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Statistical Error Propagation in 3D Modeling From Monocular Video

Statistical Error Propagation in 3D Modeling From Monocular ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Amit Roy Chowdhury Rama Chellappa Center for Automation Research University of Maryland College Park MD USA Center for Automation Research University of Maryland College Park MD

A significant portion of recent research in computer vision has focused on issues related to sensitivity and robustness of existing techniques. In this paper, we study the classical structure from motion problem and analyze how the statistics representing the quality of the input video propagates through the reconstruction algorithm and affects the quality of the output reconstruction. Specifically, we show that it is possible to derive analytical expressions of the first and second order statistics (bias and error covariance) of the solution as a function of the statistics of the input. We concentrate on the case of reconstruction from a monocular video, where the small baseline makes any algorithm very susceptible to noise in the motion estimates from the video sequence. We derive an expression relating the error covariance of the reconstruction to the error covariance of the feature tracks in the input video. This is done using the implicit function theorem of real analysis and does not require strong statistical assumptions. Next, we prove that the 3D reconstruction is statistically biased, derive an expression for it and show that it is numerically significant. Combining these two results, we also establish a new bound on the minimum error in the depth reconstruction. We present the numerical significance of these analytical results on real video data.

关键词： Three dimensional displays Image reconstruction computer vision Cameras Uncertainty Equations Adaptive optics

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A Study on Bayes Feature Fusion for Image Classification

A Study on Bayes Feature Fusion for Image Classification

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： X. Shi R. Manduchi Department of Computer Engineering University of California Santa Cruz USA

We consider here the problem of image classification when more than one visual feature are available. In these cases, Bayes fusion offers an attractive solution by combining the results of different classifiers (one classifier per feature). This is a general form of the so-called "naive Bayes" approach. Analyzing the performance of Bayes fusion with respect to a Bayesian classifier over the joint feature distribution, however, is tricky. On the one hand, it is well-known that the latter has lower bias than the former, unless the features are conditionally independent, in which case the two coincide. On the other hand, as noted by Friedman, the low variance associated with naive Bayes estimation may dramatically mitigate the effect of its bias. In this paper, we attempt to assess the tradeoff between these two factors by means of experimental tests on two image data sets using color and texture features. Our results suggest that (1) the difference between the correct classification rates using Bayes fusion and using the joint feature distribution is a function of the conditional dependence of the features (measured in terms of mutual information), however: (2) for small training data size, Bayes fusion performs almost as well as the classifier on the joint distribution.

关键词： Image color analysis Bayesian methods Error analysis Mutual information Training data Correlation Joints

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computer vision Algorithms for Quantifying the Growth and Behavior of Neurons Cultured on Nanofabricated Surfaces

Computer Vision Algorithms for Quantifying the Growth and Be...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Muhammad-Amri Abdul Karim Khalid Al-Kofahi Badrinath Roysam Natalie Dowell-Mesfin Rifat J. Hussain William Shain James N. Turner Electrical Computer & Systems Engineering Dept. Rensselaer Polytechnic Institute Troy NY Electrical Computer & Systems Engineering Dept. Rensselaer Polytechnic Institute Troy NY USA The Wadsworth Center Department of Health Albany NY The Wadsworth Center Department of Health Albany NY USA

Neuron growth on nanofabricated silicon surfaces, once understood, is fundamental to engineer circuits involving neurons and electronic components, leading to applications such as next-generation brain implants, and high-throughput neurologic drug screening assay systems. Precise quantification of neuronal growth and behavior from image samples requires computer vision algorithms for automatic tracing of neurons, and flexible algorithms for reliable registration and analysis of multi-fluorophore imagery. The tracing algorithms must be robust to high levels of clutter and common imaging artifacts, process discontinuities, and quantum noise, especially when live neurons are imaged. Finally, the large numbers of images that must be processed for a hypothesis test or assay call for computationally efficient algorithms. A fully automated neuron tracing algorithm based on the use of robust detection and estimation principles is described here that meets the above needs. It takes 2 seconds to trace and extract morphological statistics from a typical 1280 × 1024 image on a Pentium III, 1 GHz personal computer. It was validated against manually generated traces and the errors were in the range of 1-5% per image.

关键词： Neurons Surface topography Algorithm design and analysis Robustness computer vision Surface morphology Nerve fibers

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A Web-based System for Retrieving Document Images from Digital Library

A Web-based System for Retrieving Document Images from Digit...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Li Zhang Yue Lu Chew Lim Tan Department of Computer Science School of Computing National University of Singapore Singapore

A web-based system for retrieving imaged documents from a digital library is described in this paper. First, some image preprocessing is performed off-line on the underlying imaged document to extract its word objects. Then, each word object is represented by a string known as its feature code, based on which a feature code file of the corresponding document is constructed. On the web interface side, the system allows the user to input a set of query words and indicate either to perform "AND" or "OR" operation on them. Once receiving user's request, the system will process each query word and combine the results based on the "AND" or "OR" operation the user has chosen. As for each query word, it is first looked up in an index table that stores words being queried before. If matches are found, results will be retrieved from the index table directly and stored temporarily for subsequent merging. This speeds up searching and makes the system an incremental intelligence system. Otherwise, the system will convert the query word to a feature code string and employ a partial word matching approach to perform search on the pre-generated feature code files. Preliminary experimental results with the imaged documents of students' theses provided by our digital library show that the proposed system is efficient and promising for document image retrieval, and thus has potential applications to digital libraries.

关键词： Feature extraction Indexes Servers Libraries Shape Probability density function Image retrieval

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Accurately Estimating Sherd 3D Surface Geometry with Application to Pot Reconstruction

Accurately Estimating Sherd 3D Surface Geometry with Applica...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Andrew Willis Xavier Orriols David B. Cooper Brown University RI USA Computer Vision Laboratory UAB Bellaterra Spain

This paper deals with the problem of precise automatic estimation of the surface geometry of pot sherds uncovered at archaeological excavation sites using dense 3D laser-scan data. Critical to ceramic fragment analysis is the ability to geometrically classify excavated sherds, and, if possible, reconstruct the original pots using the sherd fragments. To do this, archaelogists must estimate the pot geometry in terms of an axis and associated profile curve from the discovered fragments. In this paper, we discuss an automatic method for accurately estimating an axis/profile curve pair for each archeological sherd (even when they are small) based on axially symmetric implicit polynomial surface models. Our method estimates the axis/profile curve for a sherd by finding the axially symmetric algebraic surface which best fits the measured set of dense 3D points and associated normals. We note that this method will work on 3D point data alone and does not require any local surface computations such as differentiation. Axis/profile curve estimates are accompanied by a detailed statistical error analysis. Estimation and error analysis are illustrated with application to a number of sherds. These fragments, excavated from Petra, Jordan, are chosen as exemplars of the families of geometrically diverse sherds commonly found on an archeological excavation site. We then briefly discuss how the estimation results may be integrated into a larger pot reconstruction program.

关键词： Three dimensional displays Surface fitting Estimation Fitting Data models Geometry Shape

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Real Time Face Detection and Facial Expression recognition: Development and Applications to Human computer Interaction.

Real Time Face Detection and Facial Expression Recognition: ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Marian Stewart Bartlett Gwen Littlewort Ian Fasel Javier R. Movellan Machine Perception Laboratory University of California San Diego CA USA Intelligent Robotics and Communication Laboratories ATR Kyoto Japan

computer animated agents and robots bring a social dimension to human computer interaction and force us to think in new ways about how computers could be used in daily life. Face to face communication is a real-time process operating at a a time scale in the order of 40 milliseconds. The level of uncertainty at this time scale is considerable, making it necessary for humans and machines to rely on sensory rich perceptual primitives rather than slow symbolic inference processes. In this paper we present progress on one such perceptual primitive. The system automatically detects frontal faces in the video stream and codes them with respect to 7 dimensions in real time: neutral, anger, disgust, fear, joy, sadness, surprise. The face finder employs a cascade of feature detectors trained with boosting techniques [15, 2]. The expression recognizer receives image patches located by the face detector. A Gabor representation of the patch is formed and then processed by a bank of SVM classifiers. A novel combination of Adaboost and SVM's enhances performance. The system was tested on the Cohn-Kanade dataset of posed facial expressions [6]. The generalization performance to new subjects for a 7- way forced choice correct. Most interestingly the outputs of the classifier change smoothly as a function of time, providing a potentially valuable representation to code facial expression dynamics in a fully automatic and unobtrusive manner. The system has been deployed on a wide variety of platforms including Sony's Aibo pet robot, ATR's RoboVie, and CU animator, and is currently being evaluated for applications including automatic reading tutors, assessment of human-robot interaction.

关键词： Face Real time systems Robots Pixel Training Support vector machines Gabor filters

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A Statistical Assessment of Subject Factors in the PCA recognition of Human Faces

A Statistical Assessment of Subject Factors in the PCA Recog...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Geof Givens J Ross Beveridge Bruce A. Draper David Bolme Statistics Department Colorado State University CO Computer Science Department Colorado State University Fort Collins CO USA

Some people's faces are easier to recognize than others, but it is not obvious what subject-specific factors make individual faces easy or difficult to recognize. This study considers 11 factors that might make recognition easy or difficult for 1,072 human subjects in the FERET dataset. The specific factors are: race (white, Asian, African-American, or other), gender, age (young or old), glasses (present or absent), facial hair (present or absent), bangs (present or absent), mouth (closed or other), eyes (open or other), complexion (clear or other), makeup (present or absent), and expression (neutral or other). An ANOVA is used to determine the relationship between these subject covariates and the distance between pairs of images of the same subject in a standard Eigenfaces subspace. Some results are not terribly surprising. For example, the distance between pairs of images of the same subject increases for people who change their appearance, e.g., open and close their eyes, open and close their mouth or change expression. Thus changing appearance makes recognition harder. Other findings are surprising. Distance between pairs of images for subjects decreases for people who consistently wear glasses, so wearing glasses makes subjects more recognizable. Pairwise distance also decreases for people who are either Asian or African-American rather than white. A possible shortcoming of our analysis is that minority classifications such as African-Americans and wearers-of-glasses are underrepresented in training. Followup experiments with balanced training addresses this concern and corroborates the original findings. Another possible shortcoming of this analysis is the novel use of pairwise distance between images of a single person as the predictor of recognition difficulty. A separate experiment confirms that larger distances between pairs of subject images implies a larger recognition rank for that same pair of images, thus confirming that the subject is harder to recog

关键词： Distance measurement Glass Image recognition Hair Principal component analysis Mouth Training

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Proceedings of ieee conference on computer vision and pattern recognition

Proceedings of IEEE Conference on Computer Vision and Patter...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

Presents the front cover of the proceedings record.

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An algorithm for self calibration from several views

An algorithm for self calibration from several views

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hartley GE Corporate Research and Development Schenectady NY USA

This paper gives a practical algorithm for the self-calibration of a camera from several views. The method involves non-iterative methods for finding an initial calibration for the camera, followed by least-squares iteration to an optimum solution. At the same time, a scaled Euclidean reconstruction of the scene appearing in the images is computed.< >

关键词： Calibration Image reconstruction Geometric modeling Machine vision

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Efficient parallel multigrid relaxation algorithms for Markov random field-based low-level vision applications

Efficient parallel multigrid relaxation algorithms for Marko...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Memin Heitz Charot Campus de Beaulieu IRISA/INRIA Rennes France

We present a new algorithmic framework which enables making a full use of the large potential of data parallelism available on 2D processor arrays for the implementation of nonlinear multigrid relaxation methods. This framework leads to fast convergence towards quasi-optimal solutions. It is demonstrated on two different low-level vision applications.< >

关键词： Parallel algorithms Markov processes Machine vision

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