Shi, A patch-based convolutional neural network for remote sensing image classification, Neural Netw. 95 (2017) 19-28.Sharma, A.; Liu, X.; Yang, X.; Shi, D. A Patch-based Convolutional Neural Network for Remote
we attempt to enhance the motion information in HAR systems and mitigate the influence of bad samples through a Siamese architecture, termed as Motion-patch-based Siamese Convolutional Neural Network (MSCNN).The term motion patch is defined as a specific square region that includes critical motion ...
论文[Patch-based Convolutional Neural Network for WholeSlide Tissue Image Classification]为此类问题提出了 一个解决方案。基本原理就是把一个高分辨率图像分成很多小patch,然后对每个patch做patch-level classification,最后集合patch-level classification得到一个image-level classification。
The proposed method is based on a deep convolutional neural network that labels each...doi:10.1007/978-3-319-67561-9_11Qichao SunSun Yat-sen UniversityLijie DengSun Yat-sen University Carnegie Mellon University (SYSU-CMU) SHUNDE International Joint Research InstituteJianwei Liu...
Optimal image Denoising using patch-based convolutional neural network architecture. Multimed Tools Appl 82, 29805–29821 (2023). https://doi.org/10.1007/s11042-023-15014-8 Download citation Received20 April 2022 Revised10 October 2022 Accepted27 February 2023 Published16 March 2023 Issue DateAugust...
Local features play an important role in remote sensing image matching, and handcrafted features have been excessively used in this area for a long time. This article proposes a pyramid convolutional neural triplet network that extracts a 128-dimensional deep descriptor that significantly improves the...
Patch-based Discriminative Feature Learning for Unsupervised Person Re-identification 一、摘要 尽管有可分辨的局部特征早已被用于有效地解决person ReID问题,但是需要大量昂贵的手工标注。本文提出了一种基于patch的无监督学习框架,以便从patch而不是整幅图像中学习识别特征,即利用patch之间的相似性来学习一个有区别的...
The performance of the LCNN was compared to that of a deep convolutional neural network, support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF). SVM, KNN, and RF were tested with both patch-based and pixel-based systems. Three 30 km × 30 km test sites of ...
Carse, J., McKenna, S. (2019). Active Learning for Patch-Based Digital Pathology Using Convolutional Neural Networks to Reduce Annotation Costs. In: Reyes-Aldasoro, C., Janowczyk, A., Veta, M., Bankhead, P., Sirinukunwattana, K. (eds) Digital Pathology. ECDP 2019. Lecture Notes in ...
Improving Multi-atlas Segmentation by Convolutional Neural Network Based Patch Error EstimationMulti-atlas segmentation (MAS) is widely used in automatically labeling medical images. The performance of patch-based MAS approaches relies on accurate estimation of local patch similarity, a proxy......