scholarly journals High-definition human visual attention mapping using wavelets

2021 ◽  
Author(s):  
Yusuf Saber
Keyword(s):  
Visual Attention ◽  
Saliency Detection ◽  
High Definition ◽  
Biological Inspiration ◽  
Saliency Maps ◽  
Spectral Residual ◽  
Salient Regions ◽  
Novel Approaches ◽  
Salient Edges ◽  
Human Visual Attention

In this work, three novel approaches to detecting visual attention in images are presented. The idea behind detecting areas within images or video that naturally attract a viewer’s attention is based on the concept of generating pre-attentive saliency maps. Saliency, in and of itself, relates to some measure of “conspicuity” in the visual field and is believed to be an important precursor for many tasks in computer vision. One of the proposed methods in this thesis detects salient regions, while the other two detect salient edges. The classical approach to saliency detection proposed by Itti is extended by introducing wavelets as a lossless resizing tool while maintaining the aspect of biological inspiration. In addition to this, the spectral residual method and the frequency tuned method are modified using wavelets to allow for salient edge detection. Tests show that the proposed methods yield results that are not only comparable to leading,cutting-edge methods, but also exceed them in terms of correct and complete object detection as well as noise reduction.

scholarly journals High-definition human visual attention mapping using wavelets

2021 ◽  
Author(s):  
Yusuf Saber
Keyword(s):  
Visual Attention ◽  
Saliency Detection ◽  
High Definition ◽  
Biological Inspiration ◽  
Saliency Maps ◽  
Spectral Residual ◽  
Salient Regions ◽  
Novel Approaches ◽  
Salient Edges ◽  
Human Visual Attention

In this work, three novel approaches to detecting visual attention in images are presented. The idea behind detecting areas within images or video that naturally attract a viewer’s attention is based on the concept of generating pre-attentive saliency maps. Saliency, in and of itself, relates to some measure of “conspicuity” in the visual field and is believed to be an important precursor for many tasks in computer vision. One of the proposed methods in this thesis detects salient regions, while the other two detect salient edges. The classical approach to saliency detection proposed by Itti is extended by introducing wavelets as a lossless resizing tool while maintaining the aspect of biological inspiration. In addition to this, the spectral residual method and the frequency tuned method are modified using wavelets to allow for salient edge detection. Tests show that the proposed methods yield results that are not only comparable to leading,cutting-edge methods, but also exceed them in terms of correct and complete object detection as well as noise reduction.

Insulator detection and recognition of explosion based on convolutional neural networks

2019 ◽  
Vol 17 (02) ◽  
pp. 1940008 ◽  
Author(s):  
Bin Yan ◽  
Qing Chen ◽  
Run Ye ◽  
Xiaojia Zhou
Keyword(s):  
Computer Technology ◽  
Transmission Lines ◽  
Saliency Detection ◽  
Recognition Algorithm ◽  
Contour Detection ◽  
High Definition ◽  
Multiple Faults ◽  
Saliency Maps ◽  
Som Network ◽  
Fully Connected

Unmanned aerial vehicles (UAVs) equipped with high definition (HD) cameras can obtain a large number of detailed inspection images. The insulator is an indispensable component in the transmission lines. Detecting insulator in image video quickly and accurately can provide a reliable basis for the ranging and the obstacle avoidance flight of UAV close to the tower and transmission line. At the same time, the insulator is a serious threat to the safety of the power grid due to the multiple faults of the insulator, and the computer technology should be fully utilized to diagnose the fault. Detection of the insulator images with the complex aerial background is implemented by constructing a convolutional neural network (CNN), which has the classic architecture of five modules of convolution and pooling, two modules of fully connected layers. In this paper, we propose a recognition algorithm for explosion fault based on saliency detection, which uses the trained network model to extract the features. Then, we put the saliency maps into a self-organizing feature map (SOM) network and build the mathematical module via super pixel segmentation, contour detection and other image processing methods. The test shows that the algorithm can reduce the error that may be caused by manual analysis. It also demonstrates that the detection of the insulator and the recognition of explosion fault can effectively improve the efficiency and intelligence level.

scholarly journals Few-Shot Personalized Saliency Prediction Based on Adaptive Image Selection Considering Object and Visual Attention

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2170 ◽  
Author(s):  
Yuya Moroto ◽  
Keisuke Maeda ◽  
Takahiro Ogawa ◽  
Miki Haseyama
Keyword(s):  
Visual Attention ◽  
Experimental Results ◽  
The Other ◽  
Target Person ◽  
Selection Scheme ◽  
Training Images ◽  
Saliency Maps ◽  
Saliency Prediction ◽  
The Relationship ◽  
Human Visual Attention

A few-shot personalized saliency prediction based on adaptive image selection considering object and visual attention is presented in this paper. Since general methods predicting personalized saliency maps (PSMs) need a large number of training images, the establishment of a theory using a small number of training images is needed. To tackle this problem, although finding persons who have visual attention similar to that of a target person is effective, all persons have to commonly gaze at many images. Thus, it becomes difficult and unrealistic when considering their burden. On the other hand, this paper introduces a novel adaptive image selection (AIS) scheme that focuses on the relationship between human visual attention and objects in images. AIS focuses on both a diversity of objects in images and a variance of PSMs for the objects. Specifically, AIS selects images so that selected images have various kinds of objects to maintain their diversity. Moreover, AIS guarantees the high variance of PSMs for persons since it represents the regions that many persons commonly gaze at or do not gaze at. The proposed method enables selecting similar users from a small number of images by selecting images that have high diversities and variances. This is the technical contribution of this paper. Experimental results show the effectiveness of our personalized saliency prediction including the new image selection scheme.

scholarly journals R³Net: Recurrent Residual Refinement Network for Saliency Detection

2018 ◽  
Author(s):  
Zijun Deng ◽  
Xiaowei Hu ◽  
Lei Zhu ◽  
Xuemiao Xu ◽  
Jing Qin ◽  
...  
Keyword(s):  
Saliency Detection ◽  
Ground Truth ◽  
Input Image ◽  
Convolutional Network ◽  
Low Level ◽  
Saliency Maps ◽  
Saliency Prediction ◽  
Benchmark Datasets ◽  
Salient Regions ◽  
High Level

Saliency detection is a fundamental yet challenging task in computer vision, aiming at highlighting the most visually distinctive objects in an image. We propose a novel recurrent residual refinement network (R^3Net) equipped with residual refinement blocks (RRBs) to more accurately detect salient regions of an input image. Our RRBs learn the residual between the intermediate saliency prediction and the ground truth by alternatively leveraging the low-level integrated features and the high-level integrated features of a fully convolutional network (FCN). While the low-level integrated features are capable of capturing more saliency details, the high-level integrated features can reduce non-salient regions in the intermediate prediction. Furthermore, the RRBs can obtain complementary saliency information of the intermediate prediction, and add the residual into the intermediate prediction to refine the saliency maps. We evaluate the proposed R^3Net on five widely-used saliency detection benchmarks by comparing it with 16 state-of-the-art saliency detectors. Experimental results show that our network outperforms our competitors in all the benchmark datasets.

scholarly journals Leveraging colour-based pseudo-labels to supervise saliency detection in hyperspectral image datasets

2021 ◽  
Author(s):  
Annalisa Appice ◽  
Angelo Cannarile ◽  
Antonella Falini ◽  
Donato Malerba ◽  
Francesca Mazzia ◽  
...  
Keyword(s):  
Visual Attention ◽  
Hyperspectral Image ◽  
Saliency Detection ◽  
Hyperspectral Images ◽  
Spectral Information ◽  
Imaging Analysis ◽  
Analysis Task ◽  
Visual Attention Mechanism ◽  
Land Use Monitoring ◽  
Research Resources

AbstractSaliency detection mimics the natural visual attention mechanism that identifies an imagery region to be salient when it attracts visual attention more than the background. This image analysis task covers many important applications in several fields such as military science, ocean research, resources exploration, disaster and land-use monitoring tasks. Despite hundreds of models have been proposed for saliency detection in colour images, there is still a large room for improving saliency detection performances in hyperspectral imaging analysis. In the present study, an ensemble learning methodology for saliency detection in hyperspectral imagery datasets is presented. It enhances saliency assignments yielded through a robust colour-based technique with new saliency information extracted by taking advantage of the abundance of spectral information on multiple hyperspectral images. The experiments performed with the proposed methodology provide encouraging results, also compared to several competitors.

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.

scholarly journals Eight-Channel Multispectral Image Database for Saliency Prediction

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 970
Author(s):  
Miguel Ángel Martínez-Domingo ◽  
Juan Luis Nieves ◽  
Eva M. Valero
Keyword(s):  
Imaging Techniques ◽  
Image Database ◽  
Multispectral Image ◽  
Urban Scenes ◽  
Saliency Maps ◽  
Saliency Prediction ◽  
New Models ◽  
Rgb Images ◽  
Salient Regions ◽  
Rgb Image

Saliency prediction is a very important and challenging task within the computer vision community. Many models exist that try to predict the salient regions on a scene from its RGB image values. Several new models are developed, and spectral imaging techniques may potentially overcome the limitations found when using RGB images. However, the experimental study of such models based on spectral images is difficult because of the lack of available data to work with. This article presents the first eight-channel multispectral image database of outdoor urban scenes together with their gaze data recorded using an eyetracker over several observers performing different visualization tasks. Besides, the information from this database is used to study whether the complexity of the images has an impact on the saliency maps retrieved from the observers. Results show that more complex images do not correlate with higher differences in the saliency maps obtained.

A Unified Approach of Object-Level Saliency Detection in Images and Videos

2013 ◽  
Vol 765-767 ◽  
pp. 1401-1405
Author(s):  
Chi Zhang ◽  
Wei Qiang Wang
Keyword(s):  
Quality Evaluation ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Unified Approach ◽  
Feature Maps ◽  
Still Images ◽  
Saliency Maps ◽  
Novel Method ◽  
Object Level ◽  
Important Branch

Object-level saliency detection is an important branch of visual saliency. In this paper, we propose a novel method which can conduct object-level saliency detection in both images and videos in a unified way. We employ a more effective spatial compactness assumption to measure saliency instead of the popular contrast assumption. In addition, we present a combination framework which integrates multiple saliency maps generated in different feature maps. The proposed algorithm can automatically select saliency maps of high quality according to the quality evaluation score we define. The experimental results demonstrate that the proposed method outperforms all state-of-the-art methods on both of the datasets of still images and video sequences.

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