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Practical Methods for Geometric and Photometric Correction of Tiled Projector

Practical Methods for Geometric and Photometric Correction o...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： M. Harville B. Culbertson I. Sobel D. Gelb A. Fitzhugh D. Tanguay Hewlett Packard Laboratories Palo Alto CA USA

We describe a novel, practical method to create largescale, immersive displays by tiling multiple projectors on curved screens. Calibration is performed automatically with imagery from a single uncalibrated camera, without requiring knowledge of the 3D screen shape. Composition of 2D-mesh-based coordinate mappings, from screen-tocamera and from camera-to-projectors, allows image distortions imposed by the screen curvature and camera and projector lenses to be geometrically corrected together in a single non-parametric framework. For screens that are developable surfaces, we show that the screen-to-camera mapping can be determined without some of the complication of prior methods, resulting in a display on which imagery is undistorted, as if physically attached like wallpaper. We also develop a method of photometric calibration that unifies the geometric blending, brightness scaling, and black level offset maps of prior approaches. The functional form of the geometric blending is novel in itself. The resulting method is more tolerant of geometric correction imprecision, so that visual artifacts are significantly reduced at projector edges and overlap regions. Our efficient GPUbased implementation enables a single PC to render multiple high-resolution video streams simultaneously at frame rate to arbitrary screen locations, leaving the CPU largely free to do video decompression and other processing.

关键词： Photometry Large screen displays Cameras Calibration Brightness computer vision computer displays Laboratories Shape Lenses

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Estimating Mixing Factors Simultaneously in Multilinear Tensor Decomposition for Robust Face recognition and Synthesis

Estimating Mixing Factors Simultaneously in Multilinear Tens...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Sung Won Park M. Savvides Electrical and Computer Engineering Department Carnegie Mellon University Pittsburgh PA USA

Facial images change appearance due to multiple factors such as poses, lighting variations, facial expressions, etc. Tensor approach, an extension of conventional matrix, is appropriate to analyze facial factors since tensors make it possible to construct multilinear models using a multiple factor structure. We use higher-order tensors to model multiple factors of facial variations, but this provides some difficulty in use. First, it is difficult to decompose the multiple factors of a test image, especially when the factor parameters are unknown or are not in the training set. Second, for face recognition and face synthesis tasks, it is also difficult to construct reliable multilinear models which have more than two factors. In this paper, we propose a novel tensor factorization method to decompose mixing factors for unseen test images. We set up tensor factorization problem as a least squares problems with a quadratic equality constraint, and solve it using numerical optimization techniques. The novelty in our approach compared to previous work is that our tensor factorization method does not require any knowledge or assumption of test images; we can attain parameters of facial factors by our tensor factorization method. We have conducted several experiments to show the versatility of the method for both face recognition and synthesis tasks. Thus, we demonstrate the proposed method produces reliable results for trilinear models as well as bilinear models.

关键词： Tensile stress Robustness Face recognition Testing Matrix decomposition Image recognition Least squares methods Constraint optimization Lighting Performance analysis

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Video Annotation by Active Learning and Cluster Tuning

Video Annotation by Active Learning and Cluster Tuning

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Guo-jun Qi Yan Song Xian-Sheng Hua Hong-Jiang Zhang Li-Rong Dai Department of Automation University of Science and Technology Hefei Anhui China Department of EEIS University of Science and Technology Hefei Anhui China Microsoft Research Asia Beijing China

Supervised and semi-supervised learning are frequently applied methods to annotate videos by map..ing low-level features into high-level semantic concepts. Though they work well for certain concepts, the performance is still far from reality due to the large gap between the features and the semantics. The main constraint of these methods is that the information contained in a limited number of labeled training samples can hardly represent the distributions of the semantic concepts. In this paper, we propose a novel semi-automatic video annotation framework, active learning with clustering tuning, to tackle the disadvantages of current video annotation solutions. In this framework, firstly an initial training set is constructed based on clustering the entire video dataset. And then a SVM-based active learning scheme is proposed, which aims at maximizing the margin of the SVM classifier by manually selectively labeling a small set of samples. Moreover, in each round of active learning, we tune/refine the clustering results based on the prediction results of current stage, which is beneficial for selecting the most informative samples in the active learning process, as well as helps further improve the final annotation accuracy in the post-processing step. Experimental results show that the proposed scheme performs superior to typical active learning algorithms in terms of both annotation accuracy and stability.

关键词： Support vector machines Support vector machine classification Clustering algorithms Histograms Spatial databases Automation Asia computer vision pattern recognition conferences

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Pose-Invariant Physiological Face recognition in the Thermal Infrared Spectrum

Pose-Invariant Physiological Face Recognition in the Thermal...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： P. Buddharaju I.T. Pavlidis P. Tsiamyrtzis Department of Computer Science University of Houston USA Department of Statistics Athens University of Economics and Business Greece

We present a novel framework for face recognition based on physiological information. The motivation behind this effort is to capitalize on the permanency of innate characteristics that are under the skin. To establish feasibility, we propose a specific methodology to capture facial physiological patterns using the bioheat information contained in thermal imagery. First, the algorithm delineates the human face from the background using the Bayesian framework. Then, it localizes the superficial blood vessel network using image morphology. The extracted vascular network produces contour shapes that are characteristic to each individual. The branching points of the skeletonized vascular network are referred to as Thermal Minutia Points (TMP) and constitute the feature database. To render the method robust to facial pose variations we collect for each subject to be stored in the database five (5) different pose images (center, mid-left profile, left profile, mid-right profile, and right profile). During the classification stage, the algorithm first estimates the pose of the test image. Then, it matches the local and global TMP structures extracted from the test image with those of the corresponding pose images in the database. We have conducted experiments on a sizeable database of thermal facial images collected in our lab. The good experimental results show that the proposed methodology has merit. More important, the results demonstrate the feasibility of the physiological framework in face recognition and open the way for further methodological and experimental research in the area.

关键词： Face recognition Infrared spectra Image databases Spatial databases Testing Skin Humans Bayesian methods Blood vessels Biomedical imaging

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Structure and View Estimation for Tomographic Reconstruction: A Bayesian Approach

Structure and View Estimation for Tomographic Reconstruction...

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conference on computer vision and pattern recognition (CVPR)

作者： S.P. Mallick S. Agarwal D.J. Kriegman S.J. Belongie B. Carragher C.S. Potter Computer Science and Engineering University of California San Diego USA Department of Cell Biology National Resource for Automated Molecular Microscopy The Scripps Research Institute La Jolla USA

This paper addresses the problem of reconstructing the density of a scene from multiple projection images produced by modalities such as x-ray, electron microscopy, etc. where an image value is related to the integral of the scene density along a 3D line segment between a radiation source and a point on the image plane. While computed tomography (CT) addresses this problem when the absolute orientation of the image plane and radiation source directions are known, this paper addresses the problem when the orientations are unknown - it is akin to the structure-from-motion (SFM) problem when the extrinsic camera parameters are unknown. We study the problem within the context of reconstructing the density of protein macro-molecules in Cryogenic Electron Microscopy (cryo-EM), where images are very noisy and existing techniques use several thousands of images. In a non-degenerate configuration, the viewing planes corresponding to two projections, intersect in a line in 3D. Using the geometry of the imaging setup, it is possible to determine the projections of this 3D line on the two image planes. In turn, the problem can be formulated as a type of orthographic structure from motion from line correspondences where the line correspondences between two views are unreliable due to image noise. We formulate the task as the problem of denoising a correspondence matrix and present a Bayesian solution to it. Subsequently, the absolute orientation of each projection is determined followed by density reconstruction. We show results on cryo-EM images of proteins and compare our results to that of Electron Micrograph Analysis (EMAN) - a widely used reconstruction tool in cryo-EM.

关键词： Bayesian methods Image reconstruction Layout X-ray imaging Electron microscopy Computed tomography Proteins Image segmentation Cameras Cryogenics

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The Tyzx DeepSea G2 vision System, ATaskable, Embedded Stereo Camera

The Tyzx DeepSea G2 Vision System, ATaskable, Embedded Stere...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： J.I. Woodfill G. Gordon D. Jurasek T. Brown R. Buck Tyzx Inc. Menlo Park CA USA

Our goal is to build vision systems suitable for deployment in devices that operate in demanding dynamic, variably lit, real-world environments. For such systems to be successful, not only must they perform their visual analysis well and robustly, but they must also be small, cheap and consume little power. Further, since volume deployments of such vision systems are still nascent, the systems must be taskable -- flexible enough to support many different uses. We have met our goal with the Tyzx DeepSea G2 Stereo vision System, an embedded stereo camera consisting of two CMOS imagers, a DeepSea II stereo ASIC, an FPGA, a DSP/Co-processor and a PowerPC running Linux, connected to the Ethernet. It is made practically taskable by the definition of a set of configurable visual primitives supported by specific hardware acceleration. These primitives include stereo correlation, color and depth background modeling, and 2D and 3D quantized representations or projections of the range data. We have defined a common programming interface in which the visual primitives are available both in traditional workstation environments, supported in software, and on the G2 with hardware acceleration. Single G2s are deployed in mobile platforms such as robots and automobiles, while networks of G2s are deployed in tracking systems in public and private sites around the world.

关键词： Machine vision Cameras Hardware Acceleration Vehicle dynamics Performance analysis Robustness Stereo vision CMOS image sensors Application specific integrated circuits

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A Comparative Study of Boosted and Adaptive Particle Filters for Affine-Invariant Target Detection and Tracking

A Comparative Study of Boosted and Adaptive Particle Filters...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Guoliang Fan V. Venkataraman Li Tang J. Havlicek School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA School of Electrical and Computer Engineering University of Oklahama Norman OK USA

This paper addresses a specific multi-aspect target detection and tracking problem where the dynamics of the target’s aspect is modeled by an affine model following a first-order Markov process. We are interested in how to achieve robust and accurate Monte Carlo estimation in a high-dimensional state space with poor target visibility by re-visiting two recent improvements to particle filters, i.e., "boosting" and "adapting". The impetus of this work is a tracking indicator that estimates the tracking performance based on the observation model and may trigger either one of two actions when it is necessary. One is "boosting", i.e., the detector specified by the tracker’s previous output is involved to induce more promising particles, and the original idea of "boosting" is extended here by encouraging positive interaction between the detector and the tracker. The other is "adapting", i.e., the system model can self-adjust to enhance the tracking capability. We compare two methods in the context of affine-invariant target tracking and with respect to their contributions to improve the particle quality. Experiments on simulated image sequences with real infrared background show that both techniques can improve the tracking performance by balancing the focus and the diversity of particle distribution.

关键词： Particle filters Object detection Target tracking Particle tracking Boosting Detectors Markov processes Robustness Monte Carlo methods State estimation

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Robust Video-Based Surveillance by Integrating Target Detection with Tracking

Robust Video-Based Surveillance by Integrating Target Detect...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Junxian Wang G. Bebis R. Miller Computer Vision Laboratory University of Nevada Reno NV USA Vehicle Design R&A Department Ford Motor Company Limited Dearborn MI USA

Target detection and tracking represent two fundamental steps in automatic video-based surveillance systems where the goal is to provide intelligent recognition capabilities by analyzing target behavior. This paper presents a framework for video-based surveillance where detection and tracking are addressed simultaneously in a unified framework (i.e., detection results trigger tracking, and tracking re-enforces detections)to improve detection results. In contrast to methods that apply target detection and tracking sequentially and independently from each other (i.e., "detect-then-track"), we feed the results of tracking back to the detection stage to adaptively optimize the threshold used in the detection stage and improve system robustness (i.e., "detect-and-track"). Specifically, the initial locations and representations of the targets are extracted by background subtraction. To model the background, we employ Support Vector Regression (SVR) along with an on-line learning scheme to update it efficiently over time. Target detection is performed by thresholding the outputs of the SVR model. Tracking uses shape projection histograms to iteratively localize the targets and achieve a high shape matching confidence level. Feeding back the results of tracking to the detection stage restricts the range of threshold values, suppress false alarms due to noise, and allows to continuously detect small targets as well as targets undergoing projection distortions. We have validated the proposed framework by detecting vehicles and pedestrians in traffic scenes using both visible and thermal video sequences. Experimental results and comparisons with framebased detection and kernel-based tracking methods illustrate the robustness of our approach.

关键词： Robustness Surveillance Object detection Target tracking Shape Vehicle detection Target recognition Feeds Optimization methods Histograms

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recognition Mechanism of a Neural Network for Character recognition

Recognition Mechanism of a Neural Network for Character Reco...

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Annual International conference of the ieee Engineering in Medicine and Biology society (EMBC)

作者： H. Shintani M. Akutagawa H. Nagashino Y. Kinouchi Faculty of Engineering University of Tokushima Tokushima Japan School of health sciences University of Tokushima Tokushima Japan

We have developed a small scale four-layered neural network (NN) model for simple character recognition, which can recognize the patterns transformed by affine conversion. In this study 24 patterns are presented as input patterns. An input pattern is divided into 64 local patterns and connected with the 1st hidden layer. After the training, we investigated the recognition mechanism of NN using Alopex algorithm. Effectiveness of this method is demonstrated

关键词： Character recognition Neural networks pattern recognition Brain modeling Neurons Pixel Backpropagation algorithms Multi-layer neural network Power system modeling computer vision

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Partial & Holistic Face recognition on FRGC-II data using Support Vector Machine

Partial & Holistic Face Recognition on FRGC-II data using Su...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： M. Savvides R. Abiantun J. Heo S. Park C. Xie B.V.K. Vijayakumar Electrical & Computer Eng Department Carnegie Mellon University Pittsburgh PA USA

In this paper we investigate how to perform face recognition on the hardest experiment (Exp4) in Face recognition Grand Challenge(FRGC) phase-II data which deals with subjects captured under uncontrolled conditions such as harsh overhead illumination, some pose variations and facial expressions in both indoor and outdoor environments. Other variations include the presence and absence of eye-glasses. The database consists of a generic dataset of 12,776 images for training a generic face subspace, a target set of 16,028 images and a query set of 8,014 images are given for matching. We propose to use our novel face recognition algorithm using Kernel Correlation Feature Analysis for dimensionality reduction (222 features) coupled with Support Vector Machine discriminative training in the Target KCFA feature set for providing a similarity distance measure of the probe to each target subject. We show that this algorithm configuration yields the best verification rate at 0.1% FAR (87.5%) compared to PCA+SVM, GSLDA+SVM, SVM+SVM, KDA+SVM. Thus we explore with our proposed algorithm which facial regions provide the best discrimination ability, we analyze performing partial face recognition using the eye-region, nose region and mouth region. We empirically find that the eye-region is the most discriminative feature of the faces in FRGC data and yields a verification rate closest to the holistic face recognition of 83.5% @ 0.1% FAR compared to 87.5%. We use Support Vector Machines for fusing these two to boost the performance to ~90@0.1 % FAR on the first large-scale face database such as the FRGC dataset.

关键词： Face recognition Support vector machines Spatial databases Algorithm design and analysis Lighting Image databases Kernel Probes Performance analysis Nose

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