Object Detection Through Exploration With A Foveated Visual Field

We present a foveated object detector (FOD) as a biologically-inspired alternative to the sliding window (SW) approach which is the dominant method of search in computer vision object detection. Similar to the human visual system, the FOD has higher resolution at the fovea and lower resolution at the visual periphery. Consequently, more computational resources are allocated at the fovea and relatively fewer at the periphery. The FOD processes the entire scene, uses retino-specific object detection classifiers to guide eye movements, aligns its fovea with regions of interest in the input image and integrates observations across multiple fixations. Our approach combines modern object detectors from computer vision with a recent model of peripheral pooling regions found at the V1 layer of the human visual system. We assessed various eye movement strategies on the PASCAL VOC 2007 dataset and show that the FOD performs on par with the SW detector while bringing significant computational cost savings.Comment: An extended version of this manuscript was published in PLOS Computational Biology (October 2017) at https://doi.org/10.1371/journal.pcbi.100574

Predicting complexity perception of real world images

The aim of this work is to predict the complexity perception of real world images.We propose a new complexity measure where different image features, based on spatial, frequency and color properties are linearly combined. In order to find the optimal set of weighting coefficients we have applied a Particle Swarm Optimization. The optimal linear combination is the one that best fits the subjective data obtained in an experiment where observers evaluate the complexity of real world scenes on a web-based interface. To test the proposed complexity measure we have performed a second experiment on a different database of real world scenes, where the linear combination previously obtained is correlated with the new subjective data. Our complexity measure outperforms not only each single visual feature but also two visual clutter measures frequently used in the literature to predict image complexity. To analyze the usefulness of our proposal, we have also considered two different sets of stimuli composed of real texture images. Tuning the parameters of our measure for this kind of stimuli, we have obtained a linear combination that still outperforms the single measures. In conclusion our measure, properly tuned, can predict complexity perception of different kind of images

General models in min-max continous location

In this paper, a class of min-max continuous location problems is discussed. After giving a complete characterization of th stationary points, we propose a simple central and deep-cut ellipsoid algorithm to solve these problems for the quasiconvex case. Moreover, an elementary convergence proof of this algorithm and some computational results are presented

Integrating Mechanisms of Visual Guidance in Naturalistic Language Production

Situated language production requires the integration of visual attention and lin-guistic processing. Previous work has not conclusively disentangled the role of perceptual scene information and structural sentence information in guiding visual attention. In this paper, we present an eye-tracking study that demonstrates that three types of guidance, perceptual, conceptual, and structural, interact to control visual attention. In a cued language production experiment, we manipulate percep-tual (scene clutter) and conceptual guidance (cue animacy), and measure structural guidance (syntactic complexity of the utterance). Analysis of the time course of lan-guage production, before and during speech, reveals that all three forms of guidance affect the complexity of visual responses, quantified in terms of the entropy of atten-tional landscapes and the turbulence of scan patterns, especially during speech. We find that perceptual and conceptual guidance mediate the distribution of attention in the scene, whereas structural guidance closely relates to scan-pattern complexity. Furthermore, the eye-voice span of the cued object and its perceptual competitor are similar; its latency mediated by both perceptual and structural guidance. These results rule out a strict interpretation of structural guidance as the single dominant form of visual guidance in situated language production. Rather, the phase of the task and the associated demands of cross-modal cognitive processing determine the mechanisms that guide attention

A bridge between worlds: understanding network structure to understand change strategy

A number of scholars are exploring district and site relations in organizational change efforts in the larger policy context of No Child Left Behind. These studies suggest the importance of the central office as a support to the work of reform and offer strategies for building relations between district offices and sites in order to implement and sustain change efforts. What is frequently overlooked in these studies is that organizational change efforts are often socially constructed. Therefore, examining the underlying social networks may provide insight into structures that support or constrain efforts at change. This exploratory case study uses social network analysis and interviews to examine the communication and knowledge network structures of central office and site leaders in an ‘in need of improvement’ district facing sanctions under No Child Left Behind. Findings indicate sparse ties among and between school site and central office administrators, as well as a centralized network structure that may constrain the exchange of complex information and ultimately inhibit efforts at change