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

作者： Ros, German Ramos, Sebastian Granados, Manuel Bakhtiary, Amir Vazquez, David Lopez, Antonio M. Comp Vis Ctr Campus UAB Barcelona Spain Univ Autonoma Barcelona Barcelona Spain Univ Oberta Catalunya Barcelona Spain

ISBN: (纸本)9781479966837

Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in real-time. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community.

关键词： computer vision geometry real-time systems road safety traffic information systems 3D geometry KITTI dataset autonomous driving computer vision algorithms future mobility geometric information real-time system safe driving semantic information vision-based offline-online perception paradigm Cameras computer vision Semantics Sensors Three-dimensional displays Vehicle dynamics Vehicles images computer vision real-time systems Defensive driving three-dimensional displays Semantics Traffic information systems vehicle Vehicle dynamics Road safety Cameras Sensors semiotics geometry

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computer vision-Based Estimation of Respiration Signals 8th

Computer Vision-Based Estimation of Respiration Signals

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8th Latin American Conference on Biomedical Engineering (CLAIB) / 42nd National Conference on Biomedical Engineering (CNIB)

作者： Paz Reyes, Maria Eugenia Dorta Palmero, Jasiel Leon Diaz, Jessica Aragon, Efren Taboada-Crispi, Alberto Univ Cent Marta Abreu Las Villas Comp Sci Santa Clara Cuba Univ Cent Marta Abreu Las Villas Informat Res Ctr Santa Clara Cuba

ISBN: (纸本)9783030306489;9783030306472

Respiration movement and respiration rate have been used to monitor breathing status for diagnosis and fitness purposes. From a given video sequence of a person facing a camera, this system here automatically detects and tracks a region of interest (ROI) on the chest, using the Kanade-Lucas-Tomasi method, after applying the Viola-Jones algorithm and identifying the Harris-Stephens features for tracking the ROI across frames. The displacement in the vertical direction of the ROI (frame by frame) was used to estimate the respiration movement, after low-pass filtering at a proper cutoff frequency. Finally, the respiration rate is estimated from the respiration movement signal by a root MUSIC-based estimator. For the 12 video files provided, we obtained respiration movement signals with correlation indexes respect to the corresponding 'references' of above 90%, in most cases, and respiration rate signals with normalized root mean-squared errors with respect to the inst_freq around 10%. A global ranking index of around 0.8 was consistently obtained. computer vision algorithms are well-suited for estimating respiration movement and respiration rate signals from frontal video sequences.

关键词： Respiration Time-varying signals Estimation/evaluation Video processing computer vision algorithms

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Utilization of computer vision and Deep Learning in Fault Identification and Repair of Mechanical and Electronic Equipment

Utilization of Computer Vision and Deep Learning in Fault Id...

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作者： Wu, Jun Zhenjiang Higher Vocational and Technical School Jiangsu Province Zhenjiang City China

With the rapid development of computer technology, network communication, and digital signal processing, more and more professionals have begun to focus on learning and researching new knowledge and using advanced software tools to comprehensively upgrade existing equipment. In this context, deep learning machines were used to replace some of the difficulties and shortcomings in traditional teaching. This article focused on the theory, methods, and operational principles of deep neural networks. At the same time, fault diagnosis and repair strategies based on computer vision technology, machine vision, and digital signal processing were analyzed, and data simulation results were achieved through performance testing of the model. The results showed that the accuracy of the model in identifying normal equipment parts was 100%;the accuracy in identifying internal equipment parts was 94%;and the accuracy in identifying external equipment parts was 97%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer vision algorithms Deep learning Equipment failures Mechanical and electronic equipment

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Radial textures: a new algorithm to analyze meat quality on MRI

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第29-30期79卷 21557-21578页

作者： Caballero, Daniel Univ Copenhagen Chemometr & Analyt Technol Dept Food Sci Rolighedsvej 26 DK-1958 Frederiksberg C Denmark Univ Extremadura Comp Sci Dept Res Inst Meat & Meat Prod IproCar Av Ciencias S-N ES-10003 Caceres Spain

Magnetic Resonance Imaging (MRI) was introduced as an alternative to destructive methods for determining the quality parameters of meat products, since MRI is a non-destructive, non-ionizing, and innocuous technique. The use of MRI is linked to the use of computer vision algorithms and data mining techniques. Thus, the success of MRI could not be understood without referring to the implementation of powerful algorithms to handle all data generated. In this study, a new texture algorithm was developed in order to obtain new features based on the radial distribution of the textures from MRI. This new algorithm is called Radial Texture Algorithm (RTA) and it was compared with four classical texture features approaches. The results obtained allow describing the semantic context of the texture features for this new algorithm and the relationship among the features from this algorithm and the classical approaches. Besides, the results obtained by means of these computer vision algorithms were correlated with the results obtained by means of physico-chemical and sensory parameters of the Iberian loins. All of computer vision algorithms achieved correlation coefficients higher than 0.500, noting that RTA reached the highest correlation coefficients in almost all cases. These high correlation coefficients confirm the proposed algorithm as an alternative to the other computational texture features approaches in order to compute the physico-chemical and sensory parameters of meat products in an accurate, fast, non-destructive and efficient way.

关键词： Texture features computer vision algorithms Data mining Iberian loin Food technology

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Statistical evaluation of corner detectors: does the statistical test have an effect?

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IET computer vision 2018年第7期12卷 1018-1030页

作者： Anjum, Fatima Kanwal, Nadia Clark, Adrian F. Bostanci, Erkan Lahore Coll Women Univ Dept Comp Sci Jail Rd Lahore Pakistan Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England Univ Ankara Dept Comp Engn Ankara Turkey

This study explores the use of several non-parametric statistical tests for evaluating the performances of computer vision algorithms, specifically corner detectors, as a more reliable alternative to the graphical approaches that have been commonly employed to date. Using synthetic images carrying corners of different internal angles and orientations and a carefully designed testing framework, a ranking of the performances of corner detectors was established. It was found that Harris & Stephens and SUSAN out-performed more modern detectors. These are one of the few examples where evaluation of vision operators independent of the application has predicted performance in a real-world problem. A similar exercise on real images of the same patterns produced similar results and the findings of a real-world application that uses corners to identify signage were also consistent. Together, all of the tests considered essentially perform pairwise comparisons of performance, so when many algorithms are involved it is important to take account of the potential for type I statistical errors. Several approaches were evaluated and none were found to affect the conclusions.

关键词： statistical testing edge detection computer vision graph theory nonparametric statistics statistical evaluation corner detectors nonparametric statistical tests computer vision algorithms performance evaluation graphical approaches synthetic images internal angles testing framework vision operators type I statistical errors signage identification

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Tensor total variation regularised low-rank approximation framework for video deraining

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IET IMAGE PROCESSING 2020年第14期14卷 3602-3612页

作者： Baiju, P. S. Jayan, P. Deepak George, Sudhish N. Natl Inst Technol Calicut Dept Elect & Commun Engn Calicut Kerala India

Outdoor monitoring systems are known to exhibit better performance under normal weather conditions, while it lacks effectiveness under inclement conditions. Often video footage captured by the camera under rainy conditions comprises several visual distortions. It eventually leads to flaws when handled with succeeding computer vision algorithms, namely the object identification and tracking. Additionally, eliminating such unpleasant rainy effects is essential prior to the processing of video footage by suitable algorithms. The present work attempts to formulate a new low-rank tensor recovery based deraining algorithm that enables to remove the rain streaks from video footage. The proposed method detects the rain streaks by adopting optical flow estimation along with the brightness features inherent with the rain streaks. A unified framework comprised of tensor singular value decomposition (t-SVD) based weighted nuclear norm minimisation and tensor total variation (TTV) regularisation effectively removes rain streaks and recovers the original rain-free data from the available rainy data. The use of t-SVD enforces the concept of low rankness and also exploits the temporal redundancy among the video frames. Furthermore, TTV regularisation facilitates to promote the temporal continuity for discriminating most of the natural image contents from sparse rain streaks by preserving piece-wise smoothness of video frames. Comprehensive experimental findings based on real and synthetic data with dynamic background show that the rain streaks are more efficaciously eliminated by adopting the proposed method without much loss in the information.

关键词： image motion analysis computer vision minimisation image segmentation tensors rain singular value decomposition image representation video signal processing image sequences tensor total variation low-rank approximation framework video deraining outdoor monitoring systems normal weather conditions inclement conditions video footage rainy conditions computer vision algorithms unpleasant rainy effects suitable algorithms low-rank tensor recovery tensor singular value decomposition original rain-free data low rankness video frames TTV regularisation facilitates sparse rain streaks

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Random perturbation models for boundary extraction sequence

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MACHINE vision AND APPLICATIONS 1997年第5-6期9卷 229-239页

作者： Ramesh, V Haralick, RM UNIV WASHINGTON DEPT EESEATTLEWA 98195

computer vision algorithms are composed of different sub-algorithms often applied in sequence. Determination of the performance of a total computer vision algorithm is possible if the performance of each of the sub-algorithm constituents is given. The performance characterization of an algorithm has to do with establishing the correspondence between the random variations and imperfections in the output data and the random variations and imperfections in the input data. In this paper we illustrate how random perturba tion models can be set up for a vision algorithm sequence involving edge finding, edge linking, and gap filling. By starting with an appropriate noise model for the input data we derive random perturbation models for the output data at each stage of our example sequence. By utilizing the perturbation model for edge detector output derived, we illustrate how pixel noise can be successively propagated to derive an error model for the boundary extraction output. It is shown that the fragmentation of an ideal boundary can be described by an alternating renewal process and that the parameters of the renewal process are related to the probability of correct detection and grouping at the edge linking step. It is also shown that the characteristics of random segments generated due to gray-level noise are functions of the probability of false alarm of the edge detector. Theoretical results are validated through systematic experiments.

关键词： random perturbation models boundary extraction sequence computer vision algorithms performance characterization

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Efficient CRC concatenation scheme for polar codes

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ELECTRONICS LETTERS 2017年第13期53卷 860-861页

作者： Zhang Qingshuang Liu Aijun Pan Xiaofei PLA Univ Sci & Technol Coll Commun Engn 1 Haifuxiang Rd Nanjing 210007 Jiangsu Peoples R China

A new cyclic redundancy check (CRC) concatenation with polar codes based on partial protection is proposed, where only the crucial bits chosen from the total information bits are protected with the CRC. The index set of crucial bits is determined by the row weight of generator matrix of polar codes and the indexes with minimum row weight make up this crucial set. Using this new concatenation, the number of polar codewords with minimum Hamming weight can be efficiently decreased. Simulation results show that the performance of the proposed partial protection scheme owns a lower error floor than that of the classical blind protection.

关键词： computer vision image sensors Kinect v1 depth images computer vision algorithms empirical noise model synthetic Kinect depth images

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Performance and precision analysis of 3D surface modeling through UAVs: validation and comparison of different photogrammetric data processing software

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PHYSICA SCRIPTA 2024年第3期99卷 035017-035017页

作者： Mora-Felix, Zuriel D. Rangel-Peraza, Jesus Gabriel Monjardin-Armenta, Sergio Alberto Sanhouse-Garcia, Antonio J. Tecnol Nacl Mexico Div Estudios Posgrad Invest Inst Tecnol Culiacan Juan de Dios Batiz 310 Col Guadalupe Culiacan 80220 Mexico Univ Autonoma Sinaloa Fac Ciencias Tierra & Espacio Circuito Interior Oriente SN Culiacan 80040 Mexico

The planimetric and altimetric accuracies of the Digital Elevation Model (DEM) obtained from Unmanned Aerial Vehicle (UAV) depend on some photogrammetric parameters such as flight height, flight speed, and/or ground sample distance. In the last few years, the study of computer vision algorithms has increased focusing on their importance on the photogrammetric reconstruction process for land surface mapping. According to this, the main goal of this paper was to evaluate the performance of different photogrammetric processing software, such as Open Drone Map (ODM), Agisoft PhotoScan, and Pix4D, on DEM accuracy. For this purpose, a DJI Phantom 4 Pro drone was used for the acquisition of 600 images in a difficult topography area (630 ha) with 27 Ground Control Points (GCP) previously established. The photogrammetric products were generated, and a statistical analysis was carried out for the comparison of the DEMs. The results of the Root Mean Square Error (RMSE) show that the planimetric and altimetric accuracy of Agisoft PhotoScan (RMSExy = 0.514 m;RMSEz = 0.162 m) is greater than those obtained using Pix4D and ODM. Unsatisfactory results were obtained with ODM since deformations and high planimetric and altimetric errors were identified in the orthomosaic and DEM, which was related to inefficient key point detection and an incomplete mosaic construction process. Agisoft PhotoScan modeled the anthropogenic objects as part of the land surface, while Pix4d smoothed the terrain, eliminating these anthropogenic objects and not considering them as part of the ground surface.

关键词： computer vision algorithms photogrammetric reconstruction process digital elevation model UAV photogrammetry planimetric and altimetric accuracy

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Expert, Crowd, Students or Algorithm: who holds the key to deep-sea imagery 'big data' processing?

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METHODS IN ECOLOGY AND EVOLUTION 2017年第8期8卷 996-1004页

作者： Matabos, Marjolaine Hoeberechts, Maia Doya, Carol Aguzzi, Jacopo Nephin, Jessica Reimchen, Thomas E. Leaver, Steve Marx, Roswitha M. Albu, Alexandra Branzan Fier, Ryan Fernandez-Arcaya, Ulla Juniper, S. Kim IFREMER Ctr Bretagne REM EEP Lab Environm Profond F-29280 Plouzane France Univ Victoria Ocean Networks Canada Victoria BC V8W 2Y2 Canada Univ Victoria Dept Comp Sci Victoria BC Canada CSIC Inst Marine Sci ICM Paseo Maritimo Barceloneta 37-49 E-08003 Barcelona Spain Univ Victoria Dept Biol Victoria BC Canada Univ Victoria Dept Elect & Comp Engn Victoria BC Canada Univ Victoria Sch Earth & Ocean Sci Victoria BC Canada

1. Recent technological development has increased our capacity to study the deep sea and the marine benthic realm, particularly with the development of multidisciplinary seafloor observatories. Since 2006, Ocean Networks Canada cabled observatories, have acquired nearly 65 TB and over 90 000 h of video data from seafloor cameras and remotely operated vehicles. Manual processing of these data is time-consuming and highly labour-intensive, and cannot be comprehensively undertaken by individual researchers. These videos are a crucial source of information for assessing natural variability and ecosystem responses to increasing human activity in the deep sea. 2. We compared the performance of three groups of humans and one computer vision algorithm in counting individuals of the commercially important sablefish (or black cod) Anoplopoma fimbria, in recorded video from a cabled camera platform at 900 m depth in a submarine canyon in the Northeast Pacific. The first group of human observers were untrained volunteers recruited via a crowdsourcing platform and the second were experienced university students, who performed the task for their ichthyology class. Results were validated against counts obtained from a scientific expert. 3. All groups produced relatively accurate results in comparison to the expert and all succeeded in detecting patterns and periodicities in fish abundance data. Trained volunteers displayed the highest accuracy and the algorithm the lowest. 4. As seafloor observatories increase in number around the world, this study demonstrates the value of a hybrid combination of crowdsourcing and computer vision techniques as a tool to help process large volumes of imagery to support basic research and environmental monitoring. Reciprocally, by engaging large numbers of online participants in deep-sea research, this approach can contribute significantly to ocean literacy and informed citizen input to policy development.

关键词： computer vision algorithms crowdsourcing deep-sea imagery Digital Fishers fish counting OceanNetworks Canada seafloor observatories underwater video

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