scholarly journals A CNN-Based Method for Counting Grains within a Panicle

Machines ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 30
Author(s):  
Liang Gong ◽  
Shengzhe Fan
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Large Deviation ◽  
Rice Breeding ◽  
Processing Methods ◽  
Automatic Counting ◽  
Important Index ◽  
Final Error ◽  
Complex Images

The number of grains within a panicle is an important index for rice breeding. Counting manually is laborious and time-consuming and hardly meets the requirement of rapid breeding. It is necessary to develop an image-based method for automatic counting. However, general image processing methods cannot effectively extract the features of grains within a panicle, resulting in a large deviation. The convolutional neural network (CNN) is a powerful tool to analyze complex images and has been applied to many image-related problems in recent years. In order to count the number of grains in images both efficiently and accurately, this paper applied a CNN-based method to detecting grains. Then, the grains can be easily counted by locating the connected domains. The final error is within 5%, which confirms the feasibility of CNN-based method for counting grains within a panicle.

scholarly journals Image Processing Failure and Deep Learning Success in Lawn Measurement

2020 ◽  
Author(s):  
J. Wilkins Wilkins ◽  
M. V. Nguyen Nguyen ◽  
B. Rahmani Rahmani
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Area Measurement ◽  
Learning Methods ◽  
Hierarchical Networks ◽  
Processing Methods ◽  
Learning Success ◽  
Segmented Images

Lawn area measurement is an application of image processing and deep learning. Researchers used hierarchical networks, segmented images, and other methods to measure the lawn area. Methods’ effectiveness and accuracy varies. In this project, image processing and deep learning methods were used to find the best way to measure the lawn area. Three image processing methods using OpenCV compared to convolutional neural network, which is one of the most famous, and effective deep learning methods. We used Keras and TensorFlow to estimate the lawn area. Convolutional neural network or shortly CNN shows very high accuracy (94-97%). In image processing methods, thresholding with 80-87% accuracy and edge detection are the most effective methods to measure the lawn area while the method ofcontouring with 26-31% accuracy does not calculate the lawn area successfully. We may conclude that deep learning methods, especially CNN, could be the best detective method comparing to image processing learning techniques.

scholarly journals Face Recognition based on Convolutional Neural Network

2020 ◽  
Vol 9 (4) ◽  
pp. 192-196
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Initial Level ◽  
Great Precision ◽  
Processing Methods

Recognition holds great significance to give biometric authentications that are utilized in various applications particularly in attendance and security. A gathered database of the subjects is converted applying image processing methods to make this task. This paper suggests a cascade object detector based face detection and convolutional neural network alexnet based face recognition that can recognize the faces. The techniques used for face recognition are machine learning-based methods because of their great precision as associated with different methods. Face detection is the initial level before face recognition that is done utilizing a cascade object detector classifier. Face recognition is performed utilizing Deep Learning's sub-field that is Convolutional Neural Network (CNN). It is a multi-layer network which is used to train the network, to perform a particular task using classification. Check learning of a trained CNN model that is AlexNet is used for face recognition.

scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

scholarly journals Brain Tumor Segmentation using K-Means Clustering and Detection using Convolutional Neural Network

2020 ◽  
Vol 9 (5) ◽  
pp. 1452-1455
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Clustering Algorithm ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Mri Scans ◽  
Two Phases ◽  
Processing Techniques

This paper presents brain tumor detection and segmentation using image processing techniques. Convolutional neural networks can be applied for medical research in brain tumor analysis. The tumor in the MRI scans is segmented using the K-means clustering algorithm which is applied of every scan and the feed it to the convolutional neural network for training and testing. In our CNN we propose to use ReLU and Sigmoid activation functions to determine our end result. The training is done only using the CPU power and no GPU is used. The research is done in two phases, image processing and applying neural network.

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