Saliency detection in synthetic aperture radar (SAR) image is a difficult problem. This paper proposed a multitask saliency detection (MSD) model for the saliency detection task of SAR image. Firstly, we extract four features of SAR image as the input of the MSD model, which include the intensity, orientation, uniqueness and global contrast. Then, the saliency map is generated by the multitask ...

The automatic detection of regions of interest (ROI) is useful for remote sensing image analysis, such as land cover classification, object recognition, image compression, and various computer vision related applications. Recently, approaches based on visual saliency have been utilized for ROI detection. However, most existing methods focus on detecting ROIs from a single image, which generally...

Although the light field has been recently recognized helpful in saliency detection, it is not comprehensively explored yet. In this work, we propose a new saliency detection model with light field data. The idea behind the proposed model originates from the following observations. (1) People can distinguish regions at different depth levels via adjusting the focus of eyes. Similarly, a light f...

There has been profound progress in visual saliency thanks to the deep learning architectures, however, there still exist three major challenges that hinder the detection performance for scenes with complex compositions, multiple salient objects, and salient objects of diverse scales. In particular, output maps of the existing methods remain low in spatial resolution causing blurred edges due t...

The saliency map is a computational model and has been constructed for simulating human saliency processing, e.g. pop-out target detection (e.g. Itti & Koch, 2000). In this study the spatial structure on the saliency map was investigated. It is proposed that the saliency map is structured into processing units whose size is increasing with retinal eccentricity. In two experiments the distance b...

By the guidance of attention, human visual system is able to locate objects of interest in complex scene. In this paper, we propose a novel visual saliency detection method the conditional saliency for both image and video. Inspired by biological vision, the definition of visual saliency follows a strictly local approach. Given the surrounding area, the saliency is defined as the minimum uncert...

Detection of visually salient image regions has been of great research interest in recent years. It is useful for a wide range of applications such as object detection, video summarization and object segmentation. In this paper, we propose a modified method based on frequency-tuned (FT) model for salient region detection that outputs full resolution saliency maps with well-defined boundaries of...

Salient object detection in videos is challenging because of the competing motion in the background, resulting from camera tracking an object of interest, or motion of objects in the foreground. The authors present a fast method to detect salient video objects using particle filters, which are guided by spatiotemporal saliency maps and color feature with the ability to quickly recover from fals...

Saliency detection has drawn a lot of attention of researchers in various fields over the past several years. Saliency is the perceptual quality that makes an object, person to draw the attention of humans at the very sight. Salient object detection in an image has been used centrally in many computational photography and computer vision applications like video compression [1], object recogniti...

In this paper, we propose an end-to-end group-wise deep co-saliency detection approach to address the co-salient object discovery problem based on the fully convolutional network (FCN) with group input and group output. The proposed approach captures the group-wise interaction information for group images by learning a semantics-aware image representation based on a convolutional neural network...