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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XII"

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image processing Based Lie Detector Model

Image Processing Based Lie Detector Model

引用

Innovations in Intelligent systems and Applications conference

作者： Aybüke Akyakı Büşra Karaca Doğay Altınel Filiz Gürkan Elektrik-Elektronik Mühendisliği Bölümü İstanbul Medeniyet Üniversitesi İstanbul Türkiye

ISBN: (数字)9798350379433

ISBN: (纸本)9798350379440

image processing refers to information processing techniques used with computers integrated into images in various industrial and technological fields. These techniques can be used in various applications involving the process of object recognition and are developed within the scope of artificial intelligence. In recent years, image processing techniques have been utilized in facial recognition systems to detect crime and criminals. This paper presents a study on the creation of a lie detector that provides information on whether individuals are lying by using image processing techniques. For this purpose, algorithms that analyze eye blinking and emotional state behaviors from the facial region detected in the video image for lie detection were developed. These algorithms were coded in the Python programming language and combined to obtain a lie detector model. Additionally, a balanced test set consisting of video images of individuals telling the truth and lying were prepared to validate the model. In tests conducted on this set, the success rate of the obtained lie detector model was found to be 83.3%.

关键词： Ear image processing Detectors Brain modeling Python Ubiquitous computing Technological innovation Skin Object recognition Neural networks

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On the Influence of image Features on the Performance of Deep Learning Models in Human-Object Interaction Detection

On the Influence of Image Features on the Performance of Dee...

引用

International conference on Computer Vision and Graphics, ICCVG 2022

作者： GrzĄbka, Marcin Iwanowski, Marcin Sarwas, Grzegorz Institute of Control and Industrial Electronics Warsaw University of Technology ul. Koszykowa 75 Warszawa00-662 Poland

ISBN: (纸本)9783031220241

In recent years Human Object Interaction (HOI) detection has experienced rapid performance growth mainly due to the development of various deep learning-based methods and algorithms. One of the most popular approaches to this task is three-stream architecture consisting of three processing paths: human-stream, object-stream, and relation-stream, gathering information on the scene using features extracted from particular image elements. The total amount of features depends on the detailed processing schemes and model parameters. The increase in the number of features does not necessarily increase the final efficiency of the HOI detection method. This paper focuses on the relation between the quantity of features used to detect HOI and its predictive efficiency. In our study, we investigate the influence of the number of features on the final detection results. Several experiments were conducted to validate various model configurations of different features considered. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Efficiency

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An Evaluation of Medical image Analysis using image Segmentation and Deep Learning Techniques for Brain-Tumour 2

An Evaluation of Medical Image Analysis using Image Segmenta...

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2nd International conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Sinha, Himanshu Kelley School of Business Indiana University Bloomington NapervilleIL United States

ISBN: (纸本)9798331541217

An aberrant mass of rapidly proliferating brain cells, known as a brain tumour, may develop into various forms of cancer. The segmentation of brain tumours by MRI scans is a challenging but crucial process with several medical analytic applications. Radiologists or clinical specialists are responsible for performing a tedious and time-consuming process that involves identifying, segmenting, and extracting contaminated tumour regions from MRI images. The different anatomical structures of human organs may be imagined via image-processing ideas. The human brain is notoriously difficult for basic imaging techniques to identify abnormal structures. A large number of researchers looked at several rapid and accurate algorithms for brain tumour (BT) detection and classification. Recent advances in DL-based automated systems have allowed for faster and more accurate segmentation of DT. This research makes use of a DenseNet50 model for medical picture classification of brain malignancies that is built on a pre-trained CNN model made possible by DL. The model is available on Kaggle. An optimiser like Adam and a categorical cross-entropy loss function were utilised to train a model. A suggested DenseNet50 model outperformed other comparative advanced DL models and attained an experimental accuracy of 95%. The visual results also highlight the impressive outcomes of the MRI brain tumour segmentation system, which will help doctors automatically detect and treat brain tumours. The results acquired demonstrate the effective identification of tumorous images, which is extremely beneficial for the diagnosis process and future applications. © 2024 IEEE.

关键词： Magnetic resonance imaging

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A microfluidic system, utilising image processing methods, for the detection of blood coagulation and erythrocyte aggregation 5

A microfluidic system, utilising image processing methods, f...

引用

5th IEEE International conference on image processing Applications and systems (IPAS)

作者： Louka, Marinos Passos, Andreas Inglezakis, Antonis Loizou, Constantinos Kaliviotis, Efstathios Cyprus Univ Technol Dept Mech Engn & Mat Sci & Engn Limassol Cyprus EMBIO Diagnost Nicosia Cyprus

ISBN: (纸本)9781665462198

Hemostasis is a defence mechanism that prevents blood losses in cases of vessel injuries, and other related disorders. In many cases, patients need to frequently monitor their blood coagulation tendency in order to regulate their medication. In addition, red blood cell aggregation (RBCA) is related to blood inflammation, and it appears elevated in many pathological conditions. Blood coagulation and RBCA can be studied by analysing the dynamic changes of light transmittance though a clotting/aggregating sample, and indeed various works in the literature exploit this approach. In this work, blood coagulation and RBCA are examined by utilising single drops of blood in an inexpensive camera-based microfluidic system, designed for low computational and production cost. Results are compared with a microscopy-camera system, with both setups utilizing the same custom made microchannel. Three image processing algorithms are developed to analyze the averaged light intensity, and the local structural chracteristics of blood, through a binarization and region classification method, using logical operations. The results illustrate the repeatability of the technique and the donorto-donor variation within the proposed approach. Based on the image processing analysis, the developed coagulation and aggregation indices show great potential of utilisation in an inexpensive and robust point of care device.

关键词： Blood coagulation red blood cell aggregation image processing medical diagnostics POC

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Color Terms and Stable Diffusion 32

Color Terms and Stable Diffusion

引用

32st Color and Imaging conference - Color Science and Engineering systems, Technologies, and Applications, CIC 2024

作者： Moroney, Nathan Numantic Solutions United States

ISBN: (纸本)9780892083688

Stable diffusion is a generative algorithm for creating images from text prompts. This paper explores prompts with color terms and proposes a process to generate, visualize and assess these results. Automated prompts are used to generate and render a color term, an object and a context. The results are then evaluated using two dashboard views of the underlying images. First is a sampling based on a collection of frequently used color terms. Second is a sampling by object prompts, such as apples and boxes. This paper considers the following questions: how effectively are the colored objects generated? how do the colors generated by stable diffusion compare to human color naming? How might color terms be useful in visualizing properties and features of generative algorithms? The dashboard view of color terms suggests that less frequently used color terms may be generated less consistently. In addition, even the most common color terms can fail to be correctly generated. Likewise, objects with more frequent color associations, such as apples or pumpkins, will result in less accurate color generation. ©2024 Society for Imaging Science and Technology.

关键词： Color image processing

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An Adaptive BSCO Algorithm of Solid Color Optimization for 3D Reconstruction System with PIFuHD 17th

An Adaptive BSCO Algorithm of Solid Color Optimization for 3...

引用

17th International conference on Wireless algorithms, systems, and Applications (WASA)

作者： Hsieh, Chao-Hsien Song, Yubo Wang, Zhen Li, Changfeng Qufu Normal Univ Jining 273165 Shandong Peoples R China

ISBN: (纸本)9783031192135;9783031192142

PIFuHD can generate high-resolution model in the process of human 3D reconstruction. However, PIFuHD will produce debris outside the human body when the image background is more complex. In order to solve this problem, this paper develops an adaptive BSCO algorithm for the background of human body and image of human body in 3D reconstruction system. The BSCO algorithm is divided into four steps in processing. First, BSCO algorithm uses Go-selfies to separate the background. Second, BSCO algorithm converges the RGB of all pixels of the character into a set. Third, BSCO algorithm finds the greatest difference from the set through HSV conversion. Fourth, BSCO algorithm weighs the set and then calculates the RGB score. The highest score of RGB is used as the RGB of the background after solid color optimization. The experimental results show that the proposed method improves the reconstruction effect of PIFuHD.

关键词： PIFuHD 3D reconstruction image processing Background adaptive

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A generic model of semiconductor-based photon-counting detectors for spectral CT

A generic model of semiconductor-based photon-counting detec...

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conference on Medical Imaging - Physics of Medical Imaging

作者： Bhattarai, Mridul Panta, Raj Kumar Clark, Darin Segars, W. Paul Abadi, Ehsan Samei, Ehsan Duke Univ Med Phys Grad Program Durham NC 27708 USA Duke Univ Sch Med Dept Radiol Ctr Virtual Imaging Trials CVIT Durham NC 27708 USA Duke Univ Sch Med Dept Radiol Quantitat Imaging & Anal Lab Durham NC 27708 USA

ISBN: (纸本)9781510671553;9781510671546

Photon-counting CT (PCCT) is an emerging x-ray spectral imaging technology. There are several PCCT prototypes available for clinical and experimental applications. Despite many published results, objective quantitative evaluation of PCCT systems remains challenging due to varied and unknown influences. In addition, CT acquisition geometry and image processing mechanisms are often proprietary, complicating the correlations between the detector's physical characteristics and the corresponding CT image quality, hindering fair comparisons between different technologies. This study aimed to provide a resource to address these challenges by developing a versatile model that accurately replicates the physics of any semiconductor-based photon-counting detector (PCD) and integrating it into a virtual imaging framework to generate detector-specific CT images. The methodology involved a Monte Carlo simulation to model x-ray photon interactions with PCDs and an analytical Gaussian charge sharing model to model charge-diffusion and -repulsion in the detector. Finally, the energy deposited post-crosstalk by source photons (1-120 keV) in the 3x3-pixel-neighborhood was translated into photon counts across two energy thresholds to compute detector-specific spatio-energetic covariance correlation matrices. This framework was validated against experimental measurements of a clinical CdTe-based PCCT, showcasing its accuracy in reproducing real-world scenarios. Additionally, to demonstrate the model's efficacy, we simulated key PCD materials (Si, CdTe, CZT) and designed standardized scanner components, developing virtual systems (DukeCounter scanners). These systems were used to "scan" a physics phantom (ACR) and anthropomorphic-computational models (XCAT). In low-energy (i.e., 5 keV for Si, 20 keV for CdTe and CZT) threshold images, for 125 mAs acquisitions, (noise magnitudes, HU for bone insert) measured in ACR phantom were (76.0 HU, 889.4 HU), (76.9 HU, 932.9 HU), (81.7 HU, 939.4 HU)

关键词： Sensors Silicon Computed tomography image quality Monte Carlo methods X-ray computed tomography Scanners systems modeling Reconstruction algorithms Semiconductors Cadmium telluride Photon counting

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Wasserstein distributional robustness of neural networks 37

Wasserstein distributional robustness of neural networks

引用

37th conference on Neural Information processing systems, NeurIPS 2023

作者： Bai, Xingjian Jiang, Yifan He, Guangyi Oblój, Jan Department of Computer Science University of Oxford United Kingdom Mathematical Institute University of Oxford United Kingdom

ISBN: (纸本)9781713899921

Deep neural networks are known to be vulnerable to adversarial attacks (AA).For an image recognition task, this means that a small perturbation of the original can result in the image being *** of such attacks as well as methods of adversarial training against them are subject of intense *** re-cast the problem using techniques of Wasserstein distributionally robust optimization (DRO) and obtain novel contributions leveraging recent insights from DRO sensitivity *** consider a set of distributional threat *** the traditional pointwise attacks, which assume a uniform bound on perturbation of each input data point, distributional threat models allow attackers to perturb inputs in a nonuniform *** link these more general attacks with questions of out-of-sample performance and Knightian *** evaluate the distributional robustness of neural networks, we propose a first-order AA algorithm and its multistep *** attack algorithms include Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) as special ***, we provide a new asymptotic estimate of the adversarial accuracy against distributional threat *** bound is fast to compute and first-order accurate, offering new insights even for the pointwise *** also naturally yields out-of-sample performance *** conduct numerical experiments on CIFAR-10, CIFAR-100, imageNet datasets using DNNs on RobustBench to illustrate our theoretical *** code is available at https://***/JanObloj/W-DRO-Adversarial-Methods. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： image recognition

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Application of computer vision methods for automated wooden planks length measurement 18

Application of computer vision methods for automated wooden ...

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18th Baltic Electronics conference

作者： Zabulis, Lukas Lipnickas, Arunas Augustauskas, Rytis Kaunas Univ Technol Dept Automat Kaunas Lithuania

ISBN: (数字)9781665462952

ISBN: (纸本)9781665462952

Automated inspection is a principal component in modern manufacturing aiming for Industry 4.0. With the fast development of digital image processing and machine learning algorithms, quality control is often performed by computer vision systems (CV). Following the presented topic, this paper applies the CV system for non-direct length measuring of objects moving through a conveyor. The approach involves image segmentation by a convolutional neural network and segmented object processing principles. Since high-resolution images were used, segmentation was performed in a patch manner. The algorithm also involves a custom contour analysis and the application of the least-squares method for the midline of wooden plank extraction. The proposed approach is robust to the object's non-alignment and merging factors as well as shape and wooden texture variance. The presented algorithm was tested on a custom wooden planks database gathered in a real EURO pallets production environment and confirmed to work as expected ensuring +/- 1mm length measuring accuracy and 0.97s processing timing meeting the real-time requirement of the specified system.

关键词： object segmentation deep learning computer vision digital image processing non-direct measurement

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Exploiting Richness of Learned Compressed Representation of images for Semantic Segmentation

Exploiting Richness of Learned Compressed Representation of ...

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2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Kakaiya, Ravi Sathish, Rakshith Sheet, Debdoot Sethuraman, Ramananthan Indian Institute of Technology Kharagpur West Bengal India Intel Technology India Pvt. Ltd. Bengaluru India

ISBN: (纸本)9798350313154

Autonomous vehicles and Advanced Driving Assistance systems (ADAS) have the potential to radically change the way we travel. Many such of such vehicles currently rely on segmentation and object detection algorithms to detect and track objects around its surrounding. The data collected from the vehicles are often sent to cloud servers to facilitate continual/life-long learning of these algorithms. Considering the bandwidth constraints, the data is compressed before sending it to servers, where it is typically decompressed for training and analysis. In this work, we propose the use of a learning-based compression Codec to reduce the overhead in latency incurred for the decompression operation in the standard pipeline. We demonstrate that the learned compressed representation can also be used to perform tasks like semantic segmentation in addition to decompression to obtain the images. We experimentally validate the proposed pipeline on the Cityscapes dataset, where we achieve a compression factor up to 66× while preserving the information required to perform segmentation with a dice coefficient of 0.84 as compared to 0.88 achieved using decompressed images while reducing the overall compute by 11%. © 2023 IEEE.

关键词： image compression

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