IEEE Transactions on Pattern Analysis and Machine Intelligence

Modeling visual attention-particularly stimulus-driven, saliency-based attention-has been a very active research area over the past 25 years. Many different models of attention are now available which, aside from lending theoretical contributions to other fields, have demonstrated successful applications in computer vision, mobile robotics, and cognitive systems. Here we review, from a computational perspective, the basic concepts of attention implemented in these models. We present a taxonomy of nearly 65 models, which provides a critical comparison of approaches, their capabilities, and shortcomings. In particular, 13 criteria derived from behavioral and computational studies are formulated for qualitative comparison of attention models. Furthermore, we address several challenging issues with models, including biological plausibility of the computations, correlation with eye movement datasets, bottom-up and top-down dissociation, and constructing meaningful performance measures. Finally, we highlight current research trends in attention modeling and provide insights for future.

State-of-the-Art in Visual Attention Modeling

http://cs.uef.fi/sipu/pub/SRE-review-Kinnunen-and-Li.pdf

An overview of text-independent speaker recognition: From features to supervectors

We provide a review of recent advances in 3D surface imaging technologies. We focus particularly on noncontact 3D surface measurement techniques based on structured illumination. The high-speed and high-resolution pattern projection capability offered by the digital light projection technology, together with the recent advances in imaging sensor technologies, may enable new generation systems for 3D surface measurement applications that will provide much better functionality and performance than existing ones in terms of speed, accuracy, resolution, modularization, and ease of use. Performance indexes of 3D imaging system are discussed, and various 3D surface imaging schemes are categorized, illustrated, and compared. Calibration techniques are also discussed, since they play critical roles in achieving the required precision. Numerous applications of 3D surface imaging technologies are discussed with several examples.

Structured-light 3D surface imaging: a tutorial

http://eia.udg.edu/~qsalvi/papers/2004-PR.pdf

Pattern codification strategies in structured light systems

Visual attention is the ability of the human vision system to detect salient parts of the scene, on which higher vision tasks, such as recognition, can focus. In human vision, it is believed that visual attention is intimately linked to the eye movements and that the fixation points correspond to the location of the salient scene parts. In computer vision, the paradigm of visual attention has been widely investigated and a saliency-based model of visual attention is now available that is commonly accepted and used in the field, despite the fact that its biological grounding has not been fully assessed. This work proposes a new method for quantitatively assessing the plausibility of this model by comparing its performance with human behavior. The basic idea is to compare the map of attention - the saliency map - produced by the computational model with a fixation density map derived from eye movement experiments. This human attention map can be constructed as an integral of single impulses located at the positions of the successive fixation points. The resulting map has the same format as the computer-generated map, and can easily be compared by qualitative and quantitative methods. Some illustrative examples using a set of natural and synthetic color images show the potential of the validation method to assess the plausibility of the attention model.

/pdf/empirical-validation-of-the-saliency-based-model-of-visual-2ljqclbmew.pdf

Empirical Validation of the Saliency-based Model of Visual Attention

The iterative closest point (ICP) algorithm is widely used for the registration of 3D geometric data. One of the main drawbacks of the algorithm is its quadratic time complexity O(N/sup 2/) with the number of points N. Consequently, several methods have been proposed to accelerate the process. We present a new solution for the speeding up of the ICP algorithm and special care is taken to avoid any tradeoff with the quality of the registration. The proposed solution combines a coarse to fine multiresolution approach with the neighbor search algorithm. The multiresolution approach permits to successively improve the registration using finer levels of representation and the neighbor search algorithm speeds up the closest point search by using a heuristic approach. Both multiresolution scheme and neighbor search algorithm main features are presented. Confirming the success of the proposed solution, typical results show that this combination permits to create a very fast ICP algorithm, with a closest point search complexity of O(N), while preserving the matching quality.

/pdf/a-multi-resolution-icp-with-heuristic-closest-point-search-3ha2d90vjf.pdf

A multi-resolution ICP with heuristic closest point search for fast and robust 3D registration of range images

Visual attention is the ability to rapidly detect the interesting parts of a given scene. Inspired by biological vision, the principle of visual attention is used with a similar goal in computer vision. Several previous works deal with the computation of visual attention from images provided by standard video cameras, but little attention has been devoted so far to scene depth as source for visual attention. The investigation presented in this paper aims at an extension of the visual attention model to the scene depth component. The first part of the paper is devoted to the integration of depth in the computational model built around conspicuity and saliency maps. The second part is devoted to experimental work in which results of visual attention, obtained form the extended model and for various 3D scenes, are presented. The results speak for the usefulness of the enhanced computational model.

/pdf/computing-visual-attention-from-scene-depth-ycn1znru3l.pdf

Computing visual attention from scene depth

https://doc.rero.ch/record/10746/files/Jost_Timoth_e_-_Assessing_the_contribution_of_color_20081124.pdf

Heinz Hügli

Papers

Empirical Validation of the Saliency-based Model of Visual Attention

A multi-resolution ICP with heuristic closest point search for fast and robust 3D registration of range images

Computing visual attention from scene depth

Assessing the contribution of color in visual attention

Usefulness of the LPC-residue in text-independent speaker verification