Publications by Kevin Smith | EPFL Graph Search

Related publications (10)

Bayesian methods for visual multi-object tracking with applications to human activity recognition

In recent years, we have seen a dramatic increase in the amount of video data recorded and stored around the world. Driven by the availability of low-cost video cameras, the ever-decreasing cost of digital media storage, and the explosion in popularity of ...

EPFL2007

Tracking the visual focus of attention for a varying number of wandering people

Daniel Gatica-Perez, Jean-Marc Odobez, Kevin Smith, Silèye Oumar Ba

In this article, we define and address the problem of finding the visual focus of attention for a varying number of wandering people (VFOA-W) -- determining where a person is looking when their movement is unconstrained. VFOA-W estimation is a new and impo ...

2008

Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks with Learned Shape Features

Pascal Fua, Graham Knott, Radhakrishna Achanta, Kevin Smith, Aurélien Lucchi

It is becoming increasingly clear that mitochondria play an important role in neural function. Recent studies show mitochondrial morphology to be crucial to cellular physiology and synaptic function and a link between mitochondrial defects and neuro-degene ...

Institute of Electrical and Electronics Engineers2012

SLIC Superpixels Compared to State-of-the-art Superpixel Methods

Pascal Fua, Sabine Süsstrunk, Radhakrishna Achanta, Kevin Smith, Aurélien Lucchi, Appu Shaji

Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically c ...

Institute of Electrical and Electronics Engineers2012

General Constraints for Batch Multiple-Target Tracking Applied to Large-Scale Videomicroscopy

Vincent Lepetit, Kevin Smith

While there is a large class of Multiple-Target Tracking (MTT) problems for which batch processing is possible and desirable, batch MTT remains relatively unexplored in comparison to sequential approaches. In this paper, we give a principled probabilistic ...

IEEE2008

Learning Structured Models for Segmentation of 2D and 3D Imagery

Pascal Fua, Graham Knott, Carlos Joaquin Becker, Kevin Smith, Aurélien Lucchi, Pablo Marquez Neila, Yunpeng Li

Efficient and accurate segmentation of cellular structures in microscopic data is an essential task in medical imaging. Many state-of-the-art approaches to image segmentation use structured models whose parameters must be carefully chosen for optimal perfo ...

Institute of Electrical and Electronics Engineers2015

Fast Ray Features for Learning Irregular Shapes

Vincent Lepetit, Kevin Smith

We introduce a new class of image features, the Ray feature set, that consider image characteristics at distant contour points, capturing information which is difficult to represent with standard feature sets. This property allows Ray features to efficient ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

CIDRE: an illumination-correction method for optical microscopy

Kevin Smith, Yunpeng Li

Uneven illumination affects every image acquired by a microscope. It is often overlooked, but it can introduce considerable bias to image measurements. The most reliable correction methods require special reference images, and retrospective alternatives do ...

Nature Publishing Group2015

Structured Image Segmentation using Kernelized Features

Pascal Fua, Kevin Smith, Aurélien Lucchi, Yunpeng Li

Most state-of-the-art approaches to image segmentation formulate the problem using Conditional Random Fields. These models typically include a unary term and a pairwise term, whose parameters must be carefully chosen for optimal performance. Recently, stru ...

2012

A Fully Automated Approach to Segmentation of Irregularly Shaped Cellular Structures in EM Images

Pascal Fua, Vincent Lepetit, Radhakrishna Achanta, Kevin Smith, Aurélien Lucchi

While there has been substantial progress in segmenting natural im-ages, state-of-the-art methods that perform well in such tasks unfortunately tend to underperform when confronted with the different challenges posed by electron microscope (EM) data. For e ...

Springer2010