References [1] Vikram T.N, Tscherepanow M. and Wrede B. A Visual Saliency Map based on Random Sub-Window Means. In proceedings of Iberian Conference on Pattern Recognition and Image Analysis, pp. 33-40 (2011). [2] Vikram T.N, Tscherepanow M. and Wrede B. A Random Center Surround Bottom up Visual Attention Model useful for Salient Region Detection. In IEEE Workshop on Applications of Computer Vi...

Saliency Detection is very important for image and video processing application. This paper presents Saliency Detection for video processing. The sample video is converted in the form of Frames. Now Saliency algorithm is apply to the frames of images to filter the background from the video frames. The frames are filter in four parts, first the Hyper Complex Form algorithm is apply to separate t...

In this paper we propose a novel approach to the task of salient object detection. In contrast to previous salient object detectors that are based on a spotlight attention theory, we follow an object-based attention theory and incorporate the notion of an object directly into our saliency measurements. Particularly, we consider proto-objects as units of the analysis, where a protoobject is a co...

This paper presents a co-salient object detection method to find common salient regions in a set of images. We utilize deep saliency networks to transfer co-saliency prior knowledge and better capture high-level semantic information, and the resulting initial co-saliency maps are enhanced by seed propagation steps over an integrated graph. The deep saliency networks are trained in a supervised ...

Saliency Detection by Multi-Task Sparsity Pursuit Congyan Lang, Guangcan Liu, Member, IEEE, Jian Yu, and Shuicheng Yan, Senior Member, IEEE, Abstract—This paper addresses the problem of detecting salient areas within natural images. We shall mainly study the problem under unsupervised setting, namely saliency detection without learning from labeled images. A solution of multi-task sparsity purs...

In this paper we propose a Kalman filter aided saliency detection model which is based on the conjecture that salient regions are considerably different from our ”visual expectation” or they are ”visually surprising” in nature. In this work, we have structured our model with an immediate objective to predict saliency in static images. However, the proposed model can be easily extended for space...

Due to stringing time constraints, saliency models are becoming popular tools for building situated robotic systems requiring, for instance, object recognition and vision-based localisation capabilities. This paper contributes to this endeavour by applying saliency into two new tasks: modulation of stereobased obstacle detection and ground-plane estimation, both to operate on-board off-road veh...

This paper presents a method for detecting salient objects in videos where temporal information in addition to spatial information is fully taken into account. Following recent reports on the advantage of deep features over conventional handcrafted features, we propose the SpatioTemporal Deep (STD) feature that utilizes local and global contexts over frames. We also propose the SpatioTemporal C...

Almost all existing visual saliency models focus on predicting a universal saliency map across all observers. Yet psychology studies suggest that visual attention of different observers can vary a lot under some specific circumstances, especially when they view scenes with multiple salient objects. However, few work explores this visual attention difference probably because of lacking a proper ...

The problem of finding locations where people look at first in images, known as saliency detection, spans decades of research from multiple disciplines including psychology, neuroscience, and computer vision. Because of the complexity of the problem it can hardly be considered as solved. Here, we give an overview of the methods for saliency detection starting from early biologically-plausible m...