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Affine-based Deformable Attention and Selective Fusion for Semi-dense Matching

Affine-based Deformable Attention and Selective Fusion for S...

引用

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

作者： Hongkai Chen Zixin Luo Yurun Tian Xuyang Bai Ziyu Wang Lei Zhou Mingmin Zhen Tian Fang David McKinnon Yanghai Tsin Long Quan Apple Inc Hong Kong University of Science and Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Identifying robust and accurate correspondences across images is a fundamental problem in computer vision that enables various downstream tasks. Recent semi-dense matching methods emphasize the effectiveness of fusing relevant cross-view information through Transformer. In this paper, we propose several improvements upon this paradigm. Firstly, we introduce affine-based local attention to model cross-view deformations. Secondly, we present selective fusion to merge local and global messages from cross attention. Apart from network structure, we also identify the importance of enforcing spatial smoothness in loss design, which has been omitted by previous works. Based on these augmentations, our network demonstrate strong matching capacity under different settings. The full version of our network achieves state-of-the-art performance among semi-dense matching methods at a similar cost to LoFTR, while the slim version reaches LoFTR baseline’s performance with only 15% computation cost and 18% parameters.

关键词： Geometry Deformable models computer vision Costs Deformation Shape Fuses

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Fine-Grained recognition in High-throughput Phenotyping

Fine-Grained Recognition in High-throughput Phenotyping

引用

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

作者： Beichen Lyu Stuart D. Smith Keith A. Cherkauer Purdue University West Lafayette IN

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Fine-Grained recognition aims to classify sub-category objects such as bird species and car models from imagery. In High-throughput Phenotyping, the required task is to classify individual plant cultivars to assist plant breeding, which has posed three challenges: 1) it is easy to overfit complex features and models, 2) visual conditions change during and between image collection opportunities, and 3) analysis of thousands of cultivars require high-throughput data collection and analysis. To tackle these challenges, we propose a simple but intuitive descriptor, Radial Object Descriptor, to represent plant cultivar objects based on contour. This descriptor is invariant under scaling, rotation, and translation, as well as robust under changes to the plant's growth stage and camera's view angle. Furthermore, we complement this mid-level feature by fusing it with the low-level features (Histogram of Oriented Gradients) and deep features (ResNet-18), respectively. We extensively test our fusion approaches using two real world experiments. One experiment is on a novel benchmark dataset (HTP-Soy) in which we collect ~ 2, 000 high-resolution aerial images of outdoor soybean plots. Another experiment is on three datasets of indoor rosette plants. For both experiments, our fusion approaches achieve superior accuracies while maintaining better generalization as compared with traditional approaches.

关键词： Feature extraction Histograms Image recognition Task analysis pattern recognition Biological system modeling Visualization

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A Drowsy Driver Detection and security system

A Drowsy Driver Detection and security system

引用

International conference on Ultra Modern Telecommunications and workshops (ICUMT)

作者： Rajat Garg Vikrant Gupta Vineet Agrawal Department of Electronics and Communication Vellore of Institute of Technology Vellore India

This paper presents a new approach towards automobile safety and security. We propose three distinct but closely related concepts viz. an Iris recognition system, a Drowsy Driver Detection system and a Distress Signalling system using non-intrusive machine vision based concepts. In recent time's automobile theft and fatigue related crashes have really magnified. In order to minimize these issues, we have incorporated Biometric security through Iris recognition that will help in authentication and an improved sleep detection and driver alert system by monitoring both the driver's eyes as well as senseing the heat variation of the body via infrared thermal sensor. Distress Signalling system is incorporated for drivers to get assistance from the Police in need without revealing it to people present around him. This paper combines computer vision, pattern recognition and optics. All image processing was performed using NI vision Assistant. Also NI LabVIEW was used to take the current body temperature from the temperature sensors attached to the DAQ (Data Acquisition) Signal Accessory.

关键词： Security Automobiles Iris recognition Infrared detectors Infrared heating Temperature sensors Data acquisition Vehicle safety Machine vision Fatigue

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Thermal Image Super-Resolution Challenge Results - PBVS 2024

Thermal Image Super-Resolution Challenge Results - PBVS 2024

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

作者： Rafael E. Rivadeneira Angel D. Sappa Chenyang Wang Junjun Jiang Zhiwei Zhong Peilin Chen Shiqi Wang Escuela Superior Politécnica del Litoral ESPOL Guayaquil Ecuador Computer Vision Center Campus UAB Barcelona Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper outlines the advancements and results of the Fifth Thermal Image Super-Resolution challenge, hosted at the Perception Beyond the Visible Spectrum CVPR 2024 workshop. The challenge employed a novel benchmark cross-spectral dataset consisting of 1000 thermal images, each paired with its corresponding registered RGB image. The challenge featured two tracks: Track-1 focused on Single Thermal Image Super-Resolution with an ×8 upscale factor, while Track-2 extended its evaluation to include both ×8 and ×16 scaling factors, utilizing high-resolution RGB images to guide the super-resolution process for low-resolution thermal images. The participation of over 175 teams highlights the research community’s strong engagement and dedication to enhancing image resolution techniques across both single and cross-spectral methodologies. This year’s challenge sets new benchmarks and provides valuable insights into future directions for research in thermal image super-resolution.

关键词： Image quality computer vision Thermal factors conferences Superresolution Benchmark testing Transformers

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SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category recognition

SVM-KNN: Discriminative Nearest Neighbor Classification for ...

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conference on computer vision and pattern recognition (CVPR)

作者： Hao Zhang A.C. Berg M. Maire J. Malik Computer Science Division EECS Department University of California Berkeley CA USA

We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While nearest neighbor classifiers are natural in this setting, they suffer from the problem of high variance (in bias-variance decomposition) in the case of limited sampling. Alternatively, one could use support vector machines but they involve time-consuming optimization and computation of pairwise distances. We propose a hybrid of these two methods which deals naturally with the multiclass setting, has reasonable computational complexity both in training and at run time, and yields excellent results in practice. The basic idea is to find close neighbors to a query sample and train a local support vector machine that preserves the distance function on the collection of neighbors. Our method can be applied to large, multiclass data sets for which it outperforms nearest neighbor and support vector machines, and remains efficient when the problem becomes intractable for support vector machines. A wide variety of distance functions can be used and our experiments show state-of-the-art performance on a number of benchmark data sets for shape and texture classification (MNIST, USPS, CUReT) and object recognition (Caltech- 101). On Caltech-101 we achieved a correct classification rate of 59.05%(±0.56%) at 15 training images per class, and 66.23%(±0.48%) at 30 training images.

关键词： Nearest neighbor searches Shape Humans Support vector machines Prototypes Support vector machine classification computer vision pattern recognition computer science Sampling methods

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Accurate Tracking of Monotonically Advancing Fronts

Accurate Tracking of Monotonically Advancing Fronts

引用

conference on computer vision and pattern recognition (CVPR)

作者： M.S. Hassouna A.A. Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY USA

A wide range of computer vision applications such as distance field computation, shape from shading, and shape representation require an accurate solution of a particular Hamilton-Jacobi (HJ) equation, known as the Eikonal equation. Although the fast marching method (FMM) is the most stable and consistent method among existing techniques for solving such equation, it suffers from large numerical error along diagonal directions as well as its computational complexity is not optimal. In this paper, we propose an improved version of the FMMthat is both highly accurate and computationally efficient for Cartesian domains. The new method is called the multi-stencils fast marching (MSFM), which computes the solution at each grid point by solving the Eikonal equation along several stencils and then picks the solution that satisfies the fast marching causality relationship. The stencils are centered at each grid point x and cover its entire nearest neighbors. In 2D space, 2 stencils cover the 8-neighbors of x, while in 3D space, 6 stencils cover its 26-neighbors. For those stencils that are not aligned with the natural coordinate system, the Eikonal equation is derived using directional derivatives and then solved using a higher order finite difference scheme.

关键词： computer vision Shape Finite difference methods Application software Computational complexity Nonlinear equations Partial differential equations Image processing Laboratories Grid computing

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Efficient Light Field Image Super-Resolution via Progressive Disentangling

Efficient Light Field Image Super-Resolution via Progressive...

引用

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

作者： Gaosheng Liu Huanjing Yue Jingyu Yang School of Electrical and Information Engineering Tianjin University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The performance of light field (LF) image super-resolution (SR) has been significantly improved with the development of deep learning techniques. In recent state-of-the-art methods, increasingly deeper and wider networks with a massive number of layers are employed to improve SR performance. However, these approaches often incur heavy computational costs, hindering efficient inference and practical applications. In this paper, we address the problem by introducing an efficient network for LF image SR. Specifically, we propose an efficient progressive disentangling block (PDistgB), where the intermediate LF feature is progressively channel-wise split and selectively domain-specific disentangled. The PDistgB can well incorporate the LF structure prior while requiring fewer computational costs compared with existing disentangling strategies. In addition, we apply Transformer on the angular domain to incorporate angular correlations from all views for further improving the SR accuracy. Experimental results on public datasets demonstrate that our method achieves state-of-the-art performance with high efficiency. Codes and models are available at https://***/GaoshengLiu/PDistgNet.

关键词： Deep learning computer vision Correlation conferences Computational modeling Superresolution Transformers

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A New Joint Clustering and Diffeomorphism Estimation Algorithm for Non-Rigid Shape Matching

A New Joint Clustering and Diffeomorphism Estimation Algorit...

引用

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

作者： Hongyu Guo A. Rangarajan S.C. Joshi L. Younes Department of CISE University of Florida USA Departments of Radiation Oncology and Biomedical Engineering North Carolina State University Chapel Hill USA Department of Applied Mathematics and Statistics Johns Hopkins University USA

Matching shapes parameterized as unlabeled point-sets is a challenging problem since we have to solve for point correspondences in a non-rigid setting. Previous work on this problem such as modal matching, linear assignment, shape contexts etc. has focused more on the correspondence aspect and not on the non-rigid deformations. The principal motivation for the present work is to establish a distance measure between shapes on a shape manifold. A pre-requisite for achieving this goal is the diffeomorphic matching of point-sets. We show that a joint clustering and diffeomorphism estimation strategy is capable of simultaneously estimating correspondences and a diffeomorphism between unlabeled point-sets. Cluster centers for the two point-sets having the same label are always in correspondence. Essentially, as the cluster centers evolve during the iterations of an incremental EM algorithm, we estimate a diffeomorphism between the two sets of cluster centers. We apply our algorithm to 2D corpus callosum shapes.

关键词： Clustering algorithms Shape measurement Image analysis computer vision Probability distribution Oncology Biomedical engineering Mathematics Statistics Biomedical measurements

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Efficient Nonparametric Belief Propagation with Application to Articulated Body Tracking

Efficient Nonparametric Belief Propagation with Application ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： T.X. Han Huazhong Ning T.S. Huang Beckman Institute and ECE Department University of Illinois Urbana-Champaign Urbana-Champaign IL USA

An efficient Nonparametric Belief Propagation (NBP) algorithm is developed in this paper. While the recently proposed nonparametric belief propagation algorithm has wide applications such as articulated tracking [22, 19], superresolution [6], stereo vision and sensor calibration [10], the hardcore of the algorithm requires repeatedly sampling from products of mixture of Gaussians, which makes the algorithm computationally very expensive. To avoid the slow sampling process, we applied mixture Gaussian density approximation by mode propagation and kernel fitting [2, 7]. The products of mixture of Gaussians are approximated accurately by just a few mode propagation and kernel fitting steps, while the sampling method (e.g. Gibbs sampler) needs many samples to achieve similar approximation results. The proposed algorithm is then applied to articulated body tracking for several scenarios. The experimental results show the robustness and the efficiency of the proposed algorithm. The proposed efficient NBP algorithm also has potentials in other applications mentioned above.

关键词： Belief propagation Sampling methods Inference algorithms Gaussian approximation Kernel Random variables computer vision Graphical models Robustness Gaussian processes

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Modeling Individual and Group Actions in Meetings: A Two-Layer HMM Framework

Modeling Individual and Group Actions in Meetings: A Two-Lay...

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

作者： Dong Zhang D. Gatica-Perez S. Bengio I. McCowan G. Lathoud IDIAP Research Institute Martigny Switzerland

We address the problem of recognizing sequences of human interaction patterns in meetings, with the goal of structuring them in semantic terms. The investigated patterns are inherently group-based (defined by the individual activities of meeting participants, and their interplay), and multimodal (as captured by cameras and microphones). By defining a proper set of individual actions, group actions can be modeled as a two-layer process, one that models basic individual activities from low-level audio-visual features, and another one that models the interactions. We propose a two-layer Hidden Markov Model (HMM) framework that implements such concept in a principled manner, and that has advantages over previous works. First, by decomposing the problem hierarchically, learning is performed on low-dimensional observation spaces, which results in simpler models. Second, our framework is easier to interpret, as both individual and group actions have a clear meaning, and thus easier to improve. Third, different HMM models can be used in each layer, to better reflect the nature of each subproblem. Our framework is general and extensible, and we illustrate it with a set of eight group actions, using a public five-hour meeting corpus. Experiments and comparison with a single-layer HMM baseline system show its validity.

关键词： Hidden Markov models pattern recognition Humans Speech recognition Cameras Microphones Sensor systems Psychology Teamwork Context modeling

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