A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail.

/pdf/a-model-of-saliency-based-visual-attention-for-rapid-scene-wv0n3yqxix.pdf

A model of saliency-based visual attention for rapid scene analysis

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting "spatial pyramid" is a simple and computationally efficient extension of an orderless bag-of-features image representation, and it shows significantly improved performance on challenging scene categorization tasks. Specifically, our proposed method exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories. The spatial pyramid framework also offers insights into the success of several recently proposed image descriptions, including Torralbas "gist" and Lowes SIFT descriptors.

/pdf/beyond-bags-of-features-spatial-pyramid-matching-for-4aa5cbe2fz.pdf

Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

The Potato Operation: computer vision for agricultural robotics and quality control

In data-hiding the issue of the achievable rate maximization is closely related to the problem of host interference cancellation. The optimal host interference cancellation relies on the knowledge of the host realization and the channel statistics (the additive white Gaussian noise (AWGN) variance) available at the encoder a priori to the transmission. The latter assumption can be rarely met in practical situations. Contrarily to the Costa set-up where the encoder is optimized for the particular state of the independent and identically distributed (i.i.d.) Gaussian attacking channel, we address the problem of asymmetrically informed data-hiding optimal encoder design assuming that the host interference probability density function (pdf) is an i.i.d. Laplacian and the channel variance lies on some known interval. The presented experimental results advocate the advantages of the developed embedding strategy.

/pdf/asymmetrically-informed-data-hiding-optimization-of-dzl29ocbst.pdf

Asymmetrically informed data-hiding optimization of achievable rate for Laplacian host

From non-stationary Gaussian modelling to independent component analysis: an fMRI study

Robust digital watermarking with channel state estimation: Part I Approaching theoretical capacity

A new process for preparing 3,3 - thio - bis[2-Methoxy-1-azabenzanthrone], a useful intermediate in the synthesis of dyestuffs, is disclosed. The process involves reacting 2-methoxy-1-azabenzanthrone with sulfur chlorides in suspension or solution in an inert solvent under heating, and recovering the reaction product from the reaction mass, e.g., by filtering.

Thierry Pun

Papers

The Potato Operation: computer vision for agricultural robotics and quality control

Asymmetrically informed data-hiding optimization of achievable rate for Laplacian host

From non-stationary Gaussian modelling to independent component analysis: an fMRI study

Robust digital watermarking with channel state estimation: Part I Approaching theoretical capacity

The Potato Operation: germs detection by shadows analysis and controlled lighting