Morphology-Based Steels Automatic Counting Method

2010 ◽  
Vol 29-32 ◽  
pp. 1907-1912
Author(s):  
Wen Cheng Wang ◽  
Fa Liang Chang
Keyword(s):  
Mathematical Morphology ◽  
Image Recognition ◽  
Binary Image ◽  
Experimental Results ◽  
Counting Method ◽  
Recognition Method ◽  
Counting Problem ◽  
Automatic Counting ◽  
Binary Segmentation

In order to solve the automatic counting problem of steels, this paper has proposed a image recognition method based on mathematical morphology. It captured the tiling steels image by CCD firstly. Then, the image is sent to computer and preprocessed by using denoising operation and binary segmentation et al.. Finally, the binary image was thinned using hit and miss transform which based on morphology, and the number of steels was obtained. Experimental results showed that this method is convenient and can enhance the accuracy of the steels automatic counting.

scholarly journals Research on Recognition Method of COVID-19 Images Based on Deep Learning

2020 ◽  
Author(s):  
dongshen ji ◽  
yanzhong zhao ◽  
zhujun zhang ◽  
qianchuan zhao
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Small Sample ◽  
Experimental Results ◽  
Ct Image ◽  
Recognition Method ◽  
Sample Recognition

In view of the large demand for new coronary pneumonia covid19 image recognition samples,the recognition accuracy is not ideal.In this paper,a new coronary pneumonia positive image recognition method proposed based on small sample recognition. First, the CT image pictures are preprocessed, and the pictures are converted into the picture formats which are required for transfer learning. Secondly, perform small-sample image enhancement and expansion on the converted picture, such as miscut transformation, random rotation and translation, etc.. Then, multiple migration models are used to extract features and then perform feature fusion. Finally,the model is adjusted by fine-tuning.Then train the model to obtain experimental results. The experimental results show that our method has excellent recognition performance in the recognition of new coronary pneumonia images,even with only a small number of CT image samples.

scholarly journals Research on Recognition Method of COVID-19 Images Based on Deep Learning

2021 ◽  
Vol 271 ◽  
pp. 01039
Author(s):  
Dongsheng Ji ◽  
Yanzhong Zhao ◽  
Zhujun Zhang ◽  
Qianchuan Zhao
Keyword(s):  
Image Recognition ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Small Sample ◽  
Experimental Results ◽  
Fine Tuning ◽  
Ct Image ◽  
Recognition Method ◽  
Sample Recognition

In view of the large demand for new coronary pneumonia covid19 image recognition samples, the recognition accuracy is not ideal. In this paper, a new coronary pneumonia positive image recognition method proposed based on small sample recognition. First, the CT image pictures are preprocessed, and the pictures are converted into the picture formats which are required for transfer learning. Secondly, small-sample image enhancement and extension are performed on the transformed image, such as staggered transformation, random rotation and translation, etc.. Then, multiple migration models are used to extract features and then perform feature fusion. Finally,the model is adjusted by fine-tuning. Then train the model to obtain experimental results. The experimental results show that our method has excellent recognition performance in the recognition of new coronary pneumonia images, even with only a small number of CT image samples.

An Automatic Microalgal Cells Counting Method

2014 ◽  
Vol 1010-1012 ◽  
pp. 178-181
Author(s):  
Yun Zhao ◽  
De Jian Zheng ◽  
Ying Shen
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Binary Image ◽  
Morphological Operations ◽  
Microscopic Image ◽  
Median Filtering ◽  
Counting Method ◽  
Automatic Counting

An automatic counting method for microalgal cells were proposed using digital image processing with characteristics of microalgae microscopic image considered. Firstly, the microalgae image was pretreated with graying, and was then applied the median filtering to remove the noise. Secondly, the image was applied Bot-hat conversion to enhance the contrast. Thirdly, the image was segmented with Otsu algorithm, and was then applied morphological operations. Finally, the binary image segments representing microalgal cells were labeled and counted. The results of experiments showed that this method was simple, efficient and accurate in counting microalgal cells in the microscopic image.

One Extended OTSU Flame Image Recognition Method Using RGBL and Stripe Segmentation

2011 ◽  
Vol 121-126 ◽  
pp. 2141-2145 ◽  
Author(s):  
Wei Gang Yan ◽  
Chang Jian Wang ◽  
Jin Guo
Keyword(s):  
Image Segmentation ◽  
Water Vapor ◽  
Image Recognition ◽  
Recognition Accuracy ◽  
New Method ◽  
Recognition Method ◽  
Otsu’S Method ◽  
Flame Shape ◽  
Image Segmentation Algorithm ◽  
Rgb Image

This paper proposes a new image segmentation algorithm to detect the flame image from video in enclosed compartment. In order to avoid the contamination of soot and water vapor, this method first employs the cubic root of four color channels to transform a RGB image to a pseudo-gray one. Then the latter is divided into many small stripes (child images) and OTSU is employed to perform child image segmentation. Lastly, these processed child images are reconstructed into a whole image. A computer program using OpenCV library is developed and the new method is compared with other commonly used methods such as edge detection and normal Otsu’s method. It is found that the new method has better performance in flame image recognition accuracy and can be used to obtain flame shape from experiment video with much noise.

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