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Image processing is manipulation of an image that has been digitised and uploaded into a computer. Software programs modify the image to make it more useful, and can for example be used to enable image recognition.

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RETFound-enhanced community-based fundus disease screening: real-world evidence and decision curve analysis

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Cell Painting-based bioactivity prediction boosts high-throughput screening hit-rates and compound diversity

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A comparative analysis of pairwise image stitching techniques for microscopy images

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Creating a universal cell segmentation algorithm

Cell segmentation currently involves the use of various bespoke algorithms designed for specific cell types, tissues, staining methods and microscopy technologies. We present a universal algorithm that can segment all kinds of microscopy images and cell types across diverse imaging protocols.

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EfficientBioAI: making bioimaging AI models efficient in energy and latency

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Moving towards a generalized denoising network for microscopy

The visualization and analysis of biological events using fluorescence microscopy is limited by the noise inherent in the images obtained. Now, a self-supervised spatial redundancy denoising transformer is proposed to address this challenge.

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Editorial article, editorial: current trends in image processing and pattern recognition.

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  • PAMI Research Lab, Computer Science, University of South Dakota, Vermillion, SD, United States

Editorial on the Research Topic Current Trends in Image Processing and Pattern Recognition

Technological advancements in computing multiple opportunities in a wide variety of fields that range from document analysis ( Santosh, 2018 ), biomedical and healthcare informatics ( Santosh et al., 2019 ; Santosh et al., 2021 ; Santosh and Gaur, 2021 ; Santosh and Joshi, 2021 ), and biometrics to intelligent language processing. These applications primarily leverage AI tools and/or techniques, where topics such as image processing, signal and pattern recognition, machine learning and computer vision are considered.

With this theme, we opened a call for papers on Current Trends in Image Processing & Pattern Recognition that exactly followed third International Conference on Recent Trends in Image Processing & Pattern Recognition (RTIP2R), 2020 (URL: http://rtip2r-conference.org ). Our call was not limited to RTIP2R 2020, it was open to all. Altogether, 12 papers were submitted and seven of them were accepted for publication.

In Deshpande et al. , authors addressed the use of global fingerprint features (e.g., ridge flow, frequency, and other interest/key points) for matching. With Convolution Neural Network (CNN) matching model, which they called “Combination of Nearest-Neighbor Arrangement Indexing (CNNAI),” on datasets: FVC2004 and NIST SD27, their highest rank-I identification rate of 84.5% was achieved. Authors claimed that their results can be compared with the state-of-the-art algorithms and their approach was robust to rotation and scale. Similarly, in Deshpande et al. , using the exact same datasets, exact same set of authors addressed the importance of minutiae extraction and matching by taking into low quality latent fingerprint images. Their minutiae extraction technique showed remarkable improvement in their results. As claimed by the authors, their results were comparable to state-of-the-art systems.

In Gornale et al. , authors extracted distinguishing features that were geometrically distorted or transformed by taking Hu’s Invariant Moments into account. With this, authors focused on early detection and gradation of Knee Osteoarthritis, and they claimed that their results were validated by ortho surgeons and rheumatologists.

In Tamilmathi and Chithra , authors introduced a new deep learned quantization-based coding for 3D airborne LiDAR point cloud image. In their experimental results, authors showed that their model compressed an image into constant 16-bits of data and decompressed with approximately 160 dB of PSNR value, 174.46 s execution time with 0.6 s execution speed per instruction. Authors claimed that their method can be compared with previous algorithms/techniques in case we consider the following factors: space and time.

In Tamilmathi and Chithra , authors carefully inspected possible signs of plant leaf diseases. They employed the concept of feature learning and observed the correlation and/or similarity between symptoms that are related to diseases, so their disease identification is possible.

In Das Chagas Silva Araujo et al. , authors proposed a benchmark environment to compare multiple algorithms when one needs to deal with depth reconstruction from two-event based sensors. In their evaluation, a stereo matching algorithm was implemented, and multiple experiments were done with multiple camera settings as well as parameters. Authors claimed that this work could be considered as a benchmark when we consider robust evaluation of the multitude of new techniques under the scope of event-based stereo vision.

In Steffen et al. ; Gornale et al. , authors employed handwritten signature to better understand the behavioral biometric trait for document authentication/verification, such letters, contracts, and wills. They used handcrafter features such as LBP and HOG to extract features from 4,790 signatures so shallow learning can efficiently be applied. Using k-NN, decision tree and support vector machine classifiers, they reported promising performance.

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The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Santosh, KC, Antani, S., Guru, D. S., and Dey, N. (2019). Medical Imaging Artificial Intelligence, Image Recognition, and Machine Learning Techniques . United States: CRC Press . ISBN: 9780429029417. doi:10.1201/9780429029417

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Santosh, KC, Das, N., and Ghosh, S. (2021). Deep Learning Models for Medical Imaging, Primers in Biomedical Imaging Devices and Systems . United States: Elsevier . eBook ISBN: 9780128236505.

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Santosh, KC (2018). Document Image Analysis - Current Trends and Challenges in Graphics Recognition . United States: Springer . ISBN 978-981-13-2338-6. doi:10.1007/978-981-13-2339-3

Santosh, KC, and Gaur, L. (2021). Artificial Intelligence and Machine Learning in Public Healthcare: Opportunities and Societal Impact . Spain: SpringerBriefs in Computational Intelligence Series . ISBN: 978-981-16-6768-8. doi:10.1007/978-981-16-6768-8

Santosh, KC, and Joshi, A. (2021). COVID-19: Prediction, Decision-Making, and its Impacts, Book Series in Lecture Notes on Data Engineering and Communications Technologies . United States: Springer Nature . ISBN: 978-981-15-9682-7. doi:10.1007/978-981-15-9682-7

Keywords: artificial intelligence, computer vision, machine learning, image processing, signal processing, pattern recocgnition

Citation: Santosh KC (2021) Editorial: Current Trends in Image Processing and Pattern Recognition. Front. Robot. AI 8:785075. doi: 10.3389/frobt.2021.785075

Received: 28 September 2021; Accepted: 06 October 2021; Published: 09 December 2021.

Edited and reviewed by:

Copyright © 2021 Santosh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: KC Santosh, [email protected]

This article is part of the Research Topic

Current Trends in Image Processing and Pattern Recognition

Research Topics

Biomedical Imaging

Biomedical Imaging

The current plethora of imaging technologies such as magnetic resonance imaging (MR), computed tomography (CT), position emission tomography (PET), optical coherence tomography (OCT), and ultrasound provide great insight into the different anatomical and functional processes of the human body.

Computer Vision

Computer Vision

Computer vision is the science and technology of teaching a computer to interpret images and video as well as a typical human. Technically, computer vision encompasses the fields of image/video processing, pattern recognition, biological vision, artificial intelligence, augmented reality, mathematical modeling, statistics, probability, optimization, 2D sensors, and photography.

Image Segmentation/Classification

Image Segmentation/Classification

Extracting information from a digital image often depends on first identifying desired objects or breaking down the image into homogenous regions (a process called 'segmentation') and then assigning these objects to particular classes (a process called 'classification'). This is a fundamental part of computer vision, combining image processing and pattern recognition techniques.

Multiresolution Techniques

Multiresolution   Techniques

The VIP lab has a particularly extensive history with multiresolution methods, and a significant number of research students have explored this theme. Multiresolution methods are very broad, essentially meaning than an image or video is modeled, represented, or features extracted on more than one scale, somehow allowing both local and non-local phenomena.

Remote Sensing

Remote Sensing

Remote sensing, or the science of capturing data of the earth from airplanes or satellites, enables regular monitoring of land, ocean, and atmosphere expanses, representing data that cannot be captured using any other means. A vast amount of information is generated by remote sensing platforms and there is an obvious need to analyze the data accurately and efficiently.

Scientific Imaging

Scientific Imaging

Scientific Imaging refers to working on two- or three-dimensional imagery taken for a scientific purpose, in most cases acquired either through a microscope or remotely-sensed images taken at a distance.

Stochastic Models

Stochastic Models

In many image processing, computer vision, and pattern recognition applications, there is often a large degree of uncertainty associated with factors such as the appearance of the underlying scene within the acquired data, the location and trajectory of the object of interest, the physical appearance (e.g., size, shape, color, etc.) of the objects being detected, etc.

Video Analysis

Video Analysis

Video analysis is a field within  computer vision  that involves the automatic interpretation of digital video using computer algorithms. Although humans are readily able to interpret digital video, developing algorithms for the computer to perform the same task has been highly evasive and is now an active research field.

Deep Evolution Figure

Evolutionary Deep Intelligence

Deep learning has shown considerable promise in recent years, producing tremendous results and significantly improving the accuracy of a variety of challenging problems when compared to other machine learning methods.

Discovered Radiomics Sequencer

Discovery Radiomics

Radiomics, which involves the high-throughput extraction and analysis of a large amount of quantitative features from medical imaging data to characterize tumor phenotype in a quantitative manner, is ushering in a new era of imaging-driven quantitative personalized cancer decision support and management. 

Discovered Radiomics Sequencer

Sports Analytics

Sports Analytics is a growing field in computer vision that analyzes visual cues from images to provide statistical data on players, teams, and games. Want to know how a player's technique improves the quality of the team? Can a team, based on their defensive position, increase their chances to the finals? These are a few out of a plethora of questions that are answered in sports analytics.

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Understanding metric-related pitfalls in image analysis validation

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Apr 22, 2024, 2:39 AM

Reinke, A., Tizabi, M. D., Baumgartner, M., Eisenmann, M., Heckmann-Nötzel, D., Kavur, A. E., Rädsch, T., Sudre, C. H., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Benis, A., Buettner, F., Cardoso, M. J., Cheplygina, V., Chen, J., Christodoulou, E., Cimini, B. A., … Maier-Hein, L. (2024). Understanding metric-related pitfalls in image analysis validation. Nature Methods, 21(2), 182–194. https://doi.org/10.1038/S41592-023-02150-0

A recent study tackles the issue of choosing the right validation metrics in image analysis, crucial for both advancing scientific research and applying findings in practical settings. The researchers used a multistage Delphi process involving experts from various disciplines, coupled with extensive community feedback, to gather and synthesize information about common pitfalls in selecting validation metrics. This effort led to the creation of a comprehensive, easily accessible resource that categorizes these pitfalls using a new, universally applicable taxonomy. While this work specifically addresses biomedical image analysis, the insights it provides are applicable across various fields, aiming to improve understanding and decision-making regarding validation metrics globally. This is a significant step towards closing the gap between artificial intelligence research and real-world application.

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A deep neural network for hand gesture recognition from RGB image in complex background

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  • Tsung-Han Tsai 1 ,
  • Yuan-Chen Ho 1 ,
  • Po-Ting Chi 1 &
  • Ting-Jia Chen 1  

Deep learning research has gained significant popularity recently, finding applications in various domains such as image preprocessing, segmentation, object recognition, and semantic analysis. Deep learning has gradually replaced traditional algorithms such as color-based methods, contour-based methods, and motion-based methods. In the context of hand gesture recognition, traditional algorithms heavily rely on depth information for accuracy, but their performance is often subpar. This paper introduces a novel approach using a deep neural network for hand gesture recognition, requiring only a single complementary metal oxide semiconductor (CMOS) camera to operate amidst complex backgrounds. The neural network design incorporates depthwise separable convolutional layers, dividing the model into segmentation and recognition components. As our proposed single-stage model, we avoid the use of the whole model and thus reduce the number of weights and calculations. Additionally, in the training phase, the data augmentation and iterative training strategy further increase recognition accuracy. The results show that the proposed work uses little parameter usage while still having a higher gesture recognition rate than the other works.

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Ghadi, Y.Y., et al.: MS-DLD: multi-sensors based daily locomotion detection via kinematic-static energy and body-specific HMMs. IEEE Access (2022). https://doi.org/10.1109/ACCESS.2022.3154775

Article   Google Scholar  

Azmat, U., Jalal, A., Javeed, M.: Multi-sensors fused IoT-based home surveillance via bag of visual and motion features. In: 2023 international conference on communication, computing and digital systems (C-CODE), pp. 1–6. IEEE

Hajjej, F., et al.: Deep human motion detection and multi-features analysis for smart healthcare learning tools. IEEE Access (2022). https://doi.org/10.1109/ACCESS.2022.3214986

Kumar, P., Rautaray, S.S., Agrawal, A.: Hand data glove: A new generation real-time mouse for human-computer interaction. In: International conference on recent advances in information technology (RAIT), pp. 750–755 (2012). https://doi.org/10.1109/RAIT.2012.6194548 .

Sun, J., Ji, T., Zhang, S., Yang, J., Ji, G.: Research on the hand gesture recognition based on deep learning. In: 2018 12th international symposium on antennas, propagation and EM theory (ISAPE), pp. 1–4 (2018). https://doi.org/10.1109/ISAPE.2018.8634348

Rautaray, S.S., Agrawal, A.: Vision based hand gesture recognition for human computer interaction: a survey. Artif. Intell. Rev. 43 (1), 1–54 (2015). https://doi.org/10.1007/s10462-012-9356-9

Hu, B., Wang, J.: Deep learning based hand gesture recognition and UAV flight controls. Int. J. Autom. Comput. 17 (1), 17–29 (2020). https://doi.org/10.1007/s10462-012-9356-9

Mummadi, C., Leo, F., Verma, K., et al.: Real-time and embedded detection of hand gestures with an IMU-based glove. Informatics 5 (2), 28 (2018). https://doi.org/10.3390/informatics5020028

Oudah, M., Al-Naji, A., Chahl, J.: Hand gesture recognition based on computer vision: a review of techniques. J. Imaging 6 (8), 73 (2020). https://doi.org/10.3390/jimaging6080073

Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp. 1097–1105 (2012). https://doi.org/10.1145/3065386

Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp. 91–99 (2015). https://doi.org/10.1109/TPAMI.2016.2577031

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431–3440 (2015) https://doi.org/10.1109/CVPR.2015.7298965

Chevtchenko, S.F., Vale, R.F., Macario, V., Cordeiro, F.R.: A convolutional neural network with feature fusion for real-time hand posture recognition. Appl. Soft Comput. (2018). https://doi.org/10.1016/j.asoc.2018.09.010

Xing K., et al.: Hand gesture recognition based on deep learning method. In: 2018 IEEE third interna-tional conference on data science in cyberspace (DSC), pp. 542-546 (2018) https://doi.org/10.1109/DSC.2018.00087

Bilal, S., Akmeliawati, R., El Salami, M. J., Shafie, A.A., & Bouhabba, E.M.: A hybrid method using haar-like and skin-color algorithm for hand posture detection, recognition and tracking. In: 2010 IEEE international conference on mechatronics and automation, Xi'an, pp. 934–939 (2010) https://doi.org/10.1109/ICMA.2010.5588576

Guo, J., Cheng, J., Pang J., Guo, Y.: Real-time hand detection based on multi-stage HOG-SVM classifier. In: 2013 IEEE international conference on image processing, melbourne, VIC, pp. 4108–4111 (2013). https://doi.org/10.1109/ICIP.2013.6738846

Long, J., Shelhamer, E., Darrell. T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the ieee conference on computer vision and pattern recognition, pp. 3431–3440 (2015). https://doi.org/10.1109/CVPR.2015.7298965

Bilal, S., et al.: A hybrid method using haar-like and skin-color algorithm for hand posture detection, recognition and tracking. In: 2010 IEEE international conference on mechatronics and automation, Xi'an, pp. 934–939 (2010). https://doi.org/10.1109/ICMA.2010.5588576

Nguyen, V.-T., et al.: A method for hand detection based on Internal Haar-like features and Cascaded AdaBoost Classifier. ICCE, pp. 608–613 (2012)

Chen, L.-C., Papandreou, G., Schroff, F., Adam. H.: Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587 (2017)

Chen, L.-C., Papandreou, G., Schroff, F., Adam. H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. arXiv:1802.02611 , https://doi.org/10.1007/978-3-030-01234-2_49 (2018)

Lin, G., Milan, A., Shen, C., Reid, I.: Refinenet: multipath refinement networks with identity mappings for highresolution semantic segmentation. arXiv:1611.06612 , https://doi.org/10.1109/CVPR.2017.549 (2016)

Barros, P., Magg, S., Weber, C., Wermter, S.: A multichannel convolutional neural network for hand posture recognition. In: International conference on artificial neural networks, pp. 403– 410 (2014). https://doi.org/10.1007/978-3-319-11179-7_51 .

Zhang, W., Wang, J., Lan, F.: Dynamic hand gesture recognition based on short-term sampling neural networks. IEEE/CAA J. Autom. Sin. 8 (1), 110–120 (2020). https://doi.org/10.1109/JAS.2020.1003465

Saboo, S., Singha, J., Laskar, R.H.: Dynamic hand gesture recognition using combination of two-level tracker and trajectory-guided features. Multim. Syst. 28 (1), 183–194 (2022). https://doi.org/10.1007/s00530-021-00811-8

Yirtici, T., Yurtkan, K.: Regional-CNN-based enhanced Turkish sign language recognition. Signal Image Video Process. 5 , 1305–1311 (2022). https://doi.org/10.1007/s11760-021-02082-2

Sun, S., et al.: ShuffleNetv2-YOLOv3: a real-time recognition method of static sign language based on a lightweight network. Signal Image Video Process. 17 (6), 2721–2729 (2023)

Zhou, W., Li, X.: PEA-YOLO: a lightweight network for static gesture recognition combining multiscale and attention mechanisms. Signal Image Video Process. 18 (1), 597–605 (2023)

Dadashzadeh, A., Targhi, A.T., Tahmasbi, M., Mirmehdi, M.: HGR-Net: a fusion network for hand gesture segmentation and recognition. arXiv:1806.05653 , https://doi.org/10.1049/iet-cvi.2018.5796 (2018)

Matilainen, M., Sangi, P., Holappa, J., Silven, O.: Ouhands database for hand detection and pose recognition. In: Image Processing theory tools and applications, 6th international conference, pp. 1–5. IEEE (2016). https://doi.org/10.1109/IPTA.2016.7821025 .

Pinto, R.F., et al.: Static hand gesture recognition based on convolutional neural networks. J. Electr. Comput. Eng. 2019 , 1–12 (2019). https://doi.org/10.1155/2019/4167890

http://sun.aei.polsl.pl/mkawulok/gestures/ . Accessed 30 July 2019

Bambach, S., Lee, S., Crandall, D., Yu, C.: Lending a hand: detecting hands and recognizing activities in complex ego- centric interactions. In: IEEE international conference on computer vision (ICCV) (2015). https://doi.org/10.1109/ICCV.2015.226

Khan, A.U., Borji, A.: Analysis of hand segmentation in the wild. In: CVPR (2018). https://doi.org/10.1109/CVPR.2018.00495

Everingham, M., John, W.: The PASCAL visual object classes challenge 2012 (VOC2012) development kit. Pattern Anal. Stat. Model. Comput. Learn. Tech. Rep (2012). https://doi.org/10.1007/s11263-014-0733-5

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90 .

Howard, A. G., et al.: MobileNets: Efficient convolutional neural networks for mobile vision applications,” arXiv:1704.04861 [cs], https://doi.org/10.1109/IJCNN52387.2021.9534076 (2017)

Verma, M., Gupta, A., et al.: One for all: an end-to-end compact solution for hand gesture recognition, arXiv:2105.07143 (2021)

Zhou, W., Chen, K.: A lightweight hand gesture recognition in complex backgrounds. Displays 74 , 102226 (2022). https://doi.org/10.1016/j.displa.2022.102226

Dayananda Kumar, N. C., Suresh, K. V., Dinesh, R.: Depth Based Static Hand Gesture Segmentation and Recognition. In: Cognition and Recognition: 8th international conference, ICCR 2021, Mandya, India, December 30–31, 2021, Revised Selected Papers. Springer, Cham (2023)

Bansal, S.R., Savita, W., Rajeev, G.: mrmr-pso: a hybrid feature selection technique with a multiobjective approach for sign language recognition. Arab. J. Sci. Eng. 47 (8), 10365–10380 (2022). https://doi.org/10.1007/s13369-021-06456-z

Bousbai, K., et al.: Improving hand gestures recognition capabilities by ensembling convolutional networks. Exp. Syst. 39 (5), e12937 (2022). https://doi.org/10.1111/exsy.12937

Sadeghzadeh, A., Islam, M. B.: BiSign-Net: Fine-grained Static Sign Language Recognition based on Bilinear CNN. In: 2022 international symposium on intelligent signal processing and communication systems (ISPACS). IEEE (2022)

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Tsai, TH., Ho, YC., Chi, PT. et al. A deep neural network for hand gesture recognition from RGB image in complex background. SIViP (2024). https://doi.org/10.1007/s11760-024-03198-x

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    Within the domain of image processing, a wide array of methodologies is dedicated to tasks including denoising, enhancement, segmentation, feature extraction, and classification. These techniques collectively address the challenges and opportunities posed by different aspects of image analysis and manipulation, enabling applications across various fields. Each of these methodologies ...

  6. IET Image Processing

    IET Image Processing journal publishes the latest research in image and video processing, covering the generation, ... IET Image Processing is a major venue for pioneering research that's open to all, in areas related to the generation, processing and communication of visual information. Articles Most Recent;

  7. Recent Trends in Image Processing and Pattern Recognition

    Dear Colleagues, The 5th International Conference on Recent Trends in Image Processing and Pattern Recognition (RTIP2R) aims to attract current and/or advanced research on image processing, pattern recognition, computer vision, and machine learning. The RTIP2R will take place at the Texas A&M University—Kingsville, Texas (USA), on November 22 ...

  8. Advances in image processing using machine learning techniques

    Image processing has been an important area of investigation for many years, finding applications in medicine, astronomy, biology, different areas of engineering etc. With the recent advances in digital technology, there is an eminent integration of ML and image processing to help resolve complex problems.

  9. Real-time intelligent image processing for the internet of things

    The topics covered in this special issue include (i) intelligent image processing applications and services to fulfill the real-time processing and performance demands, (ii) real-time deep learning and machine learning solutions to improve computational speed and increase recognition rates at network edges, (iii) new frameworks to optimize real-time AIoT image processing, and (iv) combining ...

  10. (PDF) A Review on Image Processing

    Abstract. Image Processing includes changing the nature of an image in order to improve its pictorial information for human interpretation, for autonomous machine perception. Digital image ...

  11. Frontiers

    Technological advancements in computing multiple opportunities in a wide variety of fields that range from document analysis (Santosh, 2018), biomedical and healthcare informatics (Santosh et al., 2019; Santosh et al., 2021; Santosh and Gaur, 2021; Santosh and Joshi, 2021), and biometrics to intelligent language processing.These applications primarily leverage AI tools and/or techniques, where ...

  12. Image Processing

    Image processing applied to medical research has made many clinical diagnosis protocols and treatment plans more efficient and accurate. For example, a sophisticated nodule detection algorithm applied to digital mammogram images can aid in the early detection of breast cancer. However, image processing applications usually require significant ...

  13. Computer Vision and Image Processing

    At any rate, computer vision and image processing are two closely related fields which can be considered as a work area used in almost any research involving cameras or any image sensor to acquire information from the scenes or working environments. Thus, the main aim of this Topic is to cover some of the relevant areas where computer vision ...

  14. Research Topics

    Research Topics. Biomedical Imaging. The current plethora of imaging technologies such as magnetic resonance imaging (MR), computed tomography (CT), position emission tomography (PET), optical coherence tomography (OCT), and ultrasound provide great insight into the different anatomical and functional processes of the human body. Computer Vision.

  15. Viewpoints on Medical Image Processing: From Science to Application

    Multi-modal image processing for enhancing multi-modal imaging procedures primarily deals with image reconstruction and artifact reduction. ... In summary, medical image processing is a progressive field of research, and more and more applications are becoming part of the clinical practice. These applications are based on one or more of the ...

  16. Image Processing Technology Based on Machine Learning

    Machine learning is a relatively new field. With the deepening of people's research in this field, the application of machine learning is increasingly extensive. On the other hand, with the development of science and technology, image has become an indispensable medium of information transmission, and image processing technology is also booming. This paper introduces machine learning into ...

  17. Leveraging Image-Processing Techniques for Empirical Research

    Photos play a critical role in online shopping. To examine their impact on consumers, most previous studies rely on human assessments to develop measures for photos. Such an approach limits the number of dimensions and samples that can be investigated in one study. This study exploits image-processing techniques to tackle this challenge. We develop a framework and differentiate two types of ...

  18. Research Methodology in Image Processing

    Research techniques in Image processing field ate very innovative and uniquely poised from approach of any other field of research. Published in: 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS) Article #: Date of Conference: 14-15 June 2018 Date Added ...

  19. 471383 PDFs

    All kinds of image processing approaches. | Explore the latest full-text research PDFs, articles, conference papers, preprints and more on IMAGE PROCESSING. Find methods information, sources ...

  20. Digital Image Processing

    In this paper we give a tutorial overview of the field of digital image processing. Following a brief discussion of some basic concepts in this area, image processing algorithms are presented with emphasis on fundamental techniques which are broadly applicable to a number of applications. In addition to several real-world examples of such techniques, we also discuss the applicability of ...

  21. IET Image Processing: Vol 18, No 6

    In previous research on video stabilization, many methods have been proposed to stabilize shaky videos. However, these methods fail to strike a balance between image content integrity and stability. Some methods sacrifice image content for better stability. Other methods ignore the subtle jitters, which leads to poor stability.

  22. Research on Image Processing Technology of Computer Vision Algorithm

    Abstract: With the gradual improvement of artificial intelligence technology, image processing has become a common technology and is widely used in various fields to provide people with high-quality services. Starting from computer vision algorithms and image processing technologies, the computer vision display system is designed, and image distortion correction algorithms are explored for ...

  23. Understanding metric-related pitfalls in image analysis validation

    While this work specifically addresses biomedical image analysis, the insights it provides are applicable across various fields, aiming to improve understanding and decision-making regarding validation metrics globally. This is a significant step towards closing the gap between artificial intelligence research and real-world application.

  24. Applied Sciences

    The focus of this research is creating an automated interpretation system for antimicrobial susceptibility testing using the disk diffusion technique, thus simplifying the measurement and interpretation of inhibition zone sizes. ... In our study, we developed an algorithm that utilizes image processing techniques to detect the inhibition zones ...

  25. A deep neural network for hand gesture recognition from RGB image in

    Deep learning research has gained significant popularity recently, finding applications in various domains such as image preprocessing, segmentation, object recognition, and semantic analysis. Deep learning has gradually replaced traditional algorithms such as color-based methods, contour-based methods, and motion-based methods. In the context of hand gesture recognition, traditional ...

  26. Research on Traffic Vehicle Target Detection Method based on Improved

    ICIGP '24: Proceedings of the 2024 7th International Conference on Image and Graphics Processing Research on Traffic Vehicle Target Detection Method based on Improved YOLOv7. Pages 25-31. Previous Chapter Next Chapter. ABSTRACT. With the increase in the number of vehicles in our country, traffic accidents have become frequent. The real-time ...

  27. Research on Image Processing of Big Data Based on Computer Vision

    With the advent of the era of big data, the image processing of big data based on Computer Vision (CV) has become a research field of great concern. The purpose of this study is to explore how to improve image processing methods through deep learning technology to better adapt to large-scale and high-dimensional image data. We propose an innovative image processing framework, based on the ...

  28. Measuring Spherical and Nonspherical Binary Particles: Mixing and

    Rotating drum experiments on binary mixtures of plastic spheres with wood spheres, wood cylinders, plastic cylinders, and wood cubes were investigated, respectively. A high-resolution camera was employed to record the flow behaviors of the binary mixtures. A machine learning-assisted image processing method was developed to segment the particles of different shapes, and its superiority was ...