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3rd International Conference on Innovative Mechanisms for Industry applications, ICIMIA 2023

作者： Karthik, Raja Pavan Vamsi, Kalla Yadu Reddy, Veeramreddy Sourya Tejarsha Abhishek, S. Anjali, T. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India Rolls-Royce Inc Bangalore India

ISBN: (纸本)9798350343632

In the era of digital imagery, there is a great interest in finding new and creative ways to express ourselves and make our images look beautiful. One such fascinating method is cartoonization, a process that transforms ordinary images into visually appealing cartoon images. This paper explores the integration of cutting-edge computer vision algorithms, traditional image processing methods, and Neural Networks to achieve cartoonization. The main focus is on combining object segmentation with cartoonization in a smooth and seamless way, which offers a unique and innovative approach to improving images. By thoroughly considering various techniques and how they can be used together, our research not only gives a complete understanding of these methods but also highlights how they can transform the field of digital artistry. By exploring the integration between methods, the study sheds light on how these techniques contribute to the evolving landscape of digital artistry. The research suggests that the fusion of computer vision, traditional image processing, and machine Learning techniques holds promising potential for pushing the boundaries of creative expression in the digital realm, offering new ways for creating efficient cartoon images. © 2023 IEEE.

关键词： Cartoonification Cartooning Color Quantization Edge Detection Region-Based Convolutional Neural Networks (R-CNN)

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Soil type identification model using a hybrid computer vision and machine learning approach

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MULTIMEDIA TOOLS AND applications 2023年第1期83卷 575-589页

作者： Abeje, Bekalu Tadele Salau, Ayodeji Oalelan Gela, Belsti Mulualem Mengistu, Abrham Debasu Haramaya Univ Dept Informat Technol Dire Dawa Ethiopia Afe Babalola Univ Dept Elect Elect & Comp Engn Ado Ekiti Nigeria Saveetha Inst Med & Tech Sci Saveetha Sch Engn Chennai India Bahir Dar Univ Inst Technol Bahir Dar Ethiopia Bahir Dar Univ Inst Technol Dept Comp Sci Bahir Dar Ethiopia

Computer vision and its technologies are being used in the area of agricultural automation to identify, locate, and track targets for further image processing. Mostly, agricultural production has been highly dependent on natural resources like soil, water, and other related natural minerals from the soil. Soil classification is a way of arranging soils that have similar characteristics into groups. Identifying and classifying soils has a great role to play in agricultural productivity as it helps to provide relevant information which aids agricultural experts to recommend the type of crop best suited for a specific type of soil. This study mainly concentrated on classifying soil types such as clay soil, loam soil, sandy soil, peat soil, silt soil, and chalk soil. The soil images were collected from Amhara region at different locations by using a sony digital camera. To reduce image noise due to handshake we used a camera stand or arm to avoid other types of noises like environmental lighting effects and shadow. Once the dataset was collected, preprocessing such as resizing and gamma correction was performed to remove noise from the images, and contrast adjustment was also performed. Experimental research was applied as a general methodology and the experiment was conducted based on two approaches. The first approach used CNN as an end-to-end classifier and the second used a hybrid approach which used CNN as a feature extractor and SVM as a classifier. When CNN was used as an end-to-end classifier, a classification accuracy of 88% was achieved, whereas when the hybrid approach which used CNN as feature extractor and SVM as classifier was employed, a classification accuracy of 95% was achieved. Finally, we conclude that the hybrid approach is better than that of the End-to-End classification using our proposed model.

关键词： Soil type identification CNN PCA SVM

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Currency Detector For Visually Impaired

Currency Detector For Visually Impaired

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2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023

作者： MacRiga, G. Adiline Agustin Jebakumar, V. Ariharan, S. Sri Sairam Engineering College Dept of Information Technology Chennai India

ISBN: (纸本)9798350394580

The currency has a great meaning in everyday *** each of us using the Currency notes in our day to day lives through Cash or online Payment. Thus currency recognisation has gained a great interest for many researchers across the varoius part of the globe. Currency Identification System is an important area of processing of the image. This is used worldwide for various purposes. Our project aims to develop an intelligent system for recognizing Indian paper currency, which has diverse applications in electronic banking, currency monitoring systems, money exchange machines, and other related fields. With the rise of modern currency automation systems, the ability to accurately identify and process paper currency is becoming increasingly important. By leveraging advanced computer vision and machine learning techniques, we hope to create a system that can quickly and accurately recognize Indian paper currency, enabling it to be used in a range of applications. This project is essential to addressing the current needs of currency automation systems and has the potential to make a significant impact on various industries. In this project We are using Opencv(Open Computer vision) which is a image processing libraray, the Approach is correlation technique is applied to extract Gandhiji images and Thin Strip, and forming clusters of featuresIt is a very helpful system for blind people toknow the denomination on the paper currency © 2023 IEEE.

关键词： Currency Recognition RGB image,Correlation

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Tomato leaf disease classification by exploiting transfer learning and feature concatenation

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IET image processing 2022年第3期16卷 913-925页

作者： Al-gaashani, Mehdhar S. A. M. Shang, Fengjun Muthanna, Mohammed S. A. Khayyat, Mashael Abd El-Latif, Ahmed A. Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing Peoples R China St Petersburg State Univ Dept Math St Petersburg 199034 Russia Univ Jeddah Coll Comp Sci & Engn Dept Informat Syst & Technol Jeddah Saudi Arabia Menoufia Univ Fac Sci Math & Comp Sci Dept Shibin Al Kawm Egypt

Tomato is one of the most important vegetables worldwide. It is considered a mainstay of many countries' economies. However, tomato crops are vulnerable to many diseases that lead to reducing or destroying production, and for this reason, early and accurate diagnosis of tomato diseases is very urgent. For this reason, many deep learning models have been developed to automate tomato leaf disease classification. Deep learning is far superior to traditional machine learning with loads of data, but traditional machine learning may outperform deep learning for limited training data. The authors propose a tomato leaf disease classification method by exploiting transfer learning and features concatenation. The authors extract features using pre-trained kernels (weights) from MobileNetV2 and NASNetMobile;then, they concatenate and reduce the dimensionality of these features using kernel principal component analysis. Following that, they feed these features into a conventional learning algorithm. The experimental results confirm the effectiveness of concatenated features for boosting the performance of classifiers. The authors have evaluated the three most popular traditional machine learning classifiers, random forest, support vector machine, and multinomial logistic regression;among them, multinomial logistic regression achieved the best performance with an average accuracy of 97%.

关键词： agricultural products Regression analysis machine learning (artificial intelligence) Neural nets concatenated features image recognition random forests transfer learning Computer vision and image processing techniques deep learning (artificial intelligence) Other topics in statistics feature extraction image classification pattern classification crops tomato leaf disease classification method Agriculture Data handling techniques popular traditional machine learning classifiers feature concatenation support vector machines Biology and medical computing Agriculture, forestry and fisheries computing deep learning models logistic regression learning (artificial intelligence) plant diseases regression analysis Support vector machines Information technology applications tomato crops principal component analysis conventional learning algorithm important vegetables worldwide diseases tomato diseases

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Hearing Anything Anywhere

Hearing Anything Anywhere

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IEEE/CVF Conference on Computer vision and Pattern Recognition (CVPR)

作者： Wang, Mason Long Sawata, Ryosuke Clark, Samuel Gao, Ruohan Wu, Shangzhe Wu, Jiajun Stanford Univ Stanford CA 94305 USA Sony AI Tokyo Japan Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9798350353006

Recent years have seen immense progress in 3D computer vision and computer graphics, with emerging tools that can virtualize real-world 3D environments for numerous Mixed Reality (XR) applications. However, alongside immersive visual experiences, immersive auditory experiences are equally vital to our holistic perception of an environment. In this paper, we aim to reconstruct the spatial acoustic characteristics of an arbitrary environment given only a sparse set of (roughly 12) room impulse response (RIR) recordings and a planar reconstruction of the scene, a setup that is easily achievable by ordinary users. To this end, we introduce DIFFRIR, a differentiable RIR rendering framework with interpretable parametric models of salient acoustic features of the scene, including sound source directivity and surface reflectivity. This allows us to synthesize novel auditory experiences through the space with any source audio. To evaluate our method, we collect a dataset of RIR recordings and music in four diverse, real environments. We show that our model outperforms state-of-the-art baselines on rendering monaural and binaural RIRs and music at unseen locations, and learns physically interpretable parameters characterizing acoustic properties of the sound source and surfaces in the scene.

关键词： Audio Binauralization Differentiable Audio Rendering image-Source Inverse Rendering machine Learning Physics-Based Models Room Acoustics Room Impulse Response Signal processing Sound Sound Dataset Soundfield Interpolation Spatial Audio

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Digital image processing: Advanced Technologies and applications

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APPLIED SCIENCES-BASEL 2024年第14期14卷 6051页

作者： Mahmood, Zahid COMSATS Univ Islamabad Dept Elect & Comp Engn Abbottabad Campus Abbottabad 22060 Pakistan

A few decades ago, conventional image processing methods mostly focused on basic tasks such as image enhancement, registration, or edge detection. Early attempts to achieve these tasks mostly utilized grayscale images. Over time, simple methods to process grayscale images resulted in performance degradation for RGB images [1]. Ultimately, RGB image processing received more attention and subsequent advancements were made, including color preservation and fusion-based processing [2]. Currently, deep learning is used extensively in various fields, such as speech recognition and healthcare domains, with encouraging outcomes in image processing, such as image classification and segmentation [3]. A recent study showed that deep learning-based approaches significantly improve the performance of many image-related tasks, such as object detection, recognition, or segmentation compared, compared to conventional *** the past three decades, a large number of diverse methods have appeared in computer vision and machine learning. Many of them utilize conventional machine learning. However, the recent trend in deep learning has yielded encouraging results. This section gives a brief overview of the works collected in this Special *** contributions listed in this Special Issue can be combined into three major groups with the following key *** the rapid advancements in AI and machine learning, the use of deep learning in various applications has become obvious in many industries due to its ability to process complex patterns and make reliable predictions. Therefore, deep learning algorithms have found their place in crucial fields, including object detection and recognition, natural language processing, and medical imaging. For instance, CNNs are extensively employed for tasks such as image classification, object detection, segmentation, and medical imaging. Similarly, recurrent neural networks (RNNs) and transformers have advanced the capabilities of ma

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A Survey on Automated Disease Diagnosis and Classification of Herbal Plants Using Digital image processing 6

A Survey on Automated Disease Diagnosis and Classification o...

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6th International Conference on Information Systems and Computer Networks, ISCON 2023

作者： Khandelwal, Ankit Shukla, Aasheesh Sain, Mangal GLA University Electronics and Communication Dept. Mathura India Dongseo University Information Engineering Dept. Busan Korea Republic of

ISBN: (纸本)9798350346961

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 inconsistent, therefore techniques for classifying plants diseases using digital method are necessary as they speed up the identification process. This literature discusses various digital methods for classifying plant diseases. The first step is image processing of herbal plants, which make use of traditional image processing techniques and algorithms to categories diseases in herbal plants based on attributes like texture, color, and shape. The second step is plant categorization based on deep learning and machine learning, such as CNN, KNN, logistic regression etc. Deep learning applications mainly lessen the reliance on designs and preprocessing techniques by joining the entire process. This review provides a basic understanding and knowledge of the current state-of-the-art researches, as well as some dynamic trends for future work.. © 2023 IEEE.

关键词： Computer vision

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In-Scene Material Detection for Real-Time Autonomous Hyperspectral Reflectance Correction 11

In-Scene Material Detection for Real-Time Autonomous Hypersp...

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Conference on image Sensing Technologies - Materials, Devices, Systems, and applications XI

作者： Pietersen, Randall A. Robinson, Brian M. Diltz, Robert A. Einstein, Herbert H. MIT Dept Civil & Environm Engn Room 1-342 77 Massachusetts Ave Cambridge MA 02139 USA USAF Civil Engn Ctr 104 Res Rd Bldg T9719 Tyndall AFB FL 32403 USA Torch Technol 4090 South Mem Pkwy Huntsville AL 35802 USA

ISBN: (纸本)9781510673793;9781510673786

If a U.S. Air Force operated airfield is attacked, the current methodology for assessing its condition is a slow manual inspection process, exposing personnel to dangerous conditions. Advances in drone technology, remote sensing, deep learning, and computer vision have sparked interest in developing autonomous remote solutions. While digital image processing techniques have matured in recent decades, a lack of application-specific training data presents significant obstacles for developing reliable solutions to detect specific objects amongst rubble, debris, variations in pavement types, changing surface features, and other variable runway conditions. Consequently, near-surface hyperspectral imaging has been proposed as an alternative to RGB digital images, due to its discriminatory power in classifying materials. Spatio-spectral data acquired by hyperspectral imagers help address common challenges presented by data scarcity and scene complexity;however, raw data acquired by hyperspectral sensors must first undergo a reflectance correction process before it can be of use. This paper presents an expedient method, tailored to airfield damage assessment, for performing autonomous reflectance correction on near-surface hyperspectral data using in-scene pavement materials with a known spectral reflectance. Unlike most reflectance correction methods, this process eliminates the need for human intervention with the sensor (or its data) pre or post flight and does not require pre-staged reference targets or an additional downwelling irradiance sensor. Positive initial results from real-world flights over pavements are presented and compared to traditional methods of reflectance correction. Three separate flight tests report mean errors between 2% and 2.5% using the new method.

关键词： Hyperspectral Imaging Pavement Reflectance Correction machine Learning Computer vision

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Multi-Scale ConvLSTM Attention-Based Brain Tumor Segmentation

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND applications 2022年第11期13卷 849-856页

作者： Ait Skourt, Brahim Nikolov, Nikola S. Majda, Aicha Begdouri, Ahlame Univ Sidi Mohammed Ben Abdellah Lab Intelligent Syst & Applicat Fes Morocco Univ Limerick Comp Sci & Informat Syst Dept Limerick Ireland Univ Moulay Ismail Networks & Comp Syst Res Team Meknes Morocco

In computer vision, there are various machine learning algorithms that have proven to be very effective. Convolutional Neural Networks (CNNs) are a kind of deep learning algorithms that became mostly used in image processing with a remarkable success rate compared to conventional machine learning algorithms. CNNs are widely used in different computer vision fields, especially in the medical domain. In this study, we perform a semantic brain tumor segmentation using a novel deep learning architecture we called multi-scale ConvLSTM Attention Neural Network, that resides in Convolutional Long-Short-Term-Memory (ConvLSTM) and Attention units with the use of multiple feature extraction blocks such as Inception, Squeeze-Excitation and Residual Network block. The use of such blocks separately is known to boost the performance of the model, in our case we show that their combination has also a beneficial effect on the accuracy. Experimental results show that our model performs brain tumor segmentation effectively compared to standard U-Net, Attention U-net and Fully Connected Network (FCN), with 79.78 Dice score using our method compared to 78.61, 73.65 and 72.89 using Attention U-net, standard U-net and FCN respectively.

关键词： Convolutional neural networks image processing semantic brain tumor segmentation convolutional long short term memory inception squeeze-excitation residual-network attention units

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A machine vision Approach for Bioreactor Foam Sensing

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SLAS TECHNOLOGY 2021年第4期26卷 408-414页

作者： Austerjost, Jonas Soeldner, Robert Edlund, Christoffer Trygg, Johan Pollard, David Sjoegren, Rickard Sartorius Stedim Biotech GmbH Sartorius Corp Res August Spindler Str 11 D-37079 Gottingen Germany Sartorius Stedim Data Analyt AB Sartorius Corp Res Umea Sweden Sartorius Stedim North Amer Inc Sartorius Corp Res Boston MA USA

machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

关键词： foam sensor machine vision deep learning process analytical technology bioprocessing

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