A matting method based on full feature coverage

Xiao, Chunxia; He, Fazhi; Yu, Haiping

doi:10.1007/s11042-018-6690-1

Cited by 50 publications

(12 citation statements)

References 55 publications

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“…However, this adds more computational complexity due to bilateral filtering. Chen et al developed a matting scheme based on full feature coverage [ 60 ] and this also adds more complexity.…”

Section: Proposed Approachesmentioning

confidence: 99%

Automated crack segmentation via saturation channel thresholding, area classification and fusion of modified level set segmentation with Canny edge detection

Nnolim

2020

Heliyon

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Automatic detection of complex cracks on rough concrete surfaces via image processing is a challenging task. The most current effective methods involve deep learning schemes. These are usually computationally and structurally complex. Recently, relatively simplified algorithms were developed for effective segmentation of crack features. However, these approaches still could not consistently and accurately extract such features from extremely noisy images of rough concrete surfaces with complex crack patterns. This study describes crack feature segmentation algorithms based on wavelet coefficient adjustment, nonlinear filter pre-processing, saturation channel extraction, adaptive threshold-based edge detection and fuzzy clustering-based area classification. Additional modifications include a new energy function for active contour segmentation algorithm. Adaptive localized mask generation is also proposed for automatic region-based segmentation. Furthermore, a binary fusion stage is incorporated for improved edge feature extraction. The quantitative and visual evaluation of the proposed schemes show improvement in results compared to several recent state-of-the-art algorithms.

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Section: Proposed Approachesmentioning

confidence: 99%

Automated crack segmentation via saturation channel thresholding, area classification and fusion of modified level set segmentation with Canny edge detection

Nnolim

2020

Heliyon

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“…Step 3) M step: a) Update µ k by using (29) b) Update k by using (30). c) Update π ik by using (31); d) Update w and b i by using (32) and (33);…”

Section: B Outlier Elimination By MI Maximizationmentioning

confidence: 99%

A Robust Spatial Information-Theoretic GMM Algorithm for Bias Field Estimation and Brain MRI Segmentation

et al. 2020

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Due to their simplicity and flexibility, the unsupervised statistical models such as Gaussian mixture model (GMM) are powerful tools to address the brain magnetic resonance (MR) images segmentation problems. However, the GMM is based only on the intensity information, which makes it sensitive to noise. Lots of GMM-based segmentation algorithms have the low segmentation accuracy because of the influence caused by noise and intensity inhomogeneity. To further improve the segmentation accuracy, a robust spatial information-theoretic GMM algorithm is proposed in this paper to simultaneously estimate the intensity inhomogeneity and segment brain MR images. First, a novel spatial factor containing the non-local spatial information is incorporated to reduce the impact of noise. The proposed spatial factor not only takes into account the local neighboring information, but also considers the spatial structure information of pixels. Thus, our algorithm can retain more image details while reducing the influence of noise. Second, the mutual information (MI) maximization method is used to identify and eliminate outliers. Finally, to overcome the impact of intensity inhomogeneity, we use a linear combination of a set of orthogonal polynomials to approximate the bias field. The objective function is integrated with the bias field estimation model to segment the images and estimate the bias field simultaneously. The experimental results on both synthetic and clinical brain MR images show that the proposed algorithm can overcome the influence of noise and intensity inhomogeneity, and achieve more accurate segmentation results. INDEX TERMS Brain MRI segmentation, Gaussian mixture model, Markov random fields, non-local spatial information, information-theoretic approach, intensity inhomogeneity.

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“…With the rapid development of general image/video sensors in recent years [15], [16], image recognition and classification technology draw a lot of attention for tree species identification [17]- [21]. In reference [10], local contrast and statistical features were extracted from a window centered at the image pixel and were used to plant identification, which proved the feasibility of the identification method based on tree leaves.…”

Section: Related Workmentioning

confidence: 99%

To Identify Tree Species With Highly Similar Leaves Based on a Novel Attention Mechanism for CNN

Song

Zhang

2019

IEEE Access

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Image identification technology has great significance for forestry production and forestry management. Highly similar object identification tasks, such as tree species with similar leaves, are extremely challenging. Simply using typical Convolutional Neural Networks (CNNs) or simply adding more convolutional layers still performs poorly in the above tasks. In this paper, we present a novel attention mechanism to enhance the CNN for identification of tree species with highly similar leaves. This paper presents a highly discriminative network, namely attention branch based convolutional neural networks (ABCNN), to better distinguish the differences between leaves features. Firstly, we proposed a novel structure, in which an attention branch is added in all block layers of network besides the typical normal branch. Secondly, our attention branch adopts a condensation process to obtain a region of interest (ROI) from global information of input and designs a reconstruction process to amplify the features difference to focus on the ROI. Thirdly, we design a fusion process, which carefully combines the attention branch with a normal branch to improve the network performance in the training process. The proposed ABCNN is tested on special dataset of Leafsnap with highly similar tree leaves. Our approach achieved 91.43% classification accuracy, which is higher than previous methods. Furthermore, ABCNN is also tested on general data set of SVHN and obtains 98.27% classification accuracy, which is the most competitive when considering the lower computational resources for ordinary applications. Both above experiments demonstrate the discrimination and robustness of the proposed method.

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A matting method based on full feature coverage

Cited by 50 publications

References 55 publications

Automated crack segmentation via saturation channel thresholding, area classification and fusion of modified level set segmentation with Canny edge detection

Automated crack segmentation via saturation channel thresholding, area classification and fusion of modified level set segmentation with Canny edge detection

A Robust Spatial Information-Theoretic GMM Algorithm for Bias Field Estimation and Brain MRI Segmentation

To Identify Tree Species With Highly Similar Leaves Based on a Novel Attention Mechanism for CNN

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