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.

Mineral Belt Image Segmentation Using Firefly Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 4074-4077
Author(s):  
Li Fang He ◽  
Xiong Tong ◽  
Song Wei Huang
Keyword(s):  
Image Segmentation ◽  
Firefly Algorithm ◽  
Computation Time ◽  
Segmentation Algorithm ◽  
Shaking Table ◽  
Optimal Thresholds ◽  
Mineral Belt ◽  
Otsu’S Method ◽  
Optimum Threshold ◽  
Image Segmentation Algorithm

Otsu is one of better image segmentation algorithm, which obtains optimum threshold by maximizing the between-class variance of an image. However, computation time will sharply increase as the threshold increase. In order to greatly improve the problem, a new segmentation algorithm called firefly algorithm based on Otsu’s method is presented in this paper, and applied the algorithm to segmentation of mineral belt image of shaking table, which lay the foundation for automation of shaking table. Experimental results show that the new algorithm can accurately segment different mineral belts and can obtain the same optimal thresholds and fitness values with Otsu’s method.

scholarly journals Implementation of Watershed Based Image Segmentation Algorithm using Distance Transform for Grid Computing

2013 ◽  
Vol 10 (6) ◽  
pp. 1715-1719
Author(s):  
Megha Sharma ◽  
Seema Verma
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Grid Computing ◽  
Segmentation Algorithm ◽  
New Method ◽  
Distance Transform ◽  
Image Information ◽  
Watershed Transform ◽  
Image Segmentation Algorithm

A new method for image segmentation using watershed transform algorithm is presented in this paper. It takes advantage of the fact that the proposed algorithm produced good results even if the same parameters are used for the standard segmentation algorithm. The proposed segmentation algorithm will be very effective for grid computing as it seems to possess specific tasks of image information and detection in order to obtain a detailed and accurate image analysis.

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.

Experimental Study of Human Fingerprint Image Recognition Analysis

2014 ◽  
Vol 971-973 ◽  
pp. 1616-1619
Author(s):  
Su Ling Zhang
Keyword(s):  
Image Processing ◽  
Experimental Study ◽  
Image Segmentation ◽  
Image Enhancement ◽  
Image Recognition ◽  
Fingerprint Image ◽  
Image Preprocessing ◽  
Advantages And Disadvantages ◽  
Demand Pattern ◽  
Image Segmentation Algorithm

respectively cited the fingerprint image preprocessing for image segmentation , demand pattern, image enhancement and binarization of several algorithms , and each algorithm were compared. Image segmentation algorithm studied in this paper , image enhancement algorithms, can be very good to complete the project requirements. Because each method has its advantages and disadvantages , and therefore use different methods to get different results after image processing .

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.

scholarly journals An Novel Image Recognition method based on Three-way Decision

2018 ◽  
Vol 176 ◽  
pp. 01035
Author(s):  
Jin Dai ◽  
Shuai Shao ◽  
Zu Wang ◽  
Xianjing Zhao
Keyword(s):  
Image Recognition ◽  
Iterative Process ◽  
Recognition Accuracy ◽  
Recognition Algorithm ◽  
Positive Sample ◽  
Human Cognition ◽  
Recognition Method ◽  
Decision Region ◽  
Level Information ◽  
Delay Decision

The traditional recognition method takes the low-level information of the image as the foundation. The image recognition center of gravity is biased towards the typical features, and achieves the effect of recognition by region-dependent segmentation. Because the general image segmentation is a regular rectangle, easily lead to the same target is divided into different sub-blocks, ignoring the image of the fuzzy part, so the image recognition is not complete. An image recognition algorithm based on threeway decision is proposed. It takes full advantage of effective information in the image, improving the image recognition accuracy. First, this method divided the image into three regions: positive region, negative region and delay decision region. Second, an iterative process is performed on the region of the delay decision. Final, image recognition is performed on the positive sample region. Based on the basic theory of the three-way decision, the more obvious the decision result is, the more iterations are, and the information is added to the classifier until the blurred part of image cannot be divided. Finally, to achieve the realize effective image recognition. This method simulates the process of human cognition effectively, and makes the utilization of the effective information reach the maximum in the recognition process. The results of the experimental analysis showed that the method is more concise and efficient, and the recognition accuracy is more accurate.

scholarly journals Research on Maize Disease Recognition Method Based on Improved ResNet50

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Guowei Wang ◽  
Haiye Yu ◽  
Yuanyuan Sui
Keyword(s):  
Image Recognition ◽  
Learning Strategy ◽  
Recognition Accuracy ◽  
Experimental Comparison ◽  
Training Set ◽  
Recognition Method ◽  
Data Set ◽  
Disease Identification ◽  
Maize Field ◽  
Convolution Kernels

In order to solve the problem of accuracy and speed of disease identification in real-time spraying operation in maize field, an improved ResNet50 maize disease identification model was proposed. Firstly, this paper uses the Adam algorithm to optimize the model, adjusts the learning strategy through the inclined triangle learning rate, increases L2 regularization to reduce over fitting, and adopts exit strategy and ReLU incentive function. Then, the first convolution kernel of the ResNet50 model is modified into three 3 x 3 small convolution kernels. Finally, the ratio of training set to verification set is 3 : 1. Through experimental comparison, the recognition accuracy of the maize disease recognition model proposed in this paper is higher than that of other models. The image recognition accuracy in the data set is 98.52%, the image recognition accuracy in the farmland is 97.826%, and the average recognition speed is 204 ms, which meets the accuracy and speed requirements of maize field spraying operation and provides technical support for the research of maize field spraying equipment.

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