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Learn OpenCV 4 by Building Projects: Build real-world computer vision and image processing applications with OpenCV and C++, 2nd Edition

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2018年

作者： David Millán Escrivá Vinícius G. Mendonça Prateek Joshi

ISBN: (数字)9781789347623

ISBN: (纸本)9781789341225

Explore OpenCV 4 to create visually appealing cross-platform computer vision applications Key Features • Understand basic OpenCV 4 concepts and algorithms • Grasp advanced OpenCV techniques such as 3D reconstruction, machine learning, and artificial neural networks • Work with Tesseract OCR, an open-source library to recognize text in images Book Description OpenCV is one of the best open source libraries available, and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. Whether you’re completely new to computer vision, or have a basic understanding of its concepts, Learn OpenCV 4 by Building Projects – Second edition will be your guide to understanding OpenCV concepts and algorithms through real-world examples and projects. You’ll begin with the installation of OpenCV and the basics of image processing. Then, you’ll cover user interfaces and get deeper into image processing. As you progress through the book, you'll learn complex computer vision algorithms and explore machine learning and face detection. The book then guides you in creating optical flow video analysis and background subtraction in complex scenes. In the concluding chapters, you'll also learn about text segmentation and recognition and understand the basics of the new and improved deep learning module. By the end of this book, you'll be familiar with the basics of Open CV, such as matrix operations, filters, and histograms, and you'll have mastered commonly used computer vision techniques to build OpenCV projects from scratch. What you will learn • Install OpenCV 4 on your operating system • Create CMake scripts to compile your C++ application • Understand basic image matrix formats and filters • Explore segmentation and feature extraction techniques • Remove backgrounds from static scenes to identify moving objects for surveillance • Employ various techniques to track objects in a live video • Work with new OpenCV functions for

关键词： Computer vision OpenCV OpenCV 4 Tesseract OCR Open CV book

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In-Sensor Visual Perception and Inference

INTELLIGENT COMPUTING

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INTELLIGENT COMPUTING 2023年 2卷

作者： Liu, Yanan Fan, Rui Guo, Jianglong Ni, Hepeng Bhutta, Muhammad Usman Maqboo Shanghai Univ Sch Microelect Shanghai Peoples R China Tongji Univ Coll Elect & Informat Engn State Key Lab Intelligent Autonomous Syst Shanghai Peoples R China Tongji Univ Frontiers Sci Ctr Intelligent Autonomous Syst Shanghai Peoples R China Harbin Inst Technol Sch Sci Shenzhen Peoples R China Shandong Jianzhu Univ Sch Mech & Elect Engn Jinan Peoples R China Chinese Univ Hong Kong Robot Lab Hong Kong Peoples R China

Conventional machine vision systems have separate perception, memory, and processing architectures, which may exacerbate the increasing need for ultrahigh image processing rates and ultralow power consumption. In contrast, in-sensor visual computing performs signal processing at the pixel level using the collected analog signals directly, without sending data to other processors. Therefore, the in-sensor computing paradigm may hold the key to realizing extremely efficient and low power visual signal processing by integrating sensing, storage, and computation onto focal planes using either novel circuit designs or new materials. The focal-plane sensor-processor (FPSP), which is a typical in-sensor visual computing device, is a vision chip that has been developed for nearly 2 decades in domains such as image processing, computer vision, robotics, and neural networks. In contrast to conventional computer vision systems, the FPSP gives vision systems in-sensor image processing capabilities, thus decreasing system complexity, reducing power consumption, and enhancing information processing efficiency and security. Although many studies on in-sensor computing using the FPSP have been conducted since its invention, no thorough and systematic summary of these studies exists. This review explains the use of image processing algorithms, neural networks, and applications of in-sensor computing in the fields of machine vision and robotics. The objective is to assist future developers, researchers, and users of unconventional visual sensors in understanding in-sensor computing and associated applications.

关键词： image enhancement

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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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image Registration Algorithm for Stamping Process Monitoring Based on Improved Unsupervised Homography Estimation

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APPLIED SCIENCES-BASEL 2024年第17期14卷 7721页

作者： Zhang, Yujie Du, Yinuo Shaanxi Univ Sci & Technol Sch Elect & Control Engn Xian 710021 Peoples R China

Featured Application This paper is applied in the industrial monitoring of the stamping process, where vibrations are common, necessitating the use of image registration and homography estimation methods to align template images with test images. By employing machine-vision and image-processing techniques, the process is monitored in real-time to detect any anomalies, ultimately aiming to protect the stamping *** Homography estimation is a crucial task in aligning template images with target images in stamping monitoring systems. To enhance the robustness and accuracy of homography estimation against random vibrations and lighting variations in stamping environments, this paper proposes an improved unsupervised homography estimation model. The model takes as input the channel-stacked template and target images and outputs the estimated homography matrix. First, a specialized deformable convolution module and Group Normalization (GN) layer are introduced to expand the receptive field and enhance the model's ability to learn rotational invariance when processing large, high-resolution images. Next, a multi-scale, multi-stage unsupervised homography estimation network structure is constructed to improve the accuracy of homography estimation by refining the estimation through multiple stages, thereby enhancing the model's resistance to scale variations. Finally, stamping monitoring image data is incorporated into the training through data fusion, with data augmentation techniques applied to randomly introduce various levels of perturbation, brightness, contrast, and filtering to improve the model's robustness to complex changes in the stamping environment, making it more suitable for monitoring applications in this specific industrial context. Compared to traditional methods, this approach provides better homography matrix estimation when handling images with low texture, significant lighting variations, or large viewpoint changes. Compared to other deep-lear

关键词： machine vision stamping mold protection image alignment single-stress estimation unsupervised learning data enhancement

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Sliding Window Detection Algorithm of Grabbing Safety and Grabbing Effect Based on DEM with applications to Unmanned Bridge Grabbing Work

Sliding Window Detection Algorithm of Grabbing Safety and Gr...

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International Conference on image processing, Computer vision and machine Learning (ICICML)

作者： Zou, Yifei Wuhan Univ WHU Wuhan Peoples R China

ISBN: (纸本)9781665464680

In the process of grabbing and issuing of the unattended system for fine particles dry bulk materials, the three-dimensional distribution of dry bulk materials is continuous, extremely irregular and fast dynamic. The selection of grabbing area is related to the grabbing safety and grabbing effect. In the extreme case of three-dimensional distribution, the improper selection of grabbing area may lead to accidents such as turning of grabs, closing rope out of groove, poor grabbing effect, or unsatisfied grab efficiency. Based on the distribution characteristics of dry bulk materials, a sliding window detection algorithm based on three-dimensional feature distribution is proposed in this paper. Before grabbing, the detection and analysis are carried out according to the distribution of materials, and the influence characteristics of relatively safe and effective distribution are judged, such as central depression, superelevation slope and other characteristics. The Digital Elevation Model (DEM)three-dimensional feature distribution model of the whole area is established, and the sliding window is further used to detect safety and efficient grab areas. The simulation results show that the sliding window detection algorithm based on DEM three-dimensional feature distribution can predict a more efficient and safe grab area according to the distribution of stacking materials, which provides a new detection method for the application of unattended bridge crane grab engineering.

关键词： three-dimensional distribution grabbing safety grabbing effect sliding window DEM

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Automated Computer vision-Enabled Manufacturing of Nanowire Devices

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ACS NANO 2022年第11期16卷 18009-18017页

作者： Potocnik, Teja Christopher, Peter J. Mouthaan, Ralf Albrow-Owen, Tom Burton, Oliver J. Jagadish, Chennupati Tan, Hark Hoe Wilkinson, Timothy D. Hofmann, Stephan Joyce, Hannah J. Alexander-Webber, Jack A. Univ Cambridge Dept Engn Cambridge CB3 0FA England Australian Natl Univ Australian Res Council Ctr Excellence Transformat Res Sch Phys & Engn Dept Elect Mat Engn Canberra ACT 2600 Australia

We present a high- throughput method for identifying and characterizing individual nanowires and for automatically designing electrode patterns with high alignment accuracy. Central to our method is an optimized machine-readable, lithographically processable, and multi-scale fiducial marker system-dubbed LithoTag-which provides nanostructure position determination at the nanometer scale. A grid of uniquely defined LithoTag markers patterned across a substrate enables image alignment and mapping in 100% of a set of >9000 scanning electron microscopy (SEM) images (>7 gigapixels). Combining this automated SEM imaging with a computer vision algorithm yields location and property data for individual nanowires. Starting with a random arrangement of individual InAs nanowires with diameters of 30 +/- 5 nm on a single chip, we automatically design and fabricate >200 single-nanowire devices. For >75% of devices, the positioning accuracy of the fabricated electrodes is within 2 pixels of the original microscopy image resolution. The presented LithoTag method enables automation of nanodevice processing and is agnostic to microscopy modality and nanostructure type. Such high-throughput experimental methodology coupled with data-extensive science can help overcome the characterization bottleneck and improve the yield of nanodevice fabrication, driving the development and applications of nanostructured materials.

关键词： nanowires computer vision automation nanofabrication microscopy

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MNPDenseNet: Automated Monkeypox Detection Using Multiple Nested Patch Division and Pretrained DenseNet201

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MULTIMEDIA TOOLS AND applications 2024年第30期83卷 75061页

作者： Demir, Fahrettin Burak Baygin, Mehmet Tuncer, Ilknur Barua, Prabal Datta Dogan, Sengul Tuncer, Turker Ooi, Chui Ping Ciaccio, Edward J. Acharya, U. Rajendra Bandirma Onyedi Eylul Univ Fac Engn & Nat Sci Dept Software Engn Balikesir Turkiye Erzurum Tech Univ Coll Engn Dept Comp Engn Erzurum Turkiye Interior Minist Elazig Governorship Elazig Turkiye Univ Southern Queensland Sch Business Informat Syst Darling Hts Qld Australia Firat Univ Technol Fac Dept Digital Forens Engn Elazig Turkiye Singapore Univ Social Sci Sch Sci & Technol Singapore 599494 Singapore Columbia Univ Irving Med Ctr Dept Med New York NY 10032 USA Univ Southern Queensland Ctr Hlth Res Darling Hts Qld Australia Univ Southern Queensland Sch Math Phys & Comp Springfield Qld Australia

BackgroundMonkeypox is a viral disease caused by the monkeypox virus (MPV). A surge in monkeypox infection has been reported since early May 2022, and the outbreak has been classified as a global health emergency as the situation continues to worsen. Early and accurate detection of the disease is required to control its spread. machine learning methods offer fast and accurate detection of COVID-19 from chest X-rays, and chest computed tomography (CT) images. Likewise, computer vision techniques can automatically detect monkeypoxes from digital images, videos, and other *** this paper, we propose an automated monkeypox detection model as the first step toward controlling its global *** and methodA new dataset comprising 910 open-source images classified into five categories (healthy, monkeypox, chickenpox, smallpox, and zoster zona) was created. A new deep feature engineering architecture was proposed, which contained the following components: (i) multiple nested patch division, (ii) deep feature extraction, (iii) multiple feature selection by deploying neighborhood component analysis (NCA), Chi2, and ReliefF selectors, (iv) classification using SVM with 10-fold cross-validation, (v) voted results generation by deploying iterative hard majority voting (IHMV) and (vi) selection of the best vector by a greedy *** proposal attained a 91.87% classification accuracy on the collected dataset. This is the best result of our presented framework, which was automatically selected from 70 generated *** computed classification results and findings demonstrated that monkeypox could be successfully detected using our proposed automated model.

关键词： MNPDenseNet Monkeypox detection image processing Biomedical engineering

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MUWS'2023: The 2nd International Workshop on Multimodal Human Understanding for the Web and Social Media 23

MUWS'2023: The 2nd International Workshop on Multimodal Huma...

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32nd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Cheema, Gullal S. Hakimov, Sherzod Kastner, Marc A. Garcia, Noa Leibniz Univ Hannover L3S Res Ctr Hannover Germany Univ Potsdam Potsdam Germany Kyoto Univ Kyoto Japan Osaka Univ Osaka Japan

ISBN: (纸本)9798400701245

Multimodal human understanding and analysis is an emerging research area that cuts through several disciplines like Computer vision (CV), Natural Language processing (NLP), Speech processing, Human-Computer Interaction (HCI), and Multimedia. Several multimodal learning techniques have recently shown the benefit of combining multiple modalities in image-text, audio-visual and video representation learning and various downstream multimodal tasks. At the core, these methods focus on modelling the modalities and their complex interactions by using large amounts of data, different loss functions and deep neural network architectures. However, for many Web and Social media applications, there is the need to model the human, including the understanding of human behaviour and perception. For this, it becomes important to consider interdisciplinary approaches, including social sciences, semiotics and psychology. The core is understanding various cross-modal relations, quantifying bias such as social biases, and the applicability of models to real-world problems. Interdisciplinary theories such as semiotics or gestalt psychology can provide additional insights and analysis on perceptual understanding through signs and symbols via multiple modalities. In general, these theories provide a compelling view of multimodality and perception that can further expand computational research and multimedia applications on the Web and Social media. The theme of the MUWS workshop, multimodal human understanding, includes various interdisciplinary challenges related to social bias analyses, multimodal representation learning, detection of human impressions or sentiment, hate speech, sarcasm in multimodal data, multimodal rhetoric and semantics, and related topics. The MUWS workshop will be an interactive event and include keynotes by relevant experts, poster and demo sessions, research presentations and discussion.

关键词： multimodality machine learning image-text relations social media web human understanding semiotics

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Road Infrastructure Defect Detection using Yolo8Seg Based Approach

Road Infrastructure Defect Detection using Yolo8Seg Based Ap...

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International image processing, applications and Systems Conference (IPAS)

作者： Norah A. AlSubaie Ghayda A. AlMalki Ghada N. AlMutairi Sarah A. AlRumaih Department of Computer Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

This research introduces "Jaddah," an innovative AI-based system for the automated detection of road infrastructure defects using advanced computer vision and machine learning techniques. The system addresses the limitations of traditional road inspection methods, which are often slow and prone to human error. Jaddah develops a mobile application that efficiently detects, classifies, and segments road defects at the pixel level. By utilizing a comprehensive dataset of high-resolution images, the model training process is significantly enhanced. The YOLOv8-seg model is implemented to achieve precise defect localization and segmentation, ensuring high accuracy in identifying and categorizing road defects. Performance metrics show an impressive 87% mAP50, demonstrating reliable defect detection. These results contribute to improved infrastructure maintenance, enhanced road safety, and greater operational efficiency.

关键词： Measurement Location awareness Computer vision Urban planning machine learning Inspection Road safety Maintenance Mobile applications Defect detection

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A novel image processing technique based on deep learning for water consumption detection

A novel image processing technique based on deep learning fo...

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

作者： Carratu, Marco Dello Iacono, Salvatore Di Leo, Giuseppe Gallo, Vincenzo Liguori, Consolatina Pietrosanto, Antonio Univ Salerno Dept Ind Engn Via Giovanni Paolo II 132 Fisciano SA Italy

ISBN: (纸本)9781665483605

In recent years, traditional image processing techniques have seen the introduction of novel tools, able to face issues that are not always handy with classical vision algorithms. For example, classical image processing algorithms (measurement, detection of features, and many others) require a controlled environment, like illumination, target positioning, and vibration that can influence the scene for the correct operation. On the other hand, the machine learning approaches enabled image processing techniques also in non-controlled environments. One of these applications can be represented by developing a leak detector at the household level, based on processing pictures of the mechanical water meter dial. The proposed research investigates using a deep learning approach to detect the minimal movement of the water meter needles related to water leakage. In particular, a CNN was trained to correlate successive differences on the water meter dial images taken with an applied calibrated water flow. From this analysis, it is possible to detect the absence of periods with null consumption and thus detect small water losses.

关键词： Deep Learning CNN Water leakage image processing

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