OpenRaiser/Intern-Atlas huggingface dataset:
On the use of independent tasks for face recognition
Authors: Àgata Lapedriza, David Masip, Jordi Vitrià
Venue: CVPR
Year: 2008

We present a method for learning discriminative linear feature extraction using independent tasks. More concretely, given a target classification task, we consider a complementary classification task that is independent of the target one. For example, in face classification field, subject recognition can be a target task while facial expression classification can be a complementary task. Then, we use labels of the complementary task in order to obtain a more robust feature extraction, being the new feature space less sensitive to the complementary classification. To learn the proposed feature extraction we use the mutual information measure between the projected data and both labels from the target and the complementary tasks. In our experiments, this framework has been applied to a face recognition problem, in order to inhibit this classification task from environmental artifacts, and to mitigate the effects of the small sample size problem. Our classification experiments show an improved feature extraction process using the proposed method.
Volumetric reconstruction from multi-energy single-view radiography
Authors: Sang N. Le, Mei Kay Lee, Shamima Banu, Anthony C. Fang
Venue: CVPR
Year: 2008

We address the volumetric reconstruction problem that takes as input a series of orthographic multi-energy x-ray images, producing as output a reconstructed model space consisting of uniform-size mass density voxels. Our approach solves the non-linear constrained optimization for mulation problem by constructing a compliant estimate of volumetric distribution, subject to projective and domain constraints, and minimizes variational irregularities. To resolve the inherent ambiguities of single-view formulation, an optional shape model may be introduced to aid the reconstruction process. We demonstrate our method’s practical usage as a new in-vivo method for estimating three dimensional body segmental compositions, and compare its results with those of existing methods.
Rotation symmetry group detection via frequency analysis of frieze-expansions
Authors: Seungkyu Lee 0001, Robert T. Collins, Yanxi Liu 0001
Venue: CVPR
Year: 2008

We present a novel and effective algorithm for rotation symmetry group detection from real-world images. We propose a frieze-expansion method that transforms rotation symmetry group detection into a simple translation symmetry detection problem. We define and construct a dense symmetry strength map from a given image, and search for potential rotational symmetry centers automatically. Frequency analysis, using discrete Fourier transform (DFT), is applied to the frieze-expansion patterns to uncover the types and the cardinality of multiple rotation symmetry groups in an image, concentric or otherwise. Furthermore, our detection algorithm can discriminate discrete versus continuous and cyclic versus dihedral symmetry groups, and identify the corresponding supporting regions in the image. Experimental results on over 80 synthetic and natural images demonstrate superior performance of our rotation detection algorithm in accuracy and in speed over the state of the art rotation detection algorithms.
Rank-based distance metric learning: An application to image retrieval
Authors: Jung-Eun Lee, Rong Jin 0001, Anil K. Jain 0001
Venue: CVPR
Year: 2008

We present a novel approach to learn distance metric for information retrieval. Learning distance metric from a number of queries with side information, i.e., relevance judgements, has been studied widely, for example pairwise constraint-based distance metric learning. However, the capacity of existing algorithms is limited, because they usually assume that the distance between two similar objects is smaller than the distance between two dissimilar objects. This assumption may not hold, especially in the case of information retrieval when the input space is heterogeneous. To address this problem explicitly, we propose rank-based distance metric learning. Our approach overcomes the drawback of existing algorithms by comparing the distances only among the relevant and irrelevant objects for a given query. To avoid over-fitting, a regularizer based on the Burg matrix divergence is also introduced. We apply the proposed framework to tattoo image retrieval in forensics and law enforcement application domain. The goal of the application is to retrieve tattoo images from a gallery database that are visually similar to a tattoo found on a suspect or a victim. The experimental results show encouraging results in comparison to the standard approaches for distance metric learning.
Semi-supervised boosting using visual similarity learning
Authors: Christian Leistner, Helmut Grabner, Horst Bischof
Venue: CVPR
Year: 2008

The required amount of labeled training data for object detection and classification is a major drawback of current methods. Combining labeled and unlabeled data via semi-supervised learning holds the promise to ease the tedious and time consuming labeling effort. This paper presents a novel semi-supervised learning method which combines the power of learned similarity functions and classifiers. The approach capable of exploiting both labeled and unlabeled data is formulated in a boosting framework. One classifier (the learned similarity) serves as a prior which is steadily improved via training a second classifier on labeled and unlabeled samples. We demonstrate the approach on challenging computer vision applications. First, we show how we can train a classifier using only a few labeled samples and many unlabeled data. Second, we improve (specialize) a state-of-the-art detector by using labeled and unlabeled data.
FusionFlow: Discrete-continuous optimization for optical flow estimation
Authors: Victor S. Lempitsky, Stefan Roth 0001, Carsten Rother
Venue: CVPR
Year: 2008

Accurate estimation of optical flow is a challenging task, which often requires addressing difficult energy optimization problems. To solve them, most top-performing methods rely on continuous optimization algorithms. The modeling accuracy of the energy in this case is often traded for its tractability. This is in contrast to the related problem of narrow-baseline stereo matching, where the top-performing methods employ powerful discrete optimization algorithms such as graph cuts and message-passing to optimize highly non-convex energies. In this paper, we demonstrate how similar non-convex energies can be formulated and optimized discretely in the context of optical flow estimation. Starting with a set of candidate solutions that are produced by fast continuous flow estimation algorithms, the proposed method iteratively fuses these candidate solutions by the computation of minimum cuts on graphs. The obtained continuous-valued fusion result is then further improved using local gradient descent. Experimentally, we demonstrate that the proposed energy is an accurate model and that the proposed discrete-continuous optimization scheme not only finds lower energy solutions than traditional discrete or continuous optimization techniques, but also leads to flow estimates that outperform the current state-of-the-art.
Smoothing-based Optimization
Authors: Marius Leordeanu, Martial Hebert
Venue: CVPR
Year: 2008

We propose an efficient method for complex optimization problems that often arise in computer vision. While our method is general and could be applied to various tasks, it was mainly inspired from problems in computer vision, and it borrows ideas from scale space theory. One of the main motivations for our approach is that searching for the global maximum through the scale space of a function is equivalent to looking for the maximum of the original function, with the advantage of having to handle fewer local optima. Our method works with any non-negative, possibly non-smooth function, and requires only the ability of evaluating the function at any specific point. The algorithm is based on a growth transformation, which is guaranteed to increase the value of the scale space function at every step, unlike gradient methods. To demonstrate its effectiveness we present its performance on a few computer vision applications, and show that in our experiments it is more effective than some well established methods such as MCMC, Simulated Annealing and the more local Nelder-Mead optimization method.
Probabilistic multi-tensor estimation using the Tensor Distribution Function
Authors: Alex D. Leow, Siwei Zhu, Katie McMahon, Greig I. de Zubicaray, Matthew Meredith, Margie Wright, Paul M. Thompson
Venue: CVPR
Year: 2008

Diffusion weighted magnetic resonance (MR) imaging is a powerful tool that can be employed to study white matter microstructure by examining the 3D displacement profile of water molecules in brain tissue. By applying diffusion-sensitized gradients along a minimum of 6 directions, second-order tensors can be computed to model dominant diffusion processes. However, conventional DTI is not sufficient to resolve crossing fiber tracts. A number of high-angular resolution schemes with greater than 6 gradient directions have been employed to address this issue. In this paper, we introduce the tensor distribution function (TDF), a probability function defined on the space of symmetric positive definite matrices. Here, fiber crossing is modeled as an ensemble of Gaussian diffusion processes with weights specified by the TDF once this optimal TDF is determined, the diffusion orientation distribution function (ODF) can easily be computed by analytic integration of the resulting displacement probability function.
A linear approach to motion estimation using generalized camera models
Authors: Hongdong Li, Richard I. Hartley, Jae-Hak Kim
Venue: CVPR
Year: 2008

A well-known theoretical result for motion estimation using the generalized camera model is that 17 corresponding image rays can be used to solve linearly for the motion of a generalized camera. However, this paper shows that for many common configurations of the generalized camera models (e.g., multi-camera rig, catadioptric camera etc.), such a simple 17-point algorithm does not exist, due to some previously overlooked ambiguities. We further discover that, despite the above ambiguities, we are still able to solve the motion estimation problem effectively by a new algorithm proposed in this paper. Our algorithm is essentially linear, easy to implement, and the computational efficiency is very high. Experiments on both real and simulated data show that the new algorithm achieves reasonably high accuracy as well.
Visual tracking via incremental Log-Euclidean Riemannian subspace learning
Authors: Xi Li 0001, Weiming Hu 0004, Zhongfei Zhang, Xiaoqin Zhang 0002, Mingliang Zhu, Jian Cheng 0002
Venue: CVPR
Year: 2008

Recently, a novel Log-Euclidean Riemannian metric is proposed for statistics on symmetric positive definite (SPD) matrices. Under this metric, distances and Riemannian means take a much simpler form than the widely used affine-invariant Riemannian metric. Based on the Log-Euclidean Riemannian metric, we develop a tracking framework in this paper. In the framework, the covariance matrices of image features in the five modes are used to represent object appearance. Since a nonsingular covariance matrix is a SPD matrix lying on a connected Riemannian manifold, the Log-Euclidean Riemannian metric is used for statistics on the covariance matrices of image features. Further, we present an effective online Log-Euclidean Riemannian subspace learning algorithm which models the appearance changes of an object by incrementally learning a low-order Log-Euclidean eigenspace representation through adaptively updating the sample mean and eigenbasis. Tracking is then led by the Bayesian state inference framework in which a particle filter is used for propagating sample distributions over the time. Theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of the proposed framework.
Joint Conditional Random Field of multiple views with online learning for image-based rendering
Authors: Wenfeng Li 0004, Baoxin Li
Venue: CVPR
Year: 2008

There are many applications, such as image-based rendering, where multiple views of a scene are considered simultaneously for improved analysis through employing strong correlation among the set of pixels corresponding to the same physical scene point. While being a useful tool for modeling pixel interactions, Markov random field (MRF) models encounter challenges in such cases since they assume strong independence of the observed data for tractability, rendering it difficult to take advantage of having multiple correlated views. In this paper we propose joint conditional random field (CRF) for multiple views in the context of virtual view synthesis in image-based rendering. The model is enabled by the adoption of steerable spatial filters for capturing not only the pixel dependence in a single image but also their correlations among multiple views. Furthermore, a novel on-line learning scheme is proposed for the CRF model, which learns the CRF parameters from the same input data for synthesizing virtual views. This effectively makes the model adaptive to the input and thus optimal results can be expected. Experiments are designed to validate the proposed approach and its effectiveness.
Unsupervised learning of human perspective context using ME-DT for efficient human detection in surveillance
Authors: Liyuan Li, Maylor K. H. Leung
Venue: CVPR
Year: 2008

A novel and automated technique for learning human perspective context (HPC) from a scene is proposed in this paper. It is found that two models are required to describe HPC for camera tilt angle ranging from 0° to 50°. From a scene, the tilt angle can be inferred from the observed human shapes and head/foot positions. Afterward, a novel ME-DT (Model Estimation - Data Tuning) algorithm is proposed to learn human perspective context from live data of various degrees of uncertainties. The uncertainties may come from the variations of human individual heights and poses, and segmentation/recognition errors. ME-DT not only estimates the model parameters from the training data but also tunes the data to achieve a better head-foot correlation. The human perspective context provides a feasible constraint on the scales, positions, and orientations of humans in the scene. Applying this constraint to the HOG human detection, great reduction of the detection windows and improved performances have been obtained compared to conventional methods.
3D face tracking and expression inference from a 2D sequence using manifold learning
Authors: Wei-Kai Liao, Gérard G. Medioni
Venue: CVPR
Year: 2008

We propose a person-dependent, manifold-based approach for modeling and tracking rigid and nonrigid 3D facial deformations from a monocular video sequence. The rigid and nonrigid motions are analyzed simultaneously in 3D, by automatically fitting and tracking a set of landmarks. We do not represent all nonrigid facial deformations as a simple complex manifold, but instead decompose them on a basis of eight 1D manifolds. Each 1D manifold is learned offline from sequences of labeled expressions, such as smile, surprise, etc. Any expression is then a linear combination of values along these 8 axes, with coefficient representing the level of activation. We experimentally verify that expressions can indeed be represented this way, and that individual manifolds are indeed 1D. The manifold dimensionality estimation, manifold learning, and manifold traversal operation are all implemented in the N-D Tensor Voting framework. Using simple local operations, this framework gives an estimate of the tangent and normal spaces at every sample, and provides excellent robustness to noise and outliers. The output of our system, besides the tracked landmarks in 3D, is a labeled annotation of the expression. We demonstrate results on a number of challenging sequences.
Retinal image registration from 2D to 3D
Authors: Yuping Lin, Gérard G. Medioni
Venue: CVPR
Year: 2008

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.
Image de-fencing
Authors: Yanxi Liu 0001, Tamara Belkina, James Hays, Roberto Lublinerman
Venue: CVPR
Year: 2008

We introduce a novel image segmentation algorithm that uses translational symmetry as the primary foreground/background separation cue. We investigate the process of identifying and analyzing image regions that present approximate translational symmetry for the purpose of image forground/background separation. In conjunction with texture-based inpainting, understanding the different see-through layers allows us to perform powerful image manipulations such as recovering a mesh-occluded background (as much as 53% occluded area) to achieve the effect of image and photo de-fencing. Our algorithm consists of three distinct phases- (1) automatically finding the skeleton structure of a potential frontal layer (fence) in the form of a deformed lattice, (2) separating foreground/background layers using appearance regularity, and (3) occluded foreground inpainting to reveal a complete, non-occluded image. Each of these three tasks presents its own special computational challenges that are not encountered in previous, general image de-layering or texture inpainting applications.
Video segmentation: Propagation, validation and aggregation of a preceding graph
Authors: Siying Liu, Guo Dong, Chye Hwang Yan, Sim Heng Ong
Venue: CVPR
Year: 2008

In this work, video segmentation is viewed as an efficient intra-frame grouping temporally reinforced by a strong inter-frame coherence. Traditional approaches simply regard pixel motions as another prior in the MRF-MAP framework. Since pixel pre-grouping is inefficiently performed on every frame, the strong correlation between inter-frame groupings is largely underutilized. We exploit the inter-frame correlation to propagate trustworthy groupings from the previous frame. A preceding graph is constructed and labeled for the previous frame. It is temporally propagated to the current frame and validated by similarity measures. All unlabeled subgraphs are spatially aggregated for the final grouping. Experimental results show that the proposed approach is highly efficient for spatio-temporal segmentation. It makes good use of temporal correlation and produces satisfactory grouping results.
Image partial blur detection and classification
Authors: Renting Liu, Zhaorong Li, Jiaya Jia
Venue: CVPR
Year: 2008

In this paper, we propose a partially-blurred-image classification and analysis framework for automatically detecting images containing blurred regions and recognizing the blur types for those regions without needing to perform blur kernel estimation and image deblurring. We develop several blur features modeled by image color, gradient, and spectrum information, and use feature parameter training to robustly classify blurred images. Our blur detection is based on image patches, making region-wise training and classification in one image efficient. Extensive experiments show that our method works satisfactorily on challenging image data, which establishes a technical foundation for solving several computer vision problems, such as motion analysis and image restoration, using the blur information.
Accurate polyp segmentation for 3D CT colongraphy using multi-staged probabilistic binary learning and compositional model
Authors: Le Lu 0001, Adrian Barbu, Matthias Wolf 0001, Jianming Liang, Marcos Salganicoff, Dorin Comaniciu
Venue: CVPR
Year: 2008

Accurate and automatic colonic polyp segmentation and measurement in Computed Tomography (CT) has significant importance for 3D polyp detection, classification, and more generally computer aided diagnosis of colon cancers. In this paper, we propose a three-staged probabilistic binary classification approach for automatically segmenting polyp voxels from their surrounding tissues in CT. Our system integrates low-, and mid-level information for discriminative learning under local polar coordinates which align on the 3D colon surface around detected polyp. More importantly, our supervised learning system has flexible modeling capacity, which offers a principled means of encoding semantic, clinical expert annotations of colonic polyp tissue identification and segmentation. The learning generality to unseen data is bounded by boosting [12, 11] and stacked generality [14]. Extensive experimental results on polyp segmentation performance evaluation and robustness testing with disturbances (using both training data and unseen data) are provided to validate our presented approach. The reliability of polyp segmentation and measurement has been largely increased to 98:2% (ie. errors les 3 mm), compared with other state of art work [4, 15] of about 75% ~ 80%.
Constrained spectral clustering through affinity propagation
Authors: Zhengdong Lu, Miguel Á. Carreira-Perpiñán
Venue: CVPR
Year: 2008

Pairwise constraints specify whether or not two samples should be in one cluster. Although it has been successful to incorporate them into traditional clustering methods, such as K-means, little progress has been made in combining them with spectral clustering. The major challenge in designing an effective constrained spectral clustering is a sensible combination of the scarce pairwise constraints with the original affinity matrix. We propose to combine the two sources of affinity by propagating the pairwise constraints information over the original affinity matrix. Our method has a Gaussian process interpretation and results in a closed-form expression for the new affinity matrix. Experiments show it outperforms state-of-the-art constrained clustering methods in getting good clusterings with fewer constraints, and yields good image segmentation with user-specified pairwise constraints.