A fast image segmentation algorithm based on salient features model and spatial-frequency domain adaptive kernel is proposed to solve the accurate discriminate objects problem of online visual detection in such scenes...A fast image segmentation algorithm based on salient features model and spatial-frequency domain adaptive kernel is proposed to solve the accurate discriminate objects problem of online visual detection in such scenes of variable sample morphological characteristics,low contrast and complex background texture.Firstly,by analyzing the spectral component distribution and spatial contour feature of the image,a salient feature model is established in spatial-frequency domain.Then,the salient object detection method based on Gaussian band-pass filter and the design criterion of adaptive convolution kernel are proposed to extract the salient contour feature of the target in spatial and frequency domain.Finally,the selection and growth rules of seed points are improved by integrating the gray level and contour features of the target,and the target is segmented by seeded region growing.Experiments have been performed on Berkeley Segmentation Data Set,as well as sample images of online detection,to verify the effectiveness of the algorithm.The experimental results show that the Jaccard Similarity Coefficient of the segmentation is more than 90%,which indicates that the proposed algorithm can availably extract the target feature information,suppress the background texture and resist noise interference.Besides,the Hausdorff Distance of the segmentation is less than 10,which infers that the proposed algorithm obtains a high evaluation on the target contour preservation.The experimental results also show that the proposed algorithm significantly improves the operation efficiency while obtaining comparable segmentation performance over other algorithms.展开更多
The segmentation of unlabeled medical images is troublesome due to the high cost of annotation, and unsupervised domain adaptation is one solution to this. In this paper, an improved unsupervised domain adaptation met...The segmentation of unlabeled medical images is troublesome due to the high cost of annotation, and unsupervised domain adaptation is one solution to this. In this paper, an improved unsupervised domain adaptation method was proposed. The proposed method considered both global alignment and category-wise alignment. First, we aligned the appearance of two domains by image transformation. Second, we aligned the output maps of two domains in a global way. Then, we decomposed the semantic prediction map by category, aligning the prediction maps in a category-wise manner. Finally, we evaluated the proposed method on the 2017 Multi-Modality Whole Heart Segmentation Challenge dataset, and obtained 82.1 on the dice similarity coefficient and 4.6 on the average symmetric surface distance, demonstrating the effectiveness of the combination of global alignment and category-wise alignment.展开更多
Labeled data scarcity of an interested domain is often a serious problem in machine learning.Leveraging the labeled data from other semantic-related yet co-variate shifted source domain to facilitate the interested do...Labeled data scarcity of an interested domain is often a serious problem in machine learning.Leveraging the labeled data from other semantic-related yet co-variate shifted source domain to facilitate the interested domain is a consensus.In order to solve the domain shift between domains and reduce the learning ambiguity,unsupervised domain adaptation(UDA)greatly promotes the transferability of model parameters.However,the dilemma of over-fitting(negative transfer)and under-fitting(under-adaptation)is always an overlooked challenge and potential risk.In this paper,we rethink the shallow learning paradigm and this intractable over/under-fitting problem,and propose a safer UDA model,coined as Bilateral Co-Transfer(BCT),which is essentially beyond previous well-known unilateral transfer.With bilateral co-transfer between domains,the risk of over/under-fitting is therefore largely reduced.Technically,the proposed BCT is a symmetrical structure,with joint distribution discrepancy(JDD)modeled for domain alignment and category discrimination.Specifically,a symmetrical bilateral transfer(SBT)loss between source and target domains is proposed under the philosophy of mutual checks and balances.First,each target sample is represented by source samples with low-rankness constraint in a common subspace,such that the most informative and transferable source data can be used to alleviate negative transfer.Second,each source sample is symmetrically and sparsely represented by target samples,such that the most reliable target samples can be exploited to tackle underadaptation.Experiments on various benchmarks show that our BCT outperforms many previous outstanding work.展开更多
基金supported by National Natural Science Foundation of China[grant numbers 61573233]Natural Science Foundation of Guangdong,China[grant numbers 2021A1515010661]+1 种基金Special projects in key fields of colleges and universities in Guangdong Province[grant numbers 2020ZDZX2005]Innovation Team Project of University in Guangdong Province[grant numbers 2015KCXTD018].
文摘A fast image segmentation algorithm based on salient features model and spatial-frequency domain adaptive kernel is proposed to solve the accurate discriminate objects problem of online visual detection in such scenes of variable sample morphological characteristics,low contrast and complex background texture.Firstly,by analyzing the spectral component distribution and spatial contour feature of the image,a salient feature model is established in spatial-frequency domain.Then,the salient object detection method based on Gaussian band-pass filter and the design criterion of adaptive convolution kernel are proposed to extract the salient contour feature of the target in spatial and frequency domain.Finally,the selection and growth rules of seed points are improved by integrating the gray level and contour features of the target,and the target is segmented by seeded region growing.Experiments have been performed on Berkeley Segmentation Data Set,as well as sample images of online detection,to verify the effectiveness of the algorithm.The experimental results show that the Jaccard Similarity Coefficient of the segmentation is more than 90%,which indicates that the proposed algorithm can availably extract the target feature information,suppress the background texture and resist noise interference.Besides,the Hausdorff Distance of the segmentation is less than 10,which infers that the proposed algorithm obtains a high evaluation on the target contour preservation.The experimental results also show that the proposed algorithm significantly improves the operation efficiency while obtaining comparable segmentation performance over other algorithms.
文摘The segmentation of unlabeled medical images is troublesome due to the high cost of annotation, and unsupervised domain adaptation is one solution to this. In this paper, an improved unsupervised domain adaptation method was proposed. The proposed method considered both global alignment and category-wise alignment. First, we aligned the appearance of two domains by image transformation. Second, we aligned the output maps of two domains in a global way. Then, we decomposed the semantic prediction map by category, aligning the prediction maps in a category-wise manner. Finally, we evaluated the proposed method on the 2017 Multi-Modality Whole Heart Segmentation Challenge dataset, and obtained 82.1 on the dice similarity coefficient and 4.6 on the average symmetric surface distance, demonstrating the effectiveness of the combination of global alignment and category-wise alignment.
基金supported by National Key R&D Program of China(2021YFB3100800)National Natural Science Foundation of China(62271090)+1 种基金Chongqing Natural Science Fund(cstc2021jcyjjqX0023)supported by Huawei computational power of Chongqing Artificial Intelligence Innovation Center.
文摘Labeled data scarcity of an interested domain is often a serious problem in machine learning.Leveraging the labeled data from other semantic-related yet co-variate shifted source domain to facilitate the interested domain is a consensus.In order to solve the domain shift between domains and reduce the learning ambiguity,unsupervised domain adaptation(UDA)greatly promotes the transferability of model parameters.However,the dilemma of over-fitting(negative transfer)and under-fitting(under-adaptation)is always an overlooked challenge and potential risk.In this paper,we rethink the shallow learning paradigm and this intractable over/under-fitting problem,and propose a safer UDA model,coined as Bilateral Co-Transfer(BCT),which is essentially beyond previous well-known unilateral transfer.With bilateral co-transfer between domains,the risk of over/under-fitting is therefore largely reduced.Technically,the proposed BCT is a symmetrical structure,with joint distribution discrepancy(JDD)modeled for domain alignment and category discrimination.Specifically,a symmetrical bilateral transfer(SBT)loss between source and target domains is proposed under the philosophy of mutual checks and balances.First,each target sample is represented by source samples with low-rankness constraint in a common subspace,such that the most informative and transferable source data can be used to alleviate negative transfer.Second,each source sample is symmetrically and sparsely represented by target samples,such that the most reliable target samples can be exploited to tackle underadaptation.Experiments on various benchmarks show that our BCT outperforms many previous outstanding work.
