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Automatic Kinematic Chain Building from Feature Trajectories of Articulated Objects

Automatic Kinematic Chain Building from Feature Trajectories...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jingyu Yan M. Pollefeys Department of Computer Science University of North Carolina Chapel Hill Chapel Hill NC USA

We investigate the problem of learning the structure of an articulated object, i.e. its kinematic chain, from feature trajectories under affine projections. We demonstrate this possibility by proposing an algorithm which first segments the trajectories by local sampling and spectral clustering, then builds the kinematic chain as a minimum spanning tree of a graph constructed from the segmented motion subspaces. We test our method in challenging data sets and demonstrate the ability to automatically build the kinematic chain of an articulated object from feature trajectories. The algorithm also works when there are multiple articulated objects in the scene. Furthermore, we take into account non-rigid articulated parts that exist in human motions. We believe this advance will have impact on articulated object tracking and dynamical structure from motion.

关键词： Kinematics Clustering algorithms Layout Buildings Sampling methods Automatic testing Tracking Shape computer science Tree graphs

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引用

conference on computer vision and pattern recognition (CVPR)

作者： Feng Kang Rong Jin R. Sukthankar Michigan State University USA Robotics Institute Carnegie Mellon University USA Intel Research Pittsburgh USA

Many computer vision applications, such as scene analysis and medical image interpretation, are ill-suited for traditional classification where each image can only be associated with a single class. This has stimulated recent work in multi-label learning where a given image can be tagged with multiple class labels. A serious problem with existing approaches is that they are unable to exploit correlations between class labels. This paper presents a novel framework for multi-label learning termed Correlated Label Propagation (CLP) that explicitly models interactions between labels in an efficient manner. As in standard label propagation, labels attached to training data points are propagated to test data points; however, unlike standard algorithms that treat each label independently, CLP simultaneously co-propagates multiple labels. Existing work eschews such an approach since naive algorithms for label co-propagation are intractable. We present an algorithm based on properties of submodular functions that efficiently finds an optimal solution. Our experiments demonstrate that CLP leads to significant gains in precision/recall against standard techniques on two real-world computer vision tasks involving several hundred labels.

关键词： Testing computer vision Application software Medical robotics Robot vision systems Image analysis Biomedical imaging Training data Frequency computer Society

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Generalized Single-Image-Based Morphing Attack Detection Using Deep Representations from vision Transformer

Generalized Single-Image-Based Morphing Attack Detection Usi...

引用

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

作者： Haoyu Zhang Raghavendra Ramachandra Kiran Raja Christoph Busch Norwegian University of Science and Technology Norway Darmstadt University of Applied Sciences Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face morphing attacks have posed severe threats to Face recognition Systems (FRS), which are operated in border control and passport issuance use cases. Correspondingly, morphing attack detection algorithms (MAD) are needed to defend against such attacks. MAD approaches must be robust enough to handle unknown attacks in an open-set scenario where attacks can originate from various morphing generation algorithms, post-processing and the diversity of printers/scanners. The problem of generalization is further pronounced when the detection has to be made on a single suspected image. In this paper, we propose a generalized single-image-based MAD (S-MAD) algorithm by learning the encoding from vision Transformer (ViT) architecture. Compared to CNN-based architectures, ViT model has the advantage on integrating local and global information and hence can be suitable to detect the morphing traces widely distributed among the face region. Extensive experiments are carried out on face morphing datasets generated using publicly available FRGC face datasets. Several state-of-the-art (SOTA) MAD algorithms, including representative ones that have been publicly evaluated, have been selected and benchmarked with our ViT-based approach. Obtained results demonstrate the improved detection performance of the proposed S-MAD method on inter-dataset testing (when different data is used for training and testing) and comparable performance on intra-dataset testing (when the same data is used for training and testing) experimental protocol.

关键词： Training computer vision Image coding Protocols Statistical analysis Face recognition Digital images

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How Much You Ate? Food Portion Estimation on Spoons

How Much You Ate? Food Portion Estimation on Spoons

引用

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

作者： Aaryam Sharma Chris Czarnecki Yuhao Chen Pengcheng Xi Linlin Xu Alexander Wong Vision and Image Processing Lab University of Waterloo National Research Council Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Monitoring dietary intake is a crucial aspect of promoting healthy living. In recent years, advances in computer vision technology have facilitated dietary intake monitoring through the use of images and depth cameras. However, the current state-of-the-art image-based food portion estimation algorithms assume that users take images of their meals one or two times, which can be inconvenient and fail to capture food items that are not visible from a top-down perspective, such as ingredients submerged in a stew. To address these limitations, we introduce an innovative solution that utilizes stationary user-facing cameras to track food items on utensils, not requiring any change of camera perspective after installation. The shallow depth of utensils provides a more favorable angle for capturing food items, and tracking them on the utensil’s surface offers a significantly more accurate estimation of dietary intake without the need for post-meal image capture. The system is reliable for estimation of nutritional content of liquid-solid heterogeneous mixtures such as soups and stews. Through a series of experiments, we demonstrate the exceptional potential of our method as a non-invasive, user-friendly, and highly accurate dietary intake monitoring tool.

关键词： computer vision Image segmentation Accuracy Pipelines Estimation Image capture Cameras

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Multi-User Natural Interaction System based on Real-Time Hand Tracking and Gesture recognition

Multi-User Natural Interaction System based on Real-Time Han...

引用

International conference on pattern recognition

作者： A. Del Bimbo L. Landucci A. Valli Universita degli Studi di Firenze Firenze Toscana IT Dipartimento di Sistemi e Informatica University of Florence Italy Firenze Italy

We present a computer vision based system that enables multiple people to interact naturally with a large display table using their own bare-hand gestures. The display presents and supports a particular multimedia application that can be used at the same time even by remote users. Finally we describe two different applications designed for didactic and entertainment scenarios

关键词： Real time systems Surface morphology computer displays Application software Multimedia systems pattern recognition Hidden Markov models Visualization Videoconference computer vision

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A Shape Representation for Planar Curves by Shape Signature Harmonic Embedding

A Shape Representation for Planar Curves by Shape Signature ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Sang-Mook Lee A.L. Abbott N.A. Clark P.A. Araman Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg VA USA SRS-4702 USDA Forest Service Blacksburg VA USA

This paper introduces a new representation for planar curves. From the well-known Dirichlet problem for a disk, the harmonic function embedded in a circular disk is solely dependent on specified boundary values and can be obtained from Poisson’s integral formula. We derive a discrete version of Poisson’s formula and assess its harmonic properties. Various shape signatures can be used as boundary values, whereas only the corresponding Fourier descriptors are needed for the framework. The proposed approach is similar to a scale space representation but exhibits greater generality by accommodating using any type of shape signature. In addition, it is robust to noise and computationally efficient, and it is guaranteed to have a unique solution. In this paper, we demonstrate that the approach has strong potential for shape representation and matching applications.

关键词： Shape Kernel computer vision USA Councils Poisson equations Harmonic analysis Power harmonic filters Low pass filters Embedded computing US Department of Agriculture

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Shape-Based Approach to Robust Image Segmentation using Kernel PCA

Shape-Based Approach to Robust Image Segmentation using Kern...

引用

conference on computer vision and pattern recognition (CVPR)

作者： S. Dambreville Y. Rathi A. Tannen Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta GA USA

Segmentation involves separating an object from the background. In this work, we propose a novel segmentation method combining image information with prior shape knowledge, within the level-set framework. Following the work of Leventon et al., we revisit the use of principal component analysis (PCA) to introduce prior knowledge about shapes in a more robust manner. To this end, we utilize Kernel PCA and show that this method of learning shapes outperforms linear PCA, by allowing only shapes that are close enough to the training data. In the proposed segmentation algorithm, shape knowledge and image information are encoded into two energy functionals entirely described in terms of shapes. This consistent description allows to fully take advantage of the Kernel PCA methodology and leads to promising segmentation results. In particular, our shape-driven segmentation technique allows for the simultaneous encoding of multiple types of shapes, and offers a convincing level of robustness with respect to noise, clutter, partial occlusions, or smearing.

关键词： Image segmentation Kernel Principal component analysis Shape Noise robustness computer vision Application software Active contours Training data Encoding

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Ultrasound-Specific Segmentation via Decorrelation and Statistical Region-Based Active Contours

Ultrasound-Specific Segmentation via Decorrelation and Stati...

引用

conference on computer vision and pattern recognition (CVPR)

作者： G. Slabaugh G. Unal Tong Fang M. Wels Intelligent Vision and Reasoning Department Siemens Corporate Research Inc. Princeton NJ USA Department of Computer Science Universität WÜrzburg Coburg Germany

Segmentation of ultrasound images is often a very challenging task due to speckle noise that contaminates the image. It is well known that speckle noise exhibits an asymmetric distribution as well as significant spatial correlation. Since these attributes can be difficult to model, many previous ultrasound segmentation methods oversimplify the problem by assuming that the noise is white and/or Gaussian, resulting in generic approaches that are actually more suitable to MR and X-ray segmentation than ultrasound. Unlike these methods, in this paper we present an ultrasound-specific segmentation approach that first decorrelates the image, and then performs segmentation on the whitened result using statistical region-based active contours. In particular, we design a gradient ascent flow that evolves the active contours to maximize a log likelihood functional based on the Fisher-Tippett distribution. We present experimental results that demonstrate the effectiveness of our method.

关键词： Ultrasonic imaging Decorrelation Active contours Image segmentation Speckle X-ray imaging Image quality Rayleigh scattering X-ray scattering computer science

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A Distributed Approach for Real Time 3D Modeling

A Distributed Approach for Real Time 3D Modeling

引用

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

作者： J.-S. Franco C. Menier E. Boyer B. Raffin GRAVIR-INRIA Rhône-Alpes France ID-INRIA-Rhône-Alpes France

This paper addresses the problem of real time 3D modeling from images with multiple cameras. Environments where multiple cameras and PCs are present are becoming usual, mainly due to new camera technologies and high computing power of modern PCs. However most applications in computer vision are based on a single, or few PCs for computations and do not scale. Our motivation in this paper is therefore to propose a distributed framework which allows to compute precise 3D models in real time with a variable number of cameras, this through an optimal use of the several PCs which are generally present. We focus in this paper on silhouette based modeling approaches and investigate how to efficiently partition the associated tasks over a set of PCs. Our contribution is a distribution scheme that applies to the different types of approaches in this field and allows for real time applications. Such a scheme relies on different accessible levels of parallelization, from individual task partitions to concurrent executions, yielding in turn controls on both latency and frame rate of the modeling system. We report on the application of the presented framework to visual hull modeling applications. In particular, we show that precise surface models can be computed in real time with standard components. Results with synthetic data and preliminary results in real contexts are presented.

关键词： Personal communication networks computer vision Digital cameras Concurrent computing Robot vision systems Application software Layout Computational modeling Power system modeling Real time systems

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Learning in Region-Based Image Retrieval with Generalized Support Vector Machines

Learning in Region-Based Image Retrieval with Generalized Su...

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

作者： I. Gondra D.R. Heisterkamp Computer Science Department Oklahoma State University Stillwater OK USA

Relevance feedback approaches based on support vector machine (SVM) learning have been applied to significantly improve retrieval performance in content-based image retrieval (CBIR). Those approaches require the use of fixed-length image representations because SVM kernels represent an inner product in a feature space that is a non-linear transformation of the input space. Many region-based CBIR approaches create a variable length image representation and define a similarity measure between two variable length representations. The standard SVM approach cannot be applied to this approach because it violates the requirements that SVM places on the kernel. Fortunately, a generalized SVM (GSVM) has been developed that allows the use of an arbitrary kernel. In this paper, we present an initial investigation into utilizing a GSVM-based relevance feedback learning algorithm. Since GSVM does not place restrictions on the kernel, any image similarity measure can be used. In particular, the proposed approach uses an image similarity measure developed for region-based, variable length representations. Experimental results over real world images demonstrate the efficacy of the proposed method.

关键词： Machine learning Image retrieval Support vector machines Image segmentation Content based retrieval Kernel Feedback computer science Image representation Length measurement

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