Dr. Laurent Itti Profile

Dr. Laurent Itti

at Univ of Southern California

SPIE Involvement:

Author

Publications (16)

SPIE Journal Paper | 23 November 2017

Image salient object detection with refined deep features via convolution neural network

Dandan Zhu, Lei Dai, Xuan Shao, Qiangqiang Zhou, Laurent Itti, Ye Luo, Jianwei Lu

JEI, Vol. 26, Issue 06, 063018, (November 2017) https://doi.org/10.1117/12.10.1117/1.JEI.26.6.063018

KEYWORDS: Convolution, Feature extraction, Visualization, Image compression, Data modeling, Neural networks, Visual process modeling, Sensors, Eye, Image processing

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Proceedings Article | 9 February 2006 Paper

Combining bottom-up and top-down attentional influences

Vidhya Navalpakkam, Laurent Itti

Proceedings Volume 6057, 60570E (2006) https://doi.org/10.1117/12.674148

KEYWORDS: Visualization, Signal to noise ratio, Visual process modeling, Computer science, Target detection, Electronic imaging, Neurons, Human vision and color perception, Visual analytics, Statistical modeling

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Proceedings Article | 25 October 2005 Paper

Distributed biologically-based real-time tracking in the absence of prior target information

T. Nathan Mundhenk, Jacob Everist, Chris Landauer, Laurent Itti, Kirstie Bellman

Proceedings Volume 6006, 60060D (2005) https://doi.org/10.1117/12.631076

KEYWORDS: RGB color model, Visualization, Cameras, Video, Feature extraction, Optical tracking, Automatic tracking, Target acquisition, Algorithm development, Distributed computing

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Proceedings Article | 25 October 2004 Paper

Camera localization methods for intelligent room systems using RF techniques

Pradeep Natarajan, Terrell Mundhenk, Kirstie Bellman, Laurent Itti, Michael Arbib

Proceedings Volume 5608, (2004) https://doi.org/10.1117/12.571793

KEYWORDS: Cameras, Error analysis, Algorithm development, Imaging systems, Intelligence systems, Receivers, Signal attenuation, Received signal strength, Sensors, Telecommunications

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Proceedings Article | 25 October 2004 Paper

Teaching the computer subjective notions of feature connectedness in a visual scene for real-time vision

Terrell Mundhenk, Chris Landauer, Kirstie Bellman, Michael Arbib, Laurent Itti

Proceedings Volume 5608, (2004) https://doi.org/10.1117/12.571768

KEYWORDS: Visualization, Independent component analysis, Image processing, Principal component analysis, Monte Carlo methods, Image filtering, Visual analytics, Feature selection, Feature extraction, Aerospace engineering

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We discus a tool kit for usage in scene understanding where prior information about targets is not necessarily understood. As such, we give it a notion of connectivity such that it can classify features in an image for the purpose of tracking and identification. The tool VFAT (Visual Feature Analysis Tool) is designed to work in real time in an intelligent multi agent room. It is built around a modular design and includes several fast vision processes. The first components discussed are for feature selection using visual saliency and Monte Carlo selection. Then features that have been selected from an image are mixed into useful and more complex features. All the features are then reduced in dimension and contrasted using a combination of Independent Component Analysis and Principle Component Analysis (ICA/PCA). Once this has been done, we classify features using a custom non-parametric classifier (NPclassify) that does not require hard parameters such as class size or number of classes so that VFAT can create classes without stringent priors about class structure. These classes are then generalized using Gaussian regions which allows easier storage of class properties and computation of probability for class matching. To speed up to creation of Gaussian regions we use a system of rotations instead of the traditional Psuedo-inverse method. In addtion to discussing the structure of VFAT we discuss training of the current system which is relatively easy to perform. ICA/PCA is trained by giving VFAT a large number of random images. The ICA/PCA matrix is computed by features extracted by VFAT. The non-parametric classifier NPclasify it trained by presenting it with images of objects having it decide how many objects it thinks it sees. The difference between what it sees and what it is supposed to see in terms of the number of objects is used as the error term and allows VFAT to learn to classify based upon the experimenters subjective idea of good classification.

Proceedings Article | 7 June 2004 Paper

Automatic attention-based prioritization of unconstrained video for compression

Laurent Itti

Proceedings Volume 5292, (2004) https://doi.org/10.1117/12.527057

KEYWORDS: Video, Video compression, Eye, Computer programming, Eye models, Visualization, Calibration, Visual process modeling, Televisions, Video coding

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Proceedings Article | 7 June 2004 Paper

Biologically inspired feature-based categorization of objects

T. Nathan Mundhenk, Vidhya Navalpakkam, Hendrik Makaliwe, Shrihari Vasudevan, Laurent Itti

Proceedings Volume 5292, (2004) https://doi.org/10.1117/12.527321

KEYWORDS: Algorithm development, Image segmentation, Independent component analysis, Visualization, Feature extraction, Detection and tracking algorithms, Navigation systems, Cameras, Expectation maximization algorithms, Object recognition

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Proceedings Article | 30 December 2003 Paper

Realistic avatar eye and head animation using a neurobiological model of visual attention

Laurent Itti, Nitin Dhavale, Frederic Pighin

Proceedings Volume 5200, (2003) https://doi.org/10.1117/12.512618

KEYWORDS: Eye models, Eye, Head, Visualization, Visual process modeling, Solid modeling, Video, Motion models, Brain mapping, Cameras

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Proceedings Article | 30 September 2003 Paper

Low-cost high-performance mobile robot design utilizing off-the-shelf parts and the Beowulf concept: the Beobot project

Terrell Mundhenk, Christopher Ackerman, Daesu Chung, Nitin Dhavale, Brian Hudson, Ried Hirata, Eric Pichon, Zhan Shi, April Tsui, Laurent Itti

Proceedings Volume 5267, (2003) https://doi.org/10.1117/12.515180

KEYWORDS: Robotics, RGB color model, Detection and tracking algorithms, Image segmentation, Servomechanisms, Video, Computing systems, Cameras, Operating systems, Mobile robots

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Proceedings Article | 30 September 2003 Paper

Utilization and viability of biologically inspired algorithms in a dynamic multiagent camera surveillance system

Terrell Mundhenk, Nitin Dhavale, Salvador Marmol, Elizabeth Calleja, Vidhya Navalpakkam, Kirstie Bellman, Chris Landauer, Michael Arbib, Laurent Itti

Proceedings Volume 5267, (2003) https://doi.org/10.1117/12.515176

KEYWORDS: Cameras, Visualization, Brain, Calibration, Iron, Surveillance systems, Imaging systems, Video, Detection and tracking algorithms, Brain mapping

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In view of the growing complexity of computational tasks and their design, we propose that certain interactive systems may be better designed by utilizing computational strategies based on the study of the human brain. Compared with current engineering paradigms, brain theory offers the promise of improved self-organization and adaptation to the current environment, freeing the programmer from having to address those issues in a procedural manner when designing and implementing large-scale complex systems. To advance this hypothesis, we discus a multi-agent surveillance system where 12 agent CPUs each with its own camera, compete and cooperate to monitor a large room. To cope with the overload of image data streaming from 12 cameras, we take inspiration from the primate’s visual system, which allows the animal to operate a real-time selection of the few most conspicuous locations in visual input. This is accomplished by having each camera agent utilize the bottom-up, saliency-based visual attention algorithm of Itti and Koch (Vision Research 2000;40(10-12):1489-1506) to scan the scene for objects of interest. Real time operation is achieved using a distributed version that runs on a 16-CPU Beowulf cluster composed of the agent computers. The algorithm guides cameras to track and monitor salient objects based on maps of color, orientation, intensity, and motion. To spread camera view points or create cooperation in monitoring highly salient targets, camera agents bias each other by increasing or decreasing the weight of different feature vectors in other cameras, using mechanisms similar to excitation and suppression that have been documented in electrophysiology, psychophysics and imaging studies of low-level visual processing. In addition, if cameras need to compete for computing resources, allocation of computational time is weighed based upon the history of each camera. A camera agent that has a history of seeing more salient targets is more likely to obtain computational resources. The system demonstrates the viability of biologically inspired systems in a real time tracking. In future work we plan on implementing additional biological mechanisms for cooperative management of both the sensor and processing resources in this system that include top down biasing for target specificity as well as novelty and the activity of the tracked object in relation to sensitive features of the environment.

Proceedings Article | 6 December 2002 Paper

Toward highly capable neuromorphic autonomous robots: beobots

Laurent Itti

Proceedings Volume 4787, (2002) https://doi.org/10.1117/12.455857

KEYWORDS: Neural networks, Fuzzy systems, Robotics, Standards development, Operating systems, C++, Detection and tracking algorithms, Object recognition

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Proceedings Article | 30 May 2002 Paper

Real-time high-performance attention focusing in outdoors color video streams

Laurent Itti

Proceedings Volume 4662, (2002) https://doi.org/10.1117/12.469519

KEYWORDS: Visual process modeling, Visualization, Video, Video processing, Machine vision, Target detection, Motion models, Computer vision technology, Algorithm development, Systems modeling

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Proceedings Article | 14 November 2001 Paper

Neuromorphic algorithms for computer vision and attention

Florence Miau, Constantine Papageorgiou, Laurent Itti

Proceedings Volume 4479, (2001) https://doi.org/10.1117/12.448343

KEYWORDS: Object recognition, Target detection, Visual process modeling, Visualization, Detection and tracking algorithms, Computing systems, Machine vision, Target recognition, System integration, Systems modeling

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SPIE Journal Paper | 1 September 2001

Visual attention and target detection in cluttered natural scenes

Laurent Itti, Carl Gold, Christof Koch

OE, Vol. 40, Issue 09, (September 2001) https://doi.org/10.1117/12.10.1117/1.1389063

KEYWORDS: Visualization, Target detection, Neurons, Data modeling, Performance modeling, Visual process modeling, Feature extraction, Gold, Image processing, Brain mapping

SPIE Journal Paper | 1 January 2001

Feature combination strategies for saliency-based visual attention systems

Laurent Itti, Christof Koch

JEI, Vol. 10, Issue 01, (January 2001) https://doi.org/10.1117/12.10.1117/1.1333677

KEYWORDS: Visualization, Target detection, Databases, Feature extraction, Image filtering, Performance modeling, Visual process modeling, Image processing, Neurons, Convolution

Proceedings Article | 19 May 1999 Paper

Comparison of feature combination strategies for saliency-based visual attention systems

Laurent Itti, Christof Koch

Proceedings Volume 3644, (1999) https://doi.org/10.1117/12.348467

KEYWORDS: Visualization, Target detection, Databases, Feature extraction, Neurons, Convolution, Visual process modeling, Yield improvement, Gaussian filters, Performance modeling

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