Computing visual attention from scene depth

Ouerhani, Nabil; Hügli, Heinz

doi:10.1109/icpr.2000.905356

Cited by 101 publications

(59 citation statements)

References 4 publications

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“…Jost et al [10] run similar experiments on a much larger number of test persons and could measure the quantitative improvement of the model when chromaticity channels are added to the conventional monochrome video channels. Visual attention in 3D scenes was first considered in [11] and recently, a visual attention model for 3D was quantitatively analyzed in presence of various synthetic and natural scenes [12]. This paper presents a more global analysis, where the performance of a family of visual attention models in presence of 3D color scenes is evaluated.…”

Section: Introductionmentioning

confidence: 99%

Model Performance for Visual Attention in Real 3D Color Scenes

Hügli

Jost

Ouerhani

2005

Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach

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Abstract. Visual attention is the ability of a vision system, be it biological or artificial, to rapidly detect potentially relevant parts of a visual scene. The saliency-based model of visual attention is widely used to simulate this visual mechanism on computers. Though biologically inspired, this model has been only partially assessed in comparison with human behavior. The research described in this paper aims at assessing its performance in the case of natural scenes, i.e. real 3D color scenes. The evaluation is based on the comparison of computer saliency maps with human visual attention derived from fixation patterns while subjects are looking at the scenes. The paper presents a number of experiments involving natural scenes and computer models differing by their capacity to deal with color and depth. The results point on the large range of scene specific performance variations and provide typical quantitative performance values for models of different complexity.

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Section: Introductionmentioning

confidence: 99%

Model Performance for Visual Attention in Real 3D Color Scenes

Hügli

Jost

Ouerhani

2005

Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach

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“…This is a task-dependent use of depth information and not a bottom up integration of depth features in the computation of saliency. Ouerhani and Hügli extend the approach of Itti et al from [12] by adding a conspicuity map built directly from the depth map [20]. This approach treats depth as just another channel, along with color and other cues.…”

Section: Saliency Computation With Depthmentioning

confidence: 99%

An In Depth View of Saliency

Ciptadi¹,

Hermans²,

Rehg³

2013

Procedings of the British Machine Vision Conference 2013

117

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Visual saliency is a computational process that identifies important locations and structure in the visual field. Most current methods for saliency rely on cues such as color and texture while ignoring depth information, which is known to be an important saliency cue in the human cognitive system. We propose a novel computational model of visual saliency which incorporates depth information. We compare our approach to several state of the art visual saliency methods and we introduce a method for saliency based segmentation of generic objects. We demonstrate that by explicitly constructing 3D layout and shape features from depth measurements, we can obtain better performance than methods which treat the depth map as just another image channel. Our method requires no learning and can operate on scenes for which the system has no previous knowledge. We conduct object segmentation experiments on a new dataset of registered RGB-D images captured on a mobile-manipulator robot.

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“…Maki et al [40] took particular note of the feature whereby regions that are closer in terms of depth are more salient [41], and they proposed a model for estimating human visual attention that integrates several features obtained from binocular cameras such as motion and disparity. Ouerhani and Hugli [42] directly incorporated a depth feature taken from a range finder into the Itti saliency map model. Jeong et al [43] proposed a model for computing saliency maps from every image taken from binocular cameras and correcting the maps with the help of disparity information in highly salient regions.…”

Section: Fit-based Computational Modelsmentioning

confidence: 99%

Computational Models of Human Visual Attention and Their Implementations: A Survey

Kimura

Yonetani

Hirayama

2013

IEICE Trans. Inf. & Syst.

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SUMMARYWe humans are easily able to instantaneously detect the regions in a visual scene that are most likely to contain something of interest. Exploiting this pre-selection mechanism called visual attention for image and video processing systems would make them more sophisticated and therefore more useful. This paper briefly describes various computational models of human visual attention and their development, as well as related psychophysical findings. In particular, our objective is to carefully distinguish several types of studies related to human visual attention and saliency as a measure of attentiveness, and to provide a taxonomy from several viewpoints such as the main objective, the use of additional cues and mathematical principles. This survey finally discusses possible future directions for research into human visual attention and saliency computation. key words : human visual attention, computational model, saliency, bottom-up, top-down MotivationDeveloping sophisticated algorithms for detecting and recognizing something like objects from a given image and video has been a long distance challenge in pattern recognition and computer vision research fields. In fact, a huge number of studies, techniques and theories related to object detection and recognition have already been developed. In particular, several methods for detecting certain specific categories of objects such as human bodies and human faces have already been put to practical use in for example surveillance, authentication and the human-centric enhancement of image quality, with the best possible use of the prior knowledge of target objects (human bodies and faces) [1], [2]. However, generic object detection and recognition without any constraints as regards the target objects has remained major challenge, because (1) various kinds of objects might constitute the targets and (2) target objects in the same category might have different appearances due to variations of instances in a specific category, illumination changes and so on. † † The author is with the Graduate School of Informatics, Kyoto University, Kyoto-shi, 606-8501 Japan.† † † The author is with the Graduate School of Information Science, Nagoya University, Nagoya-shi, 464-8603 Japan.a) E-mail: akisato@ieee.org b) E-mail: yonetani@vision.kuee.kyoto-u.ac.jp c) E-mail: hirayama@is.nagoya-u.ac.jp DOI: 10.1587/transinf.E96.D.562 On the other hand, human beings seem to be able to detect various kinds of objects without any thought or effort. For example, from Fig. 1 left, we can easily and instantly detect a red car, a blue traffic sign and a broad white line. Visual attention [3] is considered to play an important role in achieving this function. Visual attention is one of the built-in mechanisms of the human visual system that quickly selects regions in a visual scene, which are most likely to contain items of interest. Such a pre-selection mechanism focusing only on relevant data would be essential in enabling computers to undertake subsequent processing such as generic o...

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Computing visual attention from scene depth

Abstract: Visual attention is the ability to rapidly detect the inter

Cited by 101 publications

References 4 publications

Model Performance for Visual Attention in Real 3D Color Scenes

Model Performance for Visual Attention in Real 3D Color Scenes

An In Depth View of Saliency

Computational Models of Human Visual Attention and Their Implementations: A Survey

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