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

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Multistatic cylindrical fast backprojection 3D image reconstruction 28

Multistatic cylindrical fast backprojection 3D image reconst...

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conference on Radar Sensor Technology XXviII

作者： Sheen, David M. Clark, R. Trevor Pacific Northwest Natl Lab US DOE Battelle Mem Inst POB 999 Richland WA 99352 USA

ISBN: (纸本)9781510674158;9781510674141

The Pacific Northwest National Laboratory (PNNL) has recently developed a next-generation cylindrical millimeter-wave imaging system. This system is based on linear sparse multistatic imaging arrays. Data from this system can be focused using 3D FFT-based reconstruction algorithms, which are reasonably efficient and can be performed in near real time, or by back-projection methods that are versatile and more accurate but are computationally intensive and require lengthy post-processing. Cylindrical Fast Backprojection (CFBP) is a novel image reconstruction algorithm developed at PNNL that radically increases the efficiency of backprojection and is ideally suited to microwave and millimeter-wave imaging systems based on scanned linear arrays such as body scanners in common use for aviation security screening. This method achieves its gains in efficiency by separating a full backprojection into a sequence of three steps, range focusing, vertical focusing, and lateral focusing, with intermediate results used to avoid repetitive multidimensional computation. The method is called cylindrical fast backprojection due to the use of two-dimensional stored results, or look-up tables, that have cylindrical symmetry about the linear array. The method is well suited to cylindrically scanned linear arrays but is equally valid for linear arrays scanned to form planar or arbitrary apertures. This paper describes the CFBP algorithm and validates its performance using simulated data.

关键词： Millimeter-wave imaging microwave imaging concealed-weapon detection security screening

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A Non-deep Approach to Classifying Movie Genres Based on Multimodal Data 13th

A Non-deep Approach to Classifying Movie Genres Based on M...

引用

13th International conference on image processing and Communications, IP and C 2023, 13th International conference on Computer Recognition systems, CORES 2023

作者： Niedziólka, Pawel Zyblewski, Pawel Department of Systems and Computer Networks Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland

ISBN: (纸本)9783031416293

Multimodal data processing has recently become popular due to technological advances and easier access to real video, audio, images, or text data. This data type is often processed using deep neural networks associated with high time and computational complexity. The present work addresses the problem of classifying a multimodal MM-IMDb dataset, representing the problem of recognizing a movie genre based on a poster and a brief description of the plot. For experiments, 20 binary subsets were separated, from which features were then extracted. Features from the text were obtained using the tf-idf method, while the posters were reduced to a single color. Computer experiments on the resulting tabular data were conducted separately on both modalities, as in the concatenated feature space. The results confirmed that classical approaches to feature extraction could allow satisfactory quality classification of multimodal data to be obtained even when using relatively simple pattern recognition algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Localization and Distance Measurement of Intruding Objects in Power Transmission Lines Based on image and Point Cloud Fusion 43

Localization and Distance Measurement of Intruding Objects i...

引用

43rd Chinese Control conference, CCC 2024

作者： Zhang, Tianhao Qian, Kun Deng, Feng Southeast University School of Automation Nanjing210096 China Nari Technology Co. Ltd Nanjing211102 China

ISBN: (纸本)9789887581581

To safeguard the operation of ultra-high voltage transmission lines, this study introduces a technique for dynamically monitoring potential security threats to these lines. This technique integrates images and point cloud data to pinpoint and measure the distance of objects intruding into the vicinity of the transmission lines. The methodology focuses on achieving high-precision localization and distance measurement of such objects. Key innovations include advanced image object detection combined with efficient processing of point cloud data. This allows for precise location and reliable distance assessment of objects at significant distances. The process begins with the creation of detailed scene point clouds using unmanned aerial vehicle (UAV) SLAM mapping technology. These point clouds are then aligned with the monitoring LIDAR coordinate system through registration algorithms to use the catenary line model to reconstruct transmission lines. The intrusion's highest point is precisely located by correlating two-dimensional image detection results with the image-to-point cloud mapping and extracting the intruding object's point cloud using Euclidean clustering. The method concludes by calculating the minimum distance from the top of the intrusion to the nearest transmission line, using the catenary line equation. Experimental results indicate that this approach can detect intruders and gauge their distances efficiently and accurately across various ranges and environmental conditions, showcasing exceptional accuracy and robustness, particularly in long-range object detection scenarios. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Unmanned aerial vehicles (UAV)

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Automatic Segmentation and Extraction of Skin Lesion in Dermoscopic images using image processing

Automatic Segmentation and Extraction of Skin Lesion in Derm...

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2023 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Angeline Valentina Sweety, A. Maria Delphiya, A. Rejoline vincima, S. Kavya, N. Jeresha, J.M. Heltin Genitha, C. St.Joseph's Institute of Technology Department of CSE Chennai India St.Joseph's College of Engineering Department of IT Chennai India

ISBN: (纸本)9798350319200

Dermoscopy, an epiluminescence light microscope that magnifies lesions and enables investigation down to the dermo-epidermal interface, is a non-invasive method that doctors may use to help with the diagnosis of melanoma. The research on melanoma skin cancers and the many subtypes of each is included in our paper. The four steps of the approach are preprocessing, segmentation, extraction of features, and classification. The artifact-filled image is preprocessed during the preprocessing stage using noise filtering and contrast sharpening. The processed image (Fuzzy C-Means method) immediately initiates a segmentation method. K-means clustering outperforms fuzzy C-Means in terms of outcomes for overlapping data sets. Gabor then does feature extraction. The Gabor filter and Glcm perform feature extraction in the next stage. By computing the pair of pixels with defined values and appearing in a given spatial image in an image, Glcm is used to characterise the image. The Gabor filter determines if a picture contains any certain frequency content. Last but not least, for classification, the bulk of the algorithms described in the literature rely on a feature extraction stage that is followed by a classifier that takes the retrieved features as inputs. However, Support Vector Machine is utilised for classification in our method. SVM produces spaces with many dimensions. The picture is categorised according to the type of melanoma malignancies in the classification output. © 2023 IEEE.

关键词： Contrast Stretching Feature Extraction Fuzzy C-Means Gabor Filter Glcm Noise Filtering Preprocessing Segmentation Svm

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Real-Time image and video processing Applications Using Raspberry Pi 1

Real-Time Image and Video Processing Applications Using Rasp...

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1st IEEE Industrial Electronics Society Annual On-Line conference, ONCON 2022

作者： Yildirim, Merve Karaduman, Ozgur Kurum, Hasan University of Firat Department of Electrical and Electronics Engineering Elazig Turkey University of Firat Department of Software Engineering Elazig Turkey

ISBN: (纸本)9798350398069

image and video processing applications have significant importance in many areas which are the industrial and medical applications, especially the vehicular technology. To provide safe driving, Driving Assistance systems (DAS) including detection and tracking of a road situation, objects and behavior of the driver should be considered by using the image processing algorithms. Since these applications need the processors having high speed and power, a Raspberry Pi platform meeting these specifications is used in this study. A Canny edge detector which is one of the important edge detection methods, face and eye detection, the road line tracking, motion detection, and object detection applications are handled. Conversely the studies in the literature which are separately examined these techniques, all of them are realized as a real-time based on the Rapberry Pi according to a video image taken by a camera. For this reason, OpenCv and TensorFlow libraries having widely computer vision algorithms and Python software language are used. It is observed that the results of all image and video processing applications are successful and satisfying. As a result, a safe and comfortable driving can be provided thanks to the proposed methods. © 2022 IEEE.

关键词： Python

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An image Encryption Algorithm Suitable for Medical images 16

An Image Encryption Algorithm Suitable for Medical Images

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16th IEEE International Scientific conference on Informatics, Informatics 2022

作者： Oravec, Jakub Ovsenik, Lubos Lapcak, Maros Zdravecky, Norbert Andrejcik, Samuel Technical University of Košice Feei Dept. of Electronics and Multimedia Communications Košice Slovakia

ISBN: (纸本)9798350310344

Security of data becomes more and more important each day. The importance is even more pronounced if the data contain sensitive information, such as those shown in medical images. There are already several solutions that can be used in order to protect medical images during their storage or transmission. The most popular of those is probably encryption by conventional encryption algorithms such as Advanced Encryption Standard. This proposal tries to investigate possibility of using dedicated image encryption algorithms that have applications in fields of steganography and biometric systems for encryption of medical images. The dedicated image encryption algorithms can be easily adjusted for processing medical images, what can be verified by several experiments. Main advantage of this proposal lies in an addition of a substitution block, which should increase the security of whole algorithm. The performance of our solution will be evaluated by several numerical parameters and those will be compared with results reported in similar works. © 2022 IEEE.

关键词： Cryptography

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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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image Captioning Using Xception-Long Short-Term Memory 1

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9th International conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Panchal, Nisha Garg, Dweepna Gujarat Changa Anand India Gujarat Changa Anand India

ISBN: (数字)9783031440847

ISBN: (纸本)9783031440830

Deep Learning has shown great potential in developing applications capable of automatically generating captions or descriptions for images and video frames. The critical components of this process are image processing and natural language processing, which play a crucial role in captioning images and videos. These applications can be used in several areas, including robotic vision systems, assisting people with visual impairments, generating metadata for search engines, answering visual questions, visual grounding, and more. This paper discusses various working algorithms such as a combination of CNN-RNN, encoder-decoder, attention mechanisms, and transformation models with evaluation matrices, datasets, and limitations of existing models. Xception-LSTM shows great potential compared to the traditional encoding-decoding model using BLEU and METEOR evaluation matrics. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

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Soil Classification using Deep Learning Techniques 2

Soil Classification using Deep Learning Techniques

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2nd International conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Sivabalaselvamani, D. Rahunathan, L. Nanthini, K. Harshini, T. Hariprasath, C. Kongu Engineering College Perundurai Department of Computer Applications Tamil Nadu Erode India

ISBN: (纸本)9798350325799

The study of soil categorization is a burgeoning field nowadays. Living animals and plants depend heavily on soil for their survival. This study is to classify the different types of soils like Clay soil, Black soil, Red soil and Alluvial soil by using CNN algorithm. Classifying soil is important in fields like farming, geology, and engineering. It helps us make smart choices about the use of land, which crops to grow, plan construction, and to take care of the environment. CNN are a type of Deep Learning technique which is used for image analysis. The model assigns weights and biases to different parts of an input image, enabling them to differentiate and identify different objects within the image. Along with that other deep learning methods can also be employed to enhance accuracy. The image datasets used in this study were obtained from Kaggle and underwent processing with various algorithms. Afterwards, performance metrics were employed to compare the results. To efficiently classify the images based on their labels, VGG19 is used, which is a subset of CNN. This study enables us to effectively classify soil images. The models are trained on a dataset of soil images that had been labeled with their corresponding soil type. This model achieves an accuracy of 94.87% on the test set. The results of this study show that CNNs can be used for effective soil classification. This could lead to the development of more efficient and cost-effective methods for soil classification. © 2023 IEEE.

关键词： Learning systems

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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...

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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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