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Automated Seed Quality Assessment and Classification Using Watershed Algorithm and Ensemble learning

Automated Seed Quality Assessment and Classification Using W...

引用

Trends in Engineering Systems and Technologies (ICTEst), international conference on

作者： Ram Leeladhar Mekala Ravi Kishan Surapaneni Venkatesh Medisetty Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350307795

ISBN: (纸本)9798350307801

In response to the evolving landscape of precision agriculture, this project presents a pioneering solution for automated seed quality assessment. Leveraging the Watershed Algorithm, we conduct sophisticated image processing on Bas_370 seeds, extracting intricate features that form the backbone of a comprehensive CSV dataset. The subsequent implementation of an ensemble machine learning model, crafted through a fusion of classifiers, revolutionizes seed quality categorization, offering a robust and accurate alternative to conventional methods. Our approach not only addresses the limitations of traditional assessment techniques but also sets the stage for heightened precision and productivity in agriculture. The subsequent sections meticulously unravel the application of the Watershed Algorithm, the nuances of feature extraction, and the intricate development and evaluation processes of the ensemble machine learning model, providing a thorough comprehension of this groundbreaking seed quality assessment methodology.

关键词： Productivity machine learning algorithms Accuracy Watersheds Feature extraction Market research Seeds (agriculture)

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COVID-19 Infection Percentage Prediction via Boosted Hierarchical Vision Transformer 21st

COVID-19 Infection Percentage Prediction via Boosted Hierarc...

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21st international conference on image Analysis and processing (ICIAP)

作者： Hsu, Chih-Chung Dai, Sheng-Jay Chen, Shao-Ning Natl Cheng Kung Univ 1 Univ Rd Tainan Taiwan

ISBN: (纸本)9783031133244;9783031133237

A better backbone network usually benefits the performance of various computer vision applications. This paper aims to introduce an effective solution for infection percentage estimation of COVID-19 for the computed tomography (CT) scans. We first adopt the state-of-the-art backbone, Hierarchical Visual Transformer, as the backbone to extract the effective and semantic feature representation from the CT scans. Then, the non-linear classification and the regression heads are proposed to estimate the infection scores of COVID-19 symptoms of CT scans with the GELU activation function. We claim that multi-tasking learning is beneficial for better feature representation learning for the infection score prediction. Moreover, the maximum-rectangle cropping strategy is also proposed to obtain the region of interest (ROI) to boost the effectiveness of the infection percentage estimation of COVID-19. The experiments demonstrated that the proposed method is effective and efficient.

关键词： COVID-19 Computed tomography Deep learning Transformer

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Epidemiological image Analysis: Insights, Perspectives, and Challenges

Epidemiological Image Analysis: Insights, Perspectives, and ...

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Optimization Techniques for learning (ICOTL), international conference on

作者： Tatyana Ivanovska Markus Krüger Amro Daboul Department of Electrical Engineering Media and Computer Science East Bavarian Technical University of Applied Sciences Amberg-Weiden Amberg Germany Department of Prosthodontics Gerodontology and Biomaterials University Medicine Greifswald Greifswald Germany

The spotlight on large population-based studies is growing within the research community. Epidemiological studies, amassing extensive data through questionnaires and check-ups, often include imaging data like magnetic resonance imaging (MRI) or ultrasonography from numerous participants. This article offers an exploration of several recent epidemiological studies conducted in Germany, addressing not only the intriguing research tasks but also the multifaceted perspectives and challenges of analyzing image data. This includes potential enhancements in imaging technologies, machine learning applications for data analysis, and collaboration between different research disciplines. Key insights into the methodologies, algorithms, and techniques used for processing and interpreting complex imaging data are detailed. The potential of epidemiological image analysis to guide clinical practices and contribute to personalized medicine is also discussed.

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machine learning in Healthcare: Current Trends and the Future 1st

Machine Learning in Healthcare: Current Trends and the Futur...

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1st international conference on Artificial Intelligence for Smart Community, AISC 2020

作者： Usmani, Usman Ahmad Jaafar, Jafreezal Universiti Teknologi Petronas Seri Iskandar UTP Perak32610 Malaysia

ISBN: (纸本)9789811621826

Today, an abundance of electronically stored medical image data and DL algorithms can be used to recognize and detect patterns and anomalies in this kind of dataset. Computers and algorithms can interpret the imaging data as a very qualified radiologist can see irregular skin, lesions, tumours and brain bleeds. Consequently, the use of AI/ML tools/platforms to help radiologists is poised to grow exponentially. This approach addresses a vital issue in the healthcare sector as well-trained radiologists are challenging to come by worldwide. These professional professionals are, in most cases, under tremendous pressure due to the influx of digital medical data. We analyze and address the current state of A.I. applications in healthcare. A.I. can be applied to various healthcare data forms (structured and unstructured). Popular A.I. techniques include machine learning for structured data such as classic support vectors and neural networks, modern in-depth learning unstructured data natural language processing. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Robotic surgery

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Detection and Classification of Coffee Plant Diseases by image processing and machine learning 1st

Detection and Classification of Coffee Plant Diseases by Ima...

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1st international conference on Pan-African Intelligence and Smart Systems, PAAISS 2021

作者： Essoh, Serge Leonel Essuthi Kenfack, Hippolyte Michel Tapamo Ebele, Brice Auguste Mboule Mbietieu, Amos Mbietieu Etoua, Oscar Vianney Eone University of Yaoundé I B.P. 337 Yaoundé Cameroon UMMISCO Bondy France

ISBN: (纸本)9783030933135

With the expansion of the population, agriculture has become a major economic source. But its productivity and the quality of the resulting products are threatened by many diseases that attack plants. Thus, the detection and classification of these diseases remains a major problem in agriculture principally when the cultivated space becomes too large. The use of image processing and machine learning can be a digital contribution to resolve this problem. Methods based on pixels values processing and texture analysis of plant leaf image can extract the disease area and provide necessary information to classify the disease. In this paper, we propose a method to improve the detection of diseased areas. We also use a large number of texture descriptors extracted by the 3D-GLCM (three-dimensional Gray-level Co-occurrence Matrix) on color images to classify the diseases. The detection is done by k-means. PCA (Principal Component Analysis) selects the necessary texture descriptors for classifying the diseases. The classification is done by multi-class SVM (Support Vectors machine) and KNN (K-Nearest Neighbors) is used for comparison of classification results. Experiments were carried out on leaves images of Arabica coffee plant. The best performances show an accuracy of 96 % and a mean F1-score of 96.75 %. From those results we observed a considerable gain compared to the literature. © 2022, ICst Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Support vector machines

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Deep learning Based Application in Identifying Originality of the Hand Written Document using Convolution Neural Network 1

Deep Learning Based Application in Identifying Originality o...

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1st international conference on Connected Systems and Intelligence, CSI 2022

作者： Pallavi, M.O. Sneha, S. Sundaram, Meenakshi Preetham, N. School of CSA REVA University Bangalore India School of CS&E REVA University Bangalore India

ISBN: (纸本)9781665458153

Handwriting recognition is one of the foremost areas of exploration in pattern identification and recognition. as of now we have a system recognizes that converts the handwritten character into the printed text of many languages like English, Kannada, Tamil, Bengali, Latin, Devanagari, etc., and also the system which converts handwritten, printed copy, image or other documents into digitized format. The objective of the research is to identify the originality of handwritten documents using deep learning methods. During pandemics much of the offline work is shifted to online work, some of them are education, banking, etc. The originality of handwritten copies is checked for fraud copies to genuine verification from the original owner copies. The research includes a collection of sample written documents of around 1000 characters which consist of all the possible characters and numbers called training data, later it is cross verified by the testing data with a new input document. The framework includes proposing a CNN model and feature extraction using neural networks and proves the originality of the written copy against the test written copy. The steps involved are pre-processing, followed by segmentation, in turn, feature extraction, recognition, and comparison of each word, stroke, height, and slant of the alphabet to verify with test input. The data set will be pre-processed, CNN model extracts the features of each character, and generates a threshold value, which is compared with the testing data threshold values, if the result returned is more than 90% document will be considered to be accepted as original handwritten of the individual and if the threshold value comparison fails and less than 90% matching, the script/document is rej ected, it is categorized to fraud document. © 2022 IEEE.

关键词： Feature extraction

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Don't Wait Until the Accident Happens: Few-Shot Classification Framework for Car Accident Inspection in a Real World 21st

Don't Wait Until the Accident Happens: Few-Shot Classificati...

引用

21st international conference on image Analysis and processing (ICIAP)

作者： Park, Kyung Ho Chung, Hyunhee SOCAR Data Intelligence Grp Seoul South Korea

ISBN: (纸本)9783031064272;9783031064265

Car accident inspection is a binary classification task to recognize whether a given car image includes a damaged surface or not. While the prior studies utilized various computer vision algorithms under the fully supervised, high data availability regime, these studies bear several limits for application in the real world. First, acquiring a large amount of car accident images is challenging due to their scarcity. Second, the supervised classifier would fail to recognize a sample not seen a priori. To improve the aforementioned drawbacks, we propose a few-shot classification framework for the accident inspection task and illustrate several takeaways to the practitioners. First, we designed a few-shot classification framework and validated our approach precisely identifies the accident, although the practitioner has a few accident images. Second, we analyzed the fine-grained discriminative characteristics between normal and accident images;thus, fine-grained feature extractor architecture is adequate for our accident inspection task. Third, we scrutinized optimal image resizing strategy varies along with the feature extractor architecture;therefore, we recommend that practitioners be cautious in handling real world car images. Lastly, we analyzed a larger number of acquired accident images that are advantageous in a few-shot classification. Based on these contributions, we highly expect further studies can realize the benefits of automated car part recognition in the real world shortly.

关键词： Car accident inspection Few-shot learning Fine-grained classification

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Application of Band Combination in Landslide Identification of Remote Sensing images Driven by Deep learning

Application of Band Combination in Landslide Identification ...

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image processing, Computer Vision and machine learning (ICICML), international conference on

作者： Tianyu Yang Weijie Xu Yu Hua College of Computer Science and Cyber Security (Oxford Brookes College) Chengdu University of Technology Chengdu China

With the frequent occurrence of geological disasters, landslide identification has become an important research problem. In recent years, with the application and research of deep learning in the fields of computer vision and remote sensing image analysis, deep learning has become a popular method for landslide identification research because of its excellent feature extraction and pattern recognition capabilities. In this paper, we add new bands reflecting vegetation and water of landslide factors to the original bands of remote sensing image samples by linear combination of bands, and conduct two sets of comparison experiments on Unet and Swin-Unet to verify that adding band features can help improve landslide identification accuracy. The experimental results show that the experimental group with additional band features has a numerical improvement in landslide identification accuracy, with F1-score as the evaluation index, the Unet experimental group has a 2.29% improvement and the Swin-Unet experimental group has a 1.78% improvement. The results of this paper have implications for the subsequent application of bands combination of landslide factor features in remote sensing landslide identification driven by deep learning methods.

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Eigenfunctions of Ultrametric Morphological Openings and Closings 1st

Eigenfunctions of Ultrametric Morphological Openings and Clo...

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1st IAPR international Joint conference on Discrete Geometry and Mathematical Morphology (DGMM)

作者： Angulo, Jesus PSL Res Univ CMM Ctr Morphol Math MINES ParisTech Fontainebleau France

ISBN: (纸本)9783030766566;9783030766573

This paper deals with the relationship between spectral analysis in min-max algebra and ultrametric morphological operators. Indeed, morphological semigroups in ultrametric spaces are essentially based on that algebra. Theory of eigenfunctionals in min-max analysis is revisited, including classical applications (preference analysis, percolation and hierarchical segmentation). Ultrametric distance is the fix point functional in min-max analysis and from this result, we prove that the ultrametric distance is the key ingredient to easily define the eigenfunctions of ultrametric morphological openings and closings.

关键词： Ultrametric space Ultrametric image processing (min, max)-analysis Morphological semigroups

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A Review on Automated Annotation System for Document Text images

A Review on Automated Annotation System for Document Text Im...

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Cognitive, Green and Ubiquitous Computing (IC-CGU), international conference on

作者： Ashlin Deepa R N Boda Srija Maria Jabeen Kushi Reddy Kankar Bollepalli Jhansi Lakshmi Atul Negi Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India School of Computer and Information Sciences University of Hyderabad Hyderabad India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

Automated Annotation Systems for text play a crucial role in automatically adding descriptive metadata or labels to textual data. Over recent years, there has been a notable surge in interest among researchers and practitioners in automated annotation systems, particularly those focused on text images, including handwritten text images. This comprehensive review delves into the latest advancements in Automated Annotation Systems tailored for text images. It meticulously examines various aspects such as data creation, pre-processing steps, word detection, word recognition, and annotation models as presented across diverse research papers.

关键词： Deep learning Handwriting recognition image recognition Annotations Text recognition Reviews Computational modeling

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