Detecting and segmenting salient objects from natural scenes, often referred to as salient
object detection, has attracted great interest in computer vision. While many models have …

Over the last decade, many algorithms have been presented on an infrared target detection
problem. This paper reviews the recent approaches in infrared small target detection …

How best to evaluate a saliency model's ability to predict where humans look in images is an
open research question. The choice of evaluation metric depends on how saliency is …

In this paper we provide an extensive evaluation of fixation prediction and salient object
segmentation algorithms as well as statistics of major datasets. Our analysis identifies …

We extensively compare, qualitatively and quantitatively, 41 state-of-the-art models (29
salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over seven …

A novel Boolean Map based Saliency (BMS) model is proposed. An image is characterized
by a set of binary images, which are generated by randomly thresholding the image's color …

Low-rank recovery models have shown potential for salient object detection, where a matrix
is decomposed into a low-rank matrix representing image background and a sparse matrix …

Recently, convolutional neural networks (CNNs) have brought impressive improvements for
object detection. However, detecting targets in infrared images still remains challenging …

Audio information has been bypassed by most of current visual attention prediction studies.
However, sound could have influence on visual attention and such influence has been …

Efficient detection of targets immersed in a complex background with a low signal-to-clutter
ratio (SCR) is very important in infrared search and tracking (IRST) applications. In this …