This research propose a novel image segmentation algorithm, named as Transform Invariant Rank Cuts (TIRC). Based on salient 3D geometric information of natural scenes. The segmentationalgorithm unities an emerging ro...
详细信息
This research propose a novel image segmentation algorithm, named as Transform Invariant Rank Cuts (TIRC). Based on salient 3D geometric information of natural scenes. The segmentationalgorithm unities an emerging robust statistics technique called Robust PCA and its recent application in Transform Invariant Low-Rank Texture (TILT) extraction. This proposed novel algorithms address two critical issues that have handicapped the applications of the TILT feature. First, we propose a simple yet efficient algorithm to detect low-rank texture regions in natural images. Second, TIRC is a principled graph-cut solution to partition the TILT features into groups; each group represents a unique 3D planar structure. Using a TILT adjacency graph, the algorithm assigns a TILT feature as a node. Two nodes are connected if they are spatially adjacent, with the cut cost function defined as the total coding length of encoding the two texture regions as low-rank matrices separately. Finally, the classical graph-cut algorithm can be applied to partition the graph into sub-graphs, each of which represents a unique surface texture and 3D orientation. The efficacy and visual quality of this geometric image segmentation algorithm is demonstrated on a large urban scene database.
When a single imagesegmentation method is used for imagesegmentation of wind turbine blade images under a complex background, the results obtained are not accurate and complete. This article proposes an image segmen...
详细信息
When a single imagesegmentation method is used for imagesegmentation of wind turbine blade images under a complex background, the results obtained are not accurate and complete. This article proposes an image segmentation algorithm that applies morphology to Canny edge detection. It uses morphological opening to erode and dilate the binary image after Canny algorithm processing, and removes redundant edge information to obtain a complete fan blade image. Experimental results show that the results obtained by the imagesegmentation method proposed here have good integrity and accuracy, and can improve the segmentation effect of the image.
A hyperspectral imaging system with a moving testbed has been developed for detection of the disease caused by Rhizopus stolonifera in peaches. The all-around hyperspectral imaging of the whole peach was obtained, whi...
详细信息
A hyperspectral imaging system with a moving testbed has been developed for detection of the disease caused by Rhizopus stolonifera in peaches. The all-around hyperspectral imaging of the whole peach was obtained, which can identify the decayed area fully and is suitable for online monitoring. Three single band images (709, 807, and 874 nm) which were selected by statistical methods and an image segmentation algorithm were applied to locate the decayed area of peaches was developed based on band ratio image coupled with a simple thresholding method. The performance of image segmentation algorithm of the single-band images was evaluated. The detection accuracy of peaches classified as 'sound', 'slight-decayed', 'moderate-decayed' and 'severe-decayed' were 95%, 66.29%, 100% and 100%, respectively. The spectral information was extracted from the decayed area to improve the detection accuracy. The six optical wavelengths were selected by SPA (successive projections algorithm) from the full spectral range. The classification accuracy of sound and rotten peaches was 100% if only these two categories were applied. Our results demonstrated that the hyperspectral imaging method offers the potential to be used to automatically detect fungal infection in peaches. (C) 2017 Elsevier Ltd. All rights reserved.
Wire ropes (WRs) are widely used in various areas, such as tourist cables, mining, and elevators. Thus, it is very important to detect broken wires (BWs) to prevent safety issues. To guarantee the structural health of...
详细信息
Wire ropes (WRs) are widely used in various areas, such as tourist cables, mining, and elevators. Thus, it is very important to detect broken wires (BWs) to prevent safety issues. To guarantee the structural health of WRs, this paper proposes the use of induction thermography (IT) to detect the BWs. An image segmentation algorithm based on scale morphology is proposed to eliminate the background influence of non-uniform heating and visualize the BWs. Further, the area of the BW region, after the image binarization, subtraction, and filtering, is extracted as a feature to quantify the number of BWs. Results show that the proposed methods can effectively visualize and quantify the broken region within 1 BWs.
An unsupervised imagesegmentation method based on multidimensional (MD) particle swarm optimization (PSO) is proposed in this paper. Firstly, a clustering-based nonlinear objective function of unsupervised image segm...
详细信息
ISBN:
(纸本)9781510822030
An unsupervised imagesegmentation method based on multidimensional (MD) particle swarm optimization (PSO) is proposed in this paper. Firstly, a clustering-based nonlinear objective function of unsupervised imagesegmentation is established according to Turi's validity index. Secondly, MD PSO algorithm is adopted to minimize the objective function to seek the optimal number and cluster centers of segmented regions simultaneously. Finally, global best (GB) position of swam in each dimension is modified to avoid being trapped in local optima. Experimental results valid the performance of the proposed image segmentation algorithm.
This paper proposes a new spatial image segmentation algorithm for breast cancer detection in terahertz (THz) images of freshly excised human tumors. Region classifications of fresh tissue with 3 or more regions, such...
详细信息
ISBN:
(数字)9781728166704
ISBN:
(纸本)9781728166711
This paper proposes a new spatial image segmentation algorithm for breast cancer detection in terahertz (THz) images of freshly excised human tumors. Region classifications of fresh tissue with 3 or more regions, such as cancer, fat, and collagen, remain a challenge for cancer detection. We propose to tackle this problem by exploiting the spatial correlation among neighboring pixels in THz images, that is, pixels that are close to each other are more likely to belong to the same region. The spatial correlation among pixels is modeled by using Markov random fields (MRF). A Gaussian mixture model (GMM) with expectation maximization (EM) is then used to represent the statistical distributions of the THz images in both the frequency and spatial domain. Experiment results demonstrated that the proposed spatial image segmentation algorithm outperforms existing algorithms that do not consider spatial information.
Purpose To observe the regulation of cerebral circulation in vivo based on image segmentation algorithms for deep learning in medical imaging to automatically detect and quantify the neonatal deep medullary veins (DMV...
详细信息
Purpose To observe the regulation of cerebral circulation in vivo based on image segmentation algorithms for deep learning in medical imaging to automatically detect and quantify the neonatal deep medullary veins (DMVs) on susceptibility weighted imaging (SWI) images. To evaluate early cerebral circulation self-rescue for neonates undergoing risk of cerebral hypoxia-ischaemia in vivo. Methods SWI images and clinical data of 317 neonates with or without risk of cerebral hypoxia-ischaemia were analyzed. Quantitative parameters showing the number, width, and curvature of DMVs were obtained using an image segmentation algorithm. Results The number of DMVs was greater in males than in females (p < 0.01), and in term than in preterm infants (p = 0.001). The width of DMVs was greater in term than in preterm infants (p < 0.01), in low-risk than in high-risk group (p < 0.01), and in neonates without intracranial extracerebral haemorrhage (ICECH) than with ICECH (p < 0.05). The curvature of DMVs was greater in term than in preterm infants (P < 0.05). The width of both bilateral thalamic veins and anterior caudate nucleus veins were positively correlated with the number of DMVs;the width of bilateral thalamic veins was positively correlated with the width of DMVs. Conclusion The DMVs quantification based on image segmentation algorithm may provide more detailed and stable quantitative information in neonate. SWI vein quantification may be an observable indicator for in vivo assessment of cerebral circulation self-regulation in neonatal hypoxic-ischemic brain injury.
We study the problem of segmenting specific white matter structures of interest from Diffusion Tensor (DT-MR) images of the human brain. This is an important requirement in many Neuroimaging studies: for instance, to ...
详细信息
ISBN:
(纸本)9781617823800
We study the problem of segmenting specific white matter structures of interest from Diffusion Tensor (DT-MR) images of the human brain. This is an important requirement in many Neuroimaging studies: for instance, to evaluate whether a brain structure exhibits group level differences as a function of disease in a set of images. Typically, interactive expert guided segmentation has been the method of choice for such applications, but this is tedious for large datasets common today. To address this problem, we endow an image segmentation algorithm with "advice" encoding some global characteristics of the region(s) we want to extract. This is accomplished by constructing (using expert-segmented images) an epitome of a specific region - as a histogram over a bag of 'words' (e.g., suitable feature descriptors). Now, given such a representation, the problem reduces to segmenting a new brain image with additional constraints that enforce consistency between the segmented foreground and the pre-specified histogram over features. We present combinatorial approximation algorithms to incorporate such domain specific constraints for Markov Random Field (MRF) segmentation. Making use of recent results on image co-segmentation, we derive effective solution strategies for our problem. We provide an analysis of solution quality, and present promising experimental evidence showing that many structures of interest in Neuroscience can be extracted reliably from 3-D brain image volumes using our algorithm.
For many of the state-of-the-art computer vision algorithms, imagesegmentation is an important preprocessing step. As such, several image segmentation algorithms have been proposed, however, with certain reservation ...
详细信息
ISBN:
(纸本)9781618395993
For many of the state-of-the-art computer vision algorithms, imagesegmentation is an important preprocessing step. As such, several image segmentation algorithms have been proposed, however, with certain reservation due to high computational load and many hand-tuning parameters. Correlation clustering, a graph-partitioning algorithm often used in natural language processing and document clustering, has the potential to perform better than previously proposed image segmentation algorithms. We improve the basic correlation clustering formulation by taking into account higher-order cluster relationships. This improves clustering in the presence of local boundary ambiguities. We first apply the pairwise correlation clustering to imagesegmentation over a pairwise superpixel graph and then develop higher-order correlation clustering over a hypergraph that considers higher-order relations among superpixels. Fast inference is possible by linear programming relaxation, and also effective parameter learning framework by structured support vector machine is possible. Experimental results on various datasets show that the proposed higher-order correlation clustering outperforms other state-of-the-art image segmentation algorithms.
Interactive imagesegmentation has a better result than the automatic and manual imagesegmentation because a user can help the image segmentation algorithm by marking the sample of background and object in the image....
详细信息
ISBN:
(纸本)9781538667279;9781538667262
Interactive imagesegmentation has a better result than the automatic and manual imagesegmentation because a user can help the image segmentation algorithm by marking the sample of background and object in the image. The algorithm will merge the regions in the image based on the user marking. In interactive imagesegmentation, the calculation of the distance between regions and the sequence of the merging process is important to obtain an accurate segmentation result. In this paper, we proposed a new region merging strategy using hierarchical clustering based on interclass and intra-class variances for each region and neighborhood relationship. This research aims to improve the region merging strategy and it is expected to result better than the previous research that did not implement the hierarchical clustering. The process to segment an image concludes splitting the image into several regions, user marking to mark the sample of background and object, merging the region that is not marked by the user using the hierarchical clustering until the image fully segmented. The experimental results on dental cone beam computed tomography data show that the proposed method gives a more effective and efficient result in the segmentation process.
暂无评论