scholarly journals Image Modification Based on a Visual Saliency Map for Guiding Visual Attention

2015 ◽  
Vol E98.D (11) ◽  
pp. 1967-1975 ◽  
Author(s):  
Hironori TAKIMOTO ◽  
Tatsuhiko KOKUI ◽  
Hitoshi YAMAUCHI ◽  
Mitsuyoshi KISHIHARA ◽  
Kensuke OKUBO
Keyword(s):  
Visual Attention ◽  
Visual Saliency ◽  
Saliency Map

Image Reduction Method for Rice Leaf Disease Based on Visual Attention Model

2012 ◽  
Vol 220-223 ◽  
pp. 1393-1397
Author(s):  
Li Bo Liu ◽  
Chun Jiang Zhao ◽  
Hua Rui Wu ◽  
Rong Hua Gao
Keyword(s):  
Visual Attention ◽  
Digital Camera ◽  
Visual Saliency ◽  
Saliency Map ◽  
Brown Spot ◽  
Visual Attention Model ◽  
Attention Model ◽  
Growth Status ◽  
Leaf Disease ◽  
Brown Spot Disease

Analyzing the crop growth status through leaf disease image is one of the hottest issues in agriculture and forestry fields currently. But the size of image gathered by digital camera is too large, the focus of this research is to zooming-out image at the condition of ensuring the main information which carried by the image to distort lower. Based on the further study of visual attention model proposed by Itti and Ma YF. This paper establishes visual attention and visual saliency map of rice blast and brown spot disease image, whose size is 4272*2878 pixels. Finally, determines the reduction scale of the corresponding effective target collection and provide a new way to reduce the plant leaf images.

scholarly journals Visual Saliency Based Just Noticeable Difference Estimation in DWT Domain

Information ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 178 ◽  
Author(s):  
Chunxing Wang ◽  
Xiaoyue Han ◽  
Wenbo Wan ◽  
Jing Li ◽  
Jiande Sun ◽  
...  
Keyword(s):  
Visual Attention ◽  
Estimation Method ◽  
Human Perception ◽  
Visual Saliency ◽  
Saliency Map ◽  
Just Noticeable Difference ◽  
Cognitive Resources ◽  
Discrete Wavelet ◽  
Just Noticeable Distortion ◽  
Better Than

It has been known that human visual systems (HVSs) can be applied to describe the underlying masking properties for the image processing. In general, HVS can only perceive small changes in a scene when they are greater than the just noticeable distortion (JND) threshold. Recently, the cognitive resources of huma visual attention mechanisms are limited, which can not concentrate on all stimuli. To be specific, only more important stimuli will react from the mechanisms. When it comes to visual attention mechanisms, we need to introduce the visual saliency to model the human perception more accurately. In this paper, we presents a new wavelet-based JND estimation method that takes into account the interrelationship between visual saliency and JND threshold. In the experimental part, we verify it from both subjective and objective aspects. In addition, the experimental results show that extracting the saliency map of the image in the discrete wavelet transform (DWT) domain and then modulating its JND threshold is better than the non-modulated JND effect.

scholarly journals A New Visual Attention Model Designed for SAR Images Based on Texture Saliency

2017 ◽  
Author(s):  
Wei Xiong ◽  
Yongli Xu ◽  
Yafei Lv ◽  
Libo Yao
Keyword(s):  
Visual Attention ◽  
Satellite Image ◽  
Visual Saliency ◽  
Saliency Map ◽  
Sar Image ◽  
Sar Images ◽  
Visual Attention Model ◽  
Attention Model ◽  
Saliency Maps ◽  
Visual Models

Targets detection in synthetic aperture radar (SAR) remote sensing images, which is a fundamental but challenging problem in the field of satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. Besides, the ability of human visual system to detect visual saliency is extraordinarily fast and reliable. However, computational modeling of SAR image scene still remains a challenge. This paper analyzes the defects and shortcomings of traditional visual models applied to SAR images. Then a visual attention model designed for SAR images is proposed. The model draws the basic framework of classical ITTI model; selects and extracts the texture features and other features that can describe the SAR image better. We proposes a new algorithm for computing the local texture saliency of the input image, then the model constructs the corresponding saliency maps of features; Next, a new mechanism of feature fusion is adopted to replace the linear additive mechanism of classical models to obtain the overall saliency map; Finally, the gray-scale characteristics of focus of attention (FOA) in saliency map of all features are taken into account, our model choose the best saliency representation, Through the multi-scale competition strategy, the filter and threshold segmentation of the saliency maps can be used to select the salient regions accurately, thereby completing this operation for the visual saliency detection in SAR images. In the paper, several types of satellite image data, such as TerraSAR-X (TS-X), Radarsat-2, are used to evaluate the performance of visual models. The results show that our model provides superior performance compared with classical visual models. By further contrasting with the classical visual models, Our model reduce the false alarm caused by speckle noise, and its detection speed is greatly improved, and it is increased by 25% to 45%.

scholarly journals Saliency-Based Gaze Visualization for Eye Movement Analysis

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5178
Author(s):  
Sangbong Yoo ◽  
Seongmin Jeong ◽  
Seokyeon Kim ◽  
Yun Jang
Keyword(s):  
Eye Movements ◽  
Visual Attention ◽  
Eye Movement ◽  
Visual Stimuli ◽  
Saliency Map ◽  
Gaze Behavior ◽  
The Gaze ◽  
Movement Data ◽  
Visual Clues ◽  
Human Visual Attention

Gaze movement and visual stimuli have been utilized to analyze human visual attention intuitively. Gaze behavior studies mainly show statistical analyses of eye movements and human visual attention. During these analyses, eye movement data and the saliency map are presented to the analysts as separate views or merged views. However, the analysts become frustrated when they need to memorize all of the separate views or when the eye movements obscure the saliency map in the merged views. Therefore, it is not easy to analyze how visual stimuli affect gaze movements since existing techniques focus excessively on the eye movement data. In this paper, we propose a novel visualization technique for analyzing gaze behavior using saliency features as visual clues to express the visual attention of an observer. The visual clues that represent visual attention are analyzed to reveal which saliency features are prominent for the visual stimulus analysis. We visualize the gaze data with the saliency features to interpret the visual attention. We analyze the gaze behavior with the proposed visualization to evaluate that our approach to embedding saliency features within the visualization supports us to understand the visual attention of an observer.

An Object-Based Image Reducing Approach

2014 ◽  
Vol 1044-1045 ◽  
pp. 1049-1052 ◽  
Author(s):  
Chin Chen Chang ◽  
I Ta Lee ◽  
Tsung Ta Ke ◽  
Wen Kai Tai
Keyword(s):  
Visual Saliency ◽  
Saliency Map ◽  
Input Image ◽  
Image Size ◽  
Target Image ◽  
Feature Maps ◽  
Object Based ◽  
Wide Range

Common methods for reducing image size include scaling and cropping. However, these two approaches have some quality problems for reduced images. In this paper, we propose an image reducing algorithm by separating the main objects and the background. First, we extract two feature maps, namely, an enhanced visual saliency map and an improved gradient map from an input image. After that, we integrate these two feature maps to an importance map. Finally, we generate the target image using the importance map. The proposed approach can obtain desired results for a wide range of images.

Video Objective Quality Evaluation System Based on the Visual Saliency Map

2013 ◽  
Vol 411-414 ◽  
pp. 1362-1367 ◽  
Author(s):  
Qing Lan Wei ◽  
Yuan Zhang
Keyword(s):  
Evaluation System ◽  
Quality Evaluation ◽  
Objective Assessment ◽  
Visual Saliency ◽  
Objective Evaluation ◽  
Subjective Assessment ◽  
Saliency Map ◽  
Assessment Scores ◽  
Objective Quality ◽  
Quality Evaluation System

This paper presents the thoughts about application of saliency map to the video objective quality evaluation system. It computes the SMSE and SPSNR values as the objective assessment scores according to the saliency map, and compares with conditional objective evaluation methods as PSNR and MSE. Experimental results demonstrate that this method can well fit the subjective assessment results.

scholarly journals Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description

2020 ◽  
Vol 12 (1) ◽  
pp. 152 ◽  
Author(s):  
Ting Nie ◽  
Xiyu Han ◽  
Bin He ◽  
Xiansheng Li ◽  
Hongxing Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Saliency Map ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Ship Detection

Ship detection in panchromatic optical remote sensing images is faced with two major challenges, locating candidate regions from complex backgrounds quickly and describing ships effectively to reduce false alarms. Here, a practical method was proposed to solve these issues. Firstly, we constructed a novel visual saliency detection method based on a hyper-complex Fourier transform of a quaternion to locate regions of interest (ROIs), which can improve the accuracy of the subsequent discrimination process for panchromatic images, compared with the phase spectrum quaternary Fourier transform (PQFT) method. In addition, the Gaussian filtering of different scales was performed on the transformed result to synthesize the best saliency map. An adaptive method based on GrabCut was then used for binary segmentation to extract candidate positions. With respect to the discrimination stage, a rotation-invariant modified local binary pattern (LBP) description was achieved by combining shape, texture, and moment invariant features to describe the ship targets more powerfully. Finally, the false alarms were eliminated through SVM training. The experimental results on panchromatic optical remote sensing images demonstrated that the presented saliency model under various indicators is superior, and the proposed ship detection method is accurate and fast with high robustness, based on detailed comparisons to existing efforts.

scholarly journals SALIENCY-GUIDED CHANGE DETECTION OF REMOTELY SENSED IMAGES USING RANDOM FOREST

2018 ◽  
Vol XLII-3 ◽  
pp. 341-348
Author(s):  
W. Feng ◽  
H. Sui ◽  
X. Chen
Keyword(s):  
Random Forest ◽  
Change Detection ◽  
Learning Algorithm ◽  
Saliency Detection ◽  
Principal Component ◽  
Visual Saliency ◽  
Saliency Map ◽  
Difference Image ◽  
Fcm Clustering ◽  
Optimal Segmentation

Studies based on object-based image analysis (OBIA) representing the paradigm shift in change detection (CD) have achieved remarkable progress in the last decade. Their aim has been developing more intelligent interpretation analysis methods in the future. The prediction effect and performance stability of random forest (RF), as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method. In this paper, we present a novel CD approach for high-resolution remote sensing images, which incorporates visual saliency and RF. First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis (PCA). Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis (RCVA) algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy <i>c</i>-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for superpixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, superpixel-based CD is implemented by applying RF based on these samples. Experimental results on Ziyuan 3 (ZY3) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.

scholarly journals Bottom-up visual attention model for still image: a preliminary study

2020 ◽  
Vol 6 (1) ◽  
pp. 82
Author(s):  
Adhi Prahara ◽  
Murinto Murinto ◽  
Dewi Pramudi Ismi
Keyword(s):  
Visual Attention ◽  
Object Detection ◽  
Video Compression ◽  
Saliency Map ◽  
Bottom Up ◽  
Attention Model ◽  
Intrinsic Cues ◽  
Preliminary Study ◽  
Segmentation Image ◽  
Human Visual Attention

The philosophy of human visual attention is scientifically explained in the field of cognitive psychology and neuroscience then computationally modeled in the field of computer science and engineering. Visual attention models have been applied in computer vision systems such as object detection, object recognition, image segmentation, image and video compression, action recognition, visual tracking, and so on. This work studies bottom-up visual attention, namely human fixation prediction and salient object detection models. The preliminary study briefly covers from the biological perspective of visual attention, including visual pathway, the theory of visual attention, to the computational model of bottom-up visual attention that generates saliency map. The study compares some models at each stage and observes whether the stage is inspired by biological architecture, concept, or behavior of human visual attention. From the study, the use of low-level features, center-surround mechanism, sparse representation, and higher-level guidance with intrinsic cues dominate the bottom-up visual attention approaches. The study also highlights the correlation between bottom-up visual attention and curiosity.

Sign in / Sign up

Export Citation Format

Share Document