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Quaternion-based Spectral Saliency Detection for Eye Fixation Prediction
"... Abstract In recent years, several authors have reported that spectral saliency detection methods provide state-of-the-art performance in predicting human gaze in images (see, e.g., [1 3]). We systematically integrate and evaluate quaternion DCT- and FFT-based spectral saliency detection [3,4], weigh ..."
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Abstract In recent years, several authors have reported that spectral saliency detection methods provide state-of-the-art performance in predicting human gaze in images (see, e.g., [1 3]). We systematically integrate and evaluate quaternion DCT- and FFT-based spectral saliency detection [3,4], weighted quaternion color space components [5], and the use of multiple resolutions [1]. Furthermore, we propose the use of the eigenaxes and eigenangles for spectral saliency models that are based on the quaternion Fourier transform. We demonstrate the outstanding performance on the Bruce-Tsotsos (Toronto), Judd (MIT), and Kootstra-Schomacker eye-tracking data sets. 1
Multimodal Saliency-based Attention: A Lazy Robot’s Approach
"... Abstract — We extend our work on an integrated object-based system for saliency-driven overt attention and knowledge-driven object analysis. We present how we can reduce the amount of necessary head movement during scene analysis while still focusing all salient proto-objects in an order that strong ..."
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Abstract — We extend our work on an integrated object-based system for saliency-driven overt attention and knowledge-driven object analysis. We present how we can reduce the amount of necessary head movement during scene analysis while still focusing all salient proto-objects in an order that strongly favors proto-objects with a higher saliency. Furthermore, we integrated motion saliency and as a consequence adaptive predictive gaze control to allow for efficient gazing behavior on the ARMAR-III robot head. To evaluate our approach, we first collected a new data set that incorporates two robotic platforms, three scenarios, and different scene complexities. Second, we introduce measures for the effectiveness of active overt attention mechanisms in terms of saliency cumulation and required head motion. This way, we are able to objectively demonstrate the effectiveness of the proposed multicriterial focus of attention selection. Index Terms — active perception, saliency-based overt attention, and scene exploration I.
A modular audio-visual scene analysis and attention system for humanoid robots
- in Proc. 43rd Int. Symp. Robotics (ISR
, 2012
"... Abstract—We present an audio-visual scene analysis system, which is implemented and evaluated on the ARMAR-III robot head. The modular design allows a fast integration of new algorithms and adaptation on new hardware. Further benefits are automatic module dependency checks and determination of the e ..."
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Abstract—We present an audio-visual scene analysis system, which is implemented and evaluated on the ARMAR-III robot head. The modular design allows a fast integration of new algorithms and adaptation on new hardware. Further benefits are automatic module dependency checks and determination of the execution order. The integrated world model manages and serves the acquired data for all modules in a consistent way. The system has a state of the art performance in localization, tracking and classification of persons as well as exploration of whole scenes and unknown items. We use multimodal proto-objects to model and analyze salient stimuli in the environment of the robot to realize the robots ’ attention.
“Wow! ” BayesianSurprise forSalientAcousticEventDetection
"... We propose the use of Bayesian surprise to detect arbitrary, salient acoustic events. We use Gaussian or Gamma distributions to model the spectrogram distribution and use the Kullback-Leibler divergence of the posterior and prior distribution to calculate how “unexpected ” and thus surprising newly ..."
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We propose the use of Bayesian surprise to detect arbitrary, salient acoustic events. We use Gaussian or Gamma distributions to model the spectrogram distribution and use the Kullback-Leibler divergence of the posterior and prior distribution to calculate how “unexpected ” and thus surprising newly observed audio samples are. This way, we efficiently detect arbitrary surprising/salient acoustic events.
Salient Pattern Detection using W2 on Multivariate Normal Distributions
"... Abstract. Saliency is an attribute that is not included in an object it-self, but arises from complex relations to the scene. Common belief in neuroscience is that objects are eye-catching if they exhibit an anomaly in some basic feature of human perception. This enables detection of object-like str ..."
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Abstract. Saliency is an attribute that is not included in an object it-self, but arises from complex relations to the scene. Common belief in neuroscience is that objects are eye-catching if they exhibit an anomaly in some basic feature of human perception. This enables detection of object-like structures without prior knowledge. In this paper, we intro-duce an approach that models these object-to-scene relations based on probability theory. We rely on the conventional structure of cognitive visual attention systems, measuring saliency by local center to surround differences on several basic feature cues and multiple scales, but innovate how to model appearance and to quantify differences. Therefore, we pro-pose an efficient procedure to compute ML-estimates for (multivariate) normal distributions of local feature statistics. Reducing feature statis-tics to Gaussians facilitates a closed-form solution for the W2-distance (Wasserstein metric based on the Euclidean norm) between a center and a surround distribution. On a widely used benchmark for salient object detection, our approach, named CoDi-Saliency (for Continuous Distri-butions), outperformed nine state-of-the-art saliency detectors in terms of precision and recall. 1
Attentive Robots
"... Selective attention is a useful concept for human perception — this was elaborated in the previous chapters of this book. But why is it of interest also to machines, especially to robots? The reason is that robots share many requirements with humans: First, robots have to use limited processing reso ..."
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Selective attention is a useful concept for human perception — this was elaborated in the previous chapters of this book. But why is it of interest also to machines, especially to robots? The reason is that robots share many requirements with humans: First, robots have to use limited processing resources to process an overwhelming
