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An Adaptive Appearance Model Approach for Model-based Articulated Object Tracking

An Adaptive Appearance Model Approach for Model-based Articu...

引用

conference on computer vision and pattern recognition (CVPR)

作者： A.O. Balan M.J. Black Department of Computer Science Brown University Providence RI USA

The detection and tracking of three-dimensional human body models has progressed rapidly but successful approaches typically rely on accurate foreground silhouettes obtained using background segmentation. There are many practical applications where such information is imprecise. Here we develop a new image likelihood function based on the visual appearance of the subject being tracked. We propose a robust, adaptive, appearance model based on the Wandering-Stable-Lost framework extended to the case of articulated body parts. The method models appearance using a mixture model that includes an adaptive template, frame-to-frame matching and an outlier process. We employ an annealed particle filtering algorithm for inference and take advantage of the 3D body model to predict selfocclusion and improve pose estimation accuracy. Quantitative tracking results are presented for a walking sequence with a 180 degree turn, captured with four synchronized and calibrated cameras and containing significant appearance changes and self-occlusion in each view.

关键词： Biological system modeling Humans Cameras Robustness Annealing Filtering algorithms Predictive models Legged locomotion computer science Object detection

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Estimating Intrinsic Component Images using Non-Linear Regression

Estimating Intrinsic Component Images using Non-Linear Regre...

引用

conference on computer vision and pattern recognition (CVPR)

作者： M.F. Tappen E.H. Adelson W.T. Freeman MIT Computer Science and Artificial Intelligence Laboratory Cambridge MA USA

Images can be represented as the composition of multiple intrinsic component images, such as shading, albedo, and noise images. In this paper, we present a method for estimating intrinsic component images from a single image, which we apply to the problems of estimating shading and albedo images and image denoising. Our method is based on learning estimators that predict filtered versions of the desired image. Unlike previous approaches, our method does not require unnatural discretizations of the problem. We also demonstrate how to learn a weighting function that properly weights the local estimates when constructing the estimated image. For shading estimation, we introduce a new training set of real-world images. The accuracy of our method is measured both qualitatively and quantitatively, showing better performance on the shading/albedo separation problem than previous approaches. The performance on denoising is competitive with the current state of the art.

关键词： Layout Noise reduction Shape Surface treatment computer science Artificial intelligence Laboratories Image denoising Noise level Lighting

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Multi-modal Aerial View Image Challenge: Sensor Domain Translation

Multi-modal Aerial View Image Challenge: Sensor Domain Trans...

引用

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

作者： Spencer Low Oliver Nina Dylan Bowald Angel D. Sappa Nathan Inkawhich Peter Bruns Brigham Young University Provo Utah Air Force Research Laboratory Dayton OH ESPOL Polytechnic University Ecuador Computer Vision Center Spain Air Force Research Laboratory Rome NY University of Utah Salt Lake UT

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper describes the design, outcomes, and top methods of the 2nd annual Multi-modal Aerial View Image Challenge (MAVIC) aimed at cross modality aerial image translation. The primary objective of this competition is to stimulate research efforts towards the development of models capable of translating co-aligned images between multiple modalities. Specifically, the challenge centers on translation between synthetic aperture radar (SAR), electro-optical (EO), camera (RGB), and infrared (IR) sensor modalities, a budding area of research that has begun to garner attention. While last year’s inaugural challenge demonstrated the feasibility of SAR→EO translation, this year’s challenge made significant improvements in dataset coverage, sensor variation, experimental design, and methods covering the tasks of SAR→EO, SAR→RGB, SAR→IR, RGB→IR introducing a new dataset called translation. By Multi-modal Aerial Gathered Image Composites (MAGIC); multimodal image translation is available for different comparisons. With a more rigorous set of translation performance metrics, winners were determined from aggregation of L1-norm, LPIPS (Learned Perceptual Image Patch Similarity, and FID (Frechet Inception Distance) scores. The wining methods included the pix2pixHD and LPIPS metrics as loss functions with an aggregated score 5% better separated by the SAR→EO and RGB→IR translation scores.

关键词： Measurement computer vision Design methodology conferences Cameras pattern recognition Synthetic aperture radar

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Making a Long Video Short: Dynamic Video Synopsis

Making a Long Video Short: Dynamic Video Synopsis

引用

conference on computer vision and pattern recognition (CVPR)

作者： A. Rav-Acha Y. Pritch S. Peleg Computer Science and Engineering Hebrew University of Jerusalem Jerusalem Israel

The power of video over still images is the ability to represent dynamic activities. But video browsing and retrieval are inconvenient due to inherent spatio-temporal redundancies, where some time intervals may have no activity, or have activities that occur in a small image region. Video synopsis aims to provide a compact video representation, while preserving the essential activities of the original video. We present dynamic video synopsis, where most of the activity in the video is condensed by simultaneously showing several actions, even when they originally occurred at different times. For example, we can create a "stroboscopic movie", where multiple dynamic instances of a moving object are played simultaneously. This is an extension of the still stroboscopic picture. Previous approaches for video abstraction addressed mostly the temporal redundancy by selecting representative key-frames or time intervals. In dynamic video synopsis the activity is shifted into a significantly shorter period, in which the activity is much denser. Video examples can be found online in http://***/synopsis

关键词： Birds Videoconference Object detection Legged locomotion Displays Layout Video sequences computer science Power engineering and energy Image retrieval

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Large-Scale Bidirectional Training for Zero-Shot Image Captioning

Large-Scale Bidirectional Training for Zero-Shot Image Capti...

引用

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

作者： Taehoon Kim Mark Marsden Pyunghwan Ahn Sangyun Kim Sihaeng Lee Alessandra Sala Seung Hwan Kim LG AI Research Shutterstock

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

When trained on large-scale datasets, image captioning models can understand the content of images from a general domain but often fail to generate accurate, detailed captions. To improve performance, pretraining-and-finetuning has been a key strategy for image captioning. However, we find that large-scale bidirectional training between image and text enables zero-shot image captioning. In this paper, we introduce Bidirectional Image Text Training in largER Scale, BITTERS, an efficient training and inference framework for zero-shot image captioning. We also propose a new evaluation benchmark which comprises of high quality datasets and an extensive set of metrics to properly evaluate zero-shot captioning accuracy and societal bias. We additionally provide an efficient finetuning approach for keyword extraction. We show that careful selection of large-scale training set and model architecture is the key to achieving zero-shot image captioning.

关键词： Training Measurement Accuracy conferences computer architecture Feature extraction Data models

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How Important Is Each Dermoscopy Image?

How Important Is Each Dermoscopy Image?

引用

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

作者： Catarina Barata Carlos Santiago Institute for Systems and Robotics Lisbon Portugal

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Deep neural networks (DNNs) have revolutionized the field of dermoscopy image analysis. Systems based on DNNs are able to achieve impressive diagnostic performances, even outperforming experienced dermatologists. However, DNNs strongly rely on the quantity and quality of the training data. Real world data sets, including those related to dermoscopy, are often severely imbalanced and of reduced dimensions. Thus, models trained on these data sets typically become biased and fail to generalize well to new images. Sample weighting strategies have been proposed to overcome the previous limitations with promising results. Nonetheless, they have been poorly investigated in the context of dermoscopy image analysis. This paper addresses this issue through the extensive comparison of several sample weighting methods, namely class balance and curriculum learning. The results show that each sample weighting strategy influences the performance of the model in different ways, with most finding a compromise between correctly classifying the most common classes or biasing the model towards the less represented classes. Furthermore, the features learned by each model differ significantly, depending on the training strategy.

关键词： Training Lesions Skin Task analysis Neural networks computer architecture Image analysis

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AnonymousNet: Natural Face De-Identification With Measurable Privacy

AnonymousNet: Natural Face De-Identification With Measurable...

引用

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

作者： Tao Li Lei Lin Department of Computer Science Purdue University Goergen Institute for Data Science University of Rochester

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

With billions of personal images being generated from social media and cameras of all sorts on a daily basis, security and privacy are unprecedentedly challenged. Although extensive attempts have been made, existing face image de-identification techniques are either insufficient in photo-reality or incapable of balancing privacy and usability qualitatively and quantitatively, i.e., they fail to answer counterfactual questions such as "is it private now?", "how private is it?", and "can it be more private?" In this paper, we propose a novel framework called AnonymousNet, with an effort to address these issues systematically, balance usability, and enhance privacy in a natural and measurable manner. The framework encompasses four stages: facial attribute estimation, privacy-metric-oriented face obfuscation, directed natural image synthesis, and adversarial perturbation. Not only do we achieve the state-of-the-arts in terms of image quality and attribute prediction accuracy, we are also the first to show that facial privacy is measurable, can be factorized, and accordingly be manipulated in a photo-realistic fashion to fulfill different requirements and application scenarios. Experiments further demonstrate the effectiveness of the proposed framework.

关键词： Face Privacy Generative adversarial networks Measurement Gallium nitride Image quality

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Delaunay Triangulation based 3D Human Face Modeling from Uncalibrated Images

Delaunay Triangulation based 3D Human Face Modeling from Unc...

引用

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

作者： R. Hassanpour V. Atalay Department of Computer Engineering Çankaya University Ankara Turkey Department of Computer Engineering Middle East Technical University Ankara Turkey

In this paper, we describe an algorithm for generating three dimensional models of human faces from uncalibrated images. Input images are taken by a camera generally with a small rotation around a single axis which may cause degenerate solutions during auto-calibration. We describe a solution to this problem by a priori assumptions on the camera. To generate a specific person's head, a generic human head model is deformed according to the 3D coordinates of points obtained by reconstructing the scene using images calibrated with our algorithm. The deformation process is based on a physical based massless spring model and it requires local re-triangulation in the area with high curvatures. This is achieved by locally applying Delaunay traingulation method. However, there may occur degeneracies in Delaunay triangulation such as encroaching of edges. We describe an algorithm for removing the degeneracies during triangulation by modifying the definition of the Delaunay cavity. This algorithm has also the effect of preserving the curavature in the face area. We have compared the models generated with our algorithm with the models obtained using cyberscanners. The RMS geometric error in these comparisons are less than 1.8 x 10^{-2}.

关键词： Humans Head Deformable models Solid modeling Facial animation Cameras Rendering (computer graphics) Facial features Face detection Image reconstruction

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Dynamic Appearance Modeling for Human Tracking

Dynamic Appearance Modeling for Human Tracking

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conference on computer vision and pattern recognition (CVPR)

作者： Hwasup Lim O.I. Camps M. Sznaier V.I. Morariu Department of Electrical Engineering Pennsylvania State University University Park PA USA Department of Computer Science University of Maryland College Park MD USA

Dynamic appearance is one of the most important cues for tracking and identifying moving people. However, direct modeling spatio-temporal variations of such appearance is often a difficult problem due to their high dimensionality and nonlinearities. In this paper we present a human tracking system that uses a dynamic appearance and motion modeling framework based on the use of robust system dynamics identification and nonlinear dimensionality reduction techniques. The proposed system learns dynamic appearance and motion models from a small set of initial frames and does not require prior knowledge such as gender or type of activity. The advantages of the proposed tracking system are illustrated with several examples where the learned dynamics accurately predict the location and appearance of the targets in future frames, preventing tracking failures due to model drifting, target occlusion and scene clutter.

关键词： Humans Target tracking Nonlinear dynamical systems Predictive models computer science Robustness Histograms Educational institutions Biological system modeling Layout

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Augmentation of Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models

Augmentation of Atmospheric Turbulence Effects on Thermal Ad...

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

作者： Engin Uzun Ahmet Anı l Dursun Erdem Akagü ndü z Dept. of Image Proc. &amp Computer Vis. Technologies Aselsan Inc. Turkey Graduate School of Informatics Middle East Technical University Turkey

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Atmospheric turbulence has a degrading effect on the image quality of long-range observation systems. As a result of various elements such as temperature, wind velocity, humidity, etc., turbulence is characterized by random fluctuations in the refractive index of the atmosphere. It is a phenomenon that may occur in various imaging spectra such as the visible or the infrared bands. In this paper, we analyze the effects of atmospheric turbulence on object detection performance in thermal imagery. We use a geometric turbulence model to simulate turbulence effects on a medium-scale thermal image set, namely "FLIR ADAS v2". We apply thermal domain adaptation to state-of-the-art object detectors and propose a data augmentation strategy to increase the performance of object detectors which utilizes turbulent images in different severity levels as training data. Our results show that the proposed data augmentation strategy yields an increase in performance for both turbulent and non-turbulent thermal test images.

关键词： Adaptation models Atmospheric modeling Wind speed Training data Refractive index Imaging Object detection

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