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Stereo matching using iterated graph cuts and mean shift filtering

引用

7th Asian conference on computer vision, ACCV 2006

作者： Chang, Ju Yong Lee, Kyoung Mu Lee, Sang Uk School of Electrical Eng. ASRI Seoul National University 151-600 Seoul Korea Republic of

ISBN: (纸本)3540312196

In this paper, we propose a new stereo matching algorithm using an iterated graph cuts and mean shift filtering technique. Our algorithm consists of following two steps. In the first step, given an estimated sparse RDM (Reliable Disparity Map), we obtain an updated dense disparity map through a new constrained energy minimization framework that can cope with occlusion. The graph cuts technique is employed for the solution of the proposed stereo model. In the second step, we re-estimate the RDM from the disparity map obtained in the first step. In order to obtain accurate reliable disparities, the crosschecking technique followed by the mean shift filtering in the color-disparity space is introduced. The proposed algorithm expands the RDM repeatedly through the above two steps until it converges. Experimental results on the standard data set demonstrate that the proposed algorithm achieves comparable performance to the state-of-the-arts, and gives good results especially in the areas such as the disparity discontinuous boundaries and occluded regions, where the conventional methods usually suffer. © Springer-Verlag Berlin Heidelberg 2006.

关键词： pattern recognition

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3D Face recognition Using 3D Alignment for PCA

3D Face Recognition Using 3D Alignment for PCA

引用

conference on computer vision and pattern recognition (CVPR)

作者： T. Russ C. Boehnen T. Peters Security Technology Department Sandia National Laboratories Albuquerque NM USA Computer Science and Engineering University of Notre Dame Notre Dame IN USA

This paper presents a 3D approach for recognizing faces based on Principal Component Analysis (PCA). The approach addresses the issue of proper 3D face alignment required by PCA for maximum data compression and good generalization performance for new untrained faces. This issue has traditionally been addressed by 2D data normalization, a step that eliminates 3D object size information important for the recognition process. We achieve correspondence of facial points by registering a 3D face to a scaled generic 3D reference face and subsequently perform a surface normal search algorithm. 3D scaling of the generic reference face is performed to enable better alignment of facial points while preserving important 3D size information in the input face. The benefits of this approach for 3D face recognition and dimensionality reduction have been demonstrated on components of the Face recognition Grand Challenge (FRGC) database versions 1 and 2.

关键词： Face recognition Principal component analysis National security Image recognition Face detection computer science Power engineering and energy computer security Laboratories Data security

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Are two rotational flows sufficient to calibrate a smooth non-parametric sensor?

Are two rotational flows sufficient to calibrate a smooth no...

引用

conference on computer vision and pattern recognition (CVPR)

作者： E. Grossmann Eun-Joo Lee P. Hislop D. Nister H. Stewenius Department IRO Universite de Montréal Canada Computer Science Department University of Kentucky USA Department of Mathematics University of Kentucky USA Center for Visualization University of Kentucky USA

We present an attempt to determine whether the shape of a generic central-projection camera, such as the eye of an insect or a log-polar camera, can be determined from two motion flows resulting from purely rotational motions with non-collinear axes. Our first contribution is to write the smooth non-parametric calibration problem as a differential equation. It is unclear at present whether this problem has unique solution, up to an orthogonal transformation. Our second contribution is a discretized version of this smooth problem, for which we give a calibration algorithm - a third contribution. Using this algorithm, we explore numerically the properties of the discrete self-calibration problem, giving some insight on the nature of the problem. We show examples of successful self-calibration, but cannot give a definite affirmative answer to the question in the title.

关键词： Cameras Calibration Optical sensors Visualization computer vision Sensor phenomena and characterization computer science Mathematics Shape Insects

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Neuromorphic Lip-Reading with Signed Spiking Gated Recurrent Units

Neuromorphic Lip-Reading with Signed Spiking Gated Recurrent...

引用

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

作者： Manon Dampfhoffer Thomas Mesquida CEA List Univ. Grenoble Alpes Grenoble France

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Automatic Lip-Reading (ALR) requires the recognition of spoken words based on a visual recording of the speaker’s lips, without access to the sound. ALR with neuromorphic event-based vision sensors, instead of traditional frame-based cameras, is particularly promising for edge applications due to their high temporal resolution, low power consumption and robustness. Neuromorphic models, such as Spiking Neural Networks (SNNs), encode information using events and are naturally compatible with such data. The sparse and event-based nature of both the sensor data and SNN activations can be leveraged in an end-to-end neuromorphic hardware pipeline for low-power and low-latency edge applications. However, the accuracy of SNNs is often largely degraded compared to state-of-the-art non-spiking Artificial Neural Networks (ANNs). In this work, a new SNN model, the Signed Spiking Gated Recurrent Unit (SpikGRU2+), is proposed and used as a task head for event-based ALR. The SNN architecture is as accurate as its ANN equivalent, and outperforms the state-of-the-art on the DVS-Lip dataset. Notably, the accuracy is improved by 25% (respectively 4%) compared to the previous state-of-the-art SNN (respectively ANN). In addition, the SNN spike sparsity can be optimized to further reduce the number of operations up to 22x compared to the ANN while maintaining a high accuracy. This work opens up new perspectives for the use of SNNs for accurate and low-power end-to-end neuromorphic gesture recognition. Code is available 1 .

关键词： Visualization Accuracy Neuromorphics Gesture recognition Spiking neural networks Logic gates vision sensors

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Weakly Supervised Set-Consistency Learning Improves Morphological Profiling of Single-Cell Images

Weakly Supervised Set-Consistency Learning Improves Morpholo...

引用

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

作者： Heming Yao Phil Hanslovsky Jan-Christian Huetter Burkhard Hoeckendorf David Richmond Biology Research | AI Development (BRAID) gCS Genentech

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Optical Pooled Screening (OPS) is a powerful tool combining high-content microscopy with genetic engineering to investigate gene function in disease. The characterization of high-content images remains an active area of research and is currently undergoing rapid innovation through the application of self-supervised learning and vision transformers. In this study, we propose a set-level consistency learning algorithm, Set-DINO, that combines self-supervised learning with weak supervision to improve learned representations of perturbation effects in single-cell images. Our method leverages the replicate structure of OPS experiments (i.e., cells undergoing the same genetic perturbation, both within and across batches) as a form of weak supervision. We conduct extensive experiments on a large-scale OPS dataset with more than 5000 genetic perturbations, and demonstrate that Set-DINO helps mitigate the impact of confounders and encodes more biologically meaningful information. In particular, Set-DINO recalls known biological relationships with higher accuracy compared to commonly used methods for morphological profiling, suggesting that it can generate more reliable insights from drug target discovery campaigns leveraging OPS.

关键词： computer vision Technological innovation Biomedical optical imaging Perturbation methods Self-supervised learning Optical imaging Genetics

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Image Denoising with Shrinkage and Redundant Representations

Image Denoising with Shrinkage and Redundant Representations

引用

conference on computer vision and pattern recognition (CVPR)

作者： M. Elad B. Matalon M. Zibulevsky Department of Computer Science Technion-Israel Institute of Technology Haifa Israel Department of Electrical Engineering Israel

Shrinkage is a well known and appealing denoising technique. The use of shrinkage is known to be optimal for Gaussian white noise, provided that the sparsity on the signal’s representation is enforced using a unitary transform. Still, shrinkage is also practiced successfully with nonunitary, and even redundant representations. In this paper we shed some light on this behavior. We show that simple shrinkage could be interpreted as the first iteration of an algorithm that solves the basis pursuit denoising (BPDN) problem. Thus, this work leads to a novel iterative shrinkage algorithm that can be considered as an effective pursuit method. We demonstrate this algorithm, both on synthetic data, and for the image denoising problem, where we learn the image prior parameters directly from the given image. The results in both cases are superior to several popular alternatives.

关键词： Image denoising Wavelet transforms Noise reduction Iterative algorithms Table lookup White noise Pursuit algorithms computer science Iterative methods Maximum a posteriori estimation

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Seamless Image Stitching of Scenes with Large Motions and Exposure Differences

Seamless Image Stitching of Scenes with Large Motions and Ex...

引用

conference on computer vision and pattern recognition (CVPR)

作者： A. Eden M. Uyttendaele R. Szeliski Computer Science Division University of California Berkeley USA Interactive Visual Media Group Microsoft Research Limited USA

This paper presents a technique to automatically stitch multiple images at varying orientations and exposures to create a composite panorama that preserves the angular extent and dynamic range of the inputs. The main contribution of our method is that it allows for large exposure differences, large scene motion or other misregistrations between frames and requires no extra camera hardware. To do this, we introduce a two-step graph cut approach. The purpose of the first step is to fix the positions of moving objects in the scene. In the second step, we fill in the entire available dynamic range. We introduce data costs that encourage consistency and higher signal-to-noise ratios, and seam costs that encourage smooth transitions. Our method is simple to implement and effective. We demonstrate the effectiveness of our approach on several input sets with varying exposures and camera orientations.

关键词： Layout Cameras Dynamic range Pixel Hardware Costs Radiometry computer science Signal to noise ratio Calibration

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Real-time RGB-D mapping and 3-D modeling on the GPU using the random ball cover data structure

Real-time RGB-D mapping and 3-D modeling on the GPU using th...

引用

International conference on computer vision workshops (ICCV workshops)

作者： Dominik Neumann Felix Lugauer Sebastian Bauer Jakob Wasza Joachim Hornegger Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

The modeling of three-dimensional scene geometry from temporal point cloud streams is of particular interest for a variety of computer vision applications. With the advent of RGB-D imaging devices that deliver dense, metric and textured 6-D data in real-time, on-the-fly reconstruction of static environments has come into reach. In this paper, we propose a system for real-time point cloud mapping based on an efficient implementation of the iterative closest point (ICP) algorithm on the graphics processing unit (GPU). In order to achieve robust mappings at real-time performance, our nearest neighbor search evaluates both geometric and photometric information in a direct manner. For acceleration of the search space traversal, we exploit the inherent computing parallelism of GPUs. In this work, we have investigated the fitness of the random ball cover (RBC) data structure and search algorithm, originally proposed for high-dimensional problems, for 6-D data. In particular, we introduce a scheme that enables both fast RBC construction and queries. The proposed system is validated on an indoor scene modeling scenario. For dense data from the Microsoft Kinect sensor (640 × 480 px), our implementation achieved ICP runtimes of

关键词： Iterative closest point algorithm Graphics processing unit Runtime Nearest neighbor searches Real time systems Data structures Solid modeling

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Glaucoma Precognition: Recognizing Preclinical Visual Functional Signs of Glaucoma

Glaucoma Precognition: Recognizing Preclinical Visual Functi...

引用

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

作者： Krati Gupta Anshul Thakur Michael Goldbaum Siamak Yousefi School of Computing & Electrical Engineering Indian Institute of Technology Mandi Mandi India Department of Ophthalmology University of California San Diego USA Department of Ophthalmology University of Tennessee Health Science Center Memphis USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Deep archetypal analysis (DAA) has recently been proposed as an unsupervised approach for discovering latent structures in data. However, while a few approaches have used classical archetypal analysis (AA), DAA has not been incorporated in medical image analysis as yet. The purpose of this study is to develop a precognition framework to identify preclinical signs of glaucomatous vision loss using convex representations derived from DAA. We first develop an AA structure and a novel DAA framework to recognize hidden patterns of visual functional loss, and then project visual field data over the identified patterns to obtain a representation for glaucoma precognition several years prior to disease onset. We then develop a glaucoma classification framework using class-balanced bagging with neural networks to address the class imbalance problem. In contrast to other classification approaches, DAA, applied to a unique prospective longitudinal dataset with approximately eight years of visual field tests from normal eyes that developed glaucoma, has allowed visualization of the early signs of glaucoma and development of a construct for glaucoma precognition. Our findings suggest that our proposed glaucoma precognition approach could significantly advance state-of-the-art glaucoma prediction.

关键词： Visualization Diseases Machine learning Dictionaries Data models Neural networks pattern recognition

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Image Denoising Via Learned Dictionaries and Sparse representation

Image Denoising Via Learned Dictionaries and Sparse represen...

引用

conference on computer vision and pattern recognition (CVPR)

作者： M. Elad M. Aharon Department of Computer Science Technion-Israel Institute of Technology Haifa Israel

We address the image denoising problem, where zeromean white and homogeneous Gaussian additive noise should be removed from a given image. The approach taken is based on sparse and redundant representations over a trained dictionary. The proposed algorithm denoises the image, while simultaneously trainining a dictionary on its (corrupted) content using the K-SVD algorithm. As the dictionary training algorithm is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image. We show how such Bayesian treatment leads to a simple and effective denoising algorithm, with state-of-the-art performance, equivalent and sometimes surpassing recently published leading alternative denoising methods.

关键词： Image denoising Dictionaries Noise reduction Additive noise Bayesian methods computer science Noise measurement Gaussian noise Measurement standards Algorithm design and analysis

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