This paper brings forth a learning-based visual saliency model method for detecting diagnostic diabetic macular edema (DME) regions of interest (RoIs) in retinal image. The method introduces the cognitive process of visual selection of relevant regions that arises during an ophthalmologist's image examination. To record the process, we collected eye-tracking data of 10 ophthalmologists on 100 i...

Accurately modeling and predicting the visual attention behavior of human viewers can help a video analysis algorithm find interesting regions by reducing the search effort of tasks, such as object detection and recognition. In recent years, a great number and variety of visual attention models for predicting the direction of gaze on images and videos have been proposed. When a human views vide...

Saliency integration approaches have aroused general concern on unifying saliency maps from multiple saliency models. In fact, saliency integration is a weighted aggregation of multiple saliency maps, such that measuring the weights of saliency models is essential. In this paper, we propose an unsupervised model for saliency integration, namely the arbitrator model (AM), based on the Bayes’ pro...

Inherent in visual scene analysis is a bottleneck associated with the need to sequentially sample locations with foveating eye movements. The concept of a 'saliency map' topographically encoding stimulus conspicuity over the visual scene has proven to be an efficient predictor of eye movements. Our work reviews insights into the neurobiological implementation of visual salience computation. We ...

In this article, we propose a simple and efficient method for computing an image saliency map, which performs well on both salient region detection and as well as eye gaze prediction tasks. A large number of distinct sub-windows with random co-ordinates and scales are generated over an image. The saliency descriptor of a pixel within a random sub-window is given by the absolute difference of it...

—Saliency computation has become a popular research field for many applications due to the useful information provided by saliency maps. For a saliency map, local relations around the salient regions in multi-channel perspective should be taken into consideration by aiming uniformity on the region of interest as an internal approach. And, irrelevant salient regions have to be avoided as much as...

Detection of salient regions in images is useful for object based image retrieval and browsing applications. This task can be done using methods based on the human visual attention model [1], where feature maps corresponding to color, intensity and orientation capture the corresponding salient regions. In this paper, we propose a strategy for combining the salient regions from the individual fe...

The prediction of Visual Attention data from any kind of media is of valuable use to content creators and used to efficiently drive encoding algorithms. With the current trend in the Virtual Reality (VR) field, adapting known techniques to this new kind of media is starting to gain momentum. In this paper, we present an architectural extension to any Convolutional Neural Network (CNN) to fine-t...

We present in this paper a new and original application for saliency maps, intending to simulate the visual perception of a synthetic actor. Within computer graphics field, simulating virtual humans has become a challenging task. Animating such an autonomous actor within a virtual environment requires most of time the modeling of a perceptiondecision-action cycle. To model a part of the percept...

Visual attention has been widely used in image pre-processing, since it can rapidly detect the region of interest in the given scene. This paper presents a novel technique to track the moving object, which is based on the motion saliency model. The salient region is computed by the combination of multi-feature maps and motion saliency map, which vastly reduce the amount of information in furthe...