Picture processing is applied in all kind of fields, such as space science research, medical imaging, photography art. Because the human vision system is a complex nonlinear dynamic system, the traditional image enhan...
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Picture processing is applied in all kind of fields, such as space science research, medical imaging, photography art. Because the human vision system is a complex nonlinear dynamic system, the traditional image enhancement methods often can not meet the requirements of real-time in practical applications. In the face of a large number of images, it is of great practical significance to obtain practical information from these data. With the rapid development of computer CnTech, image compression coding CnTech has also made great progress. And through the mask r-cnn algorithm for effective segmentation and image recognition, can help people face massive image information, to their own needs as the goal, to achieve efficient retrieval, analysis, induction. The traditional methods of image classification by manually selecting features or manually extracting template matching have some defects. In this paper, a maskr-cnn image optimization processing CnTech is proposed, which can accurately identify images and has higher accuracy for image feature extraction. It is meaningful to achieve automatic and accurate segmentation of microscopic images. Through training on COCO data set, it is verified through experiments that there are problems in image segmentation and recognition. In maskr-cnn model, resource consumption is reduced and the efficiency of image segmentation and recognition is improved. Compared with the current cutting-edge algorithms, the image object detection on the strength of maskr-cnn model has significant advantages.
Train incidents with animals and even humans have gotten more attention in the past. Every year, thousands of animals are killed on train tracks, causing an imbalance in the ecosystem and significant delays in railway...
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Train incidents with animals and even humans have gotten more attention in the past. Every year, thousands of animals are killed on train tracks, causing an imbalance in the ecosystem and significant delays in railway traffic. Similarly, sometimes train accident is prevalent at the rail gates, where motor vehicles are crossing the train line. All that happens due to the lack of detecting the objects correctly on the train line. Since the detection depends on the driver or human, there is a possibility of occasionally making an error in honking the horn at the right moment, leading to the accident. That's why an automated solution for detecting objects on the train line and promptly notifying the driver is essential. In this research, we proposed a system that detects objects only on the train line. Sometimes, there is an object just beside the train line, which is at a safe distance. To eliminate this kind of wrong detection, we detected the train line first and then detected the objects on the detected train line. Here, we used the mask r-cnn algorithm to detect the train line and things on the train line. A railway traffic dataset with an input size of $512\times512$ pixels was used to test the proposed methodology, which came up with results with a mean average precision of 0.9375 and a frame rate of 30 frames per second. According to the findings of the experiments, the suggested approach can apply to identify objects on railroads in the real world.
Nuclei detection is a key step in computer assisted pathology. Due to the variability of the size, shape, appearance, and texture of breast cancer nuclei in histopathological images, automated nuclei detection has alw...
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Nuclei detection is a key step in computer assisted pathology. Due to the variability of the size, shape, appearance, and texture of breast cancer nuclei in histopathological images, automated nuclei detection has always been a difficult aspect of computer-aided pathology research. In this article, maskrcnn is presented for the automatic detection of nuclei on high-resolution histopathological images of breast cancer. maskrcnn uses the resNet network and effectively combines modules such as feature pyramid networks (FPN), rOIAlign, and fully convolutional networks (FCN). FPN can efficiently extract features of various dimensions in images. rOIAlign can improve the accuracy of the detection model in the detection task. FCN renders the prediction results more detailed. The experiment results show that the application of this algorithm is superior to otheralgorithms in terms of its intuitive vision, as well as in performance indicators such as accuracy, recall, and F-Measure.
Digital image processing technology is currently limited by the accuracy and processing speed of existing methods. To address these limitations, this paper proposes a novel computer digital image processing algorithm ...
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ISBN:
(纸本)9798400709784
Digital image processing technology is currently limited by the accuracy and processing speed of existing methods. To address these limitations, this paper proposes a novel computer digital image processing algorithm based on maskr-cnn. The proposed algorithm utilizes residual structures to replace most of the images that act on fully connected layers in the model architecture, enabling the extraction of image features based on the mask r-cnn algorithm. Furthermore, a digital image processing method is designed, an objective function is established, and feature information is obtained. Under the joint action of the encoding and decoding layers, the network structure of the generator is derived. Experimental results demonstrate that the processed images exhibit high peak signal-to-noise ratio, structural similarity coefficient, and low mean square error, with a running time consistently below 0.13 seconds. Future research directions include investigating higher precision pixel images to further enhance image processing accuracy and speed.
The length of dry beach determines the safety and stability of tailings dam. In order to measure the dry beach length more accurately, we put forward a method of measuring the dry beach length of tailings dam based on...
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ISBN:
(纸本)9781450372985
The length of dry beach determines the safety and stability of tailings dam. In order to measure the dry beach length more accurately, we put forward a method of measuring the dry beach length of tailings dam based on deep learning. This method is carried out in three steps :(1) installing monitoring cameras on both sides of tailings dam. (2) training the network model based on mask r-cnn algorithm, identify waterline and outputs waterline coordinates. (3) measuring the length of dry beach by video screen in real time through inputting the waterline coordinates into the functional relationship between waterline coordinates and measured values. The results show that this model can accurately measure the length of dry beach, and is suitable for the conditions of insufficient illumination, blurred image, rain and snow.
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