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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VIII"

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Low-Cost Hardware-Accelerated vision-Based Depth Perception for Real-Time applications

Low-Cost Hardware-Accelerated Vision-Based Depth Perception ...

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International conference on Computer vision and machine Intelligence, CVMI 2022

作者： Aditya, N.G. Dhruval, P.B. Shylaja, S.S. Katharguppe, Srinivas Department of Computer Science PES University Karnataka Bengaluru560085 India

ISBN: (纸本)9789811978661

Depth estimation and 3D object detection are critical for autonomous systems to gain context of their surroundings. In recent times, compute capacity has improved tremendously, enabling computer vision and AI on the edge. In this paper, we harness the power of CUDA and OpenMP to accelerate ELAS (a stereoscopic vision-based disparity calculation algorithm) and 3D projection of the estimated depth while performing object detection and tracking. We also examine the utility of Bayesian inference in achieving real-time object tracking. Finally, we build a drive-by-wire car equipped with a stereo camera setup to test our system in the real world. The entire system has been made public and easily accessible through a Python module. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Stereo image processing

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Depth and Thermal images in Face Detection - A Detailed Comparison Between image Modalities 2022

Depth and Thermal Images in Face Detection - A Detailed Comp...

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5th International conference on machine vision and applications (ICMVA)

作者： Mucha, Wiktor Kampel, Martin TU Wien Comp Vis Lab Vienna Austria

ISBN: (纸本)9781450395670

Face detection is a well-known issue in image processing, and numerous studies are present in this field. A prominent part of the work is devoted to RGB images, leaving depth and thermal data with less interest. However, in some conditions like low-light areas where face detection is needed, non-RGB sensors might perform better. Also, mounting an additional RGB camera could be challenging or not possible, considering privacy concerns. In this work, current deep learning methodologies are employed to train depth and thermal detection models. The training is done using combined publicly available data that is processed by us for this purpose in order to create necessary annotations for a learning process. The resulting models are validated on a new trimodal dataset collected for this experiments purpose. It contains images captured with RGB, depth, and thermal sensors. Various scenes with single and multiple faces appearances can be found. The results show that non-RGB solutions can be applied in practice with highly robust accuracy and their efficiency is close to RGB detectors. However, their performance depends on the environment and that circumstances are described later in this article.

关键词： image modalities depth face detection thermal face detection datasets modalities comparison deep learning

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An automated retinal hemorrhages and exudates detection in a Mexican image set

An automated retinal hemorrhages and exudates detection in a...

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conference on applications of machine Learning

作者： Echeveste-Vazquez, J. A. Armas-Perez, J. C. Gonzalez-Vega, A. Soto-Cruz, G. Villasenor-Mora, C. Univ Guanajuato Div Ciencias Ingn Guanajuato Mexico Univ Nacl Autonoma Mexico ENES Leon Mexico City DF Mexico

ISBN: (纸本)9781510665651;9781510665644

Retinopathy is a common complication of diabetes that can cause severe vision loss if not detected and managed promptly. In this study, we propose a comprehensive approach that leverages image processing techniques to analyze fundus images of patients with diabetic retinopathy. Our primary focus is on vein extraction and hemorrhage detection, with exudate detection being performed only on specific images to showcase advancements in the current prototype algorithm. The dataset used in this project consists of images obtained from Mexican ophthalmology institutes, ensuring its relevance and applicability to the local population. By extracting veins and hemorrhages, we aim to capture crucial features indicative of the severity of retinopathy. These generated images, along with the original dataset, are utilized to train convolutional neural network (CNN) models, enabling accurate classification of the disease's degree into three categories. The significance of this project lies in its potential to serve as an auxiliary tool in diagnosing diabetic retinopathy. By automating the analysis of fundus images and providing objective classification results, our algorithm aims to assist healthcare professionals in making informed decisions regarding treatment and management options. The proposed method can potentially enhance the efficiency and precision of diabetic retinopathy (DR) diagnosis, improving Mexican health outcomes.

关键词： Diabetic retinopathy fundus exudates eye veins eye hemorrhages CNN automatic classification.

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Pigment Epithelial Detachment Detection: A Review of Imaging Techniques and Algorithms

Pigment Epithelial Detachment Detection: A Review of Imaging...

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2022 International conference on Advanced Computing Technologies and applications, ICACTA 2022

作者： Sheeba, T.M. Albert Antony Raj, S. Anand, M. SRM Institute of Science and Technology College of Science and Humanities Department of Computer Applications Chennai India SRM Institute of Science and Technology College of Engineering and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9781665495158

Pigment epithelial detachment(PED) is a disorder in retina that happens when RPE layers of cells at the back side of the eye come apart, or get teared. The bend of layers in the retina, as well as fluid, proteins, tissue, or blood vessels, is a defining feature of PED disease, which occurs most frequently in the macula. PED can disturb the vision of the people which is often depict dark shadow, blurry vision or partial loss of vision. The optical coherence tomography (OCT) is a trend set of high resolution and non-invasive imaging modality that expedite the structure of the retina. OCT non-invasively yields cross-sectional volume of images with tissues. The major objective of this research paper is to study, state of art and to classify the retinal layer segmentation techniques, PED fluid segmentation and classification of diseases in retinal OCT images. The medical industry is suffering with more critical patients and the cases are increasing in eye diseases double the number as of now. The artificial intelligence (AI) techniques help the health sector with a great and accurate automatic detection of disease. The image classification and pattern recognition are transforming the industry with artificial intelligence techniques. Many studies are being conducted employing image processing to aid in the early diagnosis of this disease. image processing techniques have advanced as a result of the introduction of artificial intelligence and machine learning. In this review paper, the structure classification methods and the image segmentation method that are best available existing research is discussed. This review summarizes all the recent algorithms that suits for the application of machine learning algorithms for predicting retinal diseases in OCT images. The algorithms discussed from existing research paper, produce the readers to identify the best accurate algorithm for retinal classification of infected eye and normal eye, precision and less processing time for la

关键词： Optical tomography

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Accelerating Traditional Object Detection Methods on Sophisticated Embedded Systems 4th

Accelerating Traditional Object Detection Methods on Sophist...

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4th International conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, Thierry Chouireb, Fatima Ecole Normale Super Laghouat Dept Phys Lab Appl Sci & Didact Laghouat Algeria Univ Amar Telidji Laghouat Dept Elect Telecommun Signals & Syst Lab Laghouat Algeria Univ La Rochelle Dept Comp Sci Lab Math Image & Applicat La Rochelle France

ISBN: (纸本)9783031821523;9783031821530

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. Therefore, special hardware that can acquire, control, or execute in parallel is required. These specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, Visual processing Units VPUs, Tensor processing Units TPUs, Neural processing Units NPUs or Graphics processing Units GPUs. This work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. This single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4x faster than the CPU for the HOG method and 2x for the Haar Cascade Classifier method.

关键词： Object detection machine Learning Haar Cascade HOG Local feature descriptors GPU implementation OpenCV CUDA SoC Tegra X1 Jetson Nano

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Design and Optimization of a Dynamic Train image Acquisition System Utilizing Line Scan Camera Technology 4

Design and Optimization of a Dynamic Train Image Acquisition...

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4th International conference on Advances in Electrical Engineering and Computer applications, AEECA 2023

作者： Li, Changgeng Xu, Feng Li, Wei Sun, Xiaodong Yuan, Bo Crrc Qingdao Sifang Co. Ltd Qingdao266111 China

ISBN: (纸本)9798350308082

In response to the demand for efficient and high-quality daily maintenance and inspection of modern railway trains, the utilization of AI-based machine vision technology has been proven to significantly enhance maintenance efficiency and quality. image quality plays a critical role in visual inspection. Thus, this study presents the design of a dynamic train exterior image acquisition system based on line scan camera technology. Through a comprehensive analysis of maintenance requirements and business scenarios, the hardware structure and functional framework of the system are meticulously developed. The image acquisition module is strategically deployed in a 360° arrangement to enable panoramic image acquisition of the train's exterior. Leveraging the imaging principle of the line scan camera, two sets of magnetic cylinders are employed to measure the train's velocity multiple times, ensuring continuous correction of the image acquisition line frequency for stable image capture. Furthermore, a character recognition algorithm for train numbers is extensively researched, enabling accurate identification of the train number through image acquisition, preprocessing, and OCR recognition algorithms. The proposed system integrates professional image acquisition and processing techniques, enabling high-resolution and high-precision stable acquisition of dynamic train images. It provides crucial data support for precise detection of train exterior conditions and offers reliable technical means for train trajectory monitoring and safety management. © 2023 IEEE.

关键词： image acquisition

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Automatic target detection utilizing an Edge IR vision transformer (EIR-ViT) 33

Automatic target detection utilizing an Edge IR vision trans...

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conference on Automatic Target Recognition XXXIII

作者： Adams, Ethan R. Depoian, Arthur C., II Kurz, Aidan G. Bailey, Colleen P. Guturu, Parthasarathy Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510661561;9781510661578

The detection and recognition of targets within imagery and video analysis is vital for military and commercial applications. The development of infrared sensor devices for tactical aviation systems imagery has increased the performance of target detection. Due to the advancements of infrared sensors capabilities, their use for field operations such as visual operations (visops) or reconnaissance missions that take place in a variety of operational environments have become paramount. Many techniques implemented stretch back to 1970, but were limited due to computational power. The AI industry has recently been able to bridge the gap between traditional signal processing tools and machine learning. Current state of the art target detection and recognition algorithms are too bloated to be applied for on ground or aerial mission reconnaissance. Therefore, this paper proposes Edge IR vision Transformer (EIR-ViT), a novel algorithm for automatic target detection utilizing infrared images that is lightweight and operates on the edge for easier deployability.

关键词： Automatic Target Detection vision Transformer Infrared Imaging Object Detection FLIR image FLIR dataset Edge Computing

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Speech Emotion Recognition Using CNN-LSTM and vision Transformer 13th

Speech Emotion Recognition Using CNN-LSTM and Vision Transf...

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13th International conference on Innovations in Bio-Inspired Computing and applications, IBICA 2022, and 12th World Congress on Information and Communication Technologies, WICT 2022

作者： Kumar, C S Ayush Maharana, Advaith Das Krishnan, Srinath Murali Hanuma, Sannidhi Sri Sai Lal, G. Jyothish Ravi, Vinayakumar Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia

ISBN: (纸本)9783031274985

The importance of speech emotion recognition has increased as a result of the acceptance of intelligent conversational assistant services. The communication between humans and machines may be made better via emotion recognition and analysis. We propose the application of attention based deep learning techniques to process and recognize speech emotions. In this paper we look at two major approaches CNN-LSTM and Mel Spectrogram-vision Transformer based models and is compared over to the existing benchmarks. The experimental results roots for the feature extraction strategy of deep learning based approaches, eliminating the need of handpicking the features for traditional machine learning (ML) classifiers present in the current literature. A comparative study and evaluation between CNN-LSTM and vision Transformers (ViT) have been evaluated and established from the experimental results. Both the models performed similarly with CNN-LSTM giving an accuracy of 88.50% when compared to the accuracy of 85.36% by ViT surpassing the existing benchmarks and providing the scope of study of attention and image processing based learning for speech emotion recognition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image processing

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image Captioning of Satellite images Using Transfer Learning and LSTM Blending 6th

Image Captioning of Satellite Images Using Transfer Learning...

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6th International conference on Information Systems and Management Science, ISMS 2023

作者： Sharma, Rohit Shree, Aishwary katoch, Aayush Verma, Sourabh Singh Manipal University Jaipur Jaipur303007 India

ISBN: (纸本)9783031707889

In recent years, the field of image captioning has gained substantial attention, posing a complex challenge that necessitates the integration of computer vision (CV), natural language processing (NLP), and machine learning techniques. Our study introduces an advanced model designed to generate natural language descriptions for satellite images. We harnessed the capabilities of convolutional neural networks (CNNs) and utilized state-of-the-art architectures such as VGG-19, ResNet-50, DenseNet-201, and EfficientNet B7 as encoders to extract intricate features from satellite images. These encoded features were seamlessly incorporated into a Long Short-Term Memory (LSTM) network, which is a type of recurrent neural network (RNN), serving as a robust decoder. To assess the effectiveness of our approach, we conducted extensive experiments using two datasets: the Sydney Dataset and the RSIC Dataset, both widely recognized benchmarks in the field of image captioning. The comparative analysis results obtained from our evaluations not only show promise but also underscore the competitiveness of our model. By achieving significant milestones in satellite image captioning, this research not only contributes to the expanding body of knowledge in this domain but also opens avenues for real-world applications, spanning from environmental monitoring to urban planning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Satellite imagery

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FHAM: FPGA-based High-Efficiency Approximate Multipliers via LUT Encoding 40

FHAM: FPGA-based High-Efficiency Approximate Multipliers via...

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IEEE 40th International conference on Computer Design (ICCD)

作者： Yao, Shangshang Zhang, Liang Natl Univ Def Technol Changsha Peoples R China

ISBN: (数字)9781665461863

ISBN: (纸本)9781665461863

Approximate computing as a promising technique is widely used in a variety of error tolerance applications, such as computer vision, machine learning and image processing. Since multiplication is one of the basic arithmetic operations used extensively among these applications which creates a great potential to reduce the circuit's area and power consumption by exploring approximate multipliers. In this paper, we present FHAM, a novel methodology for FPGA-based approximate softcore multiplier architecture via modifying INIT parameter values in LUT primitives. Our proposed approximate multipliers can achieve up to 24.4%, 52.9%, 56.4% improvement in delay, area and power over Xilinx soft multiplier IP core, respectively. We deployed the proposed approximate multiplier designs in the application of image blending, the results demonstrate that proposed multiplier design has a better accuracy-hardware trade-off than other designs.

关键词： Error-Tolerant computing approximate multipliers image processing

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