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P-Channels: Robust Multivariate M-Estimation of Large Datasets

P-Channels: Robust Multivariate M-Estimation of Large Datase...

引用

International conference on pattern recognition

作者： M. Felsberg G. Granlund Computer Vision Laboratory Linkoping University Sweden

In this paper we introduce a new technique that allows to estimate modes of a high-dimensional probability density function with linear time-complexity in the number of dimensions and the number of samples. The method can be implemented in an order-independent incremental way, such that the space-complexity is linear in the number of dimensions and the number of modes. The number of required samples to get reliable estimates depends linearly on the number of dimensions even if we replace the assumption of independent stochastic variables with the weaker assumption of data clustered in submanifolds. These submanifolds need not to be known, but smoothness assumptions are made. The new technique is based on representing data in what we call P-channels

关键词： Robustness computer vision Laboratories Stochastic processes pattern recognition Vector quantization Clustering algorithms Principal component analysis Independent component analysis Kernel

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2D-3D Heterogeneous Face recognition Based on Deep Coupled Spectral Regression

2D-3D Heterogeneous Face Recognition Based on Deep Coupled S...

引用

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

作者： Yangtao Zheng Di Huang Weixin Li Shupeng Wang Yunhong Wang IRIP Lab School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

As one of the major branches in Face recognition (FR), 2D-3D Heterogeneous FR (HFR), where face comparison is achieved across the texture and shape modalities, has become more important. This paper proposes a novel deep learning based end-to-end approach, namely Deep Coupled Spectral Regression (DCSR), for such an issue. It jointly makes use of both the advantages of CNN based deep features and CSR based common subspace. Specifically, from 2D texture and 3D depth face maps, DCSR extracts more powerful features by a deep network with the cross-modality triplet loss, which show much better uniqueness and robustness than the hand-crafted ones. Further, DCSR learns the shared space between different modalities with the constraints of sample labels, and is thereby more discriminative than the widely used unsupervised methods. More importantly, the two steps above are integrated through a couple layer to explicitly optimize the weights of deep features and projection directions rather than a simple combination. Experiments are carried out on the FRGC v2.0 database, and the results reported clearly demonstrate the competency of our proposed method. Its generalization ability is also validated by additional experiments conducted on the CASIA NIR-VIS 2.0 database.

关键词： Face Three-dimensional displays Two dimensional displays Feature extraction Shape Databases Lighting

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ReweightOOD: Loss Reweighting for Distance-based OOD Detection

ReweightOOD: Loss Reweighting for Distance-based OOD Detecti...

引用

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

作者： Sudarshan Regmi Bibek Panthi Yifei Ming Prashnna K Gyawali Danail Stoyanov Binod Bhattarai University College London UK NepAl Applied Mathematics and Informatics Institute for Research Nepal University of Wisconsin-Madison USA West Virginia University USA University of Aberdeen UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Out-of-Distribution (OOD) detection is crucial for ensuring safety and reliability of neural networks in critical applications. Distance-based OOD detection is based on the assumption that OOD samples are mapped far from In-Distribution (ID) clusters in embedding space. A recent approach for obtaining OOD-detection-friendly embedding space has been contrastive optimization of pulling similar pairs and pushing apart dissimilar pairs. It assigns equal significance to all similarity instances with the implicit objective of maximizing the mean proximity between samples with their corresponding hypothetical class centroids. However, the emphasis should be directed towards reducing the Minimum Enclosing Sphere (MES) for each class and achieving higher inter-class dispersion to effectively mitigate the potential for ID-OOD overlap. Optimizing low-signal dissimilar pairs might potentially act against achieving maximal inter-class dispersion while less-optimized similar pairs prevent achieving smaller MES. Based on this, we propose a reweighting scheme ReweightOOD, that adopts the similarity optimization which prioritizes the optimization of less-optimized contrasting pairs while assigning lower importance to already well-optimized contrasting pairs. Such a reweighting scheme serves to minimize the MES for each class while achieving maximal interclass dispersion. Experimental results on a challenging CIFAR100 benchmark using ResNet-18 network demonstrate that ReweightOOD outperforms supervised contrastive loss by a whopping 38% in the average FPR metric. In various classification datasets, our method provides a promising solution for enhancing OOD detection capabilities in neural networks.

关键词： Measurement computer vision conferences Neural networks Benchmark testing Safety pattern recognition

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Combining Top-Down and Bottom-Up Segmentation

Combining Top-Down and Bottom-Up Segmentation

引用

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

作者： E. Borenstein E. Sharon S. Ullman Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel Division of Applied Mathematics Brown University Providence RI USA

In this work we show how to combine bottom-up and top-down approaches into a single figure-ground segmentation process. This process provides accurate delineation of object boundaries that cannot be achieved by either the top-down or bottom-up approach alone. The top-down approach uses object representation learned from examples to detect an object in a given input image and provide an approximation to its figure-ground segmentation. The bottom-up approach uses image-based criteria to define coherent groups of pixels that are likely to belong together to either the figure or the background part. The combination provides a final segmentation that draws on the relative merits of both approaches: The result is as close as possible to the top-down approximation, but is also constrained by the bottom-up process to be consistent with significant image discontinuities. We construct a global cost function that represents these top-down and bottom-up requirements. We then show how the global minimum of this function can be efficiently found by applying the sum-product algorithm. This algorithm also provides a confidence map that can be used to identify image regions where additional top-down or bottom-up information may further improve the segmentation. Our experiments show that the results derived from the algorithm are superior to results given by a pure top-down or pure bottom-up approach. The scheme has broad applicability, enabling the combined use of a range of existing bottom-up and top-down segmentations.

关键词： Image segmentation Mathematics Object detection Pixel computer science Shape Cost function Sum product algorithm Laboratories Humans

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Photoplethysmography based stratification of blood pressure using multi information fusion artificial neural network

Photoplethysmography based stratification of blood pressure ...

引用

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

作者： Dingliang Wang Xuezhi Yang Xuenan Liu Shuai Fang Likun Ma Longwei Li School of Computer and Information Hefei University of Technology Hefei China School of Software Hefei University of Technology Hefei China The First Affiliated Hospital University of Science and Technology of China Hefei China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Regular monitoring of blood pressure (BP) is an effective way to prevent cardiovascular diseases, especially for elderly people. At present, BP measurement mainly relies on cuff-based devices which are inconvenient for users and may cause discomfort. Therefore, many new approaches have been proposed to achieve cuff-less BP detection in recent years. However, the accuracy of the existing approaches still needs to be improved. In this study, holistic-based PPG and its first and second derivative features are extracted and a new multi information fusion artificial neural network (MIF-ANN) is designed to effectively fuse and exploit multiple input data. Experimental results on a public database which contains 12000 subjects show that the proposed network can model the relation between Photo-plethysmography (PPG) and BP well, achieving averagely accuracy of 91.33% for 5-category BP stratification. Additionally, this study verified that multi information fusion based on meticulously designed network plays an important role in improving the accuracy of BP detection.

关键词： Feature extraction Neural networks Blood pressure Databases Hypertension Biomedical monitoring Wavelet transforms

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Cross-Temporal Spectrogram Autoencoder (CTSAE): Unsupervised Dimensionality Reduction for Clustering Gravitational Wave Glitches

Cross-Temporal Spectrogram Autoencoder (CTSAE): Unsupervised...

引用

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

作者： Yi Li Yunan Wu Aggelos K. Katsaggelos The Department of Computer Science Northwestern University Evanston IL The Department of Electrical Computer Engineering Northwestern University Evanston IL

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The advancement of The Laser Interferometer Gravitational-Wave Observatory (LIGO) has significantly enhanced the feasibility and reliability of gravitational wave detection. However, LIGO’s high sensitivity makes it susceptible to transient noises known as glitches, which necessitate effective differentiation from real gravitational wave signals. Traditional approaches predominantly employ fully supervised or semi-supervised algorithms for the task of glitch classification and clustering. In the future task of identifying and classifying glitches across main and auxiliary channels, it is impractical to build a dataset with manually labeled ground-truth. In addition, the patterns of glitches can vary with time, generating new glitches without manual labels. In response to this challenge, we introduce the Cross-Temporal Spectrogram Autoencoder (CTSAE), a pioneering unsupervised method for the dimensionality reduction and clustering of gravitational wave glitches. CTSAE integrates a novel four-branch autoencoder with a hybrid of Convolutional Neural Networks (CNN) and vision Transformers (ViT). To further extract features across multi-branches, we introduce a novel multi-branch fusion method using the CLS (Class) token. Our model, trained and evaluated on the GravitySpy O3 dataset on the main channel, demonstrates superior performance in clustering tasks when compared to state-of-the-art semi-supervised learning methods. To the best of our knowledge, CTSAE represents the first unsupervised approach tailored specifically for clustering LIGO data, marking a significant step forward in the field of gravitational wave research. The code of this paper is available at https://***/Zod-L/CTSAE

关键词： Dimensionality reduction computer vision Sensitivity Gravitational waves Sensor phenomena and characterization Feature extraction Transformers

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Gesture recognition using Hidden Markov Models from Fragmented Observations

Gesture Recognition using Hidden Markov Models from Fragment...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Ruiduo Yang S. Sarkar Computer Science and Engineering Department University of South Florida Tampa FL USA

We consider the problem of computing the likelihood of a gesture from regular, unaided video sequences, without relying on perfect segmentation of the scene. Instead of requiring that low-and mid-level processes produce near-perfect segmentation of relevant body parts such as hands, we take into account that such processes can only produce uncertain information. The hands can only be detected as fragmented regions along with clutter. To address this problem, we propose an extension of the HMM formalism, which we call the frag-HMM, to allow for reasoning based on fragmented observations, via the use of an intermediate grouping process. In this formulation, we do not match the frag- HMMto one observation sequence, but rather to a sequence of observation sets, where each observation set is a collection of groups of fragmented observations. Based on the developed model, we show how to perform three kinds of computations. The first one is to decide on the best observation group for each frame, given a sequence of observation groups for the past frames. This allows us to incrementally compute the best segmentation of the hand for each frame, given the model. The second one involves the computation of likelihood of a sequence, averaged over all possible states sequences and possible groupings. The third is the computation of the likelihood of a sequence, maximized over all possible state sequences and group sequences. This can give us the best possible groupings for each frame, as well. We demonstrate our ideas using a publicly available hand gesture dataset that spans different subjects, is against complex background, and involves hand occlusions. The recognition performance is within 2% of that obtained with manually segmented hands and about 10% better than that obtained with segmentations that use the prior knowledge of the hand color.

关键词： Hidden Markov models Image segmentation computer vision Tracking Robust stability Skin computer science Video sequences Layout State-space methods

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A topological encoding convolutional neural network for segmentation of 3D multiphoton images of brain vasculature using persistent homology

A topological encoding convolutional neural network for segm...

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

作者： Mohammad Haft-Javaherian Martin Villiger Chris B. Schaffer Nozomi Nishimura Polina Golland Brett E. Bouma Wellman Center for Photomedicine Massachusetts General Hospital Harvard Medical School Boston MA USA Meinig School of Biomedical Engineering Cornell University Ithaca NY USA Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

The clinical evidence suggests that cognitive disorders are associated with vasculature dysfunction and decreased blood flow in the brain. Hence, a functional understanding of the linkage between brain functionality and the vascular network is essential. However, methods to systematically and quantitatively describe and compare structures as complex as brain blood vessels are lacking. 3D imaging modalities such as multiphoton microscopy enables researchers to capture the network of brain vasculature with high spatial resolutions. Nonetheless, image processing and inference are some of the bottlenecks for biomedical research involving imaging, and any advancement in this area impacts many research groups. Here, we propose a topological encoding convolutional neural network based on persistent homology to segment 3D multiphoton images of brain vasculature. We demonstrate that our model outperforms state-of-the-art models in terms of the Dice coefficient and it is comparable in terms of other metrics such as sensitivity. Additionally, the topological characteristics of our model's segmentation results mimic manual ground truth. Our code and model are open source at https://***/mhaft/DeepVess.

关键词： Image segmentation Three-dimensional displays Biological system modeling computer vision computer architecture Biomedical imaging

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RRU-Net: The Ringed Residual U-Net for Image Splicing Forgery Detection

RRU-Net: The Ringed Residual U-Net for Image Splicing Forger...

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

作者： Xiuli Bi Yang Wei Bin Xiao Weisheng Li School of Computer Science & Technology Chongqing University of Posts and Telecommunication

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Detecting a splicing forgery image and then locating the forgery regions is a challenging task. Some traditional feature extraction methods and convolutional neural network (CNN)-based detection methods have been proposed to finish this task by exploring the differences of image attributes between the un-tampered and tampered regions in a image. However, the performance of the existing detection methods is unsatisfactory. In this paper, we propose a ringed residual U-Net (RRU-Net) for image splicing forgery detection. The proposed RRU-Net is an end-to-end image essence attribute segmentation network, which is independent of human visual system, it can accomplish the forgery detection without any preprocessing and post-processing. The core idea of the proposed RRU-Net is to strengthen the learning way of CNN, which is inspired by the recall and the consolidation mechanism of the human brain and implemented by the propagation and the feedback process of the residual in CNN. The residual propagation recalls the input feature information to solve the gradient degradation problem in the deeper network; the residual feedback consolidates the input feature information to make the differences of image attributes between the un-tampered and tampered regions be more obvious. Experimental results show that the proposed detection method can achieve a promising result compared with the state-of-the-art splicing forgery detection methods.

关键词： Forgery Splicing Feature extraction Degradation Task analysis Image segmentation Network architecture

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When Person Re-identification Meets Changing Clothes

When Person Re-identification Meets Changing Clothes

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

作者： Fangbin Wan Yang Wu Xuelin Qian Yixiong Chen Yanwei Fu School of Data Science Fudan University Kyoto University School of Computer Science Fudan University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Person re-identification (ReID) is now an active research topic for AI-based video surveillance applications such as specific person search, but the practical issue that the target person(s) may change clothes (clothes inconsistency problem) has been overlooked for long. For the first time, this paper systematically studies this problem. We first overcome the difficulty of lack of suitable dataset, by collecting a small yet representative real dataset for testing whilst building a large realistic synthetic dataset for training and deeper studies. Facilitated by our new datasets, we are able to conduct various interesting new experiments for studying the influence of clothes inconsistency. We find that changing clothes makes ReID a much harder problem in the sense of bringing difficulties to learning effective representations and also challenges the generalization ability of previous ReID models to identify persons with unseen (new) clothes. Representative existing ReID models are adopted to show informative results on such a challenging setting, and we also provide some preliminary efforts on improving the robustness of existing models on handling the clothes inconsistency issue in the data. We believe that this study can be inspiring and helpful for encouraging more researches in this direction. The dataset is available on the project website: https://***/***.

关键词： Cameras Benchmark testing Lighting Buildings Games Data models Biological system modeling

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