Boundary detection using simulation of particle motion in a vector image field

Eua-Anant, Nawapak; Удпа, Лалита

doi:10.1109/83.799884

Cited by 14 publications

(7 citation statements)

References 18 publications

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“…Third, adaptive thresholding makes a calculation of the threshold by a window to find the intensity from the neighbor pixel [17] [18]. The edge-based techniques, such as zero-crossing [19], Active Canny [20], PMVIF [21][22] [23], EdgeFlow [24], and PointFlow [25], extract object boundaries in the image by creating contours around the objects. The region-based techniques, such as watershed [26] [27] are based on the principle of grouping pixels with similar properties into the same regions or the same objects.…”

Section: Introductionmentioning

confidence: 99%

Edge-based Color Image Segmentation using Particle Motion on Vector Fields Derived from Local Color Distance Images

Phornphatcharaphong¹,

Eua-Anant²

2020

Preprint

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This paper presents an Edge-based color image segmentation derived from the method of Particle Motion in a Vector Image Fields (PMVIF) that could previously be applied only to monochrome images. Instead of using an edge vector field derived from a gradient vector field and a normal compressive vector field derived from a Laplacian-gradient vector field, two novel orthogonal vector fields, directly computed from a color image, one parallel and another orthogonal to the edges, were used in the model to force a particle to move along the object edges. The normal compressive vector field is derived from the center-to-centroid vectors of local color distance images. Next, the edge vector field is derived by taking the normal compressive vector field, multiplied by differences of auxiliary image pixels to obtain a vector field analogous to a Hamiltonian gradient vector field. Using the PASCAL Visual Object Classes Challenge 2012 (VOC2012) and the Berkeley Segmentation Data Set and Benchmarks 500 (BSDS500), the benchmark score of the proposed method is provided in comparison with those of the traditional PMVIF, Watershed, SLIC, K-means, Mean shift, and JSEG. The proposed method yields better RI, GCE, NVI, BDE, Dice coefficients, faster computation time, and noise resistance.

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Section: Introductionmentioning

confidence: 99%

Edge-based Color Image Segmentation using Particle Motion on Vector Fields Derived from Local Color Distance Images

Phornphatcharaphong¹,

Eua-Anant²

2020

Preprint

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show abstract

“…Third, adaptive thresholding makes a calculation of the threshold by a window to find the intensity from the neighbor pixel [17,18]. The edge-based techniques, such as zero-crossing [19], Active Canny [20], PMVIF [21][22][23], EdgeFlow [24], and PointFlow [25], extract object boundaries in the image by creating contours around the objects. The region-based techniques, such as watershed [26,27], are based on the principle of grouping pixels with similar properties into the same regions or the same objects.…”

Section: Introductionmentioning

confidence: 99%

Edge-Based Color Image Segmentation Using Particle Motion in a Vector Image Field Derived from Local Color Distance Images

Phornphatcharaphong

Eua-Anant

2020

J. Imaging

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This paper presents an edge-based color image segmentation approach, derived from the method of particle motion in a vector image field, which could previously be applied only to monochrome images. Rather than using an edge vector field derived from a gradient vector field and a normal compressive vector field derived from a Laplacian-gradient vector field, two novel orthogonal vector fields were directly computed from a color image, one parallel and another orthogonal to the edges. These were then used in the model to force a particle to move along the object edges. The normal compressive vector field is created from the collection of the center-to-centroid vectors of local color distance images. The edge vector field is later derived from the normal compressive vector field so as to obtain a vector field analogous to a Hamiltonian gradient vector field. Using the PASCAL Visual Object Classes Challenge 2012 (VOC2012), the Berkeley Segmentation Data Set, and Benchmarks 500 (BSDS500), the benchmark score of the proposed method is provided in comparison to those of the traditional particle motion in a vector image field (PMVIF), Watershed, simple linear iterative clustering (SLIC), K-means, mean shift, and J-value segmentation (JSEG). The proposed method yields better Rand index (RI), global consistency error (GCE), normalized variation of information (NVI), boundary displacement error (BDE), Dice coefficients, faster computation time, and noise resistance.

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“…The vector image model provides a more complete description of an image in which both directions and magnitudes of image edges are considered. From the vector image model, a derivativebased edge operator is applied to yield the edge vector field [18], [19]. The proposed edge vector field is generated by averaging magnitudes and directions in the vector image.…”

Section: Introductionmentioning

confidence: 99%

Left ventricular segmentation of cardiac magnetic resonance images using a novel edge following technique

Somkantha

Theera-Umpon

Auephanwiriyakul

2008

2008 IEEE Conference on Cybernetics and Intelligent Systems

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This paper presents a novel edge following technique for image segmentation designed to segment the left ventricle in cardiac magnetic resonance (MR) images. This is a required step to determine the volume of left ventricle in a cardiac MR image, which is an essential tool for cardiac diagnosis. The traditional method for extracting them from cardiac MR images is by human delineation. This method is accuracy but time consuming. So a new ventricular segmentation technique is proposed in order to reduce the analysis time with similar accuracy level compared to doctors' opinions. Our proposed technique can detect ventricle edges in MR images using the information from the vector image model and the edge map. We also compare the proposed segmentation technique with the active contour model (ACM) and the gradient vector flow (GVF) by using the opinions of two skilled doctors as the ground truth. The experimental results show that our technique is able to provide more accurate segmentation results than the classical contour models and visually close to the manual segmentation by the experts. The results evaluated using a numerical measure by mean of the probability of error in image segmentation also confirm the visual evaluation.

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Boundary detection using simulation of particle motion in a vector image field

Cited by 14 publications

References 18 publications

Edge-based Color Image Segmentation using Particle Motion on Vector Fields Derived from Local Color Distance Images

Edge-based Color Image Segmentation using Particle Motion on Vector Fields Derived from Local Color Distance Images

Edge-Based Color Image Segmentation Using Particle Motion in a Vector Image Field Derived from Local Color Distance Images

Left ventricular segmentation of cardiac magnetic resonance images using a novel edge following technique

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