Results 201 to 210 of about 6,084,560 (257)

Locality Preserving Discriminant Projection

IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015), 2015
Face is the most powerful biometric as far as human recognition system is concerned which is not the case for machine vision. Face recognition by machine is yet incomplete due to adverse, unconstrained environment. Out of several attempts made in past few decades, subspace based methods appeared to be more accurate and robust.
Gitam Shikkenawis, Suman K. Mitra
openaire   +1 more source

Preserving digital local news

The Electronic Library, 2008
PurposeThe purpose of this paper is to explore issues and approaches for collection and management of born digital local news. Much local news – important documentation of local history – is being lost. The fact that a lot of news media is now available digitally presents new opportunities but also new challenges for such preservation.Design ...
Robert B. Allen, Kirsten A. Johnson
openaire   +1 more source

GLCP: Global-to-Local Connectivity Preservation for Tubular Structure Segmentation

International Conference on Medical Image Computing and Computer-Assisted Intervention
Accurate segmentation of tubular structures, such as vascular networks, plays a critical role in various medical domains. A remaining significant challenge in this task is structural fragmentation, which can adversely impact downstream applications ...
Feixiang Zhou, Zhuangzhi Gao, He Zhao, Jianyang Xie, Yanda Meng, Yitian Zhao, G. Lip, Yalin Zheng   +7 more
semanticscholar   +1 more source

Locality Preserving Discriminant Projections

2009
A new manifold learning algorithm called locality preserving discriminant projections (LPDP) is proposed by adding between-class scatter matrix and within-class scatter matrix into locality preserving projections (LPP). LPDP can preserve locality and utilize label information in the projection.
Jie Gui, Chao Wang, Ling Zhu
openaire   +1 more source

Preservation and evolution: Local newspapers as ambidextrous organizations

, 2020
This study uses 48 in-depth interviews with managers, editors, and reporters at local and regional newspapers and their parent companies in four countries (Finland, France, Germany, and the United Kingdom) to examine how they discuss changes to their ...
Joy Jenkins, R. Nielsen
semanticscholar   +1 more source

Deterministic Model Fitting by Local-Neighbor Preservation and Global-Residual Optimization

IEEE Transactions on Image Processing, 2020
Geometric model fitting has been widely used in many computer vision tasks. However, it remains as a challenging task when handing multiple-structural data contaminated by noises and outliers.
Guobao Xiao, Jiayi Ma, Shiping Wang, C. Chen   +3 more
semanticscholar   +1 more source

PAPILLON: PrivAcy Preservation from Internet-based and Local Language MOdel ENsembles

North American Chapter of the Association for Computational Linguistics
Users can divulge sensitive information to proprietary LLM providers, raising significant privacy concerns. While open-source models, hosted locally on the user's machine, alleviate some concerns, models that users can host locally are often less capable
Siyan Li, Vethavikashini Chithrra Raghuram, Omar Khattab, Julia Hirschberg, Zhou Yu   +4 more
semanticscholar   +1 more source

Locally linear embedding preserving local neighborhood

2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2016
Dimensionality reduction is an important issue in information processing and has popular applications in many fields, where locally linear embedding (LLE) is widely used due to accuracy and simple to implement. However, LLE is lack of robustness, and sensitive to local structure that can't preserve neighborhood character sometimes.
Tingquan Deng, Jinyan Liu, Ning Wang
openaire   +1 more source

Locality-Preserved Maximum Information Projection

IEEE Transactions on Neural Networks, 2008
Dimensionality reduction is usually involved in the domains of artificial intelligence and machine learning. Linear projection of features is of particular interest for dimensionality reduction since it is simple to calculate and analytically analyze.
Haixian Wang, Sibao Chen 0001, Zilan Hu, Wenming Zheng   +3 more
openaire   +2 more sources

Regularization Preserving Localization of Close Edges

IEEE Signal Processing Letters, 2007
In this letter, we address the problem of the influence of neighbor edges and their effect on the edge delocalization while extracting a neighbor contour by a derivative approach. The properties to be fulfilled by the regularization operators to minimize or suppress this side effect are deduced, and the best detectors are pointed out.
Laligant, Olivier, Truchetet, Frederic, Meriaudeau, Fabrice   +2 more
openaire   +1 more source