Evaluating salient object detection in natural images with multiple objects having multi‐level saliency-Reference-Cited by-同舟云学术

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Evaluating salient object detection in natural images with multiple objects having multi‐level saliency

Published:2020-07-08 Issue:10 Volume:14 Page:2249-2262
ISSN:1751-9659
Container-title:IET Image Processing
language:en
Short-container-title:IET image process

Author:

Yildirim Gökhan¹,Sen Debashis²,Kankanhalli Mohan³,Süsstrunk Sabine⁴

Affiliation:

1. Zalando ResearchBerlinGermany

2. Department of Electronics and Electrical Communication EngineeringIndian Institute of TechnologyKharagpurIndia

3. School of Computing, National University of SingaporeSingapore

4. School of Computer and Communication Sciences, École Polytechnique Fédérale de LausanneSwitzerland

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2019.0787

Reference49 articles.

1. Mechanisms of top-down attention

2. Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey

3. Frequency-tuned salient region detection

4. Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration

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1. Toward Deeper Understanding of Camouflaged Object Detection;IEEE Transactions on Circuits and Systems for Video Technology;2023-07

2. Intelligent Image Saliency Detection Method Based on Convolution Neural Network Combining Global and Local Information;Security and Communication Networks;2022-01-13

3. Structure‐aware multiple salient region detection and localization for autonomous robotic manipulation;IET Image Processing;2022-01-04

4. A Dataset Generation Framework for Evaluating Megapixel Image Classifiers and Their Explanations;Lecture Notes in Computer Science;2022

5. Simultaneously Localize, Segment and Rank the Camouflaged Objects;2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2021-06

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