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3rd International Conference on Optimization, Learning Algorithms and applications (OL2A)

作者： Leepkaln, Renan Lemes de Re, Angelita Maria Wiggers, Kelly Lais Midwestern Parana State Univ Guarapuava Parana Brazil Fed Technol Univ Parana Pato Branco Parana Brazil

ISBN: (纸本)9783031530357;9783031530364

Potato is a widely consumed food worldwide, and its productivity has increased due to new varieties and the use of technologies related to irrigation, nutrition, and soil preparation, among others. However, diseases such as late blight disease can often affect the crop, impacting many farmers around the world. As a way to help production, technology in agriculture is increasing. Among the various computational techniques that can be applied, those based on digital image processing associated with machine learning algorithms stand out, producing excellent results. This work aimed to develop a methodology for recognizing late blight disease in potato leaves using digital image processing techniques and machine learning algorithms. It was possible to obtain promising results. The experiments were carried out in a set of images from a public database containing images of healthy and unhealthy leaves (with late blight). We compare the performance of machine learning algorithms using feature vectors obtained with SIFT algorithm and RGB descriptors. The best performance was using the Decision Tree algorithm and SIFT vectors, with 99.24% of accuracy.

关键词： Automatic disease recognition Digital image machine Learning Computer vision

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Smartphone based app development with machine learning using Hibiscus sabdariffa L. extract for pH estimation

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2025年 257卷

作者： Aydin, Omer Faruk Aydin, Merve Demir, Melisa Caliskan Kahraman, Sibel Istanbul Aydin Univ Dept Comp Programming Istanbul Turkiye Marmara Univ Dept Control & Automat Technol Istanbul Turkiye Istanbul Aydin Univ Dept Ind Engn Istanbul Turkiye Istanbul Aydin Univ Dept Food Engn Istanbul Turkiye

This study presents a novel approach for pH estimation in buffer solutions using images of solutions prepared with Hibiscus sabdariffa L. as a natural pH indicator. The images of the solutions, each displaying distinctive colours indicative of their pH levels, were transformed into standardized 200x200-pixel images through the application of image processing techniques. Following this, a pH prediction model was constructed using the Adaptive Boosting regressor algorithm. The pH values of the training data used when training the model were distributed irregularly between 0-14. The models were trained with 94 pictures and 1880 experimental values. In addition, a reliable pre-processing part has been placed into the model using image processing techniques, allowing test data to be obtained in any desired environment. The obtained training and test data were separated from noise parameters, affecting the prediction results negatively. A smartphone application based on the model has been developed and made available to everyone. This innovative methodology bridges the gap between traditional pH measurement techniques and computer vision, offering amore accessible and eco-friendly means of pH assessment. The practical applications of this research extend to various fields, including environmental monitoring, agriculture, and educational settings.

关键词： machine learning image processing pH estimation Hibiscus sabdariffa L. Smartphone

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Gaussian mixture model clustering allows accurate semantic image segmentation of wheat kernels from near-infrared hyperspectral images

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2025年 259卷

作者： Kartakoullis, Andreas Caporaso, Nicola Whitworth, Martin B. Fisk, Ian D. Univ Nottingham Sch Biosci Div Div Food Nutr & Dietet Sutton Bonington Campus Nottingham LE12 5RD England Campden BRI Chipping Campden GL55 6LD Glos England Buhler UK Ltd London E16 2LD England

In this study, an ad-hoc image processing pipeline has been developed and proposed for the purpose of semantically segmenting wheat kernel data acquired through near-infrared hyperspectral imaging (HSI). The Gaussian Mixture Model (GMM), characterized as a soft clustering method, has been employed for this task, yielding noteworthy results in both kernel and germ segmentation. A comparative analysis was conducted, wherein GMM was compared with two hard clustering methods, hierarchical clustering and k-means, as well as other common clustering algorithms prevalent in food HSI applications. Notably, GMM exhibited the highest accuracy, with a Jaccard index of 0.745, surpassing hierarchical clustering at 0.698 and k-means at 0.652. Furthermore, the spectral variations observed in wheat kernel topology can be used for semantic image segmentation, especially in the context of selecting the germ portion within the wheat kernels. These findings carry practical significance for professionals in the fields of hyperspectral imaging (HSI) and machine vision, particularly for food product quality assessment and real-time inspection.

关键词： NIR hyperspectral imaging image segmentation Real time image processing Grain computer vision

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Diversified image style transfer-approaches, new methods and directed variability control

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machine vision AND applications 2025年第2期36卷 1-12页

作者： Ustyuzhanin, Alexander Kitov, Victor Kitov, Vladimir Yandex Technol LLC Artificial Intelligence & Res Dept 16 Lva Tolstogo St Moscow 119021 Russia Plekhanov Russian Univ Econ Artificial Intelligence Lab 36 Stremyanny Per Moscow 117997 Russia Lomonosov Moscow State Univ Fac Computat Math & Cybernet GSP-12nd Acad Bldg Moscow 119991 Russia

The task of image style transfer is to automatically redraw an input image in the style of another image, such as an artist's painting. The disadvantage of conventional stylization algorithms is the uniqueness of result. If the user is not satisfied with the way the style was transferred, he has no option to remake the stylization. The paper provides an overview of existing style transfer methods that generate diverse results after each run and proposes two new methods. The first method enables diversity by concatenating a random vector into inner image representation inside the neural network and by reweighting image features accordingly in the loss function. The second method allows diverse stylizations by passing the stylized image through orthogonal transformations, which impact the way the target style is transferred. These blocks are trained to replicate patterns from additional pattern images, which serve as additional input and provide an interpretable way to control stylization variability for the end user. Qualitative and quantitative comparisons demonstrate that both methods are capable to generate different stylizations with higher variability achieved by the second method. The code of both methods is available on github.

关键词： Artistic rendering Diversity image processing image generation Neural networks

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Novel Dataset Creation of Varieties of Banana and Ripening Stages for machine Learning applications 8th

Novel Dataset Creation of Varieties of Banana and Ripening S...

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8th International Conference on Computer vision and image processing (CVIP)

作者： Manasa, T. N. Pushpalatha, M. P. JSS Sci & Technol Univ Mysore Karnataka India

ISBN: (纸本)9783031581731;9783031581748

India holds the title of being the top banana producer globally, contributing approximately 25% of the total banana production. However, exporting it can be a challenge because of its shelf-life. To propose the best possible shelf-life extension methodology, it is important to classify based on the banana varieties and ripening stages to ensure sustainable growth and nutritional value. There are still not enough data sets with different varieties of bananas and their respective ripening stages. A review of research publications from the last five years has been conducted using electronic databases like Scopus, Google Scholar, and Research-Gate, as well as the details of publicly accessible dataset repository sites. The dataset captures images of different varieties of banana fruit as well as its respective different stages of ripening. Banana varieties considered include Robusta (MusaAA), Dwarf Cavendish (Musaacuminata), Nanjangud bananas, and Red bananas (Musa acuminata). The dataset contains over 41,900 processed images. In this paper, the authors provide researchers with an opportunity to develop and investigate machine learning and deep learning algorithms that are used to predict and extend the shelf life of banana fruits.

关键词： machine learning Banana type classification Ripening Stages Classification image processing

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Dimensional Accuracy Evaluation of Single-Layer Prints in Direct Ink Writing Based on machine vision

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SENSORS 2025年第8期25卷 2543-2543页

作者： Tu, Yongqiang Zhang, Haoran Chen, Hu Bao, Baohua Fang, Canmi Wu, Hao Chen, Xinkai Hassan, Alaa Boudaoud, Hakim Jimei Univ Coll Marine Equipment & Mech Engn Xiamen 361021 Peoples R China Univ Lorraine Innovat Proc Res Inst F-54000 Nancy France

The absence of standardized evaluation methodologies for single-layer dimensional accuracy significantly hinders the broader implementation of direct ink writing (DIW) technology. Addressing the critical need for precision non-contact assessment in DIW fabrication, this study develops a novel machine vision-based framework for dimensional accuracy evaluation. The methodology encompasses three key phases: (1) establishment of an optimized hardware configuration with integrated image processing algorithms;(2) comprehensive investigation of camera calibration protocols, advanced image preprocessing techniques, and high-precision contour extraction methods;and (3) development of an iterative closest point (ICP) algorithm-enhanced evaluation system. The experimental results demonstrate that our machine vision system achieves 0.04 mm x 0.04 mm spatial resolution with the ICP convergence threshold optimized to 0.001 mm. The proposed method shows an 80% improvement in measurement accuracy (0.001 mm) compared to conventional approaches. Process parameter optimization experiments validated the system's effectiveness, showing at least 76.3% enhancement in printed layer dimensional accuracy. This non-contact evaluation solution establishes a robust framework for quantitative quality control in DIW applications, providing critical insights for process optimization and standardization efforts in additive manufacturing.

关键词： machine vision dimensional accuracy evaluation single layer direct ink writing

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Retrofitting a Legacy Cutlery Washing machine Using Computer vision 16th

Retrofitting a Legacy Cutlery Washing Machine Using Computer...

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16th International Conference on Computational Collective Intelligence (ICCCI)

作者： Fwa, Hua Leong Singapore Management Univ 81 Victoria St Singapore 188065 Singapore

ISBN: (纸本)9783031702587;9783031702594

Industry 4.0, the digitalization of manufacturing promises to lead to lowered cost, efficient processes and even discovery of new business models. However, many of the enterprises have huge investments in legacy machines which are not 'smart'. In this study, we thus designed a cost-efficient solution to retrofit a legacy conveyor belt-based cutlery washing machine with a commodity web camera. We then applied computer vision (using both traditional image processing and deep learning techniques) to infer the speed and utilization of the machine. We detailed the algorithms that we designed for computing both speed and utilization. With the existing operational constraints of our client, frequent re-training of the deep learning model for object detection is not feasible. Thus, we compared the generalizability of the two techniques across 'unseen' cutleries and found traditional image processing to be generalizable across 'unseen' images. Our proposed final solution uses traditional image processing for computation of utilization but a hybrid of traditional image processing and deep learning model for speed computation as it is more reliable. Our client has implemented our proposed solution for one conveyor belt-based cutlery washing machine and will be planning to scale this to multiple conveyor belt-based cutlery washing machines.

关键词： Industry 4.0 Computer vision Deep Learning image processing

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VQA and Visual Reasoning: An overview of approaches, datasets, and future direction

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NEUROCOMPUTING 2025年 622卷

作者： Zakari, Rufai Yusuf Owusu, Jim Wilson Qin, Ke Wang, Hailin Lawal, Zaharaddeen Karami He, Tao Univ Elect Sci & Technol China Xiyuan Ave Chengdu 611731 Peoples R China Univ Brunei Darussalam Jalan Tungku Link BE-1410 Bandar Seri Begawan Brunei

Visual question answering (VQA) is a problem that researchers in both computer vision and natural language processing are interested in studying. In VQA, a system is given an image and a question in natural language about that image. The VQA system is then expected to answer in natural language. To find the right answer, a VQA algorithm may need to use common sense to make sense of the information in the image and external knowledge. In this paper, we discuss some of the main ideas behind VQA systems and provide a comprehensive literature survey of the current state of the art in VQA and visual reasoning from four perspectives: problem definition and challenges, approaches, existing datasets, and evaluation matrices. We conclude our survey with a discussion and some potential future research directions in this area to generate new ideas and creative approaches to solving current problems and developing new applications.

关键词： machine learning Deep learning VQA Natural language processing Reasoning Computer vision

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SwinSight: a hierarchical vision transformer using shifted windows to leverage aerial image classification

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Multimedia Tools and applications 2024年第39期83卷 86457-86478页

作者： Pradhan, Praveen Kumar Das, Alloy Kumar, Amish Baruah, Udayan Sen, Biswaraj Ghosal, Palash Department of Information Technology Sikkim Manipal Institute of Technology Sikkim Manipal University East Sikkim Sikkim Majitar737136 India Centre for Computers and Communication Technology Sikkim Chisopani South Sikkim737136 India CVPR Unit Indian Statistical Institute 203 Barrackpore Trunk Road West Bengal Kolkata700108 India Birangana Sati Sadhani Rajyik Vishwavidyalaya Assam Golaghat785621 India

In aerial image classification, integrating advanced vision transformers with optimal preprocessing techniques is pivotal for enhancing model performance. This study presents SwinSight, a novel hierarchical vision transformer optimized for aerial image classification, which effectively addresses the computational challenges typically associated with transformers through a shifted window mechanism. The core of the research focuses on enhancing model performance by integrating a systematic preprocessing approach using Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Fast Fourier Transform (FFT). An extensive ablation study evaluates six permutations of these techniques, aiming to identify the most effective sequence for preprocessing. Results indicate that the sequence of DCT, followed by DWT, then FFT, significantly excels, achieving a high classification accuracy of 93.16% and maintaining a rapid inference time of 0.0049 seconds per frame. This sequence’s superior performance highlights the critical role of preprocessing order in optimizing feature extraction, thereby boosting the efficacy of the classification process. SwinSight’s advancements not only set a new benchmark for aerial image analysis but also offer broader implications for enhancing image processing workflows in various applications, contributing to theoretical insights and practical improvements in image-based machine learning tasks. This paper not only offers a practical solution for aerial image classification for diverse applications such as agriculture, environmental monitoring, land use applications, security, and beyond but also presents a novel SAIOD (Sikkim Aerial images dataset for Object Detection) to the computer vision research community, fostering added advancements. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Discrete wavelet transforms

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Metric-based pill recognition with the help of textual and visual cues

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IET image processing 2024年第14期18卷 4623-4638页

作者： Radli, Richard Voroshazi, Zsolt Czuni, Laszlo Univ Pannonia Image Proc Res Lab H-8200 Veszprem Hungary

Pill image recognition by machine vision can reduce the risk of taking the wrong medications, a severe healthcare problem. Automated dispensing machines or home applications both need reliable image processing techniques to compete with the problem of changing viewing conditions, large number of classes, and the similarity in pill appearance. The problem is attacked with a multi-stream, two-phase metric embedding neural model. To enhance the metric learning procedure, dynamic margin setting is introduced into the loss function. Moreover, it is shown that besides the visual features of drug samples, even free text of drug leaflets (processed with a natural language model) can be used to set the value of the margin in the triplet loss and thus increase the recognition accuracy of testing. Thus, besides using the conventional metric learning approach, the given discriminating features can be explicitly injected into the metric model using the NLP of the free text of pill leaflets or descriptors of images of selected pills. The performance on two datasets is analysed and a 1.6% (two-sided) and 2.89% (one-sided) increase in Top-1 accuracy on the CURE dataset is reported compared to existing best results. The inference time on CPU and GPU makes the proposed model suitable for different kinds of applications in medical pill verification;moreover, the approach applies to other areas of object recognition where few-shot problems arise. The proposed high-level feature injection method (into a low-level metric learning model) can also be exploited in other cases, where class features can be well described with textual or visual cues.

关键词： image processing neural net architecture object recognition

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