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An optimized hardware architecture of 4×4, 8×8, 16×16 and 32×32 inverse transform for HEVC

An optimized hardware architecture of 4×4, 8×8, 16×16 and...

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international conference on advanced Technologies for signal and image processing (ATSIP)

作者： Manel Kammoun Emna Maamouri Ahmed Ben Atitallah Nouri Masmoudi LETI laboratory—ENIS University of Sfax Sfax TUNISIA

ISBN: (纸本)9781467385275

Most video coding standards use transform algorithms to reduce the size of data characterizing a video signal. The traditional transform matrices as in H.264 are limited to 4×4 and 8×8 sizes. However, the flexibility of coding structure presented in the next generation of video coding standard High Efficiency Video Coding standard HEVC allows the definition of various sizes of transform matrices which can vary from 4×4 to 32×32. This paper describes a unified hardware architecture for 4×4, 8×8, 16×16 and 32×32 inverse 2D core transform IDCT in HEVC standard. It used only one block 1D transform and a transpose buffer based on FIFO memory blocs instead of the traditional register array in order to further reduce the memory resources. The synthesis results under TSMC 180 nm CMOS technology show that the total gate count of the design is more than 30% improved compared to previous works. However, the operating frequency of the hardware design is about 130 MHz. This last can perform the decoding of 25 frames per second of Quad HD (3840×2160) resolution.

关键词： image processing Decision support systems Hafnium

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Real-time recognition of road traffic signs in video scenes

Real-time recognition of road traffic signs in video scenes

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international conference on advanced Technologies for signal and image processing (ATSIP)

作者： Wajdi Farhat Hassene Faiedh Chokri Souani Kamel Besbes Faculty of Sciences of Monastir University of Sousse Higher Institute of Applied Sciences & Technology University of Sousse Universite de Sousse Sousse TN Center for Research on Microelectronics and Nanotechnology CRMN of Sousse University of Sousse Faculty of Sciences of Monastir University of Monastir

ISBN: (纸本)9781467385275

This paper presents a new algorithm for the detection and classification of real-time road traffic signs in the video. Our system is able to detect and classify triangular, circular, and octagonal signs of red and blue colors. The proposed system operates into two processing steps: (1) detection and (2) classification road signs. The system has detected candidate regions as Maximally Stable Extremal Regions (MSERs) in HSV color space with available robustness to variations in lighting conditions, especially red and blue. The detected candidate regions were then classified as method template matching, such as circular, octagonal, and triangular shapes. The proposed system is operating under a range of weather conditions and recognizes all classes of road traffic sign database. Results show a high success rate. In fact, the system maintains high performance for detection and classification steps whose F-measure are set to 0.95 and 0.92 respectively. We conclude, from these results, that the proposed system is invariant to rotation, translation, scale, even to partial occlusions. Moreover, results prove that the system is suitable and reliable for real-time processing.

关键词： Roads image color analysis Shape Real-time systems Robustness Classification algorithms Feature extraction

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Segmentation of low-grade gliomas in MRI: Phase based method

Segmentation of low-grade gliomas in MRI: Phase based method

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international conference on advanced Technologies for signal and image processing (ATSIP)

作者： R. Zaouche A. Belaid S. Aloui B. Solaiman A. Bounceur D. Ben Salem S. Sid-Ahmed S. Tliba University of Abderrahmane Mira of Bejaia LIMED Laboratory Bejaia Algeria Image & Information Processing Department Institut TELECOM France University of Brest Lab-STICC Laboratory France LaTIM-INSERM UMR 1101 Neuroradiologie France Neurosurgery department France Neurosurgery department Biological Engineering of Cancers

ISBN: (纸本)9781467385275

Segmentation of gliomas in magnetic resonance imaging (MRI) images is a crucial task for early tumor diagnosis and surgical planning. Although many methods for brain tumor segmentation exist, the improvement of this process is still difficult. Indeed, MRI images show complex characteristics and the different tumor tissues are difficult to distinguish from the normal brain tissues; especially the low-grade glioma (LGG), distinguished by their infiltrating character. In fact, it is difficult to extract the tumor from the surrounding healthy parenchyma tissue without any risk of neurological functional sequelae. The purpose of this paper is to provide a new MRI brain low grade glioblastomas tumor segmentation method based on the local phase information. We applied the proposed method on a set of selected images (Flair, T1 and T1c). Those images were from patients with low-grade glioma. The preliminary results obtained seem to be interesting.

关键词： image segmentation Magnetic resonance imaging Tumors image edge detection Deformable models Cancer Clustering algorithms

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Deep random forest-based learning transfer to SVM for brain tumor segmentation

Deep random forest-based learning transfer to SVM for brain ...

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international conference on advanced Technologies for signal and image processing (ATSIP)

作者： Samya Amiri Islem Rekik Mohamed Ali Mahjoub SAGE laboratory Higher Institute of Computer Science and Communication Techniques of Hammam sousse Tunisia

ISBN: (纸本)9781467385275

Using neuroimaging techniques to diagnose brain tumors and detect both visible and invisible cancer cells infiltration boundaries motivated the emergence of diverse tumor segmentation algorithms. Noting the large variability in both tumor appearance and shape, the task of automatic segmentation becomes more difficult. In this paper, we propose a random-forest (RF) based learning transfer to SVM classifier method for segmenting tumor lesions while capturing their complex characteristics. Our framework is composed of two cascaded stages. In the first stage, we train a random forest to learn the mapping from the image space to the tumor label space. In the testing stage, we use the predicted label output from the random forest and feed it along with the testing intensity image to an SVM classifier to get the refined segmentation. Then we make our RF-SVM cascaded classification steps deep through an iterative process. We tested our method on 20 patients with high-grade gliomas from the Brain Tumor image Segmentation Challenge (BRATS) dataset. Our proposed framework significantly outperformed SVM-based segmentation and RF-based segmentation-when used solely.

关键词： Support vector machines image segmentation Radio frequency Vegetation Lesions Magnetic resonance imaging

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Low-light image Enhancement Algorithm Based on Transformer

Low-light Image Enhancement Algorithm Based on Transformer

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signal processing and Intelligent Computing (SPIC), international conference on

作者： Zeyu Wang Yan Qi Institute of Information Science and Technology Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

Low light image enhancement is an important task in computer vision. The Low-light images can adversely affect more advanced computer vision tasks such as target tracking, image segmentation, and target recognition due to the presence of large amounts of noise and low contrast. Therefore, research on low-light image enhancement is crucial. To address these issues, this paper delves into the comparison between traditional and non-traditional low-light image enhancement algorithms and has achieved the following results:We abandon the traditional approach based on a priori knowledge of images and instead redesign a new network structure for low-light image enhancement with the existing theoretical foundation for Transformer. The network performs information extraction in the channel dimension of the image through self-attention mechanism and then extracts the shallow feature information of the image through Shallow layer. After fusing these two types of information, more advanced information is extracted to guide the final image enhancement. Experimental results show that the redesigned network structure not only achieves better results but also has improved execution efficiency.

关键词： Knowledge engineering Computer vision Target tracking Target recognition Noise signal processing algorithms Feature extraction Transformers Data mining image enhancement

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Progressive DCT image Compression Based On Quad Tree Decomposition for WSN

Progressive DCT Image Compression Based On Quad Tree Decompo...

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Advances in Engineering Science and Technology (AEST), international conference on

作者： Sakena Abbas Ayona Ali Kadhim Al-Janabi Department of Electronic and Communications Engineering University of Kufa Najaf Iraq

Wireless Sensor Networks (WSNs) have nodes with limited energy, limited processing power, and limited memory. In addition, they have low bandwidth and noisy communication channels. Therefore, image compression is very important for WSNs to reduce the time or the required bandwidth for transmitting the images. Progressive image compression is useful in WSNs because even when there is a cutoff in the transmitted data due to ending the battery lifetime or channel noise, the decoder can decompress the partially received data to obtain an acceptable image. Unfortunately, the progressive Discrete Cosine Transform (DCT) standard is unsuitable for WSNs because it uses the arithmetic coder or the Huffman coding that is very complex to implement. In this paper, we replace these coders with the Quad-Tree Decomposition (QTD) which has very low complexity. Unfortunately, most of the algorithms that use the QTD require lists to record the state of the image pixels and the quad-trees. These lists require an additional memory of about (2-3) of the image size. Therefore, it is not suitable for WSNs. In this research, we present the No List-QTD (NL-QTD) image compression algorithm that solves this memory constraint. It employs marker bits of an average memory of about 1/10 the image size instead of the lists to mark these states. The low complexity of the proposed NL-QTD also preserves the energy of the sensor's battery which in turn increases its lifetime. The simulation results show that the proposed method also has a higher Peak signal-to-Noise Ratio (PSNR), and a higher Structural Similarity Index Measurement (SSIM), as compared with other related algorithms.

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Condition Monitoring of Wind Turbines Through image Similarity Evaluation

Condition Monitoring of Wind Turbines Through Image Similari...

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Digital Twins and Parallel Intelligence (DTPI), IEEE international conference on

作者： Tianyu Wang Ye Su Jiangfeng Zhang Junyu Cai Liyun Hua State Grid Zhejiang Electric Power Research Institute North China Electric Power University Hangzhou China State Grid Zhejiang Electric Power Research Institute Hangzhou China Hangzhou E. Energy Technology Co. Ltd. Hangzhou China

ISBN: (数字)9781665492270

ISBN: (纸本)9781665492287

The condition monitoring of wind turbines is essential in a wide range of industrial fields for operational safety and efficiency. A smart visual system is proposed in this paper to achieve blade speed and tower vibration measurements through image similarity evaluation and advanced signal processing techniques. First, the similarity levels of a wind turbine image sequence are determined through the zero-normalized crosscorrelation algorithm. Then, an improved short-time autocorrelation method is utilized to process the resulting image similarity signal to derive the blade speed. Meanwhile, vibration information of the wind turbine tower is obtained through the spectral analysis of the image similarity signal of the tower region. The experimental data collected on a direct drive permanent magnet wind turbine illustrate that the blade speed and the tower vibration frequency can be measured accurately, confirming the feasibility of this technique for condition monitoring of wind turbines.

关键词： Vibrations Condition monitoring Visualization image resolution Blades Poles and towers Vibration measurement

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A Comprehensive Survey of image Steganography

A Comprehensive Survey of Image Steganography

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Sustainable Computing and Smart Systems (ICSCSS), international conference on

作者： Kalaiarasi G Sudharani B Sharon Christiana Jonnalagadda Harsha Vardhan Battula Bhavana Sanagala Dept of ACSE Vignan's Foundation for Science Technology and Research Andhra Pradesh

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

This research study analyzes the multidimensional landscape of steganography, examining its historical roots, theoretical background, contemporary approaches, and various applications. Beginning with a historical overview, this study investigates the evolution of steganography from its ancient roots to its present iterations in the digital world. Next, the study progresses towards analyzing the fundamental principles and theoretical frameworks that underpin steganographic systems, such as cryptography and digital signal processing. Finally, this study presents a thorough evaluation of contemporary steganographic technologies, which range from simple LSB (Least Significant Bit) substitution techniques to advanced adaptive algorithms and machine learning methods by including deep-learning based steganography and coverless steganography. Notably, this study identifies key challenges, including detection resistance, payload capacity, and robustness against attacks. Overall, this study presents a thorough understanding of steganography, emphasizing its significance as a versatile tool for communication in the digital era, while also highlighting the challenges that pave way for future innovations.

关键词： Resistance Training Surveys Steganography Adaptation models Technological innovation Distortion

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