Diabetic Retinopathy (DR) is termed as ever-lasting retinal disorders which can cause loss of vision and even blindness in most of the cases. The procedure to classify the level of severity in DR and is complex proces...
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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.
image segmentation is a significant issue in computer vision and imageprocessing, and many segmentation techniques are found in the literature. It has several applications, including robotic perception, augmented rea...
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In computer vision tasks that are related to traffic, a method such as disparity mapping is widely used. With their help, you can estimate the distance to other objects and obstacles. The accuracy of subsequent work o...
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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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Several thousand grapevine varieties exist, with even more naming identifiers. Adequate specialized labor is not available for proper classification or identification of grapevines, making the value of commercial vine...
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Several thousand grapevine varieties exist, with even more naming identifiers. Adequate specialized labor is not available for proper classification or identification of grapevines, making the value of commercial vines uncertain. Traditional methods, such as genetic analysis or ampelometry, are time-consuming, expensive, and often require expert skills that are even rarer. New vision-based systems benefit from advanced and innovative technology and can be used by nonexperts in ampelometry. To this end, deep learning (DL) and machine learning (ML) approaches have been successfully applied for classification purposes. This work extends the state of the art by applying digital ampelometry techniques to larger grapevine varieties. We benchmarked MobileNet v2, ResNet-34, and vGG-11-BN DL classifiers to assess their ability for digital ampelography. In our experiment, all the models could identify the vines' varieties through the leaf with a weighted F1 score higher than 92%.
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.
We present a high- throughput method for identifying and characterizing individual nanowires and for automatically designing electrode patterns with high alignment accuracy. Central to our method is an optimized machi...
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We present a high- throughput method for identifying and characterizing individual nanowires and for automatically designing electrode patterns with high alignment accuracy. Central to our method is an optimized machine-readable, lithographically processable, and multi-scale fiducial marker system-dubbed LithoTag-which provides nanostructure position determination at the nanometer scale. A grid of uniquely defined LithoTag markers patterned across a substrate enables image alignment and mapping in 100% of a set of >9000 scanning electron microscopy (SEM) images (>7 gigapixels). Combining this automated SEM imaging with a computer vision algorithm yields location and property data for individual nanowires. Starting with a random arrangement of individual InAs nanowires with diameters of 30 +/- 5 nm on a single chip, we automatically design and fabricate >200 single-nanowire devices. For >75% of devices, the positioning accuracy of the fabricated electrodes is within 2 pixels of the original microscopy image resolution. The presented LithoTag method enables automation of nanodevice processing and is agnostic to microscopy modality and nanostructure type. Such high-throughput experimental methodology coupled with data-extensive science can help overcome the characterization bottleneck and improve the yield of nanodevice fabrication, driving the development and applications of nanostructured materials.
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