Many computer vision problems can be considered to consist of two main tasks: the extraction of image content descriptions and their subsequent matching. The appropriate choice of type and level of description is of course task dependent, yet it is generally accepted that the low-level or so called early vision layers in the Human Visual System are context independent. This paper concentrates on the use of low-level approaches for solving computer vision problems and discusses three inter-related aspects of this: saliency; scale selection and content description. In contrast to many previous approaches which separate these tasks, we argue that these three aspects are intrinsically related. Based on this observation, a multiscale algorithm for the selection of salient regions of an image is introduced and its application to matching type problems such as tracking, object recognition and image retrieval is demonstrated.

Thumbnail images provide users of image retrieval and browsing systems with a method for quickly scanning large numbers of images. Recognizing the objects in an image is important in many retrieval tasks, but thumbnails generated by shrinking the original image often render objects illegible. We study the ability of computer vision systems to detect key components of images so that intelligent cropping, prior to shrinking, can render objects more recognizable. We evaluate automatic cropping techniques l) based on a method that detects salient portions of general images, and 2) based on automatic face detection. Our user study shows that these methods result in small thumbnails that are substantially more recognizable and easier to find in the context of visual search.

This paper presents an overview of the Scale Saliency algorithm recently introduced in (10). Scale Saliency is a novel method for measuring the saliency of image regions and selecting optimal scales for their analysis. The model underlying the algorithm deems image regions salient if they are simultaneously unpredictable in some feature-space and over scale. The algorithm possesses a number of attractive properties: invariance to planar rotation, scaling, intensity shifts and translation; robustness to noise, changes in viewpoint, and intensity scalings. Moreover, the approach offers a more general model of feature saliency compared with conventional ones, such as those based on kernel convolution, for example wavelet analysis, since such techniques define saliency and scale only with respect to a particular set of basis morphologies. Finally, we present a generalised version of the original algorithm which is invariant to Affine transformations.

Modern real-time graphics systems are required to render millions of polygons to the screen per second. However, even with this high polygon rendering bandwidth, there are still applications which tax this rendering capability. We introduce in this paper a technique which adaptively allocates polygons to objects in a scene according to their visual importance. It is expected that using this technique, an improvement in the perceptual quality of a rendered image should result, for the same overall number of polygons being rendered. We present both a theoretical basis and a complete design for a visual attention-based level of detail management technique. We also present some preliminary assessment of output from the system. Applications for this technique are expected to be found in the areas of entertainment, visualisation and simulation.

Abstract not found

Abstract—Detecting critical changes of environments while driving is an important task in driver assistance systems. In this paper, a computational model motivated by human cognitive processing and selective attention is proposed for this purpose. The computational model consists of three major components, referred to as the sensory, perceptual, and conceptual analyzers. The sensory analyzer extracts temporal and spatial information from video sequences. The extracted information serves as the input stimuli to a spatiotemporal attention (STA) neural network embedded in the perceptual analyzer. If consistent stimuli repeatedly innervate the neural network, a focus of attention will be established in the network. The attention pattern associated with the focus, together with the location and direction of motion of the pattern, form what we call a categorical feature. Based on this feature, the class of the attention pattern and, in turn, the change in driving environment corresponding to the class are determined using a configurable adaptive resonance theory (CART) neural network, which is placed in the conceptual analyzer. Various changes in driving environment, both in daytime and at night, have been tested. The experimental results demonstrated the feasibilities of both the proposed computational model and the change detection system. Index Terms—Cognitive model, configurable adaptive resonance theory (CART) neural network, driver assistance system, sensory, perceptual, and conceptual analyzers, spatiotemporal attention (STA) neural network, system to detect change in driving environment. I.

Abstract. Attention and memory are very closely related and their aim is to simplify the acquired data into an intelligent structured data set. Two main points are discussed in this paper. The first one is the presentation of a novel visual attention model for still images which includes both a bottom-up and a top-down approach. The bottom-up model is based on structures rarity within the image during the forgetting process. The top-down information uses mousetracking experiments to build models of a global behavior for a given kind of image. The proposed models assessment is achieved on a 91-image database. The second interesting point is that the relative importance of bottom-up and top-down attention depends on the specificity of each image. In unknown images the bottom-up influence remains very important while in specific kinds of images (like web sites) top-down attention brings the major information.

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, thirteen 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.

: This paper presents a visual attention module driven by static and dynamic scene features controlling the gaze shifts of an active vision system. A preattentive processing unit computes several static features, like orientation and color, and a dynamic feature, motion. We distinguish two further processing modes of our active vision system: the hypothesis validation mode and the tracking mode. In the hypothesis validation mode the bottom-up static features and top-down information of the presence of an object are combined to guide the recognition process. The preattentive dynamic feature analysis represents an alert system. By the presence of motion it interrupts the hypothesis validation mode and triggers the tracking mode. Several experimental results are presented. Keywords: visual attention, motion detection, active vision 1 Introduction Visual attention has become a powerful and widely used tool in active vision systems (e.g. [Bajcsy 1988], [Ballard 1991]). It reduces the amoun...

this paper are only little affected by the special choice of neural dynamics as long as the dynamic leads to a synchronization between the neurons. 6.2 B) Connectivity parameters