An improvement of ship wake detection based on the radon transform

Courmontagne, Philippe

doi:10.1016/j.sigpro.2005.02.013

Cited by 43 publications

(19 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In fact, the Radon Transform is well known in the literature as an efficient method for segments detection as it has the capacity to transform thin lines to high valued peaks in the Radon space [1]. This propriety gives rise to many applications related to the computer vision such as the detection of ship wake from satellite images [2], [3], the centerline detection, line width estimation [4] and the rectangular buildings contours extraction [5]. Although the RT was mainly investigated for segments detection, its use was extended to recognize more sophisticated features, namely: rectangles, spots of circular and square shapes.…”

Section: Related Workmentioning

confidence: 99%

“…when t = 1 and the parabola equation is y = (x 2 + 1)(mod5)), the projection function R 1 (0, 1) sums the shaded pixels of the Fig.1.b. The pixels of coordinates (−2, 5) and (2,5) are wrapped back by the mod (5) respectively to (−2, 0) and (2,0). It can be seen that each function R 1 (0, 0) and R 1 (0, 1) sums exactly 5 distinct pixels and since the corresponding two parabolas are parallel, the related sets of pixels do not intersect.…”

Section: The Forward Finite Polynomial Discrete Radon Transformmentioning

confidence: 99%

See 1 more Smart Citation

The Recognition of Polynomial Position and Orientation through the Finite Polynomial Discrete Radon Transform

Elouedi

Fournier

Naït‐Ali

et al. 2013

Image Analysis and Processing – ICIAP 2013

View full text Add to dashboard Cite

In this paper, we propose to accurately detect from an image curvilinear features that can be approximated by polynomial curves. Having the a priori knowledge of a polynomial parameters (coefficients and degree), we give the possibility to recognize both the orientation and the position of the polynomial (if it exists) in the given image. For this objective, we present a new approach titled "The Finite Polynomial Discrete Radon Transform" (FPDRT) that maps the initial image into a Radon space where each point presents the amount of evidence of the existence of a polynomial at the same position. The FPDRT sums the pixels centered on a polynomial and stores the result at the corresponding position in the Radon space. The FPDRT extends the formalism of the Finite Discrete Radon Transform(FRT) which is restricted to project the image along straight lines of equation y = mx + t where m and t are integers. Our method generalizes FRT by projecting the image with respect to polynomials of equation y = mx n + t where m, n and t are integers. The FPDRT method is exactly invertible, requires only arithmetic operations and is applicable to p × p sized images where p is a prime number. Several applications are allowable by the FPDRT such as fingerprint, palm print biometric applications and multi directional roads recognition.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: The Forward Finite Polynomial Discrete Radon Transformmentioning

confidence: 99%

The Recognition of Polynomial Position and Orientation through the Finite Polynomial Discrete Radon Transform

Elouedi

Fournier

Naït‐Ali

et al. 2013

Image Analysis and Processing – ICIAP 2013

View full text Add to dashboard Cite

show abstract

“…Considering the linear characteristics of ship wakes, their detection within SAR images is well suited to be performed in the Radon domain, which is the gold standard in line detection along with the Hough transform. Thus, Radon transform based methods have dominated the literature for ship wake detection solutions [1][2][3][4][5][6]. Different from the previous approaches, Karakuş et.…”

Section: Introductionmentioning

confidence: 99%

Exploiting the Dual-Tree Complex Wavelet Transform for Ship Wake Detection in SAR Imagery

Achim

Karakuş

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

In this paper, we analyse synthetic aperture radar (SAR) images of the sea surface using an inverse problem formulation whereby Radon domain information is enhanced in order to accurately detect ship wakes. This is achieved by promoting linear features in the images. For the inverse problem-solving stage, we propose a penalty function, which combines the dual-tree complex wavelet transform (DT-CWT) with the non-convex Cauchy penalty function. The solution to this inverse problem is based on the forward-backward (FB) splitting algorithm to obtain enhanced images in the Radon domain. The proposed method achieves the best results and leads to significant improvement in terms of various performance metrics, compared to state-of-the-art ship wake detection methods. The accuracy of detecting ship wakes in SAR images with different frequency bands and spatial resolution reaches more than 90%, which clearly demonstrates an accuracy gain of 7% compared to the second-best approach.

show abstract

“…This have lead to many line detection applications on image processing [3,4,8], medical imaging [5] and seismic applications [2]. The Radon Transform was also widely used in satellite image area such as ship wakes detection [5,6], or buildings detection [14]. Especially, the authors in [9] extract buildings from high resolution images by applying the classical Radon Transform.…”

Section: Introductionmentioning

confidence: 99%

Extracting Buildings by Using the Generalized Multi Directional Discrete Radon Transform

Elouedi¹,

Hamouda²,

Rojbani³

et al. 2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. This paper presents a new method to detect and accurately locate a rectangular form object in any given image. In order to find the right coordinates of those objects in the image, we develop the Generalized Multi Directional Discrete Radon Transform (GMDRT). The GMDRT can detect any given shape whatever its form and orientation are. Experimental results on high resolution QuickBird image to extract rectangular buildings form show the efficiency of our method.

show abstract

An improvement of ship wake detection based on the radon transform

Cited by 43 publications

References 14 publications

The Recognition of Polynomial Position and Orientation through the Finite Polynomial Discrete Radon Transform

The Recognition of Polynomial Position and Orientation through the Finite Polynomial Discrete Radon Transform

Exploiting the Dual-Tree Complex Wavelet Transform for Ship Wake Detection in SAR Imagery

Extracting Buildings by Using the Generalized Multi Directional Discrete Radon Transform

Contact Info

Product

Resources

About