S. Baluja, and D. Pomerleau, "Expectation-based selective attention for visual monitoring and control of a robot vehicle," Robotics and Autonomous Systems, 22, pp. 329-344, 1997. Home/Search Document Details and Download
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Object-based Visual Attention for Computer Vision - Sun, Fisher (1 citation) (Correct)
....his model tends to produce later selection of attention and is partly similar to Duncan s integrated competition hypothesis [23] which is an object based attention theory and di#erent to the above models. Some researchers have exploited neural network approaches to model selective attention. In [2,3], the saliency maps which are derived from the residual error between the actual input and the expected input are used to create the taskspecific expectations for guiding the focus of attention. Kazanovich and Borisyuk proposed a neural network of phase oscillators with a central oscillator (CO) ....
S. Baluja, and D. Pomerleau, "Expectation-based selective attention for visual monitoring and control of a robot vehicle," Robotics and Autonomous Systems, 22, pp. 329-344, 1997.
Anticipatory Behavior: Exploiting Knowledge about the.. - Butz, Sigaud.. (2003) (1 citation) (Correct)
.... made in arti cial intelligence to integrate anticipatory mechanisms into arti cial learning systems in the framework of reinforcement learning [27, 16] learning classi er systems (as online generalizing reinforcement learners) and related systems [24, 4, 12, 31] as well as neural networks [8, 11, 28, 2]. So far, research in arti cial intelligence has included anticipatory mechanisms wrapped in model learning systems such as the model based reinforcement learning approach. Anticipatory processes were never analyzed on their own. This book suggests the investigation of the characteristic ....
Baluja, S., Pomerleau, D.A.: Expectation-based selective attention for visual monitoring and control of a robot vehicle. Robotics and Autonomous Systems 22 (1997) 329-344
Internal Models and Anticipations in Adaptive Learning.. - Butz, Sigaud, Gérard (2 citations) (Correct)
....has actually happened. As a consequence, learning to predict thanks to a back propagation algorithm is straight forward. Baluja s Attention Mechanism Baluja and Pomerleau provide an interesting anticipatory implementation of visual attention in the form of a neural network with one hidden layer [2, 3]. The mechanism is based on the ideas of visual attention modeling in [28] The system is for example able to learn to follow a line by the means of the network. Performance of the net is improved by adding another output layer, connected to the hidden layer, which learns to predict successive ....
Baluja, S., Pomerleau, D.A.: Expectation-based selective attention for visual monitoring and control of a robot vehicle. Robotics and Autonomous Systems 22 (1997) 329-344
Learning to Detect Salient Objects in Natural Scenes Using.. - Itti, Koch (1999) (3 citations) (Correct)
....task independent form of attention we study here [ Rarasuraman, 1998 ] We will disregard this component in this study. Several models have been proposed to functionally account for visual attention in primates [ Olshausen et al. 1993, Wolfe, 1994, Milanese et al. 1995, Tsotsos et al. 1995, Baluja and Pomerleau, 1997, Niebur and Koch, 1998, Itti et al. 1998 ] These models share similar general architecture. Multi scale topographic feature maps detect local spatial discontinuities in intensity, color, orientation or op Attended location attended location Inhibition of Feature maps intensity ....
S. Baluja and D.A. Pomerleau, "Expectation-based Selective Attention for Visual Monitoring and Control of a Robot Vehicle," Robotics and Autonomous Systems, 22(3-4):329--344, 1997.
A Model of Saliency-based Visual Attention for Rapid Scene Analysis - Itti (1998) (96 citations) (Correct)
....of specific temporal patterns of activity, under both top down (task dependent) and bottom up (scene dependent) control [3] 2] 1] The model proposed here (Fig. 1) builds on a second biologically plausible architecture, proposed by Koch and Ullman [4] and at the basis of several models [5] [6]. It is related to the so called feature integration theory , proposed to explain human visual search strategies [7] Visual input is first decomposed into a set of topographic feature maps. Different spatial locations then compete for saliency within each map, such that only locations which ....
S. Baluja and D.A. Pomerleau, "Expectation-based Selective Attention for Visual Monitoring and Control of a Robot Vehicle," Robotics and Autonomous Systems, vol. 22, no. 3-4, pp. 329--344, Dec. 1997.
Dynamic Relevance: Vision-Based Focus of Attention using .. - Shumeet Baluja.. (1997) (6 citations) Self-citation (Baluja Pomerleau) (Correct)
.... auto encoding networks (networks in which the output it trained to reproduce the input layer [3, 14] and principal components analysis, the irrelevant portions of the input are not encoded in the hidden units activations, and the inputs that are irrelevant to the task cannot be reconstructed 1 [4, 7]. A notion of time is necessary in order to focus attention in future frames. Instead of attempting to reconstruct the current input, the network is trained to predict the next input (this corresponds to changing the subscript of the reconstructed inputs in Figure 2 from t to t 1 ) The ....
....of attention. Once a few of the important features are determined, the system bootstraps itself. 3. Lane Marker Tracking In this section, we briefly summarize the empirical results with an autonomous road following system which uses expectation to dynamically assess the relevancy of the inputs [4, 7]. The goal of autonomous road following is to control a robot vehicle by analyzing the image of the road ahead. The direction of travel should be chosen based on the location of features like lane markings and road edges. This is a difficult task since the scene is often cluttered with extraneous ....
S. Baluja and D. Pomerleau. Expectation-based selective attention for visual monitoring and control of a robot vehicle. Robotics and Autonomous Systems Journal, To appear, 1997.
Object-based Visual Attention for Computer Vision - Sun, Fisher (2002) (1 citation) (Correct)
No context found.
S. Baluja, and D. Pomerleau, "Expectation-based selective attention for visual monitoring and control of a robot vehicle," Robotics and Autonomous Systems, 22, pp. 329-344, 1997.
Hierarchical Object-Based Visual Attention for Machine Vision - Sun (2003) (Correct)
No context found.
S. Baluja and D. Pomerleau, "Expectation-based selective attention for visual monitoring and control of a robot vehicle," Robotics and Autonomous Systems, 22, pp. 329-344, 1997.
A Neuromorphic VLSI Device for Implementing 2-D Selective.. - Indiveri (2001) (Correct)
No context found.
S. Baluja and D. A. Pomerleau, "Expectation-based selective attention for the visual monitoring and control of a robot vehicle," Robot. Autonomous Syst. J., vol. 22, pp. 329--344, 1997.
A Neuromorphic Selective Attention Architecture With.. - Bartolozzi, Indiveri (2004) (Correct)
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Baluja, S., and Pomerleau, D., 1997. "Expectation-based selective attention for the visual monitoring and control of a robot vehicle". Robotics and Autonomous Systems Journal, 22 , pp. 329--344.
Active Vision Using an Analog VLSI - Model Of Selective (Correct)
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S. Baluja and D. A. Pomerleau, "Expectation-based selective attention for the visual monitoring and control of a robot vehicle," Robot. Autom. Syst., vol. 22, pp. 329--344, 1997.
Computational Perceptual Attention - Hewett (2001) (Correct)
No context found.
Baluja, S. and D. A. Pomerleau (1997). Expectation-Based Selective Attention for Visual Monitoring and Control of a Robot Vehicle, Robotics and Autonomous Systems, 22 (3-4):329-344.
A Comparison of Feature Combination Strategies for.. - Itti, Koch (1999) (10 citations) (Correct)
No context found.
S. Baluja and D. A. Pomerleau, "Expectation-based selective attention for visual monitoring and control of a robot vehicle," Robotics Auton Sys 22(3-4), pp. 329--44, 1997.
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