Advances in Parallel &Amp; Distributed Processing, and Applications

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Camera orientation estimation using voting approach on the Gaussian sphere for in-vehicle camera

Cited by 4 publications

References 27 publications

The Utilization of the Radon Transform for the Extraction of the Orientation of Linear Features in Binary Images

The Utilization of the Radon Transform for the Extraction of the Orientation of Linear Features in Binary Images

Vanishing point detection using the teaching learning‐based optimisation algorithm

Orientation and Line Thickness Determination in Binary Images

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