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computer vision Conference (CVC)

作者： Song, Mengyu Furukawa, Tomonari Virginia Polytech Inst & State Univ Blacksburg VA 24061 USA

ISBN: (纸本)9783030177980;9783030177973

This paper presents Specular Photometric Stereo (SPS), which is a Photometric Stereo (PS) technique incorporating specular reflection. The proposed SPS uses multiple images of a surface under different lighting conditions to obtain surface normals similarly to the conventional PS, but uniquely utilizes specular components of a dark surface, which reflects little diffuse light. The proposed framework consists of two sequential numerical steps, which are the conversion of a highly non-linear specular reflection model to a non-linear equation with only one non-linear parameter, and then the iterative removal of the diffuse components. The proposed SPS can estimate normals of dark surfaces, which is not possible by the conventional PS. The proposed SPS was examined using synthesized data and then tested with real-world surfaces. The results of surface normal estimation show that the capability of the proposed SPS over the existing PS in both accuracy and computational cost.

关键词： computer vision algorithm Shape from X Photometric Stereo

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Reconfigurable Forward Homography Estimation System for Real-Time Applications 22

Reconfigurable Forward Homography Estimation System for Real...

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22nd IFIP WG 10.5/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)

作者： Popovic, Vladan Leblebici, Yusuf Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland

ISBN: (纸本)9781479960163

Image processing and computer vision algorithms extensively use projections, such as homography, as one of the processing steps. Systems for homography calculation usually observe homography as an inverse problem and provide an exact solution. However, the systems processing larger resolution images cannot meet inherently tight real-time constraints. Look-up table based systems provide an option for forward homography solutions, but they require large memory availability. Recent compressed look-up table methods reduce the memory requirements at the expense of lower peak signal-to-noise-ratio. In this work, we present a forward homography estimation algorithm which provides higher image quality than compressed look-up table methods. The algorithm is based on bounding the homography error, and neglecting the pixels out of the determined bound. The presented FPGA implementation of the estimation system requires a small amount of hardware, and no memory storage. The prototype system project an image frame onto a spherical surface at 295 Mpixels/s rate which is, up to our knowledge, currently the fastest homography system.

关键词： computer vision data compression field programmable gate arrays inverse problems table lookup FPGA implementation computer vision algorithm homography calculation homography error image frame image processing algorithm image quality inverse problem look-up table based systems memory requirement reduction peak signal-to-noise-ratio real-time applications reconfigurable forward homography estimation system Cameras Estimation Field programmable gate arrays Image resolution Real-time systems Registers Table lookup Field programmable gate arrays Table lookup homography Inverse method images real-time application program computer vision image resolution Image quality image processing algorithm Registers real-time systems Data compression Cameras Peak signal-to-noise ratio

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A Drone-based Automated Halyomorpha halys Scouting: A Case Study on Orchard Monitoring

A Drone-based Automated Halyomorpha halys Scouting: A Case S...

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IEEE International Workshop on Metrology for Agriculture and Forestry (IEEE MetroAgriFor)

作者： Sorbelli, Francesco Betti Palazzetti, Lorenzo Pinotti, Cristina M. Univ Perugia Dept Comp Sci & Math Perugia Italy Univ Florence Dept Comp Sci & Math Florence Italy

ISBN: (纸本)9798350312720

This paper presents the results of a case study focusing on automating the monitoring process of Halyomorpha halys (HH) in smart agriculture. HH is an invasive global pest that causes significant economic damages to fruit orchards. Our study aims to address the challenges associated with HH scouting, which is traditionally a time- and labor-intensive task. The study concentrates on HALY. ID project achievements of 2022 campaign, where the image acquisition is performed using solely a drone, and exploiting an autonomous drone-based navigation protocol from the top of the orchard. The protocol for the drone involves flying through predefined waypoints and capturing pictures at various tree positions. Moreover, for the sake of simplicity we conducted the detection of HH class only. We performed analyses on the acquired images, including evaluations of both image blurriness and brightness. Then, we obtain encouraging results from YOLOV5 detection algorithms trained on the novel acquired dataset of images. These outcomes show promising potential for automating HH monitoring and mark a significant step towards enhancing smart agriculture practices.

关键词： Halyomorpha halys detection Drones computer vision algorithm Technological transfer

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The Development and Research of the Indoor Navigation System for a Mobile Robot with the Possibility of Obstacle Detection 16

The Development and Research of the Indoor Navigation System...

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16th FRUCT Conference of Open-Innovations-Association

作者： Tyukin, Alexander Lebedev, Ilya Priorov, Andew PG Demidov Yaroslavl State Univ Yaroslavl Russia

ISBN: (纸本)9785757704890

This work describes a new approach to the mobile robot orientation in space and its obstacle determination. The method uses color beacons placed on the environmental objects for position determination. For obstacle detection the computer vision algorithm is used. All the system functions using one single camera. Both algorithms developed are investigated under different conditions.

关键词： collision avoidance indoor navigation mobile robots robot vision color beacons computer vision algorithm environmental object indoor navigation system mobile robot orientation obstacle detection obstacle determination position determination algorithm design and analysis Cameras Dispersion Image color analysis Navigation Streaming media Vectors Obstacle detection Mobile robots position determination algorithm design and analysis image colour analysis Streaming Video robot vision Cameras Collision avoidance navigation images computer vision Indoor navigation

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City Scale Image Geolocalization via Dense Scene Alignment 15

City Scale Image Geolocalization via Dense Scene Alignment

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IEEE Winter Conference on Applications of computer vision (WACV 2015)

作者： Yagcioglu, Semih Erdem, Erkut Erdem, Aykut Hacettepe Univ Dept Comp Engn Ankara Turkey

ISBN: (纸本)9781479966837

Predicting where a photo was taken is quite important and yet a challenging task for computer vision algorithms. Our motivation is to solve this difficult problem in a city-scale setting by employing a data-driven approach. In order to pursue this goal, we developed a fast and robust scene matching method that follows a coarse-to-fine strategy. In particular, we combine scene retrieval via global features and dense scene alignment and use a large set of geo-tagged images of downtown San Francisco in our evaluation. The experimental results show that the proposed approach, despite its simplicity, is surprisingly effective and achieves comparable results with the state-of-the-art.

关键词： computer vision feature extraction image matching image retrieval San Francisco city scale image geolocalization coarse-to-fine matching strategy computer vision algorithm data-driven approach dense scene alignment geo-tagged images global features scene matching method scene retrieval Cities and towns Digital signal processing Geology Image color analysis Prediction algorithms Robustness Visualization images computer vision Cities and towns image colour analysis Prediction algorithms image matching Digital signal processing image retrieval geology Feature extraction city size Visualization San Francisco data driven California

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Performance prediction of visual algorithms on different hardware architectures

Performance prediction of visual algorithms on different har...

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International Symposium on Optomechatronic Technologies (ISOT)

作者： Soucies, Nicolas Ouarti, Nizar Univ Pierre & Marie Curie UPMC CNRS ALTEN ISIR Paris France Univ Pierre & Marie Curie UPMC CNRS ISIR Paris France

ISBN: (纸本)9781479966660

In many specialties related to computer vision, such as video processing for object inspection, microscopy or video surveillance, a performance requirement arises, the choice of hardware architecture that sized well is crucial for experts in image processing. However, these experts are not always expert in hardware architecture and are not always able to estimate a priori the performance of their algorithms on a given architecture. Here, we propose the first elements of a tool that will be able to predict the performance, in term of time, of a given vision algorithm. Convolution is studied as an unavoidable algorithm in computer vision. We designed some benchmarks where different parameters related to the convolution where studied: size of the filter, size of the image, parallelism. Our results showed that it is possible with a correct accuracy to obtain a prediction of the timing of the convolution with three different architectures with a limited number of parameters. In future work, other algorithmic building blocks will be tested to validate our approach on a complex algorithm that will be a combination of identified building blocks. We want to propose an algorithmic language for image processing that applies to hardware performance.

关键词： component computer vision algorithm hardware architecture performance prediction

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Cross-Layer Optimization for Automated Video Surveillance 18

Cross-Layer Optimization for Automated Video Surveillance

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18th IEEE International Symposium on Multimedia (IEEE ISM)

作者： Alsmirat, Mohammad Sarhan, Nabil J. Jordan Univ Sci & Technol Dept Comp Sci Irbid 48202 Jordan Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA

ISBN: (纸本)9781509045716

This paper develops an accuracy-based cross-layer optimization solution for wireless automated video surveillance systems, in which multiple sources stream videos to a central proxy station. The proposed solution manages the application rates and transmission opportunities of various video sources based on the dynamic network conditions in such a way that maximizes the overall detection accuracy of the computer vision algorithm(s). We demonstrate the effectiveness of the proposed solution through extensive experiments.

关键词： Automated Video Surveillance Bandwidth Allocation computer vision algorithm Cross-Layer Optimization

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A vision System for Detecting and Tracking of Stop-Lines

A Vision System for Detecting and Tracking of Stop-Lines

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IEEE International Conference on Intelligent Transportation Systems

作者： Young-Woo Seo Ragunathan (Raj) Rajkumar Dept. of Electr. Comput. Eng. Carnegie Mellon Univ. Pittsburgh PA USA

ISBN: (纸本)9781479960798

This paper presents a computer vision algorithm that detects, by analyzing lane-marking detection results, stoplines and tracks, using an unscented Kalman filter, the detected stop-line over time. To detect lateral and longitudinal lane-markings, our method applies a spatial filter emphasizing the intensity contrast between lane-marking pixels and their neighboring pixels. We then examine the detected lane-markings to identify perpendicular, geometry layouts between longitudinal and lateral lane-markings for stop-line detection. To provide reliable stop-line recognition, we developed an unscented Kalman filter to track the detected stop-line over frames. Through the testings with real-world, busy urban street videos, our method demonstrated promising results, in terms of the accuracy of the initial detection accuracy and the reliability of the tracking.

关键词： Kalman filters computer vision nonlinear filters object detection object tracking traffic engineering computing video signal processing busy urban street videos computer vision algorithm detection accuracy geometry layouts lane-marking detection results lateral lane-marking longitudinal lane-marking spatial filter stop-lines detection stop-lines tracking tracking reliability unscented Kalman filter vision system Detectors Feature extraction Layout Reliability Roads Vehicles computer vision Kalman filters images Spatial filters Nonlinear filters Video signal processing layouts Object detection Road Feature extraction vehicle engineering computing tracking

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The hawk eye scan: Halyomorpha halys detection relying on aerial tele photos and neural networks

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computerS AND ELECTRONICS IN AGRICULTURE 2024年 226卷

作者： Palazzetti, Lorenzo Rangarajan, Aravind Krishnaswamy Dinca, Alexandru Boom, Bas Popescu, Dan Offermans, Peter Pinotti, Cristina M. Univ Perugia Dept Comp Sci & Math Perugia Italy Univ Florence Dept Comp Sci & Math Florence Italy Univ Polytecn Bucharest Dept Automat Control & Comp Bucharest Romania One Planet Res Ctr Imec Wageningen Netherlands

This paper faces the challenge of monitoring the Brown Marmorated Stink Bug (***) ( Halyomorpha halys) ) within orchards, utilizing drones, and computer vision. *** is an invasive species originating from East Asia, that is extremely polyphagous and poses a significant threat to various crops. Our first contribution is a drone navigation protocol, which ensures risk-free drone flights in cluttered orchard environments, preserving image quality and avoiding obstacles. We then create a pioneering *** dataset consisting of aerial telephotos captured in the field autonomously by the drone. The dataset allows the development and evaluation for the first time of multiple ML models for *** detection in the field. We trained YOLOv5, YOLOv8, RETINANET, and FASTER-RCNN models using different learning methodologies, exploiting different percentages of images without the bug, and using different slicing procedures for the images. The Medium YOLOv5 model trained with all images containing a bug detects the largest number of *** on the testing set and overall performs the best, while RETINANET and FASTER-RCNN provide the best trade-off between precision and recall. Models vary in their ability to handle occluded *** and bug-free images, which are common since the presence of the bug cannot be predicted before capturing a photo. These results show promising potential for automating *** monitoring, despite the image complexity and the early dataset stage. Our work marks a significant step towards enhancing smart agriculture practices due to the simplicity of the data acquisition process and the off-the-shelf hardware selection.

关键词： Halyomorpha halys detection Drones computer vision algorithm Technological transfer

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OpenCV Implementation of Grid-based Vertical Safe Landing for UAV using YOLOv5

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INTERNATIONAL JOURNAL OF ADVANCED computer SCIENCE AND APPLICATIONS 2022年第9期13卷 500-506页

作者： Shadakshri, V. Hrusna Chakri Veena, M. B. Dev, V. Keshihaa Rudra Gana BMS Coll Engn Elect & Commun Engn Bangalore India BMS Coll Engn Sr IEEE Member Elect & Commun Engn Bangalore India AXG Grp Intel Technol India Pvt Ltd Bangalore India

challenge of proving autonomous landing in practical situations is difficult and highly risky. Adopting autonomous landing algorithms substantially minimizes the probability of human-involved mishaps, which may enable the use of drones in populated metropolitan areas to their full potential. This paper proposes an Unmanned Aerial Vehicles (UAV) vertical safe landing & navigation pipeline that relies on lightweight computer vision modules, able to execute on the limited computational resources on-board a typical UAV. In this work, a grid-based mask technique is proposed for selecting the safe landing zones where each grid is parameterizable based on the size of the UAVs, which is implemented using OpenCV. A custom trained YOLOv5 model is the underlying building block for safe landing algorithm which is trained for aerial views of pedestrians, cars & bikes to identify as obstacles. The nearest obstacle-free zone algorithm is applied over the YOLOv5 output where boundary box locations are identified using Hue Saturation Value (HSV) filtering and then split into grids for safe landing zones where maximum coverage is taken into account while analyzing each scene. It performs a 2-level operation to prevent collisions while descending at different altitudes. Since UAV is expected to be processing only at predetermined altitudes, which will shorten the processing time, generating a PID signal for UAV actuators to navigate to the required safe zone with utmost safety and accuracy.

关键词： -Autonomous UAV system computer vision algorithm YOLOv5 safe landing site selection Haversine equations

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