This paper presents a review on automated disease detection processing. The primary issue in herbal plants is the diagnosis and stratification of its diseases. The conventional process is unpredictable and inconsisten...
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The detection and sorting of bruised apples after harvest play a crucial role in improving their economic value by eliminating surface defects. This also reduces the risk of contamination of infected apples during tra...
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The detection and sorting of bruised apples after harvest play a crucial role in improving their economic value by eliminating surface defects. This also reduces the risk of contamination of infected apples during transport and storage. It can be done by using manual detection or machinevision techniques in red, green, and blue (RGB) colors to detect bruises on apples of various skin colors;however, in the early stages of bruising, it is challenging. Therefore, the main purpose of this study is determin of the effectiveness of Deep Learning models combined with the Near Infrared (NIR) imaging system for naturally bruised Super Chief red apples immediately after harvest. In total, 1000 images for the healthy class and 500 images for the bruised class were acquired from 500 apples. After the images were acquired with the RGB and NIR cameras, the data sets were divided into training (70 %), validation (15 %), and testing (15 %) sets. The Alexnet, the Inceptipon-v3, and the vGG16 network structures were trained using the training and validation data sets, and the trained network was evaluated using the test dataset. The vGG16 model achieved the highest test accuracy (86 %) when trained on the RGB data set, while the AlexNet model exhibited the lowest test accuracy (74.6 %). When the models were trained and tested with NIR datasets, 99.33 %, 100 % and 100 % accuracy rates were obtained for AlexNet, Inception v3, and vGG16, respectively. During the experiments, the vGG16 model trained with the NIR dataset achieved the lowest loss rate of 0.0002, whereas when trained and tested with the RGB dataset, the same vGG16 model also recorded the lowest loss rate of *** findings indicate that the deep learning models, particularly when trained with NIR data, demonstrate high accuracy rates in classifying apples as healthy or bruised, making them suitable for industrial classification applications. Therefore, the NIR data set is recommended for precise and reliable apple cl
We propose a novel methodology to automatically create and continuously extend a dataset of images of retail items by synchronizing two streams of information: (1) video recordings of the items being scanned at the Po...
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Today, machinevision experiences large latency due to big data processing, which is a barrier to time-critical applications. To address this issue, in-sensor computing was presented in the past. Here, we present a sc...
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Today, machinevision experiences large latency due to big data processing, which is a barrier to time-critical applications. To address this issue, in-sensor computing was presented in the past. Here, we present a scheme of computing in a magnetic tunneling junction (MTJ) sensor array for proof-of-principle. Using the MTJ sensor array, the functions of artificial neural network (ANN) classifiers and autoencoders were verified. The time for correct classification of one picture was less than 9 mu s . The power consumed in the sensor array can be decreased according to the square law without affecting the results. Our work shows universal circuits and algorithms to compute in resistance-style ANN image sensors with promising energy efficiency.
In mobile eye-tracking, visual attention is commonly evaluated using fixation-based measures, which can be mapped to predefined objects of interest for task-specific attention analysis. Even though attention can be di...
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ISBN:
(纸本)9798400701504
In mobile eye-tracking, visual attention is commonly evaluated using fixation-based measures, which can be mapped to predefined objects of interest for task-specific attention analysis. Even though attention can be directed independently from the fovea, little research can be found on the quantification of peripheral vision for attention analysis. In this work, we discuss the benefits of enhancing traditional mapping methods with near-peripheral information and expand previous research by presenting a novel machine learning-based gaze measure, the visual attention index (vAI), for the analysis of visual attention using dynamic stimuli. Results are discussed using the data of two multi-object mobile eye tracking use cases and visualized using radar graphs. We show that by combining foveal and peripheral vision the vAI is effective for the comparison of visual attention over multiple tasks, trials and subjects, which offers new possibilities for a more realistic and detailed depiction of visual attention in multi-object tasks.
In the dynamic field of machine learning, foundation models have recently gained prominence, particularly for their application in natural language processing and computer vision. The foundational Segment Anything Mod...
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The development of international trade has facilitated the global distribution of food. Ensuring the safety of food products is a crucial process that spans from production to sale. Mismanagement of this process can p...
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The development of international trade has facilitated the global distribution of food. Ensuring the safety of food products is a crucial process that spans from production to sale. Mismanagement of this process can pose significant public health risks. The issue of food adulteration is increasingly prevalent, necessitating the development of fast and reliable methods for its detection. Deep learning, as an effective machine learning algorithm, has emerged as a new field in the food industry, offering rapid and accurate results in the identification of food adulteration. In this study, a digital image and deep learning-based method was developed to detect spinach adulteration in pistachios. A unique dataset with 6 classes was created in a laboratory environment for testing the method. The adulteration rates for each class were determined, and images were analyzed in various color spaces, including Red Green Blue (RGB), HSv (Hue Saturation value), Y,u and v (YUv), and L, a, and b (LAB). Subsequently, Convolutional Neural Network (CNN) architectures, namely ResNet-50, vGGNet-19, and DenseNet201, were employed for classification. The accuracy of all color spaces and architectural combinations exceeded 90%. Notably, the vGGNet-19 architecture achieved a 100% success rate in classifying the LAB color space. Moreover, the YUv/ResNet-50 and HSv/vGGNet-19 combinations demonstrated over 98% success in detecting peanut adulteration. The utilization of deep learning-based architectures enables swift and effortless analysis of complex food samples, eliminating the challenges associated with analyzing large quantities of food and effectively preventing food adulteration.
Brain tumor classification and segmentation for different weighted MRIs are among the most tedious tasks for many researchers due to the high variability of tumor tissues based on texture, structure, and position. Our...
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Brain tumor classification and segmentation for different weighted MRIs are among the most tedious tasks for many researchers due to the high variability of tumor tissues based on texture, structure, and position. Our study is divided into two stages: supervised machine learning-based tumor classification and imageprocessing-based region of tumor extraction. For this job, seven methods have been used for texture feature generation. We have experimented with various state-of-the-art supervised machine learning classification algorithms such as support vector machines (SvMs), K-nearest neighbors (KNNs), binary decision trees (BDTs), random forest (RF), and ensemble methods. Then considering texture features into account, we have tried for fuzzy C-means (FCM), K-means, and hybrid image segmentation algorithms for our study. The experimental results achieved a classification accuracy of 94.25%, 87.88%, 89.57%, 96.99%, and 97% with SvM, KNN, BDT, RF, and Ensemble methods, respectively, on FLAIR-, T1C-, and T2-weighted MRI, and the hybrid segmentation attaining 90.16% mean dice score for tumor area segmentation against ground-truth images.
Accompanying the increase in demand for autonomous systems and robotic solutions is the increase in the relevance of various depth estimation technologies. Monocular Depth Estimation (MDE) is used to predict distances...
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Dietary pattern assessments, essential for chronic illness management and well-being, involve time-consuming manual data input and food intake remembering. A more dependable and automated approach is needed since such...
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Dietary pattern assessments, essential for chronic illness management and well-being, involve time-consuming manual data input and food intake remembering. A more dependable and automated approach is needed since such procedures may create mistakes and inconsistencies. This study solves a long-standing problem by automating nutritional assessment using deep learning and image analysis. CNNs, deep learning models for imageprocessing, were employed in our study. Food category algorithms are trained with thousands of pictures. Even with numerous food items, these models can distinguish them in digital photographs. Our method calculates food portions after identification. Photometric food measurements are obtained using reference objects like plates and forks. Yet another deep learning model predicts portions. The method evaluates food calories last. Select food types and portions are matched to nutritional databases. These findings might automate, enhance, and user-centrically assess food intake in health informatics. Our first experiments are encouraging, but we must understand the approach's limits and need for refinement. The findings underpin future research and development. This approach envisions a future where patients can monitor their nutrition and doctors can get accurate data. This may prevent and treat lifestyle problems.
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