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ACM TRANSACTIONS ON GRAPHICS 2022年第6期41卷 p1-15页

作者： Tseng, Ethan Zhang, Yuxuan Jebe, Lars Zhang, Xuaner Xia, Zhihao Fan, Yifei Heide, Felix Chen, Jiawen Princeton Univ Princeton NJ 08544 USA Adobe San Jose CA USA

image processing pipelines are ubiquitous and we rely on them either directly, by filtering or adjusting an image post-capture, or indirectly, as image signal processing (ISP) pipelines on broadly deployed camera systems. Used by artists, photographers, system engineers, and for downstream vision tasks, traditional image processing pipelines feature complex algorithmic branches developed over decades. Recently, image-to-image networks have made great strides in image processing, style transfer, and semantic understanding. The differentiable nature of these networks allows them to fit a large corpus of data;however, they do not allow for intuitive, fine-grained controls that photographers find in modern photo-finishing tools. This work closes that gap and presents an approach to making complex photo-finishing pipelines differentiable, allowing legacy algorithms to be trained akin to neural networks using first-order optimization methods. By concatenating tailored network proxy models of individual processing steps (e.g. white-balance, tone-mapping, color tuning), we can model a non-differentiable reference image finishing pipeline more faithfully than existing proxy image-to-image network models. We validate the method for several diverse applications, including photo and video style transfer, slider regression for commercial camera ISPs, photography-driven neural demosaicking, and adversarial photo-editing.

关键词： image processing photo-finishing raw processing

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Smart and Quick Parking Spot Detection 15

Smart and Quick Parking Spot Detection

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Veeramalla, Santhosh Kumar Gali, Rama Lakshmi Ranjeeth, M. Tara, Saikumar Hindumathi, V. Bhargava Kumar, L. Bvrit Hyderabad College of Engineering for Women Department of Ece Hyderabad India

ISBN: (纸本)9798350370249

The rapid expansion of urban areas has intensified the challenge of finding parking spaces for drivers. Intelligent parking systems emerge as a crucial solution by providing real-time detection of available spaces. While various methods exist, from basic magnetic sensors to advanced computer vision algorithms, their effectiveness can be hindered by environmental factors. Recognizing the importance of automated systems, this paper focuses on implementing an image processing approach for parking space detection. By strategically placing cameras in parking lots, the system identifies vacant spaces and transforms images to determine availability. Integrating these techniques into mobile applications could further aid drivers in locating parking spots, improving their overall experience. Ultimately, accurate and efficient parking space identification is vital for reducing congestion, enhancing air quality, and improving urban environments. © 2024 IEEE.

关键词： CCTV Computer vision image processing Parking availability Parking guidance Smart parking Sustainable cities

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An Efficient Implementation of the Threshold-based VNG Demosaicing with Reduced Calculations

An Efficient Implementation of the Threshold-based VNG Demos...

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2024 Smart systems Integration conference and Exhibition

作者： Alnaser, Yousef Ravichandran, Anbumani Langer, Jan Fraunhofer ENAS Chemnitz Germany TU Chemnitz Chemnitz Germany

ISBN: (纸本)9798350388787;9798350388770

Color Filter Arrays (CFA) are essential components of digital cameras and image sensors to capture the color information needed to produce full-color images from only a single image sensor per pixel. Many methods and algorithms have been proposed to recover the missing color information of CFAs. In this work, we use a simplified version of the Theshold-based Variable Number of Gradients algorithm proposed by Chang et al. to estimate the full-color information from Bayer images. We also show that the slight modification to algorithm does not effect images quality while making it more compatible with hardware. We propose an efficient implementation of the algorithm that reduces the number of calculations per pixel at the cost of increased memory resources. Our implementation targets an image processing pipeline in an FPGA platform which is short on LUTs and FF resources but has DSPs and BRAMs to spare. We buffer the absolute differences and average color components to be shared and re-used between neighboring pixels, on two levels: within the same row, and between different rows. The latter strategy reduces the number of absolute differences calculated every cycle from 32 to 4 and average color components from 32 to 6. However, the memory requirements are increased from storing 4 image rows to 18 image rows. We implement the solutions on an FPGA using high-level synthesis (HLS) and optimize it to further reduce resources.

关键词： Demosaicing Variable Number of Gradients Bayer image FPGA

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Retrofitting a Legacy Cutlery Washing Machine Using Computer Vision 16th

Retrofitting a Legacy Cutlery Washing Machine Using Computer...

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16th International conference on Computational Collective Intelligence (ICCCI)

作者： Fwa, Hua Leong Singapore Management Univ 81 Victoria St Singapore 188065 Singapore

ISBN: (纸本)9783031702587;9783031702594

Industry 4.0, the digitalization of manufacturing promises to lead to lowered cost, efficient processes and even discovery of new business models. However, many of the enterprises have huge investments in legacy machines which are not 'smart'. In this study, we thus designed a cost-efficient solution to retrofit a legacy conveyor belt-based cutlery washing machine with a commodity web camera. We then applied computer vision (using both traditional image processing and deep learning techniques) to infer the speed and utilization of the machine. We detailed the algorithms that we designed for computing both speed and utilization. With the existing operational constraints of our client, frequent re-training of the deep learning model for object detection is not feasible. Thus, we compared the generalizability of the two techniques across 'unseen' cutleries and found traditional image processing to be generalizable across 'unseen' images. Our proposed final solution uses traditional image processing for computation of utilization but a hybrid of traditional image processing and deep learning model for speed computation as it is more reliable. Our client has implemented our proposed solution for one conveyor belt-based cutlery washing machine and will be planning to scale this to multiple conveyor belt-based cutlery washing machines.

关键词： Industry 4.0 Computer Vision Deep Learning image processing

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image Recognition and Position Technology Based on Super-pixel Fuzzy C-Means Clustering in Industrial Assembly System 3

Image Recognition and Position Technology Based on Super-pix...

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3rd International conference on image processing and Intelligent Control, IPIC 2023

作者： Yuan, Hailiang Sun, Weitao Wang, Hailing Tianjin Vocational College of Mechanics and Electricity Tianjin China Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9781510668140

Improved fuzzy c-means (FCM) clustering algorithms have been widely used for image recognition and localization. However, in industrial assembly systems, the unsatisfactory pixel merging and segmentation results between local adjacent windows, combined with the differences in the shape, size, and material of parts, as well as variations in lighting conditions, make target image recognition and localization a challenge. Most algorithms struggle to achieve the expected results and have high computational complexity. In this study, we propose a super-resolution-based FCM clustering algorithm that is faster and more accurate for image recognition and localization in industrial assembly systems with irregular part sizes. We first use multiscale morphological gradient operations to obtain high-resolution images. Then, we use the fast FCM clustering algorithm to achieve the recognition and extraction of specific target images. Finally, we use the Sobel operator to determine the target's position. The experimental results demonstrate that the proposed algorithm shows higher accuracy and efficiency in image recognition and localization for industrial assembly systems. © 2023 SPIE.

关键词： image recognition

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APPLICATION OF UNSUPERVISED LEARNING AND image processing INTO CLASSIFICATION OF DESIGNS TO BE FABRICATED WITH ADDITIVE OR TRADITIONAL MANUFACTURING 24

APPLICATION OF UNSUPERVISED LEARNING AND IMAGE PROCESSING IN...

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24th International conference on Engineering Design, ICED 2023

作者： Ördek, Baris Borgianni, Yuri Free University of Bozen-Bolzano Italy

Manufacturing process (MP) selection systems require a large amount of labelled data, typically not provided as design outputs. This issue is made more severe with the continuous development of Additive Manufacturing systems, which can be increasingly used to substitute traditional manufacturing technologies. The objective of this paper is to investigate the application of image processing for classifying MPs in an unsupervised approach. To this scope, k-means and hierarchical clustering algorithms are applied to an unlabelled image dataset. The input dataset is constructed from freely accessible web databases and consists of twenty randomly selected CAD models and corresponding images of machine elements: 35% additively manufactured parts and 65% manufactured with traditional manufacturing technologies. The input images are pre-processed to have the same colour and size. The k-means and hierarchical clustering algorithms reported 65% and 60% accuracy, respectively. The algorithms show comparable performance, however, the k-means algorithm failed to predict the correct subdivisions. The research shows promising potential for MP classification and image processing applications. © The Author(s), 2023. Published by Cambridge University Press.

关键词： 3D printing

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Camera Movement Cancellation in Video Using Phase Congruency and an FFT-Based Technique 11th

Camera Movement Cancellation in Video Using Phase Congruency...

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11st World conference on Information systems and Technologies (WorldCIST)

作者： Gharahbagh, Abdorreza Alavi Hajihashemi, Vahid Machado, J. J. M. Tavares, Joao Manuel R. S. Univ Porto Fac Engn Rua Dr Roberto Frias S-N P-4200465 Porto Portugal Univ Porto Dept Engn Mecan Fac Engn Rua Dr Roberto Frias S-N P-4200465 Porto Portugal

ISBN: (纸本)9783031456503;9783031456510

One of the interesting fields in video processing is motion detection and human action detection (HAR) in video. In some applications where both objects in the scene and the camera may be moving, camera movement cancellation is very important to increase accuracy in extracting motion features. HAR systems usually use image matching/registration algorithms to remove the camera movement. In these methods, the source (fixed) image frame is compared with moved image frame, and the best match is determined geometrically. In video processing, due to the existence of a set of frames, one can correct errors using previous data, but at the same time, it is needed a fast frame registration algorithm. According to the above explanations, this article proposes a method to detect and minimize camera movement in video using phase information. In addition to having the acceptable speed and the ability to be implemented online, the proposed method, by combining texture and phase congruency (PC), can significantly increase the accuracy of detecting the objects in the scene. The proposed method was implemented on a HAR dataset, which includes camera movement, and its ability to compensate for camera motion and pre-serve object motion was verified. Finally, the speed and accuracy of the proposed method were compared with a number of the latest image registration methods, and its efficiency in terms of camera movement cancellation and execution time is discussed.

关键词： Camera Movement Cancellation Phase Congruency (PC) Video image Registration Fast Fourier transform (FFT)

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Automatic Number Plate Recognition System Using Deep Learning algorithms and image processing for Surveillance 9

Automatic Number Plate Recognition System Using Deep Learnin...

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9th International conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Jeba Malar, A. Christy Deepika, S. Jayasree, S. Keerthana, G. Sri Krishna College of Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350365092

The increase in amount of vehicles in the past few years have made traffic management a difficult job. Technologies play an important role in these systems to regulate the traffic. Number plates are distinguished by their shape, bulk, and color in different countries. Number plates must adhere to rules in India, where a light hue serves as the background and a black color serves as the foreground. This paper recommended a new technique to locate the number plate on a vehicle by using histogram equalization and then identifying the alphabets and digits on the plates using character segmentation using Sobel and Canny edge detection image processing technique. © 2024 IEEE.

关键词： Deep learning

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Vision Based Runway Identification with marked or unmarked terrain for Automatic Landing Applications of UAV 8

Vision Based Runway Identification with marked or unmarked t...

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8th conference on Advances in Control and Optimization of Dynamical systems (ACODS)

作者： Tripathi, Amit Kumar Patel, Vijay V. Padhi, Radhakant Aeronaut Dev Agcy Bangalore Karnataka India Indian Inst Sci Fac Aerosp Engn Dept Bangalore Karnataka India

Vision based runway identification using 'marked or unmarked terrain' image sequences captured from a fixed wing unmanned aerial vehicle through onboard stereovision sensor is presented in this paper. An innovative convolutional neural netwok (CNN) based YOLO-V8 object detection algorithm is used to detect the runway during approach segment of UAV. This deep learning algorithm detects the region of interest in real time and in a computationally efficient manner. The captured unknown road segment or runway image frames are processed and examined for width, length, level and smoothness aspects to qualify as a suitable runway for UAV landings. Also, it is ensured that there are no obstacles, patches or holes on the detected road or runway. Runway start and end threshold lines and regions, touchdown point and runway edge lines are considered as the region of interest. image processing algorithms are applied on the captured runway or road images to detect strong features in the region of interest. Feature detector based image processing algorithm with stereo vision constraint is used to establish the relation between unmanned aerial vehicle's center of gravity and detected runway feature points image processing algorithms like hough line detection, RANSAC, Oriented FAST and Rotated BRIEF (ORB), median filters, morphological methods are applied to extract terrain features. Based on the detected runway orientation and position with respect to UAV position. An automatic landing manoeuvre is performed by UAV autopilot to land the UAV on intended touchdown point on runway computed through detected feature points.

关键词： image processing Runway Identification Feature detection Region of Interest threshold lines touch down point YOLOv8 ORB CNN runway width slant distance

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Data processing, analysis and Machine Learning of Electrical Atomic Force Microscopy (AFM) in Data Cube mode: conduction of a nanomaterial for Lithium-ion battery electrodes 6

Data processing, analysis and Machine Learning of Electrical...

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6th IEEE International conference on image processing, Applications and systems, IPAS 2025

作者： Germanicus, Rosine Coq De Wolf, Peter Klapetek, Petr Normandie Univ Unicaen Ensicaen CNRS CRISMAT UMR6508 Caen14000 France Bruker Nano Surfaces Santa BarbaraCA93117 United States Czech Metrology Institute Brno638 00 Czech Republic

ISBN: (纸本)9798331506520

With the increasing integration of functional systems, nanoscale characterization has become crucial not only for material investigation but also for advancing the understanding of local behavior and optimizing performances. This paper details data processing techniques and machine learning methodologies for the analysis and prediction of advanced electrical Atomic Force Microscopy datasets. The datasets are acquired using a hyperspectral Data Cube approach, which combines both mapping and spectroscopy acquisitions. In the context of decarbonization and electrification, current challenges are related to energy storage and transport. A cathode material used in Lithium-ion battery electrodes is investigated for its electrical transport properties. To comprehensively characterize the electrical properties, the conductive mode of Atomic Force Microscopy is employed using the Data Cube method, which enables current-voltage spectroscopy at each pixel of the acquisition. The analysis of experimental datasets reveals localized electrical behavior of the material's components as a function of applied voltage. Furthermore, machine learning algorithms are employed to predict current maps and cluster distinct regions based on their electrical characteristics. © 2025 IEEE.

关键词： Network security

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