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Retinal Image Registration from 2D to 3D

Retinal Image Registration from 2D to 3D

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.9

作者： Yuping Lin Gerard Medioni Computer Science Department University of Southern California Los Angeles CA USA

We propose a 2D registration method for multi-modal image sequences of the retinal fundus, and a 3D metric reconstruction of near planar surface from multiple views. there are two major contributions in our paper. For 2D registration, our method produces high registration rates while accounting for large modality differences. Compared with the state of the art method [5], our approach has higher registration rate (97.2% vs. 82.31%) while the computation time is much less. this is achieved by extracting features from the edge maps of the contrast enhanced images, and performing pairwise registration by matching the features in an iterative manner, maximizing the number of matches and estimating homographies accurately. the pairwise registration result is further globally optimized by an indirect registration process. For 3D registration part, images are registered to the reference frame by transforming points via a reconstructed 3D surface. the challenge is the reconstruction of a near planar surface, in which the shallow depth makes it a quasi-degenerate case for estimating the geometry from images. Our contribution is the proposed 4-pass bundle adjustment method that gives optimal estimation of all camera poses. With accurate camera poses, the 3D surface can be reconstructed using the images associated with the cameras with the largest baseline. Compared with state of the art 3D retinal image registration methods, our approach produces better results in all image sets.

关键词： Retina Image registration Image reconstruction Surface reconstruction Feature extraction Cameras Image sequences Geometry Iterative closest point algorithm Biomedical imaging

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In Defense of Nearest-Neighbor Based Image Classification

In Defense of Nearest-Neighbor Based Image Classification

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.7

作者： Oren Boiman Eli Shechtman Michal Irani Weizmann Institute of Science Rehovot Israel Adobe Systems Inc. USA

State-of-the-art image classification methods require an intensive learning/training stage (using SVM, Boosting, etc.) In contrast, non-parametric Nearest-Neighbor (NN) based image classifiers require no training time and have other favorable properties. However, the large performance gap between these two families of approaches rendered NN-based image classifiers useless. We claim that the effectiveness of non-parametric NN-based image classification has been considerably under-valued. We argue that two practices commonly used in image classification methods, have led to the inferior performance of NN-based image classifiers: (i) Quantization of local image descriptors (used to generate "bags-of-words", codebooks). (ii) Computation of 'Image-to-Image' distance, instead of 'Image-to-Class' distance. We propose a trivial NN-based classifier -^sNBNN, (Naive-Bayes Nearest-Neighbor), which employs NN-distances in the space of the local image descriptors (and not in the space of images). NBNN computes direct 'Image-to-Class' distances without descriptor quantization. We further show that under the Naive-Bayes assumption, the theoretically optimal image classifier can be accurately approximated by NBNN. Although NBNN is extremely simple, efficient, and requires no learning/training phase, its performance ranks among the top leading learning-based image classifiers. Empirical comparisons are shown on several challenging databases (Caltech-101, Caltech-256 and Graz-01).

关键词： Image classification Image databases Quantization Support vector machines Support vector machine classification Boosting Neural networks Classification tree analysis Degradation Rendering (computer graphics)

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Precise Detailed Detection of Faces and Facial Features

Precise Detailed Detection of Faces and Facial Features

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Liya Ding Aleix M. Martinez Department Electrical and Computer Engineering Ohio State Uinversity Columbus OH USA

Face detection has advanced dramatically over the past three decades. Algorithms can now quite reliably detect faces in clutter in or near real time. However, much still needs to be done to provide an accurate and detailed description of external and internal features. this paper presents an approach to achieve this goal. Previous learning algorithms have had limited success on this task because the shape and texture of facial features varies widely under changing expression, pose and illumination. We address this problem with the use of subclass divisions. In this approach, we use an algorithm to automatically divide the training samples of each facial feature into a set of subclasses, each representing a distinct construction of the same facial component (e.g., close versus open eye lids). the key idea used to achieve accurate detections is to not only learn the textural information of the facial feature to be detected but that of its context (i.e., surroundings). this process permits a precise detection of key facial features. We then combine this approach with edge and color segmentation to provide an accurate and detailed detection of the shape of the major facial features (brows, eyes, nose, mouth and chin). We use this face detection algorithm to obtain precise descriptions of the facial features in video sequences of American Sign Language (ASL) sentences, where the variability in expressions can be extreme. Extensive experimental validation demonstrates our method is almost as precise as manual detection, ～ 2% error.

关键词： Face detection Facial features Mouth Lighting Eyes Handicapped aids Active shape model Reliability engineering Image edge detection Nose

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Automatic Non-rigid Registration of 3D Dynamic Data for Facial Expression Synthesis and Transfer

Automatic Non-rigid Registration of 3D Dynamic Data for Faci...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.11

作者： Sen Wang Xianfeng David Gu Hong Qin Computer Science Department Stony Brook University-SUNY Stony Brook NY USA

Automatic non-rigid registration of 3D time-varying data is fundamental in many vision and graphics applications such as facial expression analysis, synthesis, and recognition. Despite many research advances in recent years, it still remains to be technically challenging, especially for 3D dynamic, densely-sampled facial data with a large number of degrees of freedom (necessarily used to represent rich and subtle facial expressions). In this paper, we present a new method for automatic non-rigid registration of 3D dynamic facial data using least-squares conformal maps, and based on this registration method, we also develop a new framework of facial expression synthesis and transfer. Nowadays more and more 3D dynamic, densely-sampled data become prevalent with the advancement of novel 3D scanning techniques. To analyze and utilize such huge 3D data, an efficient non-rigid registration algorithm is needed to establish one-to-one inter-frame correspondences. Towards this goal, a non-rigid registration algorithm of 3D dynamic facial data is developed by using least-squares conformal maps with additional feature correspondences detected by employing active appearance models (AAM). the proposed method with additional, interior feature constraints guarantees that the non-rigid data will be accurately registered. the least-squares conformal maps between two 3D surfaces are globally optimized with the least angle distortion and the resulting 2D maps are stable and one-to-one. Furthermore, by using this non-rigid registration method, we develop a new system of facial expression synthesis and transfer. Finally, we perform a series of experiments to evaluate our non-rigid registration method and demonstrate its efficacy and efficiency in the applications of facial expression synthesis and transfer.

关键词： Face detection Facial animation computer vision Graphics Humans Tracking Algorithm design and analysis Heuristic algorithms Active appearance model Deformable models

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Unsupervised Learning of Probabilistic Object Models (POMs) for Object Classification, Segmentation and recognition

Unsupervised Learning of Probabilistic Object Models (POMs) ...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.1

作者： Yuanhao Chen Long (Leo) Zhu Alan Yuille Hongjiang Zhang MOE-MS Key Laboratory of MCC University of Science and Technology China Department of Statistics University of California Los Angeles USA Department of Statistics Psychology and Computer Science University of California Los Angeles USA Microsoft Advanced Technology Center USA

We present a new unsupervised method to learn unified probabilistic object models (POMs) which can be applied to classification, segmentation, and recognition. We formulate this as a structure learning task and our strategy is to learn and combine basic POM's that make use of complementary image cues. Each POM has algorithms for inference and parameter learning, but: (i) the structure of each POM is unknown, and (ii) the inference and parameter learning algorithm for a POM may be impractical without additional information. We address these problems by a novel structure induction procedure which uses knowledge propagation to enable POM's to provide information to other POM's and "teach them" (which greatly reduced the amount of supervision required for training). In particular, we learn a POM-IP defined on Interest Points using weak supervision [1, 2] and use this to train a POM-mask, defined on regional features, which yields a combined POM which performs segmentation/localization. this combined model can be used to train POM-edgelets, defined on edgelets, which gives a full POM with improved performance on classification. We give detailed experimental analysis on large datasets which show that the full POM is invariant to scale and rotation of the object (for learning and inference) and performs inference rapidly. In addition, we show that we can apply POM's to learn objects classes (i.e. when there are several objects and the identity of the object in each image is unknown). We emphasize that these models can match between different objects from the same category and hence enable object recognition.

关键词： Unsupervised learning Image segmentation Inference algorithms Shape Object detection Statistics Laboratories Psychology computer science Data analysis

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Extracting a Fluid Dynamic Texture and the Background from Video

Extracting a Fluid Dynamic Texture and the Background from V...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.5

作者： Bernard Ghanem Narendra Ahuja Beckman Institute for Advanced Science and Technology Department of Electrical and Computer E University of Illinois Urbana-Champaign Urbana IL USA

Given the video of a still background occluded by a fluid dynamic texture (FDT), this paper addresses the problem of separating the video sequence into its two constituent layers. One layer corresponds to the video of the unoccluded background, and the other to that of the dynamic texture, as it would appear if viewed against a black background. the model of the dynamic texture is unknown except that it represents fluid flow. We present an approach that uses the image motion information to simultaneously obtain a model of the dynamic texture and separate it from the background which is required to be still. Previous methods have considered occluding layers whose dynamics follows simple motion models (e.g. periodic or 2D parametric motion). FDTs considered in this paper exhibit complex stochastic motion. We consider videos showing an FDT layer (e.g. pummeling smoke or heavy rain) in front of a static background layer (e.g. brick building). We propose a novel method for simultaneously separating these two layers and learning a model for the FDT. Due to the fluid nature of the DT, we are required to learn a model for both the spatial appearance and the temporal variations (due to changes in density) of the FDT, along with a valid estimate of the background. We model the frames of a sequence as being produced by a continuous HMM, characterized by transition probabilities based on the Navier-Stokes equations for fluid dynamics, and by generation probabilities based on the convex matting of the FDT with the background. We learn the FDT appearance, the FDT temporal variations, and the background by maximizing their joint probability using Interactive Conditional Modes (ICM). Since the learned model is generative, it can be used to synthesize new videos with different backgrounds and density variations. Experiments on videos that we compiled demonstrate the performance of our method.

关键词： Fluid dynamics Hidden Markov models Video sequences Rain Navier-Stokes equations Character generation Layout Filters Fluid flow Stochastic processes

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Multimodal surveillance: an introduction

Multimodal surveillance: an introduction

引用

ieee conference on computer vision and pattern recognition

作者： Zhu, Zhigang Huang, thomas S. CUNY City Coll Dept Comp Sci Convent Ave & 138th St New York NY 10031 USA Univ Illinois Beckman Inst Urbana IL 61801 USA

No abstract available

ISBN: (纸本)9781424411795

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LDV sensing and processing for remote hearing in a multimodal surveillance system

LDV sensing and processing for remote hearing in a multimoda...

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ieee conference on computer vision and pattern recognition

作者： Zhu, Zhigang Li, Weihong Molina, Edgardo Wolberg, George CUNY City Coll Dept Comp Sci Convent Ave & 138th St New York NY 10031 USA Grad Ctr New York NY 10031 USA

No abstract available

ISBN: (纸本)9781424411795

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Robust real-time visual SLAM using scale prediction and exemplar based feature description`

Robust real-time visual SLAM using scale prediction and exem...

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ieee conference on computer vision and pattern recognition

作者： Chekhlov, Denis Pupilli, Mark Mayol, Walterio Calway, Andrew Univ Bristol Dept Comp Sci Bristol BS8 1TH Avon England

ISBN: (纸本)9781424411795

Two major limitations of real-time visual SLAM algorithms are the restricted range of views over which they can operate and their lack of robustness when faced with erratic camera motion or severe visual occlusion. In this paper we describe a visual SLAM algorithm which addresses both of these problems. the key component is a novel feature description method which is both fast and capable of repeatable correspondence matching over a wide range of viewing angles and scales. this is achieved in real-time by using a SIFT-like spatial gradient descriptor in conjunction with efficient scale prediction and exemplar based feature representation. Results are presented illustrating robust real-time SLAM operation within an office environment.

关键词： Feature extraction

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Implicit active contours driven by local binary fitting energy

Implicit active contours driven by local binary fitting ener...

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ieee conference on computer vision and pattern recognition

作者： Li, Chunming Kao, Chiu-Yen Gore, John C. Ding, Zhaohua Vanderbilt Univ Inst Imaging Sci 221 Kirkland Hall Nashville TN 37232 USA Ohio State Univ Dept Math 231 W 18th Ave Columbus OH 43210 USA

ISBN: (纸本)9781424411795

Local image information is crucial for accurate segmentation of images with intensity inhomogeneity. However, image information in local region is not embedded in popular region-based active contour models, such as the piecewise constant models. In this paper, we propose a region-based active contour model that is able to utilize image information in local regions. the major contribution of this paper is the introduction of a local binary fitting energy with a kernel function, which enables the extraction of accurate local image information. therefore, our model can be used to segment images with intensity inhomogeneity, which overcomes the limitation of piecewise constant models. Comparisons with other major region-based models, such as the piecewise smooth model, show the advantages of our method in terms of computational efficiency and accuracy. In addition, the proposed method has promising application to image denoising.

关键词： Image segmentation

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