Biologically motivated vergence control system using human-like selective attention model

Choi, Se-Hyu; Jung, Bum-Soo; Ban, Sang-Woo; Niitsuma, Hirotaka; Lee, Minho

doi:10.1016/j.neucom.2004.12.012

Cited by 45 publications

(30 citation statements)

References 16 publications

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“…We regard a localized area with the highest intensity values in the dynamic SM as most salient regions to be analyzed for moving obstacle identification. After calculating a suitable size of candidates areas based on the entropy maximization, we use a mask-off operation to prevent duplicate selection of an interesting area in a posterior visual scan path [15,16].…”

Section: Dynamic Obstacle Detectionmentioning

confidence: 99%

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Dynamic obstacle identification based on global and local features for a driver assistance system

Woo

Lim

Lee

2010

Neural Comput & Applic

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This paper proposes a novel dynamic obstacle recognition system combining global feature with local feature to identify vehicles, pedestrians and unknown backgrounds for a driver assistance system. The proposed system consists of two main procedures: a dynamic obstacle detection model to localize an area containing a moving obstacle, and an obstacle identification model, which is a hybrid of global and local information, for recognizing an obstacle with and without occlusion. A dynamic saliency map is used for localizing an area containing a moving obstacle. For the global feature analysis, we propose a modified GIST using orientation features with MAX pooling, which is robust to translation and size variations of an object. Although the global features are a compact way to represent an object and provide a good accuracy for non-occluded objects, they are sensitive to image translation and occlusion. Thus, a local featurebased identification model is also proposed and combined with the global feature. As such, for the obstacle identification problem, the proposed system mainly follows the global feature-based object identification. If the global feature-based model identifies a candidate area as background, the system verifies the area again using the local feature-based model. As a result, the proposed system is able to provide information on both the appearance of obstacles and the class of an obstacle. Experimental results show that the proposed model can successfully detect obstacle candidates and robustly identify obstacles with and without occlusion.

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Section: Dynamic Obstacle Detectionmentioning

confidence: 99%

“…By extending Itti et al's SM model [18], Lee et al previously proposed SM models that included a symmetry feature map based on the generalized symmetry transformation (GST) algorithm and an [16,19]. And, Lee et al also proposed extensional version of stereo type SM model that includes an affective saliency map [20].…”

Section: Static Saliency Map (Sm) Modelmentioning

confidence: 99%

Dynamic obstacle identification based on global and local features for a driver assistance system

Woo

Lim

Lee

2010

Neural Comput & Applic

Self Cite

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“…Finally, the three conspicuity maps (Intensity, Colour and Orientation) are added into the saliency map (see [3] for implementation details). Other saliency algorithms, such as [7,8] use the same principles derived from [1]: selection of the visual features, center-surround differences, competition across features, and fusion of the conspicuity maps into the saliency map.…”

Section: Previous Workmentioning

confidence: 99%

Saliency maps of high dynamic range images

Petit

Brémond

Tarel

2009

Proceedings of the 6th Symposium on Applied Perception in Graphics and Visualization

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Abstract. A number of computational models of visual attention have been proposed based on the concept of saliency map. Some of them have been validated as predictors of the visual scan-path of observers looking at images and videos, using oculometric data. They are widely used for Computer Graphics applications, mainly for image rendering, in order to avoid spending too much computing time on non salient areas, and in video coding, in order to keep a better image quality in salient areas. However, these algorithms were not used so far with High Dynamic Range (HDR) inputs. In this paper, we show that in the case of HDR images, the predictions using algorithms based on Itti et al. (1998) are less accurate than with 8-bit images. To improve the saliency computation for HDR inputs, we propose a new algorithm derived from Itti & Koch (2000). From an eye tracking experiment with a HDR scene, we show that this algorithm leads to good results for the saliency map computation, with a better fit between the saliency map and the ocular fixation map than Itti et al.'s algorithm. These results may impact image retargeting issues, for the display of HDR images on both LDR and HDR display devices.

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“…The most popular visual attention mode is the one 25 proposed by Itti et al, which integrates multiple low-level features to generate a saliency map for object detection. It has been widely used in many applications [27][28][29] due to the competitive detection performance. In this paper, we propose a modified visual attention model to localize the position of Chinese license plates.…”

Section: Introductionmentioning

confidence: 99%

Vehicle license plate recognition using visual attention model and deep learning

Zang

Chai

Zhang

et al. 2015

J. Electron. Imaging

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Abstract. A vehicle's license plate is the unique feature by which to identify each individual vehicle. As an important research area of an intelligent transportation system, the recognition of vehicle license plates has been investigated for some decades. An approach based on a visual attention model and deep learning is proposed to handle the problem of Chinese car license plate recognition for traffic videos. We first use a modified visual attention model to locate the license plate, and then the license plate is segmented into seven blocks using a projection method. Two classifiers, which combine the advantages of convolutional neural network-based feature learning and support vector machine for multichannel processing, are designed to recognize Chinese characters, numbers, and alphabet letters, respectively. Experimental results demonstrate that the presented method can achieve high recognition accuracy and works robustly even under the conditions of illumination change and noise contamination. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Biologically motivated vergence control system using human-like selective attention model

Cited by 45 publications

References 16 publications

Dynamic obstacle identification based on global and local features for a driver assistance system

Dynamic obstacle identification based on global and local features for a driver assistance system

Saliency maps of high dynamic range images

Vehicle license plate recognition using visual attention model and deep learning

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