Deep Convolutional Neural Networks for Text Localisation in Figures From Biomedical Literature

Almakky, Ibrahim; Palade, Vasile; Ruiz-Garcia, Ariel

doi:10.1109/ijcnn.2019.8852353

Cited by 11 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The generally used machine learning algorithms include the SVM, i.e., support vector machine and decision trees. The SVM however is not able to deliver good results with the image datasets that contain many data [16]. SVM and decision trees can overfit the data that requires other algorithms for the process of analysis of the data-sets.…”

Section: Methodsmentioning

confidence: 99%

Research on the Identification and Application of Immovable Cultural Relics in the Historic City of Macau Based on Condition of Intelligent Remote Sensing Technology

Zhao

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The archeological sites are a heritage that we have gained from our ancestors. These sites are crucial for understanding the past and the way of life of people during those times. The monuments and the immovable relics of ancient times are a getaway to the past. The critical cultural relics however actually over the years have faced the brunt of nature. The environmental conditions have deteriorated the condition of many important immovable relics over the years since these could not be just shifted away. People also move around the ancient cultural relics that may also deform these relics. The machine learning algorithms were used to identify the location of the relics. The data from the satellite images were used and implemented machine learning algorithm to maintain and monitor the relics. This research study dwells into the importance of the area from a research point of view and utilizes machine learning techniques called CaffeNet and deep convolutional neural network. The result showed that 96% accuracy of predicting the image, which can be used for tracking human activity, protects heritage sites in a unique way.

show abstract

Section: Methodsmentioning

confidence: 99%

Research on the Identification and Application of Immovable Cultural Relics in the Historic City of Macau Based on Condition of Intelligent Remote Sensing Technology

Zhao

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…To verify the effectiveness of our artefacts extraction method, we used a simple deep neural network [59,61] defined in this section for classification. We used Python related tools such as PyTorch, TorchVision and NumPy.…”

Section: Exemplary Deep Neural Network Architecture As Referenced Classifiermentioning

confidence: 99%

RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques

Chojka

Artiemjew

Rapiński

2020

Sensors

View full text Add to dashboard Cite

Interferometric Synthetic Aperture Radar (InSAR) data are often contaminated by Radio-Frequency Interference (RFI) artefacts that make processing them more challenging. Therefore, easy to implement techniques for artefacts recognition have the potential to support the automatic Permanent Scatterers InSAR (PSInSAR) processing workflow during which faulty input data can lead to misinterpretation of the final outcomes. To address this issue, an efficient methodology was developed to mark images with RFI artefacts and as a consequence remove them from the stack of Synthetic Aperture Radar (SAR) images required in the PSInSAR processing workflow to calculate the ground displacements. Techniques presented in this paper for the purpose of RFI detection are based on image processing methods with the use of feature extraction involving pixel convolution, thresholding and nearest neighbor structure filtering. As the reference classifier, a convolutional neural network was used.

show abstract

“…To verify the effectiveness of our artifact extraction method, we used a LeNet deep neural network [46,48,50] defined in this section for classification. We used Python-related tools, such as PyTorch, TorchVision, and NumPy.…”

Section: Deep Neural Network Architecturementioning

confidence: 99%

Deep Learning for RFI Artifact Recognition in Sentinel-1 Data

2020

View full text Add to dashboard Cite

Beyond the variety of unwanted disruptions that appear quite frequently in synthetic aperture radar (SAR) measurements, radio-frequency interference (RFI) is one of the most challenging issues due to its various forms and sources. Unfortunately, over the years, this problem has grown worse. RFI artifacts not only hinder processing of SAR data, but also play a significant role when it comes to the quality, reliability, and accuracy of the final outcomes. To address this issue, a robust, effective, and—importantly—easy-to-implement method for identifying RFI-affected images was developed. The main aim of the proposed solution is the support of the automatic permanent scatters in SAR (PSInSAR) processing workflow through the exclusion of contaminated SAR data that could lead to misinterpretation of the calculation results. The approach presented in this paper for the purpose of recognition of these specific artifacts is based on deep learning. Considering different levels of image damage, we used three variants of a LeNet-type convolutional neural network. The results show the high efficiency of our model used directly on sample data.

show abstract

Deep Convolutional Neural Networks for Text Localisation in Figures From Biomedical Literature

Cited by 11 publications

References 14 publications

Research on the Identification and Application of Immovable Cultural Relics in the Historic City of Macau Based on Condition of Intelligent Remote Sensing Technology

Research on the Identification and Application of Immovable Cultural Relics in the Historic City of Macau Based on Condition of Intelligent Remote Sensing Technology

RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques

Deep Learning for RFI Artifact Recognition in Sentinel-1 Data

Contact Info

Product

Resources

About