Showing 1 - 20 results of 29 for search '"UCSD"', query time: 0.05s Refine Results
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    Bladder Cancer by A. Karim Kader

    Published 2011-01-01
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    Advanced retinal disease detection from OCT images using a hybrid squeeze and excitation enhanced model. by Gülcan Gencer, Kerem Gencer

    Published 2025-01-01
    “…The methodology was tested on UCSD and Duke's OCT datasets and produced excellent results. …”
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    CRF combined with ShapeBM shape priors for image labeling by Hao WANG, Lijun GUO, Yadong WANG, Rong ZHANG

    Published 2017-01-01
    “…Conditional random field (CRF) is a powerful model for image labeling,it is particularly well-suited to model local interactions among adjacent regions (e.g.superpixels).However,CRF doesn't consider the global constraint of objects.The overall shape of the object is used as a global constraint,the ShapeBM can be taken advantage of modeling the global shape of object,and then a new labeling model that combined the above two types of models was presented.The combination of CRF and ShapeBM was based on the superpixels,through the pooling technology was wed to establish the corresponding relationship between the CRF superpixel layer and the ShapeBM input layer.It enhanced the effectiveness of the combination of CRF and ShapeBM and improved the accuracy of the labeling.The experiments on the Penn-Fudan Pedestrians dataset and Caltech-UCSD Birds 200 dataset demonstrate that the model is more effective and efficient than others.…”
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    FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining by K. R. Seeja, Masoumeh Zareapoor

    Published 2014-01-01
    “…The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced) and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers.…”
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    Novel video anomaly detection method based on global-local self-attention network by Jing YANG, Chengmao WU, Liuping ZHOU

    Published 2023-08-01
    “…In order to improve the accuracy of video anomaly detection, a novel video anomaly detection method based on global-local self-attention network was proposed.Firstly, the video sequence and the corresponding RGB sequence were fused to highlight the motion change of the object.Secondly, the temporal correlation of the video sequence in the local area was captured by the expansion convolution layer, along with the self-attention network was utilized to compute the global temporal dependencies of the video sequence.Meanwhile, by deepening the basic network U-Net and combining the relevant motion and representation constraints, the network model was trained end-to-end to improve the detection accuracy and robustness of the model.Finally, experiments were carried out on the public data sets UCSD Ped2, CUHK Avenue and ShanghaiTech, as well as the test results were visually analyzed.The experimental results show that the detection accuracy AUC of the proposed method reaches 97.4%, 86.8% and 73.2% respectively, which is obviously better than that of the compared methods.…”
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