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Artificial Intelligence and Smart systems (ICAIS), International Conference on

作者： Milind S. Patil Pradip B. Mane All India Shri Shivaji Memoril Society’s Institute of Information Technology Pune India E&TC Engineering Department Vishwakarama Institute of Information Technology Pune India

image Quality Assessment (IQA) has got importance in the computer vision applications as it provides tool to evaluate and rate different image processing algorithms. image Fusion is a process in which information from multiple images is combined into a single image. Due to specific nature of fused images present IQA methods have limitations for evaluation of image Fusion algorithms. With the recent development of Deep Convolutional Neural Networks (Deep CNNs), No- reference image quality assessment is becomes reality. This article has proposed the pre-trained Deep CNNs based image fusion classification using Alexnet, vGG19, Inception v3 and ResNet-50. Four states–of–the-art image fusion algorithms used for image fusion are Laplacian Pyramid (LP), Shift Invariant DWT (SIDWT), Discrete Wavelet Transform (DWT) and Ratio Pyramid (RP). To achieve the effective IQA, sufficiently large dataset of synthetically fused images is created and same is evaluated by using Deep CNNs. The results show that recent deep CNN methods correctly classify the fused images into corresponding categories based on its fusion algorithms. The results are consistent with FR-IQA methods. ResNet-50 provides best classification accuracy with less number of epochs and time to converge due to sparse network connections.

关键词： Training image quality Transforms Feature extraction Discrete wavelet transforms Classification algorithms Quality assessment

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Unleashing the Power of Hierarchical variational Autoencoder for Predicting Breast Cancer

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IEEE ACCESS 2024年 12卷 195658-195670页

作者： Sreelekshmi, v. Pavithran, K. Nair, Jyothisha J. Amrita Vishwa Vidyapeetham Amrita Sch Comp Dept Comp Sci & Engn Amritapuri 690525 India Amrita Inst Med Sci Dept Med Oncol Kochi 682041 Kerala India

Breast cancer continues to be a major health concern worldwide. Early and accurate prediction is crucial for effective treatment and improving survival rates. Computer Aided Diagnosis system serves as an invaluable tool for radiologists, aiming to reduce diagnostic errors and enhance the accuracy of diagnosis. These systems incorporate various processing techniques, including pre-processing, segmentation, feature extraction, and classification. Moreover, deep learning methods frequently suffer from sub optimal performance and demand substantial computational resources. This study focuses on developing an automated classification model for mammography images to aid in breast cancer diagnosis. Our proposed model initiates with noise removal using median filters, followed by the removal of the pectoral muscle in images through the Canny-edge detection method. On these preprocessed images, we applied data augmentation using a two-point crossover technique, addressing issues of small datasets and class imbalances common in medical image analysis. The images then undergo multi-scale representation via the fourth-order complex diffusion algorithm. Feature extraction is conducted on these multi-scaled images using a Hierarchical variational Auto-encoders and then classified using a Support vector Machine. Employing fourth-order complex diffusion for initial multi-scale representation significantly enhances the accuracy of feature extraction resulting in robust classification performance. The training process involves two different datasets like MIAS and the KAU-BCMD. Test results for the KAU-BCMD dataset include: accuracy of 99.80%, Area Under the Curve of 99.30%, F1-score of 99.20%, balanced accuracy of 99.80%, and Matthews correlation coefficient of 99.20%. For the MIAS dataset, test results show accuracy of 99.30%, Area Under the Curve of 99.10%, F1-score of 98.30%, balanced accuracy of 99.00%, and Matthews correlation coefficient of 99.00%. Our validation results clearl

关键词： Feature extraction Breast cancer Muscles Accuracy Data augmentation Mammography image segmentation Noise Convolutional neural networks Classification algorithms convolutional neural network data augmentation edge detection multi-scale representation variational autoencoder

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Demographic attribute estimation in face videos combining local information and quality assessment

Demographic attribute estimation in face videos combining lo...

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作者： Becerra-Riera, Fabiola Morales-González, Annette Méndez-vázquez, Heydi Dugelay, Jean-Luc 7A #21406 Siboney Havana PlayaP.C.12200 Cuba Digital Security Department EURECOM Campus Sophia Tech 450 route des Chappes Biot Sophia Antipolis06410 France

Nowadays, video analysis applications are gaining popularity given the rise of CCTv systems and the availability of video cameras to the general public, such as cameras in mobile devices. Many image analysis and processing tasks have evolved toward video domain, with the advantage of redundant information obtained from several frames, which can help disambiguating many recognition outputs. In this context, there are also particular video problems to deal with, such as uncontrolled scenarios and poor image quality. Most existing works regarding facial demographic estimation are focused on still image datasets;therefore, we propose to address gender and age estimation in video scenarios. In order to handle known video problems such as low-quality image capture, occlusions and pose variations, we propose a threefold strategy to adapt current image-based attribute recognition algorithms. First, we employ a quality assessment step based on 12 metrics to select relevant good quality frames from a face video sequence. Second, we propose a component-based approach to determine the most discriminant local regions of the face for each specific attribute, under these varying conditions. Third, we evaluate different frame combination strategies to produce the final video prediction. In our experimental validation, conducted in 3 datasets (EURECOM Augmented, UvA-Nemo Smile and YouTube Faces datasets), we show the advantages of our proposed strategy for improving video-based demographic attribute classification. © 2022, The Author(s), under exclusive licence to Springer-verlag GmbH Germany, part of Springer Nature.

关键词： image quality

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Expanding the Medical Decathlon dataset: segmentation of colon and colorectal cancer from computed tomography images

arXiv

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arXiv 2024年

作者： Chernenkiy, I.M. Drach, Y.A. Mustakimova, S.R. Kazantseva, v.v. Ushakov, N.A. Efetov, S.K. Feldsherov, M.v. 8 Trubetskaya str. building 2 Moscow119991 Russia Department of Biomedical Technologies and Systems N.E. Bauman Moscow State Technical University 2nd Baumanskaya St. 5 Bldg. 1 Moscow105005 Russia . 8 Trubetskaya str. building 2 Moscow119991 Russia Dovatora str. 15 Moscow119991 Russia 8 Trubetskaya str. building 2 Moscow119991 Russia

Colorectal cancer is the third-most common cancer in the Western Hemisphere. The segmentation of colorectal and colorectal cancer by computed tomography is an urgent problem in medicine. Indeed, a system capable of solving this problem will enable the detection of colorectal cancer at early stages of the disease, facilitate the search for pathology by the radiologist, and significantly accelerate the process of diagnosing the disease. However, scientific publications on medical image processing mostly use closed, non-public data. This paper presents an extension of the Medical Decathlon dataset with colorectal markups in order to improve the quality of segmentation algorithms. An experienced radiologist validated the data, categorized it into subsets by quality, and published it in the public domain. Based on the obtained results, we trained neural network models of the UNet architecture with 5-part cross-validation and achieved a Dice metric quality of 0.6988 ± 0.3. The published markups will improve the quality of colorectal cancer detection and simplify the radiologist's job for study description. © 2024, CC BY-NC-SA.

关键词： Medical image processing

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Detecting Fake Faces In Smart Cities Security Surveillance Using image Recognition And Convolutional Neural Networks 1

Detecting Fake Faces In Smart Cities Security Surveillance U...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Daya Sagar, K.v. Kamesh, D.B.K. Srinivasa Rao, T. Krishna, Chinta venkata Murali Department of Electronics and Computer Engineering Koneru Lakshmaiah Education Foundation Vadeswaram Andhra Pradesh Guntur522502 India Dept.of CSE Mallareddy Engineering for Woen Hyderabad India Koneru Lakshmaiah Education Foundation India Department of C.S.E. NRI Institute of Technology India

ISBN: (纸本)9781607685395

Smart cities are planned to have millions of Internet-connected sensors and devices. Sensors can create a huge amount of data in a range of applications. In modern urban environments, quality of life in a Smart City is heavily dependent on the safety of its residents. For a long time, public safety has been a major source of anxiety. For everyone, stopping a breach of private space security has become a priority. Traditional security systems raise an alarm whenever they detect a breach of safety. It is possible to find a breach of an advanced model by using image processing and a deep analysis of convolutional neural networks to classify images. Because of the ability to reduce complicated aspects from photographs using exact algorithms for facial and body detection. The results of specific machine learning, such as deep learning techniques are outstanding. The processing time of the proposed system is reduced, and true rate of face recognition is 72.7% under varying distance from 2m to *** paper aims to show that when used together the security sector, the two can achieve more than might have been previously assumed models. © The Electrochemical Society

关键词： Face recognition

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Macro-Scale Pattern Recognition and Coordinate Identification in Real-time Spatio-temporal Overlap for Photonics Engineering Applications

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IFAC-PapersOnLine 2024年第3期58卷 70-73页

作者： Haider Al-Juboori South East Technological University Faculty of Engineering Dept. of Electronics Engineering and Communications 806 Killeshin Building Kilkenny Road Carlow Ireland R93V960

The significance of high-speed machine vision in scientific and technological fields is growing, especially with the era of Industry 4.0 technologies. There are several pattern-matching algorithms that have various intriguing applications in ultralow-latency machine vision processing. However, the low frame rate of image sensors—which usually operate at tens of hertz—fundamentally limits the processing rate. The paper will conceptualize and develop the computerized pattern recognition technique that can be applied to investigate light beam profiles and extract the desired information according to the purpose required in this case study. In the current work, the automatic detection and inspection of laser spots were designed to perform analysis and alignment for the laser beam in comparison with the electron spot beam using the LabvIEW graphical programming environment, especially when the laser and electron beams overlap. This is one of the important steps for realizing the fundamental aim of test-FEL to produce short wavelengths with the second, third, and fifth harmonics at 131.5, 88, and 53 nm, respectively. The tentative version of the program achieved the elementary purpose, which fulfilled the accurate transversal alignment of the ultrashort laser pulses with the electron beam in the system of the FEL test facility at MAX-Lab, in addition to studying the beam’s stability and jittering range.

关键词： intelligent systems pattern matching real-time tracking computer vision concepts supporting control automation semi-robotic systems

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Identification of Medicinal Plants in Ardabil Using Deep learning: Identification of Medicinal Plants using Deep learning 27

Identification of Medicinal Plants in Ardabil Using Deep lea...

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27th International Computer Conference, Computer Society of Iran, CSICC 2022

作者： Abdollahi, Jafar Islamic Azad University Ardabil Branch Department Of Computer Engineering Ardabil Iran

ISBN: (纸本)9781665480277

Ardabil is well-known for offering the ideal environment for a good, cheap medicinal herb. various plant parts are used as essential components in producing natural medicines. According to IUCN (International Union for Conservation of Nature) records, many medicinal plants are on the verge of extinction, so employing image processing and computer vision algorithms to distinguish proof of medicinal plants is critical. As a result, the digitalization of beneficial therapeutic plants is critical for biodiversity preservation. The use of Convolutional Neural Network (CNN)-based techniques to distinguish Indian leaf species is investigated in this research. Several Deep Learning frameworks have recently been used to discern, identify, and characterize various plants. This study is mostly focused on identifying medicinal plants that can be found in rural areas. The Transfer Learning technique selected a well-known pre-trained CNN architecture called mobile net v2. The medical plant dataset was built using 30 different classes of medicinal plants, totaling 3000 photos, and these models were assessed with their pre-trained weights. On a held-out test set, the trained model had an accuracy of 98.05 percent, demonstrating the practicality of this approach. © 2022 IEEE.

关键词： Deep learning Computer vision image processing Transfer learning Knowledge discovery Software Data systems

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Reducing the light scattering impact in liquid-crystal-based imaging systems

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APPLIED OPTICS 2020年第16期59卷 4780-4789页

作者： Pusenkova, Anastasiia Galstian, Tigran Univ Laval Dept Phys Engn Phys & Opt Ctr Opt Photon & Lasers 2325 Rue Terrasse Quebec City PQ G1V 0A6 Canada LensVector Inc 6203 San Ignacio AveSuite 110 San Jose CA 95119 USA

We show an experimental method of quantifying the effect of light scattering by liquid crystals (LCs) and then apply rather simple image processing algorithms (Wiener deconvolution and contrast-limited adaptive histogram equalization) to improve the quality of obtained images when using electrically tunable LC lenses (TLCLs). Better contrast and color reproduction have been achieved. We think that this approach will allow the use of thicker LC cells and thus increase the maximum achievable optical power of the TLCL without a noticeable reduction of image quality. This eliminates one of the key limitations for their use in various adaptive imaging applications requiring larger apertures. (C) 2020 Optical Society of America

关键词： Effective refractive index image processing algorithms image quality Imaging systems Liquid crystals Tunable lenses

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TexAvi: Generating Stereoscopic vR video Clips from Text Descriptions

TexAVi: Generating Stereoscopic VR Video Clips from Text Des...

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Computer vision and Machine Intelligence (CvMI), International Conference on

作者： Shruti Jayaraman R Bhavya vriksha Srihari v Mary Anita Rajam Dept. of Computer Science and Engineering College of Engineering Guindy Chennai India

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

While generative models such as text-to-image, large language models and text-to-video have seen significant progress, the extension to text-to-virtual-reality remains largely unexplored, due to a deficit in training data and the complexity of achieving realistic depth and motion in virtual environments. This paper proposes an approach to coalesce existing generative systems to form a stereoscopic virtual reality video from text. Carried out in three main stages, we start with a base text-to-image model that captures context from an input text. We then employ Stable Diffusion on the rudimentary image produced, to generate frames with enhanced realism and overall quality. These frames are processed with depth estimation algorithms to create left-eye and right-eye views, which are stitched side-by-side to create an immersive viewing experience. Such systems would be highly beneficial in virtual reality production, since filming and scene building often require extensive hours of work and post-production effort. We utilize image evaluation techniques, specifically Fréchet Inception Distance and CLIP Score, to assess the visual quality of frames produced for the video. These quantitative measures establish the proficiency of the proposed method. Our work highlights the exciting possibilities of using natural language-driven graphics in fields like virtual reality simulations.

关键词： Headphones Solid modeling visualization Stereo image processing Text to image virtual environments Training data Production Media Text to video

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A Power-efficient image Classifier using Neural Network with Pipelined FFT Architecture

TechRxiv

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TechRxiv 2023年

作者： Hai, Shafiqul Reddy, Tella Rajashekhar Department of Electrical and Computer Engineering Lakehead University BarrieON Canada

Deep Neural Network (DNN) belongs to an important class of machine learning algorithms generally used to classify digital data in the form of image and speech recognition. The computational complexity of a DNN-based image classifier is higher than traditional fully connected (FC) feed-forward NNs. Therefore, dedicated cloud servers and Graphical Processor Units (GPU) are utilized to achieve high-speed and large-capacity computation tasks in machine vision systems. However, a growing demand exists for real-time processing of complex machine-learning tasks on embedded systems. As FC layers consume the highest fraction of computational power and memory footprint, innovating novel power-efficient and low-footprint NN architecture for embedded systems is crucial. A novel design strategy and algorithms are proposed in this article, where a power-efficient FC DNN is implemented using a pipelined and parallel Fast Fourier Transform (FFT) on a circular projection-based architecture. The footprint of the DNN is further reduced using a folded FFT network. The proposed algorithm is tested using two benchmark training set examples, the "MNIST database of handwritten digits" and the "CIFAR-10 database". In both cases, we achieved > 90% accuracy, while the power consumption of the network is 37% less than the traditional FFT architecture-based DNNs. © 2023, CC BY.

关键词： Deep neural networks

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