scholarly journals Object detection in high resolution images based on multiscale and block processing

Informatics ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 7-16
Author(s):  
R. P. Bohush ◽  
I. Yu. Zakharava ◽  
S. V. Ablameyko
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Image Representation ◽  
Input Image ◽  
Image Resolution ◽  
Detection Accuracy ◽  
Block Processing ◽  
High Resolution Images

In the paper the algorithm for object detection in high resolution images is proposed. The approach uses multiscale image representation followed by block processing with the overlapping value. For each block the object detection with convolutional neural network was performed. Number of pyramid layers is limited by the Convolutional Neural Network layer size and input image resolution. Overlapping blocks splitting to improve the classification and detection accuracy is performed on each layer of pyramid except the highest one. Detected areas are merged into one if they have high overlapping value and the same class. Experimental results for the algorithm are presented in the paper.

scholarly journals Predicting placenta transcriptional regulatory interactions based on immunohistochemistry images data and convolutional neural network

2020 ◽  
Author(s):  
Yajun Liu ◽  
Yilin Guo ◽  
Ya Gao ◽  
Guiming Hu ◽  
Ju Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Human Placenta ◽  
Chorionic Villus ◽  
Gene Interaction ◽  
Gene Interaction Networks ◽  
Transcriptional Regulatory ◽  
High Resolution Images

Aims: The dysfunction of placenta development is correlated to the defects of pregnancy and fetal growth. The detailed molecular mechanism of placenta development is not identified in human due to the lack of material in vivo. Image-based reconstructions of GRN are still very underdeveloped. Methods and Results: In this study, immunohistochemistry images of different TFs in chorionic villus were obtained by a high-resolution scanner. Next, we used a convolutional neural network and machine learning method to infer gene interaction networks of human placenta from these images based on the transfer learning technique. The experimental results show that deep learning models reveals regulatory roles that have not yet been fully recognized. The spatial expression data reveal new regulatory relationships that traditional experiments have failed to recognize, and has allowed the development of gene regulation networks based on the spatial distribution of gene expression. Conclusions: We demonstrate the effectiveness of this approach in building networks using high-resolution images of the human placenta. Our analysis is of certain significance for further exploration of the development of the placenta and the occurrence of pregnancy-related diseases in the future. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.

scholarly journals IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery

2019 ◽  
Vol 11 (3) ◽  
pp. 286 ◽  
Author(s):  
Jiangqiao Yan ◽  
Hongqi Wang ◽  
Menglong Yan ◽  
Wenhui Diao ◽  
Xian Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Ground Truth ◽  
Detection Performance ◽  
Candidate Region ◽  
Detection Accuracy ◽  
Remote Sensing Images

Recently, methods based on Faster region-based convolutional neural network (R-CNN)have been popular in multi-class object detection in remote sensing images due to their outstandingdetection performance. The methods generally propose candidate region of interests (ROIs) througha region propose network (RPN), and the regions with high enough intersection-over-union (IoU)values against ground truth are treated as positive samples for training. In this paper, we find thatthe detection result of such methods is sensitive to the adaption of different IoU thresholds. Specially,detection performance of small objects is poor when choosing a normal higher threshold, while alower threshold will result in poor location accuracy caused by a large quantity of false positives.To address the above issues, we propose a novel IoU-Adaptive Deformable R-CNN framework formulti-class object detection. Specially, by analyzing the different roles that IoU can play in differentparts of the network, we propose an IoU-guided detection framework to reduce the loss of small objectinformation during training. Besides, the IoU-based weighted loss is designed, which can learn theIoU information of positive ROIs to improve the detection accuracy effectively. Finally, the class aspectratio constrained non-maximum suppression (CARC-NMS) is proposed, which further improves theprecision of the results. Extensive experiments validate the effectiveness of our approach and weachieve state-of-the-art detection performance on the DOTA dataset.

Deep learning for enhancing wavefield image quality in fast non-contact inspections

2019 ◽  
Vol 19 (4) ◽  
pp. 1003-1016 ◽  
Author(s):  
Yasamin Keshmiri Esfandabadi ◽  
Maxime Bilodeau ◽  
Patrice Masson ◽  
Luca De Marchi
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Image Quality ◽  
Convolutional Neural Network ◽  
Laser Doppler Vibrometer ◽  
High Spatial Frequency ◽  
Image Resolution ◽  
Acquisition Time ◽  
Data Set ◽  
Scanning Time

Ultrasonic wavefield imaging with a non-contact technology can provide detailed information about the health status of an inspected structure. However, high spatial resolution, often necessary for accurate damage quantification, typically demands a long scanning time. In this work, we investigate a novel methodology to acquire high-resolution wavefields with a reduced number of measurement points to minimize the acquisition time. Such methodology is based on the combination of compressive sensing and convolutional neural networks to recover high spatial frequency information from low-resolution images. A data set was built from 652 wavefield images acquired with a laser Doppler vibrometer describing guided ultrasonic wave propagation in eight different structures, with and without various simulated defects. Out of those 652 images, 326 cases without defect and 326 cases with defect were used as a training database for the convolutional neural network. In addition, 273 wavefield images were used as a testing database to validate the proposed methodology. For quantitative evaluation, two image quality metrics were calculated and compared to those achieved with different recovery methods or by training the convolutional neural network with non-wavefield images data set. The results demonstrate the capability of the technique for enhancing image resolution and quality, as well as similarity to the wavefield acquired on the full high-resolution grid of scan points, while reducing the number of measurement points down to 10% of the number of scan points for a full grid.

scholarly journals Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images

2017 ◽  
Vol 9 (5) ◽  
pp. 446 ◽  
Author(s):  
Hongzhen Wang ◽  
Ying Wang ◽  
Qian Zhang ◽  
Shiming Xiang ◽  
Chunhong Pan
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Semantic Segmentation ◽  
High Resolution Images

scholarly journals A convolutional neural network for common coordinate registration of high-resolution histology images

2020 ◽  
Author(s):  
Aidan C. Daly ◽  
Krzysztof J. Geras ◽  
Richard A. Bonneau
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
General Purpose ◽  
Image Resolution ◽  
Hybrid Network ◽  
Tissue Type ◽  
Omics Data ◽  
Multiple Sources

AbstractRegistration of histology images from multiple sources is a pressing problem in large-scale studies of spatial -omics data. Researchers often perform “common coordinate registration,” akin to segmentation, in which samples are partitioned based on tissue type to allow for quantitative comparison of similar regions across samples. Accuracy in such registration requires both high image resolution and global awareness, which mark a difficult balancing act for contemporary deep learning architectures. We present a novel convolutional neural network (CNN) architecture that combines (1) a local classification CNN that extracts features from image patches sampled sparsely across the tissue surface, and (2) a global segmentation CNN that operates on these extracted features. This hybrid network can be trained in an end-to-end manner, and we demonstrate its relative merits over competing approaches on a reference histology dataset as well as two published spatial transcriptomics datasets. We believe that this paradigm will greatly enhance our ability to process spatial -omics data, and has general purpose applications for the processing of high-resolution histology images on commercially available GPUs.

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