L. Itti

Publications269
h-index60
Citations33,452
Highly Influential Citations3,076
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A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
TLDR
A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Expand
  • 9,035
  • 1244
  • PDF
Computational modelling of visual attention
  • L. Itti, C. Koch
  • Psychology, Medicine
  • Nature Reviews Neuroscience
  • 1 March 2001
Five important trends have emerged from recent work on computational models of focal visual attention that emphasize the bottom-up, image-based control of attentional deployment. First, the… Expand
  • 3,890
  • 270
  • PDF
A saliency-based search mechanism for overt and covert shifts of visual attention
Most models of visual search, whether involving overt eye movements or covert shifts of attention, are based on the concept of a saliency map, that is, an explicit two-dimensional map that encodes… Expand
  • 2,820
  • 265
  • PDF
Bayesian surprise attracts human attention
  • L. Itti, P. Baldi
  • Computer Science, Psychology
  • Vision Research
  • 5 December 2005
TLDR
We propose a formal Bayesian definition of surprise to capture subjective aspects of sensory information. Expand
  • 1,267
  • 113
  • PDF
State-of-the-Art in Visual Attention Modeling
  • A. Borji, L. Itti
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine…
  • 2013
TLDR
We present a taxonomy of nearly 65 attentional models, which provides a critical comparison of approaches, their capabilities, and shortcomings. Expand
  • 1,470
  • 110
  • PDF
Components of bottom-up gaze allocation in natural images
Recent research [Parkhurst, D., Law, K., & Niebur, E., 2002. Modeling the role of salience in the allocation of overt visual attention. Vision Research 42 (1) (2002) 107-123] showed that a model of… Expand
  • 603
  • 80
  • PDF
Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study
TLDR
Visual attention is a process that enables biological and machine vision systems to select the most relevant regions from a scene. Expand
  • 527
  • 54
  • PDF
Automatic foveation for video compression using a neurobiological model of visual attention
  • L. Itti
  • Computer Science, Medicine
  • IEEE Transactions on Image Processing
  • 1 October 2004
TLDR
We evaluate the applicability of a biologically-motivated algorithm to select visually-salient regions of interest in video streams for multiply-foveated video compression. Expand
  • 740
  • 45
  • PDF
Exploiting local and global patch rarities for saliency detection
  • A. Borji, L. Itti
  • Computer Science
  • IEEE Conference on Computer Vision and Pattern…
  • 16 June 2012
TLDR
We introduce a saliency model based on two key ideas. Expand
  • 334
  • 45
  • PDF
A principled approach to detecting surprising events in video
  • L. Itti, P. Baldi
  • Computer Science
  • IEEE Computer Society Conference on Computer…
  • 20 June 2005
TLDR
We present a new theory of sensory surprise, which provides a principled and computable shortcut to important information. Expand
  • 412
  • 42
  • PDF