Optical simulation often plays an essential role in the field of imaging. The visualization of lightpath and setup especially worth considering for it directly illustrates the property of the optical system. However, ...
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The tremendous growth of the logistics sector has been driven by just in time principles, mass customization, omni channel distribution and a growing world population. These changes have led to the need for more flexi...
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ISBN:
(数字)9798331518523
ISBN:
(纸本)9798331518530
The tremendous growth of the logistics sector has been driven by just in time principles, mass customization, omni channel distribution and a growing world population. These changes have led to the need for more flexibility and faster speed in supply chain operations encouraging the development of smart warehouse technologies. In this review article, we discuss the latest advance in warehouse automation with the emphasis on the importance of SLAM (Simultaneous localization and mapping) in improving the efficiency and scale of autonomous warehouse robots. Effective warehouse movement would rely upon autonomous systems that can map new areas while determining their pose, and SLAM provides this capacity. This work investigates various SLAM implementations based on Visual SLAM (VSLAM), LIDAR based SLAM, and Multi sensor SLAM, and describes their merits in different warehouse scenarios. We cover challenges and recent breakthroughs in SLAM, including its deployment in a number of settings, task completion autonomously while reacting to changes in environment, and its utility for performing complicated exploratory tasks without prior knowledge or human intervention. Additionally, the importance of order picking optimization is stressed, admitting it as one of the most important warehouse processes. This analysis discusses how the SLAM (Simultaneous Localization and Mapping) technologies are allied with AGVs (Autonomous Ground Vehicles) and AMRs (Autonomous Mobile Robots) to give precise navigation, suitable order picking and overall improvement of warehouse operations.
Visually impaired people face problems with independent navigation due to limited visual information. Facing significant challenges, they often travel with an assistant or their relative. This project introduces an ap...
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ISBN:
(数字)9798331518523
ISBN:
(纸本)9798331518530
Visually impaired people face problems with independent navigation due to limited visual information. Facing significant challenges, they often travel with an assistant or their relative. This project introduces an approach, increasing the independence of a blind user by developing new software solutions. The proposed system employs DenseNet201 for feature extraction and Long Short-Term Memory (LSTM) networks for generating accurate, context-aware captions. These captions are converted into real-time auditory descriptions using the gTTS library, enabling users to interpret and navigate their environment confidently. Evaluated on the Flickr8k dataset, the system achieved a BLEU score of 0.721, demonstrating its ability to generate high-quality captions. The system's architecture is designed to balance accuracy, efficiency, and user accessibility. Additionally, the system incorporates a modular architecture optimized for computational efficiency and scalability. Future work includes exploring wearable technology for continuous real-time feedback, integrating advanced natural language processing (NLP) models for richer contextual understanding, and enhancing its applicability in complex indoor and outdoor environments. This approach represents a significant step toward empowering visually impaired individuals with improved mobility and environmental awareness.
Image fusion is a method used in image processing to provide a more complete representation by amalgamating features and data from many images. Multimodal medical image fusion involves the integration of medical image...
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ISBN:
(数字)9798331518523
ISBN:
(纸本)9798331518530
Image fusion is a method used in image processing to provide a more complete representation by amalgamating features and data from many images. Multimodal medical image fusion involves the integration of medical images from many imaging modalities, including computed tomography (CT) scans, positron emission tomography (PET), and magnetic resonance imaging (MRI), into one single dataset. This integration enhances the visualisation of anatomical structures and clinical situations, hence improving diagnostic accuracy by leveraging the strengths of each medium. This study employs MRI, CT, and PET scans as experimental modalities. This review aims to compare various multi modal medical image approach based on Stationary Wavelet Transform (SWT), Non-Subsampled Shearlet Transform (NSST), Convolutional Neural Network (CNN) and NonSubsampled Contourlet Transform (NSCT). This study examines the latest conventional and non-conventional research conducted within these disciplines. It further evaluates these approaches according to diverse image quality metrics and many quantitative assessments. According to this comparison, CNN-based fusion demonstrates superior results, as the overall visual and parametric quality of the fusion outcomes surpasses that of the other approaches evaluated.
This study aims to visualize pneumoconiosis lesions from chest X-ray images using an improved Grad-CAM method and evaluate its performance. Pneumoconiosis is a disease that causes fibrotic changes in the lungs due to ...
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ISBN:
(数字)9798350373332
ISBN:
(纸本)9798350373349
This study aims to visualize pneumoconiosis lesions from chest X-ray images using an improved Grad-CAM method and evaluate its performance. Pneumoconiosis is a disease that causes fibrotic changes in the lungs due to the inhalation of dust, and there is no cure. Diagnosis is usually performed by interpreting chest X-ray images, which requires skilled radiologists who are in short supply. Recently, diagnostic support using deep learning has garnered attention, and visualization methods such as Grad-CAM are used to explain these supports. However, these methods have not been sufficiently validated and have issues such as low-resolution visualizations. We constructed a pneumoconiosis detection model using the VGG16 model and applied GradCAM, GradCAM++, ScoreCAM, and LayerCAM methods to visualize the lesions. The VGG16 model showed high performance with both sensitivity and specificity of 0.94 in detecting pneumoconiosis. While all visualization methods exhibited high specificity, sensitivity was generally low, with LayerCAM being the lowest. Although ScoreCAM showed potential usefulness, overall visualization performance was low. It was suggested that LayerCAM is unsuitable for diffuse lung diseases like pneumoconiosis. Additionally, the sensitivity might have decreased due to narrowing down the Activation Map values to 0.7 or higher. Future research requires precise annotation and expansion of datasets. The study indicated the usefulness of ScoreCAM as a method for detecting and visualizing pneumoconiosis from chest X-ray images, although improvements in overall visualization performance are needed. LayerCAM may not be suitable for visualizing pneumoconiosis.
This paper presents a novel approach for perceptually aware image enhancement utilizing a deep residual U-Net generator combined with a Patch-based Generative Adversarial Network (PatchGAN) discriminator. Our method i...
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ISBN:
(数字)9798331523411
ISBN:
(纸本)9798331523428
This paper presents a novel approach for perceptually aware image enhancement utilizing a deep residual U-Net generator combined with a Patch-based Generative Adversarial Network (PatchGAN) discriminator. Our method incorporates a perceptual loss function based on a pre-trained VGG16 network, ensuring that enhanced images exhibit improved color sharpness, clarity, and retention of critical high-level features. Experiments show improved structural similarity, with SSIM of 0.9270 and FSIM of 0.9998. These results indicate both perceptual and structural enhancement, while the model efficiently generates high-quality images with minimal training. Our method consistently outperforms conventional approaches in visual appeal and computational efficiency, making it relevant for real-world applications such as radiology, self-driving vehicles, and low-light photography. Our implementation is open-sourced and available here: https://***/bp8zsl.
An important area of research in computer vision is feature extraction algorithms for image processing, which is the focal point of this paper's analysis. In the field of visual management, feature extraction and ...
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ISBN:
(数字)9798350368109
ISBN:
(纸本)9798350368116
An important area of research in computer vision is feature extraction algorithms for image processing, which is the focal point of this paper's analysis. In the field of visual management, feature extraction and its representation serve an important role. It can focus on desirable details that may represent the image's inherent meaning. The examined image has undergone a variety of image pre-processing techniques, such as Data Cleaning, Histogram Equalization, Skull Striping, Binarization, Feature Scaling, etc. before acquiring features about the particular medical scans. After that, feature extraction techniques are applied to obtain specific features that will be useful for the analysis of image scans. Thus, the approaches utilized for shape, texture, color and appearance-based feature extraction are the main subjects of our investigation in this paper.
Over the last two decades, Random forest (RF) has been one of the well-known and most exploited ensemble-based machine learning approaches. It has been used for computer vision, pattern recognition, medical imaging, a...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
Over the last two decades, Random forest (RF) has been one of the well-known and most exploited ensemble-based machine learning approaches. It has been used for computer vision, pattern recognition, medical imaging, and many other fields. However, it has always been a challenging task to come up with an approach that will lead to constructing a more reliable RF. In this study, we used a concept of tree strength to construct the RF, called as Enriched Random Forest (ERF). As a feature extraction, Bag of Visual Words (BoVW) is used, and Grey Wolf Optimization (GWO) as a feature selection during the construction of the ERF. The suggested method has been evaluated using the MNSIT, Caltech-101, Caltech-256, and UCI repositories—all of which are well-known and openly accessible. The outcomes of the experiments indicate that the ERF has achieved better results than other RF-based approaches.
This examines ambitions to develop a method for evaluating the neurotoxicity of mind tumours using Diffusion Tensor Imaging (DTI). DTI is an MRI method that visualizes water diffusion in the specimen and can be used t...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
This examines ambitions to develop a method for evaluating the neurotoxicity of mind tumours using Diffusion Tensor Imaging (DTI). DTI is an MRI method that visualizes water diffusion in the specimen and can be used to measure and interpret underlying tissue microarchitectural features. An approach combining the DTI with automatic segmentation and kinetic modelling is demonstrated to be a new tool for brain tumour-induced neurotoxicity assessment. In developing a diagnostic algorithm for utilization within patient-specific treatment planning, this study aims to establish an objective and quantitative method of neurotoxicity measurement in brain tumours. The test results show that DTI is a promising device for checking out tumour-caused treatment-related neurotoxicity, and destiny studies have to be orientated toward optimization of this approach once in a while.
Cycling exercise becomes more engaging when a person is immersed in a virtual reality (VR) setting that encourages adherence to daily workout routines. We developed a Metaverse application for a racetrack game that le...
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ISBN:
(数字)9798331540906
ISBN:
(纸本)9798331540913
Cycling exercise becomes more engaging when a person is immersed in a virtual reality (VR) setting that encourages adherence to daily workout routines. We developed a Metaverse application for a racetrack game that leveraged IoT infrastructure to collect and process data from a portable bike in real-time while wearing an Oculus headset. To facilitate the transmission, storage, and real-time visualization of exercise bike speed in an immersive setting, an Internet of Things interface was put into place. The VR app has been successfully interfaced with the exercise equipment, which includes a portable bike and pulse oximeter. This allows for real-time monitoring of exercise intensity, heart rate, and oxygen saturation, as well as continuous presentation of these parameters on the exercise dashboard. Individualized exercise program prescription and monitoring were facilitated by a dedicated website for the clinical rehabilitation team. The impact of this system will be evaluated in future clinical trials.
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