12,919 research outputs found

    Effective Transfer of Pretrained Large Visual Model for Fabric Defect Segmentation via Specifc Knowledge Injection

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    Fabric defect segmentation is integral to textile quality control. Despite this, the scarcity of high-quality annotated data and the diversity of fabric defects present significant challenges to the application of deep learning in this field. These factors limit the generalization and segmentation performance of existing models, impeding their ability to handle the complexity of diverse fabric types and defects. To overcome these obstacles, this study introduces an innovative method to infuse specialized knowledge of fabric defects into the Segment Anything Model (SAM), a large-scale visual model. By introducing and training a unique set of fabric defect-related parameters, this approach seamlessly integrates domain-specific knowledge into SAM without the need for extensive modifications to the pre-existing model parameters. The revamped SAM model leverages generalized image understanding learned from large-scale natural image datasets while incorporating fabric defect-specific knowledge, ensuring its proficiency in fabric defect segmentation tasks. The experimental results reveal a significant improvement in the model's segmentation performance, attributable to this novel amalgamation of generic and fabric-specific knowledge. When benchmarking against popular existing segmentation models across three datasets, our proposed model demonstrates a substantial leap in performance. Its impressive results in cross-dataset comparisons and few-shot learning experiments further demonstrate its potential for practical applications in textile quality control.Comment: 13 pages,4 figures, 3 table

    A toolbox for a structured risk-based prehabilitation program in major surgical oncology

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    Prehabilitation is a multimodal concept to improve functional capability prior to surgery, so that the patients’ resilience is strengthened to withstand any peri- and postoperative comorbidity. It covers physical activities, nutrition, and psychosocial wellbeing. The literature is heterogeneous in outcomes and definitions. In this scoping review, class 1 and 2 evidence was included to identify seven main aspects of prehabilitation for the treatment pathway: (i) risk assessment, (ii) FITT (frequency, interventions, time, type of exercise) principles of prehabilitation exercise, (iii) outcome measures, (iv) nutrition, (v) patient blood management, (vi) mental wellbeing, and (vii) economic potential. Recommendations include the risk of tumor progression due to delay of surgery. Patients undergoing prehabilitation should perceive risk assessment by structured, quantifiable, and validated tools like Risk Analysis Index, Charlson Comorbidity Index (CCI), American Society of Anesthesiology Score, or Eastern Co-operative Oncology Group scoring. Assessments should be repeated to quantify its effects. The most common types of exercise include breathing exercises and moderate- to high-intensity interval protocols. The program should have a duration of 3–6 weeks with 3–4 exercises per week that take 30–60 min. The 6-Minute Walking Testing is a valid and resource-saving tool to assess changes in aerobic capacity. Long-term assessment should include standardized outcome measurements (overall survival, 90-day survival, Dindo–Clavien/CCI®) to monitor the potential of up to 50% less morbidity. Finally, individual cost-revenue assessment can help assess health economics, confirming the hypothetic saving of 8fortreatmentfor8 for treatment for 1 spent for prehabilitation. These recommendations should serve as a toolbox to generate hypotheses, discussion, and systematic approaches to develop clinical prehabilitation standards

    Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)

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    The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.Comment: This is the second edition of the boo

    Knowledge Distillation and Continual Learning for Optimized Deep Neural Networks

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    Over the past few years, deep learning (DL) has been achieving state-of-theart performance on various human tasks such as speech generation, language translation, image segmentation, and object detection. While traditional machine learning models require hand-crafted features, deep learning algorithms can automatically extract discriminative features and learn complex knowledge from large datasets. This powerful learning ability makes deep learning models attractive to both academia and big corporations. Despite their popularity, deep learning methods still have two main limitations: large memory consumption and catastrophic knowledge forgetting. First, DL algorithms use very deep neural networks (DNNs) with many billion parameters, which have a big model size and a slow inference speed. This restricts the application of DNNs in resource-constraint devices such as mobile phones and autonomous vehicles. Second, DNNs are known to suffer from catastrophic forgetting. When incrementally learning new tasks, the model performance on old tasks significantly drops. The ability to accommodate new knowledge while retaining previously learned knowledge is called continual learning. Since the realworld environments in which the model operates are always evolving, a robust neural network needs to have this continual learning ability for adapting to new changes

    Orvosképzés 2023

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    An intelligent modular real-time vision-based system for environment perception

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    A significant portion of driving hazards is caused by human error and disregard for local driving regulations; Consequently, an intelligent assistance system can be beneficial. This paper proposes a novel vision-based modular package to ensure drivers' safety by perceiving the environment. Each module is designed based on accuracy and inference time to deliver real-time performance. As a result, the proposed system can be implemented on a wide range of vehicles with minimum hardware requirements. Our modular package comprises four main sections: lane detection, object detection, segmentation, and monocular depth estimation. Each section is accompanied by novel techniques to improve the accuracy of others along with the entire system. Furthermore, a GUI is developed to display perceived information to the driver. In addition to using public datasets, like BDD100K, we have also collected and annotated a local dataset that we utilize to fine-tune and evaluate our system. We show that the accuracy of our system is above 80% in all the sections. Our code and data are available at https://github.com/Pandas-Team/Autonomous-Vehicle-Environment-PerceptionComment: Accepted in NeurIPS 2022 Workshop on Machine Learning for Autonomous Drivin

    Visual Odometry Using Line Features and Machine Learning Enhanced Line Description

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    The research on 2D lines in images has increased strongly in the last decade; on the one hand, due to more computing power available, on the other hand, due to an increased interest in odometry methods and autonomous systems. Line features have some advantages over the more thoroughly researched point features. Lines are detected on gradients, they do not need texture to be found. Thus, as long as there are gradients between homogeneous regions, they can cope with difficult situations in which mostly homogeneous areas are present. By being detected on gradients, they are also well suited to represent structure. Furthermore, lines have a very high accuracy orthogonal to their direction, as they consist of numerous points which all lie on the gradient contributing to this locational accuracy. First, we introduce a visual odometry approach which achieves real-time performance and runs solely using lines features, it does not require point features. We developed a heuristic filter algorithm which takes neighbouring line features into account and thereby improves tracking of lines and matching of lines in images taken from arbitrary camera locations. This increases the number of tracked lines and is especially beneficial in difficult scenes where it is hard to match lines by tracking them. Additionally, we employed the Cayley representation for 3D lines to avoid overparameterization in the optimization. To show the advancement of the method, it is benchmarked on commonly used datasets and compared to other state of the art approaches. Second, we developed a machine learning based line feature descriptor for line matching. This descriptor can be used to match lines from arbitrary camera locations. The training data was created synthetically using the Unreal Engine 4. We trained a model based on the ResNet architecture using a triplet loss. We evaluated the descriptor on real world scenes and show its improvement over the famous Line Band Descriptor. Third, we build upon our previous descriptor to create an improved version. Therefor, we added an image pyramid, gabor wavelets and increased the descriptor size. The evaluation of the new descriptor additionally contains competing new approaches which are also machine learning based. It shows that our improved approach outperforms them. Finally, we provide an extended evaluation of our descriptor which shows the influences of different settings and processing steps. And we present an analysis of settings for practical usage scenarios. The influence of a maximum descriptor distance threshold, of a Left-Right consistency check and of a descriptor distance ratio threshold between the first and second best match were investigated. It turns out that, for the ratio of true to false matches, it is almost always better to use a descriptor distance ratio threshold than a maximum descriptor distance threshold

    Catchment based analysis of macronutrients (nitrogen and phosphorus) and organic carbon dynamics: new modelling and participatory tools

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    Ecosystem services (ESs) are increasingly being considered in decision-making with respect to mitigating future climate impacts. To capture complex variation in spatial and temporal dynamics, ecosystem models require spatially explicit data that are often difficult to obtain for model development and validation. Citizen science allows for the participation of trained citizen volunteers in research or regulatory activities, resulting in increased data collection and increased participation of the general public in resource management. Despite the increasing experience in citizen science, these approaches have seldom been used in the modelling of provisioning ecosystem services. The development of new approaches for the analysis of long-term changes in riverine carbon, hydrological and nutrient cycles is important to identify potential alteration on the biogeochemical cycles and potential impacts on the ecosystem services provided to the local population. The Basin scale approach is useful to evaluate the pressures on river ecosystems that may be distant from the receiving watercourse, including the effects of soil or water management activities that propagate or amplify downstream. However, the lack of process-based and basin-scale models for carbon transport has limited effective basin management of organic carbon fluxes from soils, through river networks and to receiving marine waters. In the present study, were examined the temporal and spatial drivers in macronutrient (nitrogen and phosphorus) and sediment delivery, carbon storage and sequestration and water yield in a major Italian river catchment and under different NBS scenarios. Information on climate, land use, soil and river conditions, as well as future climate scenarios, were used to explore future (2050) benefits of NBS on local and basin scales, followed the national and European directives related to water quality (Directive 2000/60/EC) and habitat (Directive 92/43/EEC). It was developed and validate a spatially semi-distributed mass balance modelling approach to estimate organic carbon delivery at a sub-basin scale and which allows exploration of alternative river basin management scenarios and their impact on DOC and POC dynamics. The model is built as an open-source plugin for QGIS and can be easily integrated with other basin scale decision support models on nutrient and sediment export. Furthermore, was performed an estimation of the benefits of individual and combined NBS approaches related to river restoration and catchment reforestation. To complete the ESs overall evaluation and prioritization was developed a new method in order to attributing a weight to the best NBS scenarios based on the natural stoichiometric ratio between the elements carbon, silicon, nitrogen, phosphorus (C:Si:N:P
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