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15th International Conference on pattern recognition, ICPR 2000

作者： Wolf, Christian Jolion, Jean-Michel Kropatsch, Walter Bischof, Horst Vienna University of Technology Pattern Recognition and Image Processing Group Favoritenstraße 9/1832 Wien1040 Austria INSA de Lyon Laboratoire Reconnaissance de Formes et Vision 3 Avenue Albert Einstein Villeurbanne Cedex69626 France

ISBN: (纸本)0769507506

Content based image retrieval is the task of searching images from a database, which are visually similar to a given example image. We present methods for content based image retrieval based on texture similarity using interest points and Gabor features. Interest point detectors are used in computer vision to detect image points with special properties, which can be geometric (corners) or non-geometric (contrast etc.). Gabor functions and Gabor filters are regarded as excellent tools for feature extraction and texture segmentation. The article combines these methods and generates a textural description of images. Special emphasis is devoted to distance measures on texture descriptions. Experimental results of a query system are given. © 2000 IEEE.

关键词： Gabor filters

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Accuracy vs Speed Trade-offs in Multi-Frame LiDAR-based Linear Correction for Visual Odometry

Accuracy vs Speed Trade-offs in Multi-Frame LiDAR-based Line...

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IEEE International Conference on Intelligent Computer Communication and processing (ICCP)

作者： Corvin-Petruţ Cobârzan Cătălin-Cosmin Golban Sergiu Nedevschi Computer Science Department Technical University of Cluj-Napoca Romania Image Processing and Pattern Recognition Group Technical University of Cluj-Napoca Romania

Our previously proposed linear approach for reducing the global drift of a video-based frame-to-frame trajectory estimation method corrects it at selected points in time based on the alignment of one past and the current 3D LiDAR measurements (see [7]). In this paper we improve on that method essentially by adding multi-past frame LiDAR point cloud alignment constraints and by performing the correction more often. While this significantly improves the accuracy of the estimation, it has the downside effect of decreasing the overall runtime performance. We thus study the trade-off between accuracy and speed and propose a couple of higher accuracy but real time correction versions. Their evaluation on the KITTI dataset results in an overall performance falling into the so called SLAM-nonSLAM gap.

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Line-based landmark recognition for self-localization of soccer robots

Line-based landmark recognition for self-localization of soc...

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International Conference on Emerging Technologies, ICET

作者： A. Baist R. Sablatnig G. Novak Pattern Recognition and Image Processing Group Vienna University of Technology Vienna Austria Institute of Computer Technology Vienna University of Technology Vienna Austria

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Multiple eigenspaces by MDL

Multiple eigenspaces by MDL

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International Conference on pattern recognition

作者： A. Leonardis H. Bischof Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria

ISBN: (纸本)0769507506

We propose an approach to constructing multiple eigenspaces from a set of training images based on the minimum description length (MDL) principle. The main idea is to systematically build a redundant set of eigenspaces, which are treated as hypotheses that are then subject to a selection procedure. The selection procedure, based on the MDL principle, selects the final resulting set of eigenspaces as an optimal representation of the training set. We have tested the proposed method on a number of standard image sets, and the significance of the approach with respect to the recognition rate has been clearly demonstrated.

关键词： Testing image recognition Principal component analysis Information science image analysis image reconstruction pattern recognition image processing Robustness Robust control

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Noise Tolerance of Linear vs Non-Linear LiDAR Based Ego-Motion Drift Correction Methods

Noise Tolerance of Linear vs Non-Linear LiDAR Based Ego-Moti...

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IEEE International Conference on Intelligent Computer Communication and processing (ICCP)

作者： Corvin-Petruț Cobârzan Cătălin-Cosmin Golban Sergiu Nedevschi Computer Science Department Technical University of Cluj-Napoca Romania Image Processing and Pattern Recognition Group Technical University of Cluj-Napoca Romania

ISBN: (纸本)9781665464383

We have previously proposed a linear approach for reducing the global drift of a video-based frame-to-frame trajectory estimation method by correcting it at selected points in time based on the alignment of past and current 3D LiDAR measurements (see [7]). In this paper we assess the tolerance to noise of a series of methods derived from the one previously proposed, this time using both linear and non-linear optimization methods to calculate the correction transform. We generate synthetic datasets with various noise pollution levels and assess the performance of each method under investigation in recovering artificially induced odometry estimation errors.

关键词： Visualization Laser radar Three-dimensional displays Pollution Optimization methods Transforms Time measurement

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Illumination insensitive eigenspaces

Illumination insensitive eigenspaces

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International Conference on Computer Vision (ICCV)

作者： H. Bischof H. Wildenauer A. Leonardis Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia

ISBN: (纸本)0769511430

Variations in illumination can have a dramatic effect on the appearance of an object in an image. In this paper we propose how to deal with illumination variations in eigenspace methods. We demonstrate that the eigenimages obtained by a training set under a single illumination condition (ambient light) can be used for recognition of objects taken under different illumination conditions. The major idea is to incorporate a set of gradient based filter banks into the eigenspace recognition framework. This can be achieved since the eigenimage coefficients are invariant for linearly filtered images (input and eigenimages). To achieve further illumination insensitivity we devised a robust procedure for coefficient recovery. The proposed approach has been extensively evaluated on a set of 2160 images and the results were compared to other approaches.

关键词： Lighting Nonlinear filters Principal component analysis image coding Object recognition Filtering Filter bank image recognition Information science image sampling

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An Evaluation of Low Cost Scanning versus Industrial 3D Scanning Devices

An Evaluation of Low Cost Scanning versus Industrial 3D Scan...

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Congress on image and Signal processing, CISP

作者： Miha Drenik Martin Kampel Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Pattern Recognition and Image Processing Group Technical University of of Vienna Vienna Austria

This paper presents an evaluation of two different 3D scanning devices. They can be put into operation depending on the monetary constraints of the user on one hand, and the accuracy needed for a specific application on the other hand. The setting up of the systems and the process of scanning with the devices are presented. Two setups were formed to evaluate the capabilities of a low costs scanning device together with industrial scanning devices. A comparison of the scans obtained was made by using a Konica Minolta VI-9i industrial scanning solution as a reference. The analysis comprises the estimation of surface flatness, the evaluation of measuring of geometric shapes, and the appraisal of the time required for obtaining a virtual 3D representation of a scanned object. The results show that both the curves as well as the shapes as a whole have been captured accurately.

关键词： Costs Shape measurement Application software Feeds Signal processing Computer industry Information science pattern recognition image processing Time measurement

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Local Structure Detection with Orientation-invariant Radial Configuration

Local Structure Detection with Orientation-invariant Radial ...

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Conference on Computer Vision and pattern recognition (CVPR)

作者： Lech Szumilas Rene Donner Georg Langs Allan Hanbury Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

Local image descriptors have proved themselves as useful tools for many computer vision tasks such as matching points between multiple images of a scene and object recognition. Current descriptors, such as SIFT, are designed to match image features with unique local neighborhoods. However, the interest point detectors used with SIFT often fail to select perceptible local structures in the image, and the SIFT descriptor does not directly encode the local neighborhood shape. In this paper we propose a symmetry based interest point detector and radial local structure descriptor which consistently captures the majority of basic local image structures and provides a geometrical description of the structure boundaries. This approach concentrates on the extraction of shape properties in image patches, which are an intuitive way to represent local appearance for matching and classification. We explore the specificity and sensitivity of this local descriptor in the context of classification of natural patterns. The implications of the performance comparison with standard approaches like SIFT are discussed.

关键词： Detectors Shape Computer vision Layout Object recognition

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Noise estimation in panoramic x-ray images: an application analysis approach

Noise estimation in panoramic x-ray images: an application a...

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IEEE/SP Workshop on Statistical Signal processing (SSP)

作者： P.M. Goebel A.N. Belbachir M. Truppe Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria Karl Landsteiner Institute for Biotelematics Danube-University of Krems Krems Austria

This paper presents an appropriate approach for the robust estimation of the noise statistics in dental panoramic X-ray images. To achieve maximum image quality after denoising, a semi-empirical scatter model is presented, leading to a local adaptive Gaussian scale mixture (GSM) model. State of the art methods use multiscale filtering of images to reduce the irrelevant part of information, based on generic estimation of noise. The usual assumption of a distribution of Gaussian and Poisson statistics only leads to overestimation of the noise variance in regions of low intensity (small photon counts), but to underestimation in regions of high intensity and therefore to non-optimal results. The analysis approach is tested on a database of 50 panoramic X-ray images and the results are cross-validated by medical experts. It is shown that the local standard deviation (SDEV) in images, stemming from homogeneous phantoms (AI, PMMA), follows a generalized Nakagami distribution (GND). The heavily tailed distribution is not covered entirely by the GND. The error density function, is hypothesized to stem from scatter-glare, degrading the image. A beam stop method, for estimation of the scatter-glare amount, verifies that hypothesis. Finally, the application of the method for a phantom image, is shown with denoising results for comparison purpose, followed by the conclusion

关键词： X-ray imaging image analysis Noise reduction Electromagnetic scattering Particle scattering X-ray scattering Imaging phantoms Noise robustness Statistics Dentistry

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Online LiDAR-Camera Extrinsic Parameters Self-checking

arXiv

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arXiv 2022年

作者： Wei, Pengjin Yan, Guohang Li, Yikang Fang, Kun Yang, Jie Liu, Wei The Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China The Autonomous Driving Group Shanghai AI Laboratory China

With the development of neural networks and the increasing popularity of automatic driving, the calibration of the LiDAR and the camera has attracted more and more attention. This calibration task is multi-modal, where the rich color and texture information captured by the camera and the accurate three-dimensional spatial information from the LiDAR is incredibly significant for downstream tasks. Current research interests mainly focus on obtaining accurate calibration results through information fusion. However, they seldom analyze whether the calibrated results are correct or not, which could be of significant importance in real-world applications. For example, in large-scale production, the LiDARs and the cameras of each smart car have to get well-calibrated as the car leaves the production line, while in the rest of the car life period, the poses of the LiDARs and cameras should also get continually supervised to ensure the security. To this end, this paper proposes a self-checking algorithm to judge whether the extrinsic parameters are well-calibrated by introducing a binary classification network based on the fused information from the camera and the LiDAR. Moreover, since there is no such dataset for the task in this work, we further generate a new dataset branch from the KITTI dataset tailored for the task. Our experiments on the proposed dataset branch demonstrate the performance of our method. To the best of our knowledge, this is the first work to address the significance of continually checking the calibrated extrinsic parameters for autonomous driving. The code is open-sourced on the Github website at https://***/OpenCalib/LiDAR2camera_self-check. Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

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