Publication

Feature point extraction using scale-space representation

Related publications (39)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Inner-Loop-Free ADMM for Cryo-EM

Michaël Unser, Laurène Donati, Emmanuel Emilien Louis Soubies

Thanks to recent advances in signal processing, the interest for fast ℓ1-regularized reconstruction algorithms in cryo-electron microscopy (cryo-EM) has intensified. The approaches based on the alternating-direction of multipliers method (ADMM) are particu ...

IEEE2019

When Stuck, Flip a Coin

Slobodan Mitrovic

Many modern services need to routinely perform tasks on a large scale. This prompts us to consider the following question: How can we design efficient algorithms for large-scale computation? In this thesis, we focus on devising a general strategy to addr ...

EPFL2018

Analyse multi-échelle de n-grammes sur 200 années d'archives de presse

Vincent Christian Buntinx

The recent availability of large corpora of digitized texts over several centuries opens the way to new forms of studies on the evolution of languages. In this thesis, we study a corpus of 4 million press articles covering a period of 200 years. The thesis ...

EPFL2017

Solving the Shortest Vector Problem in 2^n Time via Discrete Gaussian Sampling

Divesh Aggarwal

We give a randomized 2^{n+o(n)}-time and space algorithm for solving the Shortest Vector Problem (SVP) on n-dimensional Euclidean lattices. This improves on the previous fastest algorithm: the deterministic O(4^n)-time and O(2^n)-space algorithm of Miccian ...

ACM2015

Sensing the real world

Juri Ranieri

A sensor is a device that detects or measures a physical property and records, indicates, or otherwise responds to it. In other words, a sensor allows us to interact with the surrounding environment, by measuring qualitatively or quantitatively a given phe ...

EPFL2014

Low-Level Salient Region Detection Using Multi-Scale Filtering

Sabine Süsstrunk, Gökhan Yildirim

Various computer visions tasks require a summary of visually important regions in an image. Thus, developing simple yet accurate salient region detection algorithms has become an important research topic. The currently best performing state-of-the-art sali ...

2013

Mirror Match: Reliable Feature Point Matching Without Geometric Constraints

Sabine Süsstrunk, Stefan Winkler

Many algorithms have been proposed to solve the problem of matching feature points in two or more images using geometric assumptions to increase the robustness of the matching. However, these assumptions do not always hold; in particular, few methods addre ...

2013

Scale Invariant Features and Polar Descriptors in Omnidirectional Imaging

Pascal Frossard, Zafer Arican

We propose a method to compute scale invariant features in omnidirectional images. We present a formulation based on Riemannian geometry for the definition of differential operators on non-Euclidian manifolds that describe the mirror and lens structure in ...

Institute of Electrical and Electronics Engineers2012

An Efficient Algorithm for Level Set Method Preserving Distance Function

Jean-Philippe Thiran, Xavier Bresson, Dominique Zosso, Virginia Estellers Casas

The level set method is a popular technique for tracking moving interfaces in several disciplines including computer vision and fluid dynamics. However, despite its high flexibility, the original level set method is limited by two important numerical issue ...

Institute of Electrical and Electronics Engineers2012

LDAHash: Improved Matching with Smaller Descriptors

Pascal Fua, Christoph Strecha

SIFT-like local feature descriptors are ubiquitously employed in computer vision applications such as content-based retrieval, video analysis, copy detection, object recognition, photo tourism, and 3D reconstruction. Feature descriptors can be designed to ...

Institute of Electrical and Electronics Engineers2012