Results 191 to 200 of about 29,223 (229)

Siamese Networks for Chromosome Classification

2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017
Karyotying is the process of pairing and ordering 23 pairs of human chromosomes from cell images on the basis of size, centromere position, and banding pattern. Karyotyping during metaphase is often used by clinical cytogeneticists to analyze human chromosomes for diagnostic purposes.
Swati, Gaurav Gupta, Mohit Yadav, Monika Sharma, Lovekesh Vig   +4 more
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Siamese Ballistics Neural Network

2019 IEEE International Conference on Image Processing (ICIP), 2019
Firearm identification is crucial in many investigative scenario. The crime scene often contains traces left by firearms in terms of bullets and cartridges. Traces analysis is a fundamental step in the Forensics Ballistics Analysis Process to identify which firearm fired a specific cartridge.
Giudice O., Guarnera L., Paratore A. B., Farinella G. M., Battiato S.   +4 more
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Siamese Neural Networks: An Overview

2020
Similarity has always been a key aspect in computer science and statistics. Any time two element vectors are compared, many different similarity approaches can be used, depending on the final goal of the comparison (Euclidean distance, Pearson correlation coefficient, Spearman's rank correlation coefficient, and others). But if the comparison has to be
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Siamese Network for Salivary Glands Segmentation

2022
International ...
Gabin Fodop, Aurélien Olivier, Clément Hoffmann, Ali Mansour, Sandrine Jousse-Joulin, Luc Bressollette, Benoit Clement   +6 more
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Siamese Network for Classification with Optimization of AUC

2019
It is known that RankSVM can optimize area under the ROC curve (AUC) for binary classification by maximizing the margin between the positive class and the negative class. Since the objective function of Siamese Network for rank learning is the same as RankSVM, Siamese Network can also optimize AUC for binary classification. This paper proposes a method
Hideki Oki, Jun'ichi Miyao, Takio Kurita
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Siamese Networks: The Tale of Two Manifolds

2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019
Siamese networks are non-linear deep models that have found their ways into a broad set of problems in learning theory, thanks to their embedding capabilities. In this paper, we study Siamese networks from a new perspective and question the validity of their training procedure.
Soumava Kumar Roy, Mehrtash Harandi, Richard Nock, Richard I. Hartley   +3 more
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Siamese network ensemble for visual tracking

Neurocomputing, 2018
Abstract Visual object tracking is a challenging task considering illumination variation, occlusion, rotation, deformation and other problems. In this paper, we extend a Siamese INstance search Tracker (SINT) with model updating mechanism to improve its tracking robustness.
Chenru Jiang, Jimin Xiao, Yanchun Xie, Tammam Tillo, Kaizhu Huang   +4 more
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Siamese-ResNet: Implementing Loop Closure Detection based on Siamese Network

2018 IEEE Intelligent Vehicles Symposium (IV), 2018
Deep learning has made significant breakthroughs in the tasks of image classification, detection, segmentation, etc. However, the application of deep learning in robotics is still scarce. SLAM is a fundamental problem in robotics and loop closure detection is an important part of SLAM. This paper attempts to use supervised learning methods to solve the
Kai Qiu, Yunfeng Ai, Bin Tian 0003, Bin Wang 0070, Dongpu Cao   +4 more
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Palmprint Recognition Using Siamese Network

2018
Recently, palmprint representation using different descriptors under the incorporation of deep neural networks, always achieves significant recognition performance. In this paper, we proposed a novel method to achieve end-to-end palmprint recognition by using Siamese network.
Dexing Zhong, Yuan Yang, Xuefeng Du
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Attributed Network Embedding via a Siamese Neural Network

2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2019
Recently, network embedding has attracted a surge of attention due to its ability to automatically extract features from graph-structured data. Though network embedding method has been intensively studied, most of the existing approaches pay attention to either structures or attributes.
Jiong Wang, Neng Gao, Jia Peng, Jingjie Mo   +3 more
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