A Novel Active Contour Model for Texture Segmentation

Tatu, Aditya; Bansal, Sumukh

doi:10.1007/978-3-319-14612-6_17

Cited by 8 publications

(5 citation statements)

References 41 publications

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“…The ACWE model was also applied for texture segmentation by Wang et al [55], where the contour was driven by a local histogram. Tatu and Bansal [53] proposed a GAC-based model that uses intensity covariance matrices for the texture features. Gao et al introduced a factorisation-based ACM that utilises the local spectral histogram as the texture feature [16], and, more recently, a model that performs a fusion of intensity and Gabor-based features along with a factorisation scheme [17].…”

Section: Related Work On Texture Segmentationmentioning

confidence: 99%

GPU-accelerated image segmentation based on level sets and multiple texture features

Reska

Krȩtowski

2020

Multimed Tools Appl

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In this paper, we present a fast multi-stage image segmentation method that incorporates texture analysis into a level set-based active contour framework. This approach allows integrating multiple feature extraction methods and is not tied to any specific texture descriptors. Prior knowledge of the image patterns is also not required. The method starts with an initial feature extraction and selection, then performs a fast level set-based evolution process and ends with a final refinement stage that integrates a region-based model. The presented implementation employs a set of features based on Grey Level Co-occurrence Matrices, Gabor filters and structure tensors. The high performance of feature extraction and contour evolution stages is achieved with GPU acceleration. The method is validated on synthetic and natural images and confronted with results of the most similar among the accessible algorithms.

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Section: Related Work On Texture Segmentationmentioning

confidence: 99%

GPU-accelerated image segmentation based on level sets and multiple texture features

Reska

Krȩtowski

2020

Multimed Tools Appl

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“…Apply Active Contour model [21][22][23] over LST image: Later the LST based orientation Information is used as an external force in ACM model. The equation (10) can be rewritten using LST as shown in (11).…”

Section: ( ) ( )mentioning

confidence: 99%

Comparative analysis and implementation of structured Edge Active Contour

Shah¹,

Bhalani²

2020

IJECE

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This paper proposes modified chanvese model which can be implemented on image for segmentation. The structure of paper is based on Linear structure tensor (LST) as input to the variant model. Structure tensor is a matrix illustration of partial derivative information. In the proposed model, the original image is considered as information channel for computing structure tensor. Difference of Gaussian (DOG) is featuring improvement in which we can get less blurred image than original image.In this paper LST is modified by adding intensity information to enhance orientation information. Finally Active Contour Model (ACM) is used to segment the images. The proposed algorithm is tested on various images and also on some images which have intensity inhomogeneity and results are shown. Also, the results with other algorithms like chanvese, Bhattacharya, Gabor based chanvese and Novel structure tensor based model are compared.It is verified that accuracy of proposed model is the best. The biggest advantage of proposed model is clear edge enhancement.

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“…Region based segmentation models [5,[13][14][15][16][17][18] use image statistics to partition the image into regions. Ideally, the object of interest will have different statistics in comparison to surrounding areas and can then be quickly segmented.…”

Section: Active Contour Modelsmentioning

confidence: 99%

“…Other region based segmentation algorithms frequently incorporate some type of texture analysis, e.g. [13] and its references, into a pre-processing step prior to region segmentation. While the texture analysis greatly enhances the accuracy of the model, it also contributes to the complexity and computational cost.…”

Section: Active Contour Modelsmentioning

confidence: 99%

A Hybrid Morphological Active Contour for Natural Images

Fox¹,

Milanova²,

Al-Ali³

2013

IJCSEA

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Morphological active contours for image segmentation have become popular due to their low computational complexity coupled with their accurate approximation of the partial differential equations involved in the energy minimization of the segmentation process. In this paper, a morphological active contour which mimics the energy minimization of the popular Chan-Vese Active Contour without Edges is coupled with a morphological edge-driven segmentation term to accurately segment natural images. By using morphological approximations of the energy minimization steps, the algorithm has a low computational complexity. Additionally, the coupling of the edge-based and region-based segmentation techniques allows the proposed method to be robust and accurate. We will demonstrate the accuracy and robustness of the algorithm using images from the Weizmann Segmentation Evaluation Database and report on the segmentation results using the Sorensen-Dice similarity coefficient.

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A Novel Active Contour Model for Texture Segmentation

Cited by 8 publications

References 41 publications

GPU-accelerated image segmentation based on level sets and multiple texture features

GPU-accelerated image segmentation based on level sets and multiple texture features

Comparative analysis and implementation of structured Edge Active Contour

A Hybrid Morphological Active Contour for Natural Images

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