Results 61 to 70 of about 6,675,235 (374)

A Novel Weakly Supervised Remote Sensing Landslide Semantic Segmentation Method: Combining CAM and cycleGAN Algorithms

Remote Sensing, 2022
With the development of deep learning algorithms, more and more deep learning algorithms are being applied to remote sensing image classification, detection, and semantic segmentation.
Yongxiu Zhou, Honghui Wang, Ronghao Yang, Guangle Yao, Qiang Xu, Xiaojuan Zhang   +5 more
doaj   +1 more source

Learning Intelligent Dialogs for Bounding Box Annotation [PDF]

, 2018
We introduce Intelligent Annotation Dialogs for bounding box annotation. We train an agent to automatically choose a sequence of actions for a human annotator to produce a bounding box in a minimal amount of time.
Ferrari, Vittorio, Konyushkova, Ksenia, Lampert, Christoph, Uijlings, Jasper   +3 more
core   +2 more sources

Automatic human face detection for content-based image annotation [PDF]

, 2008
In this paper, an automatic human face detection approach using colour analysis is applied for content-based image annotation. In the face detection, the probable face region is detected by adaptive boosting algorithm, and then combined with a colour ...
Jiang, M, Sadka, A H, Zhou, H
core   +1 more source

Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation

, 2017
Image segmentation is a fundamental problem in biomedical image analysis. Recent advances in deep learning have achieved promising results on many biomedical image segmentation benchmarks.
B Efron, B Settles, K Sirinukunwattana, O Ronneberger, U Feige, Y Xu   +5 more
core   +1 more source

Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation [PDF]

Computer Vision and Pattern Recognition, 2016
Despite the recent advances in automatically describing image contents, their applications have been mostly limited to image caption datasets containing natural images (e.g., Flickr 30k, MSCOCO).
Hoo-Chang Shin, Kirk Roberts, Le Lu, Dina Demner-Fushman, Jianhua Yao, R. Summers   +5 more
semanticscholar   +1 more source

Image Annotation With YCbCr Color Features Based on Multiple Deep CNN- GLP

IEEE Access
Digital image collections are becoming increasingly popular due to their ease of use. Still, the need for adequate indexing information makes it difficult for users to find the specific images they need.
Myasar Mundher Adnan, Waleed Hadi Madhloom Kurdi, Sarah Alotaibi, Amjad Rehman, Saeed Ali Omer Bahaj, Mohammed Hasan Ali, Tanzila Saba   +6 more
doaj   +1 more source

Application of whole slide image markup and annotation for pathologist knowledge capture

Journal of Pathology Informatics, 2013
Objective: The ability to transfer image markup and annotation data from one scanned image of a slide to a newly acquired image of the same slide within a single vendor platform was investigated.
Walter S Campbell, Kirk W Foster, Steven H Hinrichs   +2 more
doaj   +1 more source

Automatic Image Annotation by Sequentially Learning From Multi-Level Semantic Neighborhoods

IEEE Access, 2021
Automatic image annotation is a key technology in image understanding and pattern recognition, and is becoming increasingly important in order to annotate large-scale images.
Houjie Li, Wei Li, Hongda Zhang, Xin He, Mingxiao Zheng, Haiyu Song   +5 more
doaj   +1 more source

Automatic Annotation of Images from the Practitioner Perspective

, 2005
This paper describes an ongoing project which seeks to contribute to a wider understanding of the realities of bridging the semantic gap in visual image retrieval.
Enser, Peter G.B., Lewis, Paul, Sandom, Christine J.   +2 more
core   +1 more source

Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction [PDF]

, 2019
In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks.
Bai, W., Chen, C., Duan, J., Guitton, F., Guo, Y., Matthews, P. M., Petersen, S. E., Rueckert, D., Tarroni, G.   +8 more
core   +4 more sources