The calculation of salient structures is one of the early and basic ideas of perceptual organization in Computer Vision. Saliency algorithms aim to nd image curves, maximizing some deterministic quality measure which grows with the length of the curve, its smoothness, and its continuity. This note proposes a modiied saliency estimation mechanism, which is based on probabilistically speciied gro...

Saliency detection is an important problem. Researchers in this area mainly focus on advanced models to achieve high performance on benchmark datasets with a large number of labeled images. However, most conventional saliency detection methods only use these benchmark datasets for saliency evaluation. We argue that we can use these valuable labeled data to generate precise saliency results. In ...

Data-driven saliency has recently gained a lot of attention thanks to the use of Convolutional Neural Networks. In this paper we go beyond the standard approach to saliency prediction, in which gaze maps are computed with a feedforward network, and we present a novel Saliency Attentive Model which can predict accurate saliency maps by incorporating attentive mechanisms. Our solution is composed...

Visual salience detection originated over 500 million years ago and is one of nature’s most efficient mechanisms. In contrast, many state-of-the-art computational saliency models are complex and inefficient. Most saliency models process high-resolution color (HC) images; however, insights into the evolutionary origins of visual salience detection suggest that achromatic low-resolution vision is...

We propose a saliency model to estimate the task-driven eyemovement. Human eye movement patterns is affected by observer’s task and mental state[1]. However, the existing saliency model are detected from the low-level image features such as bright regions, edges, colors, etc., In this paper, the tasks(e.g., evaluation of a piano performance) are given to the observer who is watching the music v...

Saliency detection is an active topic in multimedia field. Several algorithms have been proposed in this field. Most previous works on saliency detection focus on 2D images. However, for some complex situations which contain multiple objects or complex background, they are not robust and their performances are not satisfied. Recently, 3D visual information supplies a powerful cue for saliency d...

We present a novel method to measure saliency in molecular dynamics simulation data. This saliency measure is based on a multiscale center-surround mechanism, which is fast and efficient to compute. We explore the use of the saliency function to guide the selection of representative and anomalous timesteps for summarization of simulations. To this end, we also introduce a multiscale keyframe se...

Photography provides tangible and visceral mementos of important experiences. Recent research in content-aware image processing to automatically improve photos relies heavily on automatically identifying salient areas in images. While automatic saliency estimation has achieved estimable success, it will always face inherent challenges. Tracking the photographer’s eyes allows a direct, passive m...

Advances in artificial intelligence (AI) using techniques such as deep learning have fueled the recent progress fields computer vision. However, these algorithms are still often viewed "black boxes", which cannot easily explain how they arrived at their final output decisions. Saliency maps one commonly used form of explainable AI (XAI), indicate input features an algorithm paid attention to du...

Contrast enhancement has an important role in image processing applications. Conventional contrast enhancement techniques often fail to produce satisfactory results for broad variety of low contrast images or cannot be automatically applied to different images, because their parameters must be specified manually to produce a satisfactory result for a given image. A Power Constrained Contrast En...