Texture‐based parametric active contour for target detection and tracking

Vard, Alireza; Moallem, Payman; Nilchi, Ahmad Reza Naghsh

doi:10.1002/ima.20194

Cited by 9 publications

(7 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This pressure energy replaces the edge energy in energy function of ACM [15]. Then, texture features have been extracted using a moment-based method.…”

Section: Mathematical Description Of Acmmentioning

confidence: 99%

“…The ⊥ indicates that the texture pressure is perpendicularly applied to the tangent of the contour. T is defined as Figure 1 The masks corresponding to the moment up to order two with window size of 3 × 3 [15]. where F is the texture feature vector of the contour, Oμ and Oσ are the mean and standard deviation of the texture feature vector of the target object points, respectively.…”

Section: Mathematical Description Of Acmmentioning

confidence: 99%

“…It is modified by internal and external forces and it stops at the real boundaries of the image. Although this method was proposed for segmentation and video object tracking, it faces problems such as speed and accuracy [15].…”

Section: Introductionmentioning

confidence: 99%

“…In the method proposed by Vard et al [15], tracking of texture object in texture background is presented by means of adding a novel pressure energy, named texture pressure energy, to the energy function of the parametric active contour. Texture features of contour and object points are calculated by a method based on moment.…”

Section: Introductionmentioning

confidence: 99%

“…In this article, the texture images are synthetic images selected from Brodatz album. In [15,20], the number of iterations is not provided automatically and should be selected by the user. So, the speed of algorithm is low.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Balloon energy based on parametric active contour and directional Walsh–Hadamard transform and its application in tracking of texture object in texture background

Tahvilian

Moallem

Monadjemi

2012

EURASIP J. Adv. Signal Process.

Self Cite

View full text Add to dashboard Cite

One of the popular approaches in object boundary detecting and tracking is active contour models (ACM). This article presents a new balloon energy in parametric active contour for tracking a texture object in texture background. In this proposed method, by adding the balloon energy to the energy function of the parametric ACM, a precise detection and tracking of texture target in texture background has been elaborated. In this method, texture feature of contour and object points have been calculated using directional Walsh-Hadamard transform, which is a modified version of the Walsh-Hadamard. Then, by comparing the texture feature of contour points with texture feature of the target object, movement direction of the balloon has been determined, whereupon contour curves are expanded or shrunk in order to adapt to the target boundaries. The tracking process is iterated to the last frames. The comparison between our method and the active contour method based on the moment demonstrates that our method is more effective in tracking object boundary edges used for video streams with a changing background. Consequently, the tracking precision of our method is higher; in addition, it converges more rapidly due to it slower complexity.

show abstract

“…This pressure energy replaces the edge energy in energy function of ACM [15]. Then, texture features have been extracted using a moment-based method.…”

Section: Mathematical Description Of Acmmentioning

confidence: 99%

Section: Mathematical Description Of Acmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Balloon energy based on parametric active contour and directional Walsh–Hadamard transform and its application in tracking of texture object in texture background

Tahvilian

Moallem

Monadjemi

2012

EURASIP J. Adv. Signal Process.

Self Cite

View full text Add to dashboard Cite

show abstract

A local modified chan–vese model for segmenting inhomogeneous multiphase images

Gao

Yang

Yan

2012

Int J Imaging Syst Tech

View full text Add to dashboard Cite

In this article, we propose a novel model to overcome the drawbacks of the modified Chan–Vese (MCV) model. Our model is devoted to find an optimal partition of inhomogeneous regions accurately and computationally efficient. MCV model was proposed on the concept of using one level‐set function for one region. It needs fewer numbers of iterations and improves the efficiency of image segmentation in contrast to the multiphase Chan–Vese model. The MCV model, however, is highly dependent on initial curves placement and often leads to erroneous segmentations on images with intensity inhomogeneity. In our model, to eliminate the affection of background information on the curve evolution and speed up the curve evolution, we first use the k‐means algorithm to presegment the image to get the initial curves and then add the local image information to the total energy functional of MCV model to deal with the intensity inhomogeneity. Finally, extensive experiments are done and the segmentation results on homogeneous multiphase images verify that the proposed method has the better accuracy and efficiency comparing to MCV model. Moreover, we show results on challenging multiphase inhomogeneous image to illustrate the robust and accurate segmentation that are possible with this novel model. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 103–113, 2012

show abstract

Object Segmentation in Texture Images Using Texture Gradient Based Active Contours

Subudhi

Mukhopadhyay

2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Texture‐based parametric active contour for target detection and tracking

Cited by 9 publications

References 48 publications

Balloon energy based on parametric active contour and directional Walsh–Hadamard transform and its application in tracking of texture object in texture background

Balloon energy based on parametric active contour and directional Walsh–Hadamard transform and its application in tracking of texture object in texture background

A local modified chan–vese model for segmenting inhomogeneous multiphase images

Object Segmentation in Texture Images Using Texture Gradient Based Active Contours

Contact Info

Product

Resources

About