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

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A Hybrid Approximate Multiplier for Energy Efficient image processing

A Hybrid Approximate Multiplier for Energy Efficient Image P...

引用

Smart systems for applications in Electrical Sciences (ICSSES), International conference on

作者： V Sathiyanarayanan K Sanjaiyan J Rahul J Ram Guhanesh S Marirajan C Saravanakumar Electronics and Communication Department SRM Valliammai Engineering College Chennai India

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

In the field of Very Large-Scale Integration (VLSI) circuits, low power and energy-efficient solutions are desperately needed. “Urdhva Tiryagbhyam“ is one of the Sutras of Vedic Mathematics used as multiplication method in this paper. This study deals with the 8-bit Approximate Vedic Multiplier (AVM) based on the Vertical and Crosswise technique of Vedic mathematics is introduced in this project as a solution to the significant time and hardware resources consumed by multiplication operations, particularly in processors handling image processing applications. The Xilinx Vivado Design Suite simulator (EDA) tool is used for synthesis and simulation, and Verilog is used for implementation. The main goal of this research implements the approximate multiplier in real-time image processing applications. In comparison to traditional precise multipliers, this study intends to illustrate its energy-efficient capabilities by drastically cutting the power and space consumption of image processing circuits. In the age of energyefficient computing, the research findings demonstrate the significance of approximation multipliers in attaining optimal performance and compact device designs.

关键词： Power demand Program processors image processing Signal processing algorithms Computer architecture Very large scale integration Energy efficiency

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Real Rainy Scene Analysis: A Dual-Module Benchmark for image Deraining and Segmentation

Real Rainy Scene Analysis: A Dual-Module Benchmark for Image...

引用

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

作者： Zhao, Shilei Huang, Wenxin Yang, Mingxuan Liu, Wenxuan Wuhan Dashuiyun Technology Co. Ltd China School of Computer Science and Information Engineering Hubei University China School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)9798350313154

Scene understanding in adverse weather conditions (e.g., rainy and foggy days) has drawn increasing attention, raising some specific benchmarks and algorithms. However, rain streaks in images and videos can significantly degrade visual quality and reduce the effectiveness of computer vision algorithms. Concerning on lack of rainy-clean pairs training samples in real rainy scenes, we propose an unsupervised deraining method that does not require any explicit rain-clean image pairs for training. Instead, our approach leverages the statistical properties of rain streaks and clean regions to learn a rain removal model. Specifically, we choose contrastive learning that encourages the generator to produce derained images that are indistinguishable from clean images while the discriminator distinguishes between real clean images and rain streak patterns. Furthermore, we incorporate a semi-supervised mechanism during segmentation module that exploits low-confidence Pseudo-label to be treated as negative samples for certain classes to facilitate contrastive learning. Our experiments demonstrate that our integrated framework achieve better performance on the given challenging scenarios such as heavy rain and dynamic scenes. © 2023 IEEE.

关键词： Rain

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Robust Point Tracking Based On image Matching And Machine Learning On Video images Taken from Fast Moving Camera

Robust Point Tracking Based On Image Matching And Machine Le...

引用

IEEE Signal processing and Communications Applications (SIU)

作者： Ali Güven İmam Şamil Yetik Elektrik ve Elektronik Mühendisliği Bölümü TOBB Ekonomi ve Teknoloji Üniversitesi Ankara

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Recently, providing real-time navigation of unmanned aerial vehicles independent of global positioning systems has become of great importance. The state-of-the-art methods based on deep learning, which give good results in certain datasets, and the existing methods can not provide real-time and good solutions on images with dynamic and fast moving. Moreover, the methods, were developed so far, were focused on object-based tracking algorithms. In this paper, the tracking of the points belonging to the target pattern, found by image matching, was performed with the machine learning model we developed for 10 sequential video images. The features extracted for the machine learning model are: (i) the change between the points of the previous image and the image before that, (ii) the points of interest in the previous image, (iii) the changes found with the homography matrix between sequential images. It was experimentally shown that, point tracking can be achieved with the least error, on avarage about 23 pixels for a 2 mega-pixel resolution image, among the algorithms in the literature that can process more than 30 images per second in a CPU environment of 2 GHz or above.

关键词： Target tracking Machine learning algorithms image resolution Navigation image matching Signal processing algorithms Feature extraction

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The Hybrid Model of LSB—Technique in image Steganography Using AES and RSA algorithms 6th

The Hybrid Model of LSB—Technique in Image Steganography Us...

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6th International conference on Soft Computing and Signal processing, ICSCSP 2023

作者： Talasila, Srinivas Vijaya Kumar, Gurrala Vijaya Babu, E. Nainika, K. Veda Sahithi, M. Mohan, Pranay VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9789819984503

The process of embedding a data file or information inside another data file or picture is known as steganography. The goal of steganography is to conceal the presence of a message or data so as to avoid it being seen by an observer who does not know how or what to look for. Steganalysis techniques are capable of helping hide details in electronic data such as pictures, audio files, and video files. For instance, a message might be hidden in the unimportant portions of a picture or in the sparse components of an audio transmission. Data encryption can additionally be employed to safeguard the data on printed sheets by using magic markers or other approaches. The least significant bit (LSB) approach, an edge detection technique, has the disadvantage of poorer security and imperceptibility compared to the classic steganography techniques already in use. We propose a paradigm that integrates steganography and cryptography to increase security. To add a cryptographic flavour, we use the Advance Encryption Standard (AES) algorithm, which protects the security of the secret message bits. In this study, we compare the performance of the LSB approach using AES and the LSB technique using AES and RSA (Rivest–Shamir–Adleman) algorithms to that of the Pixel Locator Sequence (PLS) technique. To test the performance of the suggested approaches, we compute the metrics Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) and compare the findings with previously published PLS techniques that show good enough values for our proposed model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Mean square error

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A UAV-Based Remote Sensing image Automatic Monitoring System Empowered by Artificial Intelligence

A UAV-Based Remote Sensing Image Automatic Monitoring System...

引用

Intelligent algorithms for Computational Intelligence systems (IACIS), International conference on

作者： Tao Liu Bohan Zhang College of Computer Science and Technology Heilongjiang Institute of Technology Harbin China School of Information Engineering China University of Geosciences Wuhan Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

In this study, an automatic monitoring system based on convolutional neural network (CNN) is proposed to address the automation and accuracy of remote sensing image processing. With the maturity and wide application of remote sensing and UAV technologies, efficient and accurate image analysis methods have become particularly important. We developed this system through in-depth research on multiple stages of data preprocessing, feature extraction and model training. In the experimental stage, through the comparison experiments with Support Vector Machine (SVM) and Random Forest (RF) models, it is found that the CNN model has significant advantages in processing speed, anti-interference ability and accuracy. Especially in the processing of urban remote sensing images, CNN exhibits up to 90% accuracy, showing its wide applicability and excellent cross-domain application capability. Overall, this study not only successfully develops an efficient and accurate automatic monitoring system for remote sensing images, but also provides strong theoretical and experimental support for future optimization of CNN architecture in natural environments, improvement of real-time data processing capability, and extension to practical applications such as disaster monitoring and environmental protection.

关键词： Support vector machines Accuracy Disasters Computer architecture Real-time systems Data models Convolutional neural networks Protection Remote sensing Monitoring

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Infrared image Target Detection System Based on RV1126 7

Infrared Image Target Detection System Based on RV1126

引用

7th Asia Pacific conference on Optics Manufacture (APCOM)

作者： Zhu, Jiayi Zhou, Yi Jiang, Daozhong Wang, Hongtao Han, Rui Yin, Tong Li, Shu Jiangsu North Huguang Optoelect Co Ltd 25 ShengAnLu Wuxi 214035 Jiangsu Peoples R China

ISBN: (数字)9781510652095

ISBN: (纸本)9781510652095;9781510652088

The computing power of the image processor of the handheld viewing system is usually low, which brings some difficulties to the image processing. In this article, an infrared image target detection system is built with the RV1126 development board as the core. Compared with visible light, infrared image has the characteristics of low resolution and blurred details of small targets. According to the above characteristics, conventional image processing algorithms are difficult to deploy to embedded infrared image target detection systems. Therefore, this article uses SSD neural network to train the infrared target detection model, and converts the model into an infrared target detection model that can be deployed on RV1126 development board through Rknn. The actual test shows the SSD target detection network can achieve intelligent target detection and recognition on the RV1126-based embedded platform in the infrared image target detection.

关键词： RV1126 Infrared image Target detection SSD

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GLCM Based Feature Extraction and Medical X-ray image Classification Using Machine Learning Techniques 1st

GLCM Based Feature Extraction and Medical X-ray Image Classi...

引用

1st EAI International conference on Intelligent systems and Machine Learning, ICISML 2022

作者： Rout, Jyotiranjan Das, Swagat Kumar Mohalik, Priyabrata Mohanty, Subhashree Mohanty, Chandan Kumar Behera, Susil Kumar Department of Computer Science and Engineering Balasore College of Engineering and Technology Balasore India

ISBN: (纸本)9783031350771

The automated system is now created with excellent accuracy to detect abnormalities in X-ray images. To enhance the appearance of medical photographs, image pre-processing methods are applied, so that high accuracy can be achieved with constrained means. images are often classified based on their textural properties, which are measured using the Gray Level Co-occurrence Matrix (GLCM). The grey level correlation matrix provides statistical information of the second order on the grey levels of neighboring pixels in a picture (GLCM). In this proposed paper, medical X-ray images are classified and their features are extracted using an ensemble learning model. By extracting image features using the GLCM feature extraction method, this proposed model is able to distinguish between healthy and sick images (Gray level co-occurrence matrix).to improve the efficiency of the Ensemble learning classification method, it is compared against various algorithms using performance indicators, including Logistic regression, Gaussian Naive Bayes, as well as Random Forest. When this approach is compared to existing methods, the proposed ensemble model has an accuracy rate of 97% in classifying normal and diseased images. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： image enhancement

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A Survey on Privacy Preserving Content Based image Retrieval and Information Sharing in Cloud Environment 4

A Survey on Privacy Preserving Content Based Image Retrieval...

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4th International conference on Recent Trends in Computer Science and Technology, ICRTCST 2021

作者： Chouragade, Pushpanjali M. Ambhore, Premchand B. Government College Of Engineering Amravati Computer Science & Engineering MS Amravati India

ISBN: (纸本)9781665466332

As the quantity of images has expanded significantly, commonly utilized Content Based image Retrieval algorithms are commonly utilized in our ordinary routine. When it comes to processing and storing information, image retrieval systems are generally highly expensive. As a result, image owners should consider outsourcing these functions to a cloud server. However, since the cloud server can only be trusted to a certain amount, image owners' confidentiality might be jeopardized. This situation gives rise to the need of an algorithm/method which makes the images retrieval secure. For addressing this issue, the effort will be done to propose the Secure Content Based image Retrieval method with less complexity, high speed of operation, high accuracy and high retrieval efficiency. For this, various works have been studied to find best approach for achieving the efficient retrieval scheme. © 2022 IEEE.

关键词： Search engines

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Exploring Google Earth Engine Platform for Satellite image Classification Using Machine Learning algorithms 8th

Exploring Google Earth Engine Platform for Satellite Image C...

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8th International conference on Smart City Applications, SCA 2023

作者： Ouchra, Hafsa Belangour, Abdessamad Erraissi, Allae Laboratory of Information Technology and Modeling LTIM Faculty of Sciences Ben M’sik Hassan II University Casablanca Morocco Polydisciplinary Faculty of Sidi Bennour Chouaib Doukkali University El Jadida Morocco

ISBN: (纸本)9783031543753

Google Earth Engine is a geospatial data processing platform that runs in the cloud. It offers free access to massive amounts of satellite data as well as unlimited computing power to monitor, visualize, and analyze environmental features on petabyte scale. The capability of this platform to support diverse approaches for land use and land cover (LULC) classification using both pixel based and object-oriented methods has been made possible through the provision of a variety of machine learning algorithms. Earth observation data have proven to be a valuable resource of quantitative information more consistent in time and space than traditional ground surveys. They offer numerous opportunities for mapping and monitoring urban areas, as well as a variety of physical, climatic, and socioeconomic data to support urban planning and decision-making. We used Landsat 8 satellite data to perform supervised classification in this paper, and we used three advanced machine learning methods Support Vector Machine (SVM), Random Forest (RF), and Minimum Distance (MD) to classify water areas, built-up areas, cultivated areas, sandy areas, barren areas, and forest areas on Moroccan territory. The classification results are displayed using a set of accuracy indicators that includes overall accuracy (OA) and the Kappa coefficient. We obtained 0.93 as a better accuracy for the MD algorithm, however, the worst accuracy result is 0.74 for the SVM algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Engines

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Metrological characterization of different methods for recovering the optically sectioned image by means of structured light 12

Metrological characterization of different methods for recov...

引用

conference on Optical Measurement systems for Industrial Inspection xii

作者： Martinez, P. Bermudez, C. Carles, G. Cadevall, C. Matilla, A. Marine, J. Artigas, R. Sensofar Tech SL Barcelona Spain

ISBN: (数字)9781510643994

ISBN: (纸本)9781510643994

Imaging confocal microscopy (ICM) and focus variation (FV) are two of the most used technologies for 3D surface metrology. Both methods rely on the depth of focus of the microscope objective, which depends on its numerical aperture and wavelength of the light source to compute an optical section. In this paper we study how several methods of structured illumination microscopy affect the metrological characteristics of an areal optical profiler. We study the effect of the projection of different structured patterns, the sectioning algorithms, and the use of high and low frequency components onto the optically sectioned image. We characterized their performance in terms of system noise, instrument transfer function and metrological characteristics such as roughness parameters and step height values.

关键词： Focus Variation Confocal Optical sectioning Structured light

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