A factorization based active contour model for texture segmentation

Gao, Mingqi; Chen, Hengxin; Zheng, Shenhai; Fang, Bin

doi:10.1109/icip.2016.7533173

Cited by 19 publications

(18 citation statements)

References 15 publications

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“…The proposed method is based on factorization-based active contour for texture segmentation [11] [14] with modification. In geometric deformable the distance regularized active contour is used for the implementation [13].…”

Section: B Proposed Methodsmentioning

confidence: 99%

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Cotton Leaf Image Segmentation using Modified Factorization-Based Active Contour Method

Patil¹,

Amarapur²

2020

IJACSA

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Cotton plant is one of the most widely cultivated crop across worldwide. The leaf is one of the important parts which help in the food production. There are different cotton leaf diseases like Alternaria spot, foliar, bacterial blight, etc. which affects the agricultural yield. In order to detect the diseases, leaf region extraction becomes a significant task and to achieve this we use image processing techniques. Henceforth in this paper, a novel method used to extract the leaf region from a complex background. The proposed method is used for leaf extraction from complex background. The algorithm used in this method is modified factorization based active contour (MFACM) which helps in getting better output images. The database images used for research are acquired from the field using a digital camera. The proposed work is compared with existing active contour algorithms like Gradient Vector Flow (GVF), Adaptive Diffusion Flow (ADF), and Vector Flow Convolution (VFC). From the experiment, it can be observed that the proposed method is better than the other active contour methods in terms of computation time and the number of iterations. In addition to that segmented result is analyzed using specificity, sensitivity, precision which showed that our proposed method is better than the other methods.

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Section: B Proposed Methodsmentioning

confidence: 99%

“…In geometric deformable the distance regularized active contour is used for the implementation [13]. The method is modified version of a factorization based active contour model for texture segmentation [11]. So, the process of extracting the leaf region shown in Fig.…”

Section: B Proposed Methodsmentioning

confidence: 99%

Cotton Leaf Image Segmentation using Modified Factorization-Based Active Contour Method

Patil¹,

Amarapur²

2020

IJACSA

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“…Our proposed method is compared with six other classical and advanced segmentation techniques, i.e., the unsupervised J-images segmentation (JSEG) [24] method, the multiscale normalized cuts (MNCut) [26] method, the compressionbased texture merging (CTM) [20] method, the segmentation by aggregating superpixels (SAS) [44] method, the contourguided color palettes based (CCP-LAS) [45] method, and the factorization based active contour model for texture segmentation (FACM) [29] method. The test results will be given and analyzed in the following section.…”

Section: Methodsmentioning

confidence: 99%

“…Min et al [27] proposed a level set segmentation model integrating intensity and texture terms for segmentation of natural images, which can better capture intensity information of images than the Chan-Vese model [28], and the texture feature is extracted by the adaptive scale local variation degree algorithm. Gao et al [29] proposed a factorization-based level set model for texture image segmentation (referred to as FACM), which utilizes the local spectral histogram as the texture features and establishes an energy function based on the theory of matrix decomposition.…”

Section: Related Workmentioning

confidence: 99%

A Novel Color-Texture Descriptor Based on Local Histograms for Image Segmentation

Liu

et al. 2019

IEEE Access

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In this paper, we propose a novel color-texture image segmentation method based on local histograms. Starting with clustering-based color quantization, we extract a sufficient number of representative colors. For each pixel, through counting the number of pixels with each representative color within a circular neighborhood, a local histogram is obtained. After the circular neighborhood is extended to several scales, a local histogram with an appropriate scale is adopted as a color-texture descriptor at the corresponding pixel for image segmentation. Further, we correct the color-texture features near boundaries and obtain a initial segmentation by a clustering method with the color-texture descriptors. Finally, in order to obtain a better segmentation result, we merge the over segmented regions guided by the obtained boundaries. Experiments are performed on both synthetic and natural color-texture images, and the results show that our proposed method performs much better compared with state-of-the-art methods on image segmentation, particularly in textured areas.

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“…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]. Dong et al [14] also employed a factorisation-based ACM, combined with neutrosophic sets, in the task of color texture segmentation.…”

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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A factorization based active contour model for texture segmentation

Cited by 19 publications

References 15 publications

Cotton Leaf Image Segmentation using Modified Factorization-Based Active Contour Method

Cotton Leaf Image Segmentation using Modified Factorization-Based Active Contour Method

A Novel Color-Texture Descriptor Based on Local Histograms for Image Segmentation

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

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