scholarly journals Recent Advances in Saliency Estimation for Omnidirectional Images, Image Groups, and Video Sequences

2020 ◽  
Vol 10 (15) ◽  
pp. 5143
Author(s):  
Marco Buzzelli
Keyword(s):  
Evaluation Criteria ◽  
Visual Saliency ◽  
Ground Truth ◽  
Video Sequences ◽  
Domain Specific ◽  
Saliency Maps ◽  
Perceptual Property ◽  
Omnidirectional Images ◽  
Video Saliency ◽  
Selection Of

We present a review of methods for automatic estimation of visual saliency: the perceptual property that makes specific elements in a scene stand out and grab the attention of the viewer. We focus on domains that are especially recent and relevant, as they make saliency estimation particularly useful and/or effective: omnidirectional images, image groups for co-saliency, and video sequences. For each domain, we perform a selection of recent methods, we highlight their commonalities and differences, and describe their unique approaches. We also report and analyze the datasets involved in the development of such methods, in order to reveal additional peculiarities of each domain, such as the representation used for the ground truth saliency information (scanpaths, saliency maps, or salient object regions). We define domain-specific evaluation measures, and provide quantitative comparisons on the basis of common datasets and evaluation criteria, highlighting the different impact of existing approaches on each domain. We conclude by synthesizing the emerging directions for research in the specialized literature, which include novel representations for omnidirectional images, inter- and intra- image saliency decomposition for co-saliency, and saliency shift for video saliency estimation.

scholarly journals RECIST 1.1 and lesion selection: How to deal with ambiguity at baseline?

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Antoine Iannessi ◽  
Hubert Beaumont ◽  
Yan Liu ◽  
Anne-Sophie Bertrand
Keyword(s):  
Clinical Trials ◽  
Impact Analysis ◽  
Malignant Lesion ◽  
Evaluation Criteria ◽  
Ground Truth ◽  
Tumor Burden ◽  
Phase 3 ◽  
Oncology Clinical Trials ◽  
The Impact ◽  
Selection Of

AbstractResponse Evaluation Criteria In Solid Tumors (RECIST) is still the predominant criteria base for assessing tumor burden in oncology clinical trials. Despite several improvements that followed its first publication, RECIST continues to allow readers a lot of freedom in their evaluations. Notably in the selection of tumors at baseline. This subjectivity is the source of many suboptimal evaluations. When starting a baseline analysis, radiologists cannot always identify tumor malignancy with any certainty. Also, with RECIST, some findings can be deemed equivocal by radiologists with no confirmatory ground truth to rely on. In the specific case of Blinded Independent Central Review clinical trials with double reads using RECIST, the selection of equivocal tumors can have two major consequences: inter-reader variability and modified sensitivity of the therapeutic response. Apart from the main causes leading to the selection of an equivocal lesion, due to the uncertainty of the radiological characteristics or due to the censoring of on-site evaluations, several other situations can be described more precisely. These latter involve cases where an equivocal is selected as target or non-target lesions, the management of equivocal lymph nodes and the case of few target lesions. In all cases, awareness of the impact of selecting a non-malignant lesion will lead radiologists to make selections in the most rational way. Also, in clinical trials where the primary endpoint differs between phase 2 (response-related) and phase 3 (progression-related) trials, our impact analysis will help them to devise strategies for the management of equivocal lesions.

scholarly journals Guided Spatial Transformers for Facial Expression Recognition

2021 ◽  
Vol 11 (16) ◽  
pp. 7217
Author(s):  
Cristina Luna-Jiménez ◽  
Jorge Cristóbal-Martín ◽  
Ricardo Kleinlein ◽  
Manuel Gil-Martín ◽  
José M. Moya ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expert Knowledge ◽  
Visual Saliency ◽  
Expression Recognition ◽  
Spatial Transformations ◽  
Facial Landmarks ◽  
Saliency Maps ◽  
Best Fit

Spatial Transformer Networks are considered a powerful algorithm to learn the main areas of an image, but still, they could be more efficient by receiving images with embedded expert knowledge. This paper aims to improve the performance of conventional Spatial Transformers when applied to Facial Expression Recognition. Based on the Spatial Transformers’ capacity of spatial manipulation within networks, we propose different extensions to these models where effective attentional regions are captured employing facial landmarks or facial visual saliency maps. This specific attentional information is then hardcoded to guide the Spatial Transformers to learn the spatial transformations that best fit the proposed regions for better recognition results. For this study, we use two datasets: AffectNet and FER-2013. For AffectNet, we achieve a 0.35% point absolute improvement relative to the traditional Spatial Transformer, whereas for FER-2013, our solution gets an increase of 1.49% when models are fine-tuned with the Affectnet pre-trained weights.

scholarly journals Evaluation and Selection of Integrated Energy System Construction Scheme Equipped with Smart Energy Management and Control Platform Using Single-Valued Neutrosophic Numbers

2021 ◽  
Vol 13 (5) ◽  
pp. 2615
Author(s):  
Junqing Wang ◽  
Wenhui Zhao ◽  
Lu Qiu ◽  
Puyu Yuan
Keyword(s):  
Energy Management ◽  
Evaluation Criteria ◽  
Energy System ◽  
System Construction ◽  
Interval Numbers ◽  
Construction Scheme ◽  
Smart Energy Management ◽  
Integrated Energy System ◽  
And Control ◽  
Selection Of

Since application of integrated energy systems (IESs) has formed a markedly increasing trend recently, selecting an appropriate integrated energy system construction scheme becomes essential to the energy supplier. This paper aims to develop a multi-criteria decision-making model for the evaluation and selection of an IES construction scheme equipped with smart energy management and control platform. Firstly, a comprehensive evaluation criteria system including economy, energy, environment, technology and service is established. The evaluation criteria system is divided into quantitative criteria denoted by interval numbers and qualitative criteria. Secondly, single-valued neutrosophic numbers are adopted to denote the qualitative criteria in the evaluation criteria system. Thirdly, in order to accommodate mixed data types consisting of both interval numbers and single-valued neutrosophic numbers, the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is extended into a three-stage technique by introducing a fusion coefficient μ. Then, a real case in China is evaluated through applying the proposed method. Furthermore, a comprehensive discussion is made to analyze the evaluation result and verify the reliability and stability of the method. In short, this study provides a useful tool for the energy supplier to evaluate and select a preferred IES construction scheme.

The Application of Fuzzy Optimization Theory in the Selection of Foundation Improvement Methods

2015 ◽  
Vol 723 ◽  
pp. 341-344
Author(s):  
Li Juan Zhang ◽  
Jiang Han ◽  
Zhang Ming Li
Keyword(s):  
Evaluation Criteria ◽  
Fuzzy Optimization ◽  
Optimization Theory ◽  
Static Method ◽  
Optimal Selection ◽  
Construction Time ◽  
Optimal Degree ◽  
Selection Of

Research was conducted on the optimal selection of foundation improvement methods in the paper. Based on fuzzy optimization theory, four evaluation criteria such as construction time are used to evaluate the five improvement methods. The relative optimal degree 0.798 of dynamic-static consolidation method is the maximum which shows that the dynamic-static method is the optimal one; relative optimal degree and multi-evaluating criteria are used to evaluate multi-goals in the fuzzy optimization theory which will lead to the high optimal reliability result.

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.

scholarly journals Synthetic Aperture Radar Processing Approach for Simultaneous Target Detection and Image Formation

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3377 ◽  
Author(s):  
Jifang Pei ◽  
Yulin Huang ◽  
Weibo Huo ◽  
Yuxuan Miao ◽  
Yin Zhang ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Target Detection ◽  
Time Domain ◽  
Image Formation ◽  
Visual Saliency ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Saliency Maps ◽  
Sar Imaging ◽  
Sar Imagery

Finding out interested targets from synthetic aperture radar (SAR) imagery is an attractive but challenging problem in SAR application. Traditional target detection is independent on SAR imaging process, which is purposeless and unnecessary. Hence, a new SAR processing approach for simultaneous target detection and image formation is proposed in this paper. This approach is based on SAR imagery formation in time domain and human visual saliency detection. First, a series of sub-aperture SAR images with resolutions from low to high are generated by the time domain SAR imaging method. Then, those multiresolution SAR images are detected by the visual saliency processing, and the corresponding intermediate saliency maps are obtained. The saliency maps are accumulated until the result with a sufficient confidence level. After some screening operations, the target regions on the imaging scene are located, and only these regions are focused with full aperture integration. Finally, we can get the SAR imagery with high-resolution detected target regions but low-resolution clutter background. Experimental results have shown the superiority of the proposed approach for simultaneous target detection and image formation.

Assessing Product Lifecycle Sustainability Using Uncertain Design Information at Conceptual Stage

2008 ◽  
Author(s):  
Q. Z. Yang ◽  
B. Song
Keyword(s):  
Conceptual Design ◽  
Product Information ◽  
Sustainability Assessment ◽  
Early Stage ◽  
Evaluation Model ◽  
Evaluation Criteria ◽  
Design Decision ◽  
Fuzzy Evaluation ◽  
Product Lifecycle ◽  
Selection Of

This paper presents a hierarchical fuzzy evaluation approach to product lifecycle sustainability assessment at conceptual design stages. The purpose is to advocate the emerging use of lifecycle engineering methods in support of evaluation and selection of design alternatives for sustainable product development. A fuzzy evaluation model is developed with a hierarchical criteria structure to represent different sustainability considerations in the technical, economic and environmental dimensions. Using the imprecise and uncertain early-stage product information, each design option is assessed by the model with respect to the hierarchical evaluation criteria. Lifecycle engineering methods, such as lifecycle assessment and lifecycle costing analysis, are applied to the generation of the evaluation criteria. This would provide designers with a more complete lifecycle view about the product’s sustainability potentials to support decision-making in evaluation and selection of conceptual designs. The proposed approach has been implemented in a sustainable design decision-support software prototype. Illustrative examples are discussed in the paper to demonstrate the use of the approach and the prototype in conceptual design selection of a consumer product.

scholarly journals Pre-Processing Filter Reflecting Human Visual Perception to Improve Saliency Detection Performance

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2892
Author(s):  
Kyungjun Lee ◽  
Seungwoo Wee ◽  
Jechang Jeong
Keyword(s):  
Saliency Detection ◽  
Visual Saliency ◽  
Ground Truth ◽  
Bilateral Filter ◽  
Input Image ◽  
Human Visual Perception ◽  
Difference Of Gaussians ◽  
Surrounding Environment ◽  
Benchmark Datasets ◽  
Previous State

Salient object detection is a method of finding an object within an image that a person determines to be important and is expected to focus on. Various features are used to compute the visual saliency, and in general, the color and luminance of the scene are widely used among the spatial features. However, humans perceive the same color and luminance differently depending on the influence of the surrounding environment. As the human visual system (HVS) operates through a very complex mechanism, both neurobiological and psychological aspects must be considered for the accurate detection of salient objects. To reflect this characteristic in the saliency detection process, we have proposed two pre-processing methods to apply to the input image. First, we applied a bilateral filter to improve the segmentation results by smoothing the image so that only the overall context of the image remains while preserving the important borders of the image. Second, although the amount of light is the same, it can be perceived with a difference in the brightness owing to the influence of the surrounding environment. Therefore, we applied oriented difference-of-Gaussians (ODOG) and locally normalized ODOG (LODOG) filters that adjust the input image by predicting the brightness as perceived by humans. Experiments on five public benchmark datasets for which ground truth exists show that our proposed method further improves the performance of previous state-of-the-art methods.

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