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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： X. Shi R. Manduchi Department of Computer Engineering University of California Santa Cruz USA

We consider here the problem of image classification when more than one visual feature are available. In these cases, Bayes fusion offers an attractive solution by combining the results of different classifiers (one classifier per feature). This is a general form of the so-called "naive Bayes" approach. Analyzing the performance of Bayes fusion with respect to a Bayesian classifier over the joint feature distribution, however, is tricky. On the one hand, it is well-known that the latter has lower bias than the former, unless the features are conditionally independent, in which case the two coincide. On the other hand, as noted by Friedman, the low variance associated with naive Bayes estimation may dramatically mitigate the effect of its bias. In this paper, we attempt to assess the tradeoff between these two factors by means of experimental tests on two image data sets using color and texture features. Our results suggest that (1) the difference between the correct classification rates using Bayes fusion and using the joint feature distribution is a function of the conditional dependence of the features (measured in terms of mutual information), however: (2) for small training data size, Bayes fusion performs almost as well as the classifier on the joint distribution.

关键词： Image color analysis Bayesian methods Error analysis Mutual information Training data Correlation Joints

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Color constancy and non-uniform illumination: Can existing algorithms work?

Color constancy and non-uniform illumination: Can existing a...

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International conference on computer vision workshops (ICCV workshops)

作者： Michael Bleier Christian Riess Shida Beigpour Eva Eibenberger Elli Angelopoulou Tobias Tröger André Kaup Pattern Recognition Lab University of Erlangen-Nuremberg Germany Computer Vision Center Universidad Autónoma de Barcelona Spain Multimedia Communications and Signal Processing University of Erlangen-Nuremberg Germany

The color and distribution of illuminants can significantly alter the appearance of a scene. The goal of color constancy (CC) is to remove the color bias introduced by the illuminants. Most existing CC algorithms assume a uniformly illuminated scene. However, more often than not, this assumption is an insufficient approximation of real-world illumination conditions (multiple light sources, shadows, interreflections, etc.). Thus, illumination should be locally determined, taking under consideration that multiple illuminants may be present. In this paper we investigate the suitability of adapting 5 state-of-the-art color constancy methods so that they can be used for local illuminant estimation. Given an arbitrary image, we segment it into superpixels of approximately similar color. Each of the methods is applied independently on every superpixel. For improved accuracy, these independent estimates are combined into a single illuminant-color value per superpixel. We evaluated different fusion methodologies. Our experiments indicate that the best performance is obtained by fusion strategies that combine the outputs of the estimators using regression.

关键词： Image color analysis Lighting Databases Light sources Image segmentation Estimation Bayesian methods

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Face recognition in the Thermal Infrared Spectrum

Face Recognition in the Thermal Infrared Spectrum

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： P. Buddharaju I. Pavlidis I. Kakadiaris Computer Science Department University of Houston Houston TX USA

We present a two-stage face recognition method based on infrared imaging and statistical modeling. In the first stage we reduce the search space by finding highly likely candidates before arriving at a singular conclusion during the second stage. Previous work has shown that Bessel forms model accurately the marginal densities of filtered components and can be used to find likely matches but not a unique solution. We present an enhancement to this approach by applying Bessel modeling on the facial region only rather than the entire image and by pipelining a classification algorithm to produce a unique solution. The detailed steps of our method are as follows: First, the faces are separated from the background using adaptive fuzzy connectedness segmentation. Second, Gabor filtering is used as a spectral analysis tool. Third, the derivative filtered images are modeled using two-parameter Bessel forms. Fourth, high probability subjects are short-listed by applying the L^2 -norm on the Bessel models. Finally, the resulting set of highly likely matches is fed to a Bayesian classifier to find the exact match. We show experimentally that segmentation of the facial regions results in better hypothesis pruning and classification performance. We also present comparative experimental results with an eigenface approach to highlight the potential of our method.

关键词： Face recognition Infrared spectra Image segmentation Gabor filters Infrared imaging Matched filters Pipeline processing Classification algorithms Filtering Spectral analysis

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国际模式识别研究动向——第18届国际模式识别会议概述

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模式识别与人工智能 2006年第5期19卷 623-628页

作者：吴仲城中国科学院合肥智能机械研究所人机交互实验室合肥230031

介绍第18届国际模式识别会议的有关情况,对大会组织的研讨会、讲座、大会演讲报告、特邀报告、分组报告等情况进行总结和概述.

关键词：模式识别生物特征识别生物信息学自然人机交互

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One Embedding to Predict Them All: Visible and Thermal Universal Face Representations for Soft Biometric Estimation via vision Transformers

One Embedding to Predict Them All: Visible and Thermal Unive...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Nelida Mirabet-Herranz Chiara Galdi Jean-Luc Dugelay Campus SophiaTech EURECOM France

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Human faces encode a vast amount of information including not only uniquely distinctive features of the individual but also demographic information such as a person’s age, gender, and weight. Such information is referred to as soft-biometrics, which are physical, behavioral or adhered human characteristics, classifiable in pre–defined human compliant categories. As we often say ’one look is worth a thousand words’. vision Transformers have emerged as a powerful deep learning architecture able to achieve accurate classifications for different computer vision tasks, but these models have not been yet applied to soft-biometrics. In this work, we propose the Bidirectional Encoder Face representation from image Transformers (BEFiT), a model that leverages the multi-attention mechanisms to capture local and global features and produce a multi-purpose face embedding. This unique embedding enables the estimation of different demographics without having to re-train the model for each soft-biometric trait, ensuring high efficiency without compromising accuracy. Our approach explores the use of visible and thermal images to achieve powerful face embedding in different light spectra. We demonstrate that the BEFiT embeddings can capture essential information for gender, age, and weight estimation, surpassing the performance of dedicated deep learning structures for the estimation of a single soft biometric trait. The code of BEFiT implementation is publicly available 1

关键词： Biometrics Training computer vision Accuracy Computational modeling Estimation Transformers

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Real-Time Dense Stereo Embedded in a UAV for Road Inspection

Real-Time Dense Stereo Embedded in a UAV for Road Inspection

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Rui Fan Jianhao Jiao Jie Pan Huaiyang Huang Shaojie Shen Ming Liu HKUST Robotics Institute

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

The condition assessment of road surfaces is essential to ensure their serviceability while still providing maximum road traffic safety. This paper presents a robust stereo vision system embedded in an unmanned aerial vehicle (UAV). The perspective view of the target image is first transformed into the reference view, and this not only improves the disparity accuracy, but also reduces the algorithm's computational complexity. The cost volumes generated from stereo matching are then filtered using a bilateral filter. The latter has been proved to be a feasible solution for the functional minimisation problem in a fully connected Markov random field model. Finally, the disparity maps are transformed by minimising an energy function with respect to the roll angle and disparity projection model. This makes the damaged road areas more distinguishable from the road surface. The proposed system is implemented on an NVIDIA Jetson TX2 GPU with CUDA for real-time purposes. It is demonstrated through experiments that the damaged road areas can be easily distinguished from the transformed disparity maps.

关键词： Roads Stereo vision Three-dimensional displays Cameras Inspection Real-time systems Unmanned aerial vehicles

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VolRAFT: Volumetric Optical Flow Network for Digital Volume Correlation of Synchrotron Radiation-based Micro-CT Images of Bone-Implant Interfaces

VolRAFT: Volumetric Optical Flow Network for Digital Volume ...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Tak Ming Wong Julian Moosmann Berit Zeller-Plumhoff Institute of Metallic Biomaterials Helmholtz-Zentrum Hereon Germany Institute of Materials Physics Helmholtz-Zentrum Hereon Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In materials science research, digital volume correlation (DVC) analysis is commonly used to track deformations and strains to elucidate morphology-function relationships. Optical flow-based DVC is particularly popular because of its robustness to estimate the correlation as a dense deformation vector. Recently, computer vision researchers showed that network-based optical flow approaches can outperform classical iterative optical flow approaches. In this paper, we propose a supervised machine learning approach for digital volume correlation, VolRAFT, that estimates the 3D displacement vector between the reference volume and the deformed volume. The proposed approach extends the state-of-the-art network-based optical flow method, RAFT, from 2D images to 3D volumes such that it predicts the volumetric displacement vector from the input volume pairs. Experiments show that the proposed network performs well in estimating different displacement fields when compared to cutting-edge iterative DVC methods for bone-implant materials based on high resolution synchrotron-radiation micro-computed tomography imaging data.

关键词： Training computer vision Materials science and technology Correlation Three-dimensional displays Deformation Tomography

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Investigating Calibration and Corruption Robustness of Post-hoc Pruned Perception CNNs: An Image Classification Benchmark Study

Investigating Calibration and Corruption Robustness of Post-...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Pallavi Mitra Gesina Schwalbe Nadja Klein Continental AG Germany University of Lübeck Germany TU Dortmund Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many computer vision tasks. However, high computational and storage demands hinder their deployment into resource-constrained environments, such as embedded devices. Model pruning helps to meet these restrictions by reducing the model size, while maintaining superior performance. Meanwhile, safety-critical applications pose more than just resource and performance constraints. In particular, predictions must not be overly confident, i.e., provide properly calibrated uncertainty estimations (proper uncertainty calibration), and CNNs must be robust against corruptions like naturally occurring input perturbations (natural corruption robustness). This work investigates the important trade-off between uncertainty calibration, natural corruption robustness, and performance for current state-of-research post-hoc CNN pruning techniques in the context of image classification tasks. Our study reveals that post-hoc pruning substantially improves the model’s uncertainty calibration, performance, and natural corruption robustness, sparking hope for safe and robust embedded CNNs. Furthermore, uncertainty calibration and natural corruption robustness are not mutually exclusive targets under pruning, as evidenced by the improved safety aspects obtained by post-hoc unstructured pruning with increasing compression.

关键词： Performance evaluation computer vision Uncertainty Image coding Computational modeling Robustness Calibration

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Detecting Deepfake Videos using Attribution-Based Confidence Metric

Detecting Deepfake Videos using Attribution-Based Confidence...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Steven Fernandes Sunny Raj Rickard Ewetz Jodh Singh Pannu Sumit Kumar Jha Eddy Ortiz Iustina Vintila Margaret Salter Department of Computer Science Electrical and Computer Engineering University of Central Orlando Florida FL Solution Acceleration and Innovation Department Royal Bank of Canada

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Recent advances in generative adversarial networks have made detecting fake videos a challenging task. In this paper, we propose the application of the state-of-the-art attribution based confidence (ABC) metric for detecting deepfake videos. The ABC metric does not require access to the training data or training the calibration model on the validation data. The ABC metric can be used to draw inferences even when only the trained model is available. Here, we utilize the ABC metric to characterize whether a video is original or fake. The deep learning model is trained only on original videos. The ABC metric uses the trained model to generate confidence values. For, original videos, the confidence values are greater than 0.94.

关键词： Videos Face Measurement Computational modeling Training data Machine learning Databases

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Top-Down Networks: A coarse-to-fine reimagination of CNNs

Top-Down Networks: A coarse-to-fine reimagination of CNNs

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ioannis Lelekas Nergis Tomen Silvia L. Pintea Jan C. van Gemert Computer Vision Lab Delft University of Technology Netherlands

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Biological vision adopts a coarse-to-fine information processing pathway, from initial visual detection and binding of salient features of a visual scene, to the enhanced and preferential processing given relevant stimuli. On the contrary, CNNs employ a fine-to-coarse processing, moving from local, edge-detecting filters to more global ones extracting abstract representations of the input. In this paper we reverse the feature extraction part of standard bottom-up architectures and turn them upside-down: We propose top-down networks. Our proposed coarse-to-fine pathway, by blurring higher frequency information and restoring it only at later stages, offers a line of defence against adversarial attacks that introduce high frequency noise. Moreover, since we increase image resolution with depth, the high resolution of the feature map in the final convolutional layer contributes to the explainability of the network's decision making process. This favors object-driven decisions over context driven ones, and thus provides better localized class activation maps. This paper offers empirical evidence for the applicability of the top-down resolution processing to various existing architectures on multiple visual tasks.

关键词： Feature extraction Spatial resolution computer architecture Merging Task analysis Visualization Robustness

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