This study proposes a way to detect vitamin deficiency by combining machine learning and imageprocessing. Computer vision enables the system to recognise visual symptoms of specific vitamin deficiencies. The recommen...
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This paper uses the machinevision method to identify the skirt module. We have constructed three kinds of machine recognition models of skirt profile processing, structure analysis of style drawing, and size estimati...
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image segmentation is a key task in computer vision and imageprocessing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and ...
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image segmentation is a key task in computer vision and imageprocessing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among others, and numerous segmentation algorithms are found in the literature. Against this backdrop, the broad success of deep learning (DL) has prompted the development of new image segmentation approaches leveraging DL models. We provide a comprehensive review of this recent literature, covering the spectrum of pioneering efforts in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multiscale and pyramid-based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the relationships, strengths, and challenges of these DL-based segmentation models, examine the widely used datasets, compare performances, and discuss promising research directions.
The studies will be carried out using optical metrology methods on a Walter Helicheck inspection machine in reflected light and a number of images were stored to form a statistical sample. Established new indicators a...
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ISBN:
(纸本)9781510667877;9781510667884
The studies will be carried out using optical metrology methods on a Walter Helicheck inspection machine in reflected light and a number of images were stored to form a statistical sample. Established new indicators and criteria for grinding efficiency based on imageprocessing of the helical groove of the end mill. As a result, recommendations for the selection of optical control techniques were made for the first time at the intermediate stage of technological preparation for production, in real time, and after processing. In this work, for the first time, we prove the possibility of determining the camera displacement pith distance during continuous scanning of the profile of a helical surface in a radial section, the measurement accuracy and recreating a three-dimensional model of the object. As a result of the work of the new algorithm using the Haar-wavelet with new indicators, it was established that the actual one is located inside the focal zone, which proves the possibility of applied application of the method of monitoring the shape of helical flute of end mills using computer vision. The measurement accuracy of the helical flute increased from 4 to 12% along its profile.
Agriculture is often known as the art and science of nurturing soil. It involves preparing plants and animals for use in products. Agriculture is the process of growing crops and rearing animals for human consumption,...
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With the rapidly increase of population every day, it has become a major issue to fulfill everyone's need for food products (i.e., vegetables, fruits, milk, wheat, etc.) due to limited production of food products....
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With the rapidly increase of population every day, it has become a major issue to fulfill everyone's need for food products (i.e., vegetables, fruits, milk, wheat, etc.) due to limited production of food products. Moreover, healthy food utilization among people is the foremost requirement. The major factors that affect the food system includes increasing food shortage, decreasing quality, wastage, and loss of food products, limited natural resources, etc. This article addresses the various computer vision and machine learning based techniques, used to minimize the aforementioned issues. imageprocessing has become an effective technique for the analysis of many research applications. This study intends to focus on analysis of imageprocessing based applications in food products and agriculture field. Such applications help in decision making , disease prediction, classification, fruit sorting, soil quality measurement, etc. Moreover, a comprehensive review has been accomplished for various computer vision and statistical approaches used in food production and agricultural field and concludes that Deep Learning (DL) based approaches produce better results, specifically for imageprocessingapplications. Additionally, an effort has been made to provide a list of publicly available datasets for the related study.
Mamba, a State Space Model (SSM), has recently shown competitive performance to Convolutional Neural Networks (CNNs) and Transformers in Natural Language processing and general sequence modeling. Various attempts have...
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To achieve the recognition and positioning functions of indoor mobile robots under limited computing power conditions, a method based on color recognition for robot recognition and positioning is proposed. The global ...
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The fusion of visible light and infrared images has garnered significant attention in the field of imaging due to its pivotal role in various applications, including surveillance, remote sensing, and medical imaging. ...
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Diabetic Retinopathy (DR) is a health condition caused due to Diabetes Mellitus (DM). It causes vision problems and blindness due to disfigurement of human retina. According to statistics, 80% of diabetes patients bat...
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Diabetic Retinopathy (DR) is a health condition caused due to Diabetes Mellitus (DM). It causes vision problems and blindness due to disfigurement of human retina. According to statistics, 80% of diabetes patients battling from long diabetic period of 15 to 20 years, suffer from DR. Hence, it has become a dangerous threat to the health and life of people. To overcome DR, manual diagnosis of the disease is feasible but overwhelming and cumbersome at the same time and hence requires a revolutionary method. Thus, such a health condition necessitates primary recognition and diagnosis to prevent DR from developing into severe stages and prevent blindness. Innumerable machine Learning (ML) models are proposed by researchers across the globe, to achieve this purpose. Various feature extraction techniques are proposed for extraction of DR features for early detection. However, traditional ML models have shown either meagre generalization throughout feature extraction and classification for deploying smaller datasets or consumes more of training time causing inefficiency in prediction while using larger datasets. Hence Deep Learning (DL), a new domain of ML, is introduced. DL models can handle a smaller dataset with help of efficient data processing techniques. However, they generally incorporate larger datasets for their deep architectures to enhance performance in feature extraction and image classification. This paper gives a detailed review on DR, its features, causes, ML models, state-of-the-art DL models, challenges, comparisons and future directions, for early detection of DR.
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