this paper a statistical framework for incorporating contextual information in the search task is proposed

We present a method for automatically determining the perceptual importance of different regions in an image. The algorithm is based on human visual attention and eye movement characteristics. Several features known to influence human visual attention are evaluated for each region of a segmented image to produce an importance value for each factor and region. These are combined to produce an Importance Map, which classifies each region of the image in relation to its perceptual importance. Results shown indicate that the calculated Importance Maps correlate well with human perception of visually important regions. The Importance Maps can be used in a variety of applications, including compression, machine vision, and image databases. Our technique is computationally efficient and flexible, and can easily be extended to specific applications. 1.

The human visual system (HVS) is highly space-variant in sampling, coding, processing, and understanding. The spatial resolution of the HVS is highest around the point of fixation (foveation point) and decreases rapidly with increasing eccentricity. By taking advantage of this fact, it is possible to remove considerable high-frequency information redundancy from the peripheral regions and still reconstruct a perceptually good quality image. Great success has been obtained recently by a class of embedded wavelet image coding algorithms, such as the embedded zerotree wavelet (EZW) and the set partitioning in hierarchical trees (SPIHT) algorithms. Embedded wavelet coding not only provides very good compression performance, but also has the property that the bitstream can be truncated at any point and still be decoded to recreate a reasonably good quality image. In this

We show that complex visual tasks, such as position and size invariant shape recognition and navigation in the environment, can be tackled with simple architectures generated by a co-evolutionary process of active vision and feature selection. Behavioral machines equipped with primitive vision systems and direct pathways between visual and motor neurons are evolved while freely interacting with their environments. We describe the application of this methodology in three sets of experiments, namely shape discrimination, car driving, and robot navigation. We show that these systems develop sensitivity to a number of oriented, retinotopic, visual features oriented edges, corners, height – and a behavioral repertoire to locate, bring, and keep these features in sensitive regions of the vision system, resembling strategies observed in simple insects.

in dynamic scenes

Researchers have long argued that an attentional mechanism is required to perform many vision tasks. This thesis includes an implementation and evaluation of an attentional prototype as it applies to early and intermediate levels of visual computation. The model is composed of a processing hierarchy and an attention beam that traverses the hierarchy, passing through the regions of greatest interest and inhibiting the regions that are not relevant. The amount of computation required is crucial to the derivation of this model. As a result, this scheme "scales up" extremely well with the size of the problem and in fact scales to human-size problems. In addition, the domain of input to the prototype is not limited to visual stimuli, making this system applicable to many different sensory modalities. Dimensions of attention such as localizing spatial regions of interset and ordering their importance are addressed, whereas other aspects of attention such as the role of task guidance are not....

Based on an information theoretical approach, we investigate feature selection processes in saccadic object and scene analysis. Saccadic eye movements of human observers are recorded for a variety of natural and artificial test images. These experimental data are used for a statistical evaluation of the fixated image regions. Analysis of second-order statistics indicates that regions with higher spatial variance have a higher probability to be fixated, but no significant differences beyond these variance effects could be found at the level of power spectra. By contrast, an investigation with higher-order statistics, as reflected in the bispectral density, yielded clear structural differences between the image regions selected by saccadic eye movements as opposed to regions selected by a random process. These results indicate that nonredundant, intrinsically two-dimensional image features like curved lines and edges, occlusions, isolated spots, etc. play an important role in the saccadic selection process which must be integrated with top-down knowledge to fully predict object and scene analysis by human observers.

this article we present a hardware model of a selective attention mechanism implemented on a very largescale integration (VLSI) chip, using analog neuromorphic circuits. The chip exploits a spike-based representation to receive, process, and transmit signals. It can be used as a transceiver module for building multichip neuromorphic vision systems. We describe the circuits that carry out the main processing stages of the selective attention mechanism and provide experimental data for each circuit. We demonstrate the expected behavior of the model at the system level by stimulating the chip with both arti#cially generated control signals and signals obtained from a saliency map, computed from an image containing several salient features

Observers inspected normal, high quality colour displays of everyday visual scenes while their eye movements were recorded. A large display change occurred each time an eye blink occurred. Display changes could either involve “Central Interest” or “Marginal Interest” locations, as determined from descriptions obtained from independent judges in a prior pilot experiment. Visual salience, as determined by luminance, colour, and position of the Central and Marginal Interest changes were equalized. The results obtained were very similar to those obtained in prior experiments showing failure to detect changes occurring simultaneously with saccades, flicker, or “mudsplashes” in the visual scene: Many changes were very hard to detect, and Marginal Interest changes were harder to detect than Central Interest changes. Analysis of eye movements showed, as expected, that the probability of detecting a change depended on the eye’s distance from the change location. However a surprising finding was that both for Central and Marginal Interest changes, even when observers were directly fixating the change locations (within 1 degree), more than 40 % of the time they still failed to see the changes. It seems that looking at something does not guarantee you “see” it.

this document. I thank my family for the support and encouragement they have always given me. I thank my parents Les and Molly for their love and guidance. I thank my siblings, Jean, Joe, and John, or teaching me to love in the midst o stfie. I thank Robert Dodd or his continual support and encouragement and his calm unbiased eye