Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. In this paper, we introduce a method for salient region detection that outputs full resolution saliency maps with well-defined boundaries of salient objects. These boundaries are preserved by retaining substantially more frequency content from the original image than other existing techniques. Our method exploits features of color and luminance, is simple to implement, and is computationally efficient. We compare our algorithm to five state-of-the-art salient region detection methods with a frequency domain analysis, ground truth, and a salient object segmentation application. Our method outperforms the five algorithms both on the ground-truth evaluation and on the segmentation task by achieving both higher precision and better recall.

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Frequency-tuned salient region detection

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, 13 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.

State-of-the-Art in Visual Attention Modeling

When natural scenes are viewed, a multitude of objects that are stable in their environments are brought in and out of view by eye movements. The posterior parietal cortex is crucial for the analysis of space, visual attention and movement 1 . Neurons in one of its subdivisions, the lateral intraparietal area (LIP), have visual responses to stimuli appearing abruptly at particular retinal locations (their receptive fields)2. We have tested the responses of LIP neurons to stimuli that entered their receptive field by saccades. Neurons had little or no response to stimuli brought into their receptive field by saccades, unless the stimuli were behaviourally significant. We established behavioural significance in two ways: either by making a stable stimulus task-relevant, or by taking advantage of the attentional attraction of an abruptly appearing stimulus. Our results show that under ordinary circumstances the entire visual world is only weakly represented in LIP. The visual representation in LIP is sparse, with only the most salient or behaviourally relevant objects being strongly represented.

The representation of visual salience in monkey parietal cortex

Observers show a marked tendency to fixate the center of the screen when viewing scenes on computer monitors. This is often assumed to arise because image features tend to be biased toward the center of natural images and fixations are correlated with image features. A common alternative explanation is that experiments typically use a central pre-trial fixation marker, and observers tend to make small amplitude saccades. In the present study, the central bias was explored by dividing images post hoc according to biases in their image feature distributions. Central biases could not be explained by motor biases for making small saccades and were found irrespective of the distribution of image features. When the scene appeared, the initial response was to orient to the center of the screen. Following this, fixation distributions did not vary with image feature distributions when freely viewing scenes. When searching the scenes, fixation distributions shifted slightly toward the distribution of features in the image, primarily during the first few fixations after the initial orienting response. The endurance of the central fixation bias irrespective of the distribution of image features, or the observer's task, implies one of three possible explanations: First, the center of the screen may be an optimal location for early information processing of the scene. Second, it may simply be that the center of the screen is a convenient location from which to start oculomotor exploration of the scene. Third, it may be that the central bias reflects a tendency to re-center the eye in its orbit.

The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions.

Detection of salient image regions is useful for applications like image segmentation, adaptive compression, and region-based image retrieval. In this paper we present a novel method to determine salient regions in images using low-level features of luminance and color. The method is fast, easy to implement and generates high quality saliency maps of the same size and resolution as the input image. We demonstrate the use of the algorithm in the segmentation of semantically meaningful whole objects from digital images.

/pdf/salient-region-detection-and-segmentation-1uw94m4inb.pdf

Salient region detection and segmentation

Based on concepts of the human visual system, computational visual attention systems aim to detect regions of interest in images. Psychologists, neurobiologists, and computer scientists have investigated visual attention thoroughly during the last decades and profited considerably from each other. However, the interdisciplinarity of the topic holds not only benefits but also difficulties: Concepts of other fields are usually hard to access due to differences in vocabulary and lack of knowledge of the relevant literature. This article aims to bridge this gap and bring together concepts and ideas from the different research areas. It provides an extensive survey of the grounding psychological and biological research on visual attention as well as the current state of the art of computational systems. Furthermore, it presents a broad range of applications of computational attention systems in fields like computer vision, cognitive systems, and mobile robotics. We conclude with a discussion on the limitations and open questions in the field.

/pdf/computational-visual-attention-systems-and-their-cognitive-3c1msw6bdy.pdf

Computational visual attention systems and their cognitive foundations: A survey

Systems which simulate human visual attention are suited to quickly find regions of interest in images and are an interesting prepro- cessing method for a variety of applications. However, the scale-invariant computation of features in several feature dimensions is still too time con- suming to be applied to video streams at frame rate which is necessary for many practical applications. As a consequence, current implementa- tions of attention systems often make compromises between the accuracy and speed of computing a focus of attention in order to reduce the com- putation time. In this paper, we present a method for achieving fast, real-time capable system performance with high accuracy. The method involves smart feature computation techniques based on integral images. An experimental validation of the speed gain of our attention system VO- CUS is provided, too. The real-time capability of the optimized VOCUS system has already been demonstrated in robotic applications.

/pdf/a-real-time-visual-attention-system-using-integral-images-2kehyim12u.pdf

A Real-time Visual Attention System Using Integral Images

Developed societies have a high level of preparedness for natural or man-made disasters. But such incidents cannot be completely prevented, and when an incident like an earthquake or an accident in a chemical or nuclear plant hits a populated area, rescue teams need to be employed. In such situations it is a necessity for rescue teams to get a quick overview of the situation in order to identify possible locations of victims that need to be rescued and dangerous locations that need to be secured. Rescue forces must operate quickly in order to save lives, and they often need to operate in dangerous environments. Hence, robot-supported systems are increasingly used to support and accelerate search operations. The objective of the SENEKA concept is to network the various robots and sensor systems used by first responders in order to make the search for victims and survivors more quick and efficient. SENEKA targets the integration of the robot-sensor network into the operation procedures of the rescue teams. The aim of this paper is to inform on the goals and first research results of the ongoing joint research project SENEKA.

SENEKA - sensor network with mobile robots for disaster management

Embodiment of Attention.- The Embodied Dynamics of Emotion, Appraisal and Attention.- The Role of Attention in Creating a Cognitive System.- The Influence of the Body and Action on Spatial Attention.- Abstraction Level Regulation of Cognitive Processing Through Emotion-Based Attention Mechanisms.- Embodied Active Vision in Language Learning and Grounding.- Language Label Learning for Visual Concepts Discovered from Video Sequences.- Cognitive Control of Attention.- Learning to Attend - From Bottom-Up to Top-Down.- An Attentional System Combining Top-Down and Bottom-Up Influences.- The Selective Attention for Identification Model (SAIM): Simulating Visual Search in Natural Colour Images.- A Bayesian Approach to Attention Control and Concept Abstraction.- Modeling of Saliency and Visual Search.- An Information Theoretic Model of Saliency and Visual Search.- An Experimental Comparison of Three Guiding Principles for the Detection of Salient Image Locations: Stability, Complexity, and Discrimination.- A Proto-object Based Visual Attention Model.- Context Driven Focus of Attention for Object Detection.- Color Saliency and Inhibition Using Static and Dynamic Scenes in Region Based Visual Attention.- I See What You See: Eye Movements in Real-World Scenes Are Affected by Perceived Direction of Gaze.- Sequential Attention.- Selective Attention in the Learning of Viewpoint and Position Invariance.- Generating Sequence of Eye Fixations Using Decision-Theoretic Attention Model.- Reinforcement Learning for Decision Making in Sequential Visual Attention.- Biologically Inspired Framework for Learning and Abstract Representation of Attention Control.- Biological Aspects of Attention.- Modeling the Dynamics of Feature Binding During Object-Selective Attention.- The Spiking Search over Time and Space Model (sSoTS): Simulating Dual Task Experiments and the Temporal Dynamics of Preview Search.- On the Role of Dopamine in Cognitive Vision.- Differences and Interactions Between Cerebral Hemispheres When Processing Ambiguous Words.- Attention in Early Vision: Some Psychophysical Insights.- Auditory Gist Perception: An Alternative to Attentional Selection of Auditory Streams?.- Applications of Attentive Vision.- Simultaneous Robot Localization and Mapping Based on a Visual Attention System.- Autonomous Attentive Exploration in Search and Rescue Scenarios.- Attention-Based Landmark Selection in Autonomous Robotics.- Simulation and Formal Analysis of Visual Attention in Cognitive Systems.- Region-Oriented Visual Attention Framework for Activity Detection.- Autonomous Attentive Exploration in Search and Rescue Scenarios.

Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint: 4th International Workshop on Attention in Cognitive Systems, WAPCV 2007 Hyderabad, India, January 8, 2007 Revised Selected Papers

https://www.volksbot.de/surmann/papers/frintrop_etal_cviu05.pdf

Erich Rome

Papers

Computational visual attention systems and their cognitive foundations: A survey

A Real-time Visual Attention System Using Integral Images

SENEKA - sensor network with mobile robots for disaster management

Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint: 4th International Workshop on Attention in Cognitive Systems, WAPCV 2007 Hyderabad, India, January 8, 2007 Revised Selected Papers

A Bimodal Laser-Based Attention System