In livestock breeding, it is a research hotspot to conduct non-contact measuring and acquire the body size parameters of animals without stirring stress. And traditional animal body size manual measurement methods inv...
In livestock breeding, it is a research hotspot to conduct non-contact measuring and acquire the body size parameters of animals without stirring stress. And traditional animal body size manual measurement methods involve heavy workload and safety risks. This paper aims to acquire the color image of yak by shooting the traditional body size measurement position of yaks in Sanjiangyuan area. Then, a classification processing based foreground extraction method is used to extract the yak from the image; after the profile curve of the yak is extracted, the temple point and ischium end of the yak are obtained by means of calculating the distance between the image border and the central point. Finally, the body height and body dip length points of the yak are acquired. This paper specially extracts the yak in a yak image shot in Sanjiangyuan area, Qinghai Province, marks points and displays them. The experiment suggests that the dark yak body size point marking algorithm studied by this paper is quite effective to point marking of two-dimensional yak images.
At present, the environmental problems in the water are becoming more and more prominent, and many teams are working on the research of the automation of garbage collection at sea. In order to a ready-made sea on the ...
At present, the environmental problems in the water are becoming more and more prominent, and many teams are working on the research of the automation of garbage collection at sea. In order to a ready-made sea on the garbage recycling equipment automation, intelligent recognition for all white plastic and in identifying regional planning the path of the recycling of recycling equipment, aiming at different times during the day under the sea lighting situation, puts forward a visual processing and multi-step sea boundary identification method. After the image is processed by using two peak method normalized Canny boundary scan and other methods, the vertical and horizontal scanning and boundary recognition are carried out to obtain the closed Marine garbage main body area. Finally, Zigzag algorithm is used to carry out recovery path planning in the scanned area, and the results under different search radii are compared and analysed. The experimental results show that this method is applicable to a wide range of applications and has high accuracy and practicability, and the recognition results are satisfactory.
Deep Learning(DL) techniques are conquering over the prevailing traditional approaches of neural network, when it comes to the huge amount of dataset, applications requiring complex functions demanding increase accura...
Deep Learning(DL) techniques are conquering over the prevailing traditional approaches of neural network, when it comes to the huge amount of dataset, applications requiring complex functions demanding increase accuracy with lower time complexities. Neurosciences has already exploited DL techniques, thus portrayed itself as an inspirational source for researchers exploring the domain of machine learning. DL enthusiasts cover the areas of vision, speech recognition, motion planning and NLP as well, moving back and forth among fields. This concerns with building models that can successfully solve variety of tasks requiring intelligence and distributed representation. The accessibility to faster CPUs, introduction of GPUs-performing complex vector and matrix computations, supported agile connectivity to network. Enhanced software infrastructures for distributed computing worked in strengthening the thought that made researchers suffice DL methodologies. The paper emphases on the following DL procedures to traditional approaches which are performed manually for classifying medical images. The medical images are used for the study Diabetic Retinopathy(DR) and computed tomography (CT) emphysema data. Both DR and CT data diagnosis is difficult task for normal image classification methods. The initial work was carried out with basic imageprocessing along with K-means clustering for identification of image severity levels. After determining image severity levels ANN has been applied on the data to get the basic classification result, then it is compared with the result of DNNs (Deep Neural Networks), which performed efficiently because of its multiple hidden layer features basically which increases accuracy factors, but the problem of vanishing gradient in DNNs made to consider Convolution Neural Networks (CNNs) as well for better results. The CNNs are found to be providing better outcomes when compared to other learning models aimed at classification of images. CNNs are fa
Digital image Correlation (DIC) is a non-contact full-field image analysis technique which allows to retrieve strains and displacements in three dimensions at the surface of any type of material and under arbitrary lo...
Digital image Correlation (DIC) is a non-contact full-field image analysis technique which allows to retrieve strains and displacements in three dimensions at the surface of any type of material and under arbitrary loading. In recent years, high-speed and high-resolution cameras have been developed for static as well as for dynamic applications. As consequence, the application fields for DIC have broadened and it has proven to be a flexible and very accurate measurement solution for deformation analysis and material characterization. Nevertheless, nowadays DIC is often used in a qualitative manner rather than as a metrological tool. This is especially due to the time-consuming task related to the post-processing of the images. When compared to other vibration testing techniques, full-field approaches (such as DIC) allow a greater flexibility by providing a very dense number of experimental data over a single measurement. Another advantage is related to the fact that the geometry is automatically extracted from the images. In this paper, the possibility to combine global acceleration measurements on a small component with local full-field standard machinevision quasi-static camera measurements is investigated. In particular, the regularization properties of DIC and their impact on modal analysis will be studied in detail. Strains and displacements could be used in a second stage for modal analysis purpose in order to characterize the dynamic behaviour of the specimen in a certain frequency range. Different approaches could be used for combining together the data obtained during the tests. The most obvious approach would be the alignment of the time histories based on reference signals for Frequency Response Functions (FRFs) calculation prior to perform any further processing. Unfortunately this is not always possible because of synchronization issues. An alternative possibility, in case broadband random excitation is used, requires to process time data into auto and
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