scholarly journals A Crop Image Segmentation and Extraction Algorithm Based on Mask RCNN

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1160
Author(s):  
Shijie Wang ◽  
Guiling Sun ◽  
Bowen Zheng ◽  
Yawen Du
Keyword(s):  
Spatial Information ◽  
Interpolation Method ◽  
Training Set ◽  
Average Precision ◽  
Learning Framework ◽  
Sobel Operator ◽  
Extraction Algorithm ◽  
Region Extraction ◽  
Feature Pyramid ◽  
Fully Connected

The wide variety of crops in the image of agricultural products and the confusion with the surrounding environment information makes it difficult for traditional methods to extract crops accurately and efficiently. In this paper, an automatic extraction algorithm is proposed for crop images based on Mask RCNN. First, the Fruits 360 Dataset label is set with Labelme. Then, the Fruits 360 Dataset is preprocessed. Next, the data are divided into a training set and a test set. Additionally, an improved Mask RCNN network model structure is established using the PyTorch 1.8.1 deep learning framework, and path aggregation and features are added to the network design enhanced functions, optimized region extraction network, and feature pyramid network. The spatial information of the feature map is saved by the bilinear interpolation method in ROIAlign. Finally, the edge accuracy of the segmentation mask is further improved by adding a micro-fully connected layer to the mask branch of the ROI output, employing the Sobel operator to predict the target edge, and adding the edge loss to the loss function. Compared with FCN and Mask RCNN and other image extraction algorithms, the experimental results demonstrate that the improved Mask RCNN algorithm proposed in this paper is better in the precision, Recall, Average precision, Mean Average Precision, and F1 scores of crop image extraction results.

scholarly journals Boundary Loss-Based 2.5D Fully Convolutional Neural Networks Approach for Segmentation: A Case Study of the Liver and Tumor on Computed Tomography

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 144
Author(s):  
Yuexing Han ◽  
Xiaolong Li ◽  
Bing Wang ◽  
Lu Wang
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Medical Images ◽  
Tumor Segmentation ◽  
Convolutional Networks ◽  
Learning Framework ◽  
Fully Convolutional Networks ◽  
Segmentation Accuracy ◽  
Boundary Information

Image segmentation plays an important role in the field of image processing, helping to understand images and recognize objects. However, most existing methods are often unable to effectively explore the spatial information in 3D image segmentation, and they neglect the information from the contours and boundaries of the observed objects. In addition, shape boundaries can help to locate the positions of the observed objects, but most of the existing loss functions neglect the information from the boundaries. To overcome these shortcomings, this paper presents a new cascaded 2.5D fully convolutional networks (FCNs) learning framework to segment 3D medical images. A new boundary loss that incorporates distance, area, and boundary information is also proposed for the cascaded FCNs to learning more boundary and contour features from the 3D medical images. Moreover, an effective post-processing method is developed to further improve the segmentation accuracy. We verified the proposed method on LITS and 3DIRCADb datasets that include the liver and tumors. The experimental results show that the performance of the proposed method is better than existing methods with a Dice Per Case score of 74.5% for tumor segmentation, indicating the effectiveness of the proposed method.

scholarly journals Explainable multi-view framework for dissecting intercellular signaling from highly multiplexed spatial data

2021 ◽  
Author(s):  
Jovan Tanevski ◽  
Attila Gabor ◽  
Ricardo Ramirez Flores ◽  
Denis Schapiro ◽  
Julio Saez-Rodriguez
Keyword(s):  
Breast Cancer ◽  
Spatial Data ◽  
Spatial Information ◽  
Omics Data ◽  
Intercellular Signaling ◽  
Mass Cytometry ◽  
Learning Framework ◽  
Spatially Resolved ◽  
Scalable Methods ◽  
Improve Patient

Abstract The advancement of technologies to measure highly multiplexed spatial data requires the development of scalable methods that can leverage the spatial information. We present MISTy, a flexible, scalable and explainable machine learning framework for extracting interactions from any spatial omics data. MISTy builds multiple views focusing on different spatial or functional contexts to dissect different effects, such as those from direct neighbours versus those from distant cells. MISTy can be applied to different spatially resolved omics data with dozens to thousands of markers, without the need to perform cell-type annotation. We evaluate the performance of MISTy on an in silico dataset and demonstrate its applicability on three breast cancer datasets, two measured by imaging mass cytometry and one by Visium spatial transcriptomics. We show how we can estimate interactions coming from different spatial contexts that we can relate to tumor progression and clinical features. Our analysis also reveals that the estimated interactions in triple negative breast cancer are associated with clinical outcomes which could improve patient stratification. Finally, we demonstrate the flexibility of MISTy to integrate different kinds of views by modeling activities of pathways estimated from gene expression in a spatial context to analyse intercellular signaling.

A Lightweight Detection Model for SAR Aircraft in a Complex Environment

2021 ◽  
Vol 13 (24) ◽  
pp. 5020
Author(s):  
Mingwu Li ◽  
Gongjian Wen ◽  
Xiaohong Huang ◽  
Kunhong Li ◽  
Sizhe Lin
Keyword(s):  
Spatial Information ◽  
Scattering Mechanism ◽  
Complex Environment ◽  
Average Precision ◽  
Important Indicator ◽  
Practical Applications ◽  
Detection Model ◽  
The Neural Network ◽  
Series Of Experiments ◽  
Aircraft Detection

Recently, deep learning has been widely used in synthetic aperture radar (SAR) aircraft detection. However, the complex environment of the airport—consider the boarding bridges, for instance—greatly interferes with aircraft detection. Besides, the detection speed is also an important indicator in practical applications. To alleviate these problems, we propose a lightweight detection model (LDM), mainly including a reuse block (RB) and an information correction block (ICB) based on the Yolov3 framework. The RB module helps the neural network extract rich aircraft features by aggregating multi-layer information. While the RB module brings more effective information, there is also redundant and useless information aggregated by the reuse block, which is harmful to detection precision. Therefore, to accurately extract more aircraft features, we propose an ICB module combining scattering mechanism characteristics by extracting the gray features and enhancing spatial information, which helps suppress interference in a complex environment and redundant information. Finally, we conducted a series of experiments on the SAR aircraft detection dataset (SAR-ADD). The average precision was 0.6954, which is superior to the precision values achieved by other methods. In addition, the average detection time of LDM was only 6.38 ms, making it much faster than other methods.

scholarly journals GC-YOLOv3: You Only Look Once with Global Context Block

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1235
Author(s):  
Yang Yang ◽  
Hongmin Deng
Keyword(s):  
Object Detection ◽  
Irrelevant Information ◽  
Detection Algorithm ◽  
Visual Object ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Average Precision ◽  
Global Context ◽  
Pascal Voc ◽  
Feature Pyramid

In order to make the classification and regression of single-stage detectors more accurate, an object detection algorithm named Global Context You-Only-Look-Once v3 (GC-YOLOv3) is proposed based on the You-Only-Look-Once (YOLO) in this paper. Firstly, a better cascading model with learnable semantic fusion between a feature extraction network and a feature pyramid network is designed to improve detection accuracy using a global context block. Secondly, the information to be retained is screened by combining three different scaling feature maps together. Finally, a global self-attention mechanism is used to highlight the useful information of feature maps while suppressing irrelevant information. Experiments show that our GC-YOLOv3 reaches a maximum of 55.5 object detection mean Average Precision (mAP)@0.5 on Common Objects in Context (COCO) 2017 test-dev and that the mAP is 5.1% higher than that of the YOLOv3 algorithm on Pascal Visual Object Classes (PASCAL VOC) 2007 test set. Therefore, experiments indicate that the proposed GC-YOLOv3 model exhibits optimal performance on the PASCAL VOC and COCO datasets.

scholarly journals Extension of training set using mean shift procedure for aerospace images classification

2019 ◽  
Vol 75 ◽  
pp. 01010
Author(s):  
Yuriy N. Sinyavskiy ◽  
Pavel V. Melnikov ◽  
Igor A. Pestunov
Keyword(s):  
Experimental Study ◽  
Data Structure ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Mean Shift ◽  
Unlabeled Data ◽  
Training Set ◽  
Aerospace Images

An effective method of training set extension for aerospace images classification is proposed. The method is based on mean shift procedure with respect to spatial information. It allows considering the unlabeled data structure. The results of experimental study using the Salinas hyperspectral image are presented, proving the effectiveness of the proposed method.

scholarly journals An Integrated Technique for Image Forgery Detection using Block and Keypoint Based Feature Techniques

2018 ◽  
Vol 7 (3.12) ◽  
pp. 505 ◽  
Author(s):  
Leela Apurupa ◽  
J D.Dorathi Jayaseeli ◽  
D Malathi
Keyword(s):  
Human Life ◽  
Image Process ◽  
Feature Points ◽  
Forgery Detection ◽  
Digital Image Process ◽  
Satellite Imaging ◽  
Extraction Algorithm ◽  
Region Extraction ◽  
Integrated Technique ◽  
Algorithmic Rule

The invention of the net has introduced the unthinkable growth and developments within the illustrious analysis fields like drugs, satellite imaging, image process, security, biometrics, and genetic science. The algorithms enforced within the twenty first century has created the human life more leisurely and secure, however the protection to the first documents belongs to the genuine person is remained as involved within the digital image process domain. a replacement study is planned during this analysis paper to discover. The key plan in the deliberate take a look at and therefore the detection of the suspected regions are detected via the adaptive non-overlapping and abnormal blocks and this method is allotted exploitation the adaptive over-segmentation algorithmic rule. The extraction of the feature points is performed by playacting the matching between every block and its options. The feature points are step by step replaced by exploitation the super pixels within the planned Forgery Region Extraction algorithm then merge the neighboring obstructs that have comparative local shading decisions into the element squares to encourage the brought together districts; at last, it applies the morphological activity to the bound together areas to ask the recognized falsification districts The planned forgery detection algorithmic rule achieves far better detection results even below numerous difficult conditions the sooner strategies all told aspects. We have analyzed the results obtained by the each SIFT and SURF and it is well-tried that the planned technique SURF is giving more satisfactory results by both subjective and objective analysis.  

scholarly journals PLIN: A Network for Pseudo-LiDAR Point Cloud Interpolation

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1573 ◽  
Author(s):  
Haojie Liu ◽  
Kang Liao ◽  
Chunyu Lin ◽  
Yao Zhao ◽  
Meiqin Liu
Keyword(s):  
Point Cloud ◽  
Spatial Information ◽  
Interpolation Method ◽  
Low Frequency ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Sensor Data ◽  
Navigation Systems ◽  
Intermediate Point ◽  
Cascade Structure

LiDAR sensors can provide dependable 3D spatial information at a low frequency (around 10 Hz) and have been widely applied in the field of autonomous driving and unmanned aerial vehicle (UAV). However, the camera with a higher frequency (around 20 Hz) has to be decreased so as to match with LiDAR in a multi-sensor system. In this paper, we propose a novel Pseudo-LiDAR interpolation network (PLIN) to increase the frequency of LiDAR sensor data. PLIN can generate temporally and spatially high-quality point cloud sequences to match the high frequency of cameras. To achieve this goal, we design a coarse interpolation stage guided by consecutive sparse depth maps and motion relationship. We also propose a refined interpolation stage guided by the realistic scene. Using this coarse-to-fine cascade structure, our method can progressively perceive multi-modal information and generate accurate intermediate point clouds. To the best of our knowledge, this is the first deep framework for Pseudo-LiDAR point cloud interpolation, which shows appealing applications in navigation systems equipped with LiDAR and cameras. Experimental results demonstrate that PLIN achieves promising performance on the KITTI dataset, significantly outperforming the traditional interpolation method and the state-of-the-art video interpolation technique.

scholarly journals QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants

2020 ◽  
Vol 10 (7) ◽  
pp. 2411-2421
Author(s):  
Fan Lin ◽  
Elena Z. Lazarus ◽  
Seung Y. Rhee
Keyword(s):  
Machine Learning ◽  
Linkage Mapping ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Causal Gene ◽  
Training Set ◽  
Average Precision ◽  
Trait Improvement ◽  
Causal Genes ◽  
Mapping Process

Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. While it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as the training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. In addition, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.

Sign in / Sign up

Export Citation Format

Share Document