To improve the diagnostic accuracy of follicle ultrasound images detection, this paper proposed a method of cattle follicle ultrasound images detection based on HOG + Improved LBP + SVM. It calculated the Histogram of...
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To improve the diagnostic accuracy of follicle ultrasound images detection, this paper proposed a method of cattle follicle ultrasound images detection based on HOG + Improved LBP + SVM. It calculated the Histogram of Oriented Gradient (HOG) feature for all cell in detection window, used improved Local Binary Pattern method to get gray feature, combined with the Support Vector Machine (SVM), it did the feature training and test experiment, last, the proposed method was compared with that single HOG feature detection, single traditional LBP feature detection and HOG + traditional LBP feature detection. Experimental results showed, the proposed method can effectively describe and detect cattle follicle ultrasound images, and it has higher recognition accuracy.
The last few decades have witnessed the rapid development of saliency detection, which can automatically extract object-of-interest from clutter scene. However, visual saliency detection in low contrast video stream s...
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With the development of SAR technology, the SAR imaging processing data capability and computation complexity increases greatly. According to the situation, we design a virtual single node (VSN) parallel processing me...
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Salient object detection has become a hot topic in computer vision as it can substantially facilitate a wide range of applications. Conventional salient object detection models primarily rely on low-level image featur...
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Follicle Ultrasonic images detection technology plays an important role in the monitoring of bull-follicle. Because of follicle ultrasound image containing lots of speckle noise and fuzzy edges, the traditional image ...
Follicle Ultrasonic images detection technology plays an important role in the monitoring of bull-follicle. Because of follicle ultrasound image containing lots of speckle noise and fuzzy edges, the traditional image detection algorithm is difficult to get better detection results on the ultrasonic image, and the traditional image detection algorithm needs to carry out sample feature extraction for each image, which is time-consuming and time-consuming and labor-intensive. According to the characteristics of the cattle follicle ultrasound image sets, this paper proposes a model of image detection based on improved deep learning Faster R - CNN to automatically detect cattle ovarian follicles, through joint VGG-16 different network layer characteristic figure to replace single deepest characteristic figure, retain the deep semantic characteristics at the same time, also keep the shallow characterization information. The experimental results show that this method has a better effect on the ultrasonic image detection of bovine follicle.
An improved adaptive weighted median filtering method is proposed to deal with the interference noise of ultrasonic RF signal. Firstly, edge pixel points are determined to be filtered by the method of extending edge p...
An improved adaptive weighted median filtering method is proposed to deal with the interference noise of ultrasonic RF signal. Firstly, edge pixel points are determined to be filtered by the method of extending edge points; secondly, mean value is used to replace the median value which considered to be noise points; finally, weighted smoothing processing is carried out. The final experimental results in this paper show that the proposed method has better effect on RF signal processing.
With the development of artificial intelligence technology, robotics technology has become more and more mature. Ground walking robots not only develop rapidly, but also have been applied in actual production and life...
With the development of artificial intelligence technology, robotics technology has become more and more mature. Ground walking robots not only develop rapidly, but also have been applied in actual production and life. However, the development of wall climbing robot technology is still in the laboratory research and small-scale application. We live in a world where progress is continuing. Large-scale buildings, bridges and ships are becoming more and more common. In these places, it is inevitable to involve the construction, maintenance and clarity of high-rise buildings and ships. In the case of dangerous and inefficient manpower work, the application of wall climbing robots can play a very good role. Therefore, the development of wall climbing robots is of vital importance both now and in the future. Starting from the performance characteristics of wall climbing robot, this paper studies and summarizes the moving mode, control mode, conditions to be satisfied and various adsorption forms of wall climbing robot, and introduces the basic research situation in the field of wall climbing robot.
In this paper, we demonstrate an achievable implementation of Doppler parameters estimation engine. Taking advantage of FPGA, a highly parallelized and reconfigurable structure with a unified calculation is adopted. W...
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In this paper, we demonstrate an achievable implementation of Doppler parameters estimation engine. Taking advantage of FPGA, a highly parallelized and reconfigurable structure with a unified calculation is adopted. We build a prototype using single off-the-shelf Xilinx XC6VSX315T FPGA to verify the proposed method in a 16384 ×16384 SAR imaging process. The experiment result can achieve more than 20× time speedups over CPU-based solution, and the FPGA hardware resources can be balanced.
Microstructural information acquired from image analysis can be used in cell modeling. In order to obtain more precise Solid Oxide Fuel Cell (SOFC) microstructure parameters, an adaptive fuzzy approach is developed fo...
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An L band geosynchronous synthetic aperture radar (GEO SAR) will be sensitive to ionosphere scintillation because of its low carrier frequency. Meanwhile, because of the high orbit, GEO SAR has a higher probability to...
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ISBN:
(纸本)9781785616723
An L band geosynchronous synthetic aperture radar (GEO SAR) will be sensitive to ionosphere scintillation because of its low carrier frequency. Meanwhile, because of the high orbit, GEO SAR has a higher probability to meet ionospheric scintillation. In this paper, we propose an orbit strategy for eliminating the impacts of ionospheric scintillation. It is realized by selecting a proper orbit element (time past perigee) to avoid imaging for the sensitive region of ionospheric scintillation during the eruption time interval of ionospheric scintillation with the highest probability. Finally, the simulation based on systems tool kit (STK) software was conducted to verify the effectiveness of the method.
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