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Learning Approach to Delineation of Curvilinear Structures in 2D and 3D Images

Related publications (224)

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Automatic Hand Phantom Map Detection Methods

Christian Enz, Claudio Bruschini, Huaiqi Huang, Tao Li, Jörn Justiz

Many amputees have maps of referred sensation from their missing hand on their residual limb (phantom maps). This skin area can serve as a target for providing amputees with tactile sensory feedback. Providing tactile feedback on the phantom map can improv ...

2015

Learning by weakly-connected adaptive agents

Ali H. Sayed, Bicheng Ying

In this paper, we examine the learning mechanism of adaptive agents over weakly-connected graphs and reveal an interesting behavior on how information flows through such topologies. The results clarify how asymmetries in the exchange of data can mask local ...

IEEE2015

TILDE: A Temporally Invariant Learned DEtector

Pascal Fua, Vincent Lepetit, Kwang Moo Yi, Yannick Verdie

We introduce a learning-based approach to detect repeatable keypoints under drastic imaging changes of weather and lighting conditions to which state-of-the-art keypoint detectors are surprisingly sensitive. We first identify good keypoint candidates in mu ...

2015

Information Extraction on the Web with Credibility Guarantee

Thành Tâm Nguyên

The Web became the central medium for valuable sources of information extraction applications. However, such user-generated resources are often plagued by inaccuracies and misinformation due to the inherent openness and uncertainty of the Web. In this work ...

2015

SVM Active Learning Approach for Image Classification Using Spatial Information

Devis Tuia

In the last few years, active learning has been gaining growing interest in the remote sensing community in optimizing the process of training sample collection for supervised image classification. Current strategies formulate the active learning problem i ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Diffusion adaptation over networks

Ali H. Sayed

Adaptive networks are well-suited to perform decentralized information processing and optimization tasks and to model various types of self-organized and complex behavior encountered in nature. Adaptive networks consist of a collection of agents with proce ...

Elsevier2014

Perceptual Learning With Indiscriminable Stimuli

Lukasz Grzeczkowski, Fred Mast, Elisa Tartaglia

Perceptual learning is learning to perceive. For example, in a bisection task three parallel lines are presented. The central line is slightly offset towards the right or the left outer line. Observers indicate the offset direction. Training greatly improv ...

2014

Graph-based Image Inpainting

Michaël Defferrard

The project goal was to explore the applications of spectral graph theory to address the inpainting problem of large missing chunks. We used a non-local patch graph representation of the image and proposed a structure detector which leverages the graph rep ...

2014

Active Learning: Any Value for Classification of Remotely Sensed Data?

Devis Tuia

Active learning, which has a strong impact on processing data prior to the classification phase, is an active research area within the machine learning community, and is now being extended for remote sensing applications. To be effective, classification mu ...

2013

On the Influence of Informed Agents on Learning and Adaptation Over Networks

Ali H. Sayed

Adaptive networks consist of a collection of agents with adaptation and learning abilities. The agents interact with each other on a local level and diffuse information across the network through their collaboration. In this work, we consider two types of ...

Institute of Electrical and Electronics Engineers, Inc., 345 E. 47 th St. NY NY 10017-2394 United States2013