OpenRaiser/Intern-Atlas huggingface dataset:
Learning long term face aging patterns from partially dense aging databases
Authors: Jin-Li Suo, Xilin Chen 0001, Shiguang Shan, Wen Gao 0001
Venue: ICCV
Year: 2009

Studies on face aging are handicapped by lack of long term dense aging sequences for model training. To handle this problem, we propose a new face aging model, which learns long term face aging patterns from partially dense aging databases. The learning strategy is based on two assumptions: (i) short term face aging pattern is relatively simple and is possible to be learned from currently available databases; (ii) long term face aging is a continuous and smooth Markov process. Adopting a compositional face representation, our aging algorithm learns a function-based short term aging model from real aging sequences to infer facial parameters within a short age span. Based on the predefined smoothness criteria between two overlapping short term aging patterns, we concatenate these learned short term aging patterns to build the long term aging patterns. Both the subjective assessment and objective evaluations of synthetic aging sequences validate the effectiveness of the proposed model.
Stereo from flickering caustics
Authors: Yohay Swirski, Yoav Y. Schechner, Ben Herzberg, Shahriar Negahdaripour
Venue: ICCV
Year: 2009

Underwater, natural illumination typically varies strongly temporally and spatially. The reason is that waves on the water surface refract light into the water in a spatiotemporally varying manner. The resulting underwater illumination field is known as underwater caustics or flicker. In past studies, flicker has often been considered to be an undesired effect, which degrades the quality of images. In contrast, in this work, we show that flicker can actually be useful for vision in the underwater domain. Specifically, it solves very simply, accurately, and densely the stereo correspondence problem, irrespective of the object's texture. The temporal radiance variations due to flicker are unique to each object point, thus disambiguating the correspondence, with very simple calculations. This process is further enhanced by compounding the spatial variability in the flicker field. The method is demonstrated by underwater in-situ experiments.
Kernel active contour
Authors: Tan Shan, Ioannis A. Kakadiaris
Venue: ICCV
Year: 2009

Level sets and graph cuts are two state-of-the-art image segmentation methods in use today. The two methods are apparently different from each other not only because they originate from different theory foundations but also because they employ image information in different ways — level sets typically use image information in a point-wise way, whereas graph cuts use image information in a pairwise way. In this paper, we derive an equivalence relationship between the two methods through kernel technology. In particular, we show that the kernelization of the Chan-Vese (CV) functional — a functional widely used in the level set community — is exactly the energy optimized in the average association — a well-known graph cut criterion. We refer to the level sets method using the kernelized version of the CV functional as kernel active contour. The kernel active contour has computational complexity O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) due to the involved kernel technology. We propose a fast implementation for kernel active contour with computational complexity only O(n) using random projection. The kernel active contour is evaluated on synthetic and real images and compared with several existing level set and graph cut methods for image segmentation.
Computation complexity of branch-and-bound model selection
Authors: Ninad Thakoor, Venkat Devarajan, Jean Gao
Venue: ICCV
Year: 2009

Segmentation problems are one of the most important areas of research in computer vision. While segmentation problems are generally solved with clustering paradigms, they formulate the problem as recursive. Additionally, most approaches need the number of clusters to be known beforehand. This requirement is unreasonable for majority of the computer vision problems. This paper analyzes the model selection perspective which can overcome these limitations. Under this framework multiple hypotheses for cluster centers are generated using spatially coherent sampling. An optimal subset of these hypotheses is selected according to a model selection criterion. The selection can be carried out with a branch-and-bound procedure. The worst case complexity of any branch-and-bound algorithm is exponential. However, the average complexity of the algorithm is significantly lower. In this paper, we develop a framework for analysis of average complexity of the algorithm from the statistics of model selection costs.
Seeing through water: Image restoration using model-based tracking
Authors: Yuandong Tian, Srinivasa G. Narasimhan
Venue: ICCV
Year: 2009

A video sequence of an underwater scene taken from above the water surface suffers from severe distortions due to water fluctuations. In this paper, we simultaneously estimate the shape of the water surface and recover the planar underwater scene without using any calibration patterns, image priors, multiple viewpoints or active illumination. The key idea is to build a compact spatial distortion model of the water surface using the wave equation. Based on this model, we present a novel tracking technique that is designed specifically for water surfaces and addresses two unique challenges—the absence of an object model or template and the presence of complex appearance changes in the scene due to water fluctuation. We show the effectiveness of our approach on both simulated and real scenes, with text and texture.
Texel-based texture segmentation
Authors: Sinisa Todorovic, Narendra Ahuja
Venue: ICCV
Year: 2009

Given an arbitrary image, our goal is to segment all distinct texture subimages. This is done by discovering distinct, cohesive groups of spatially repeating patterns, called texels, in the image, where each group defines the corresponding texture. Texels occupy image regions, whose photometric, geometric, structural, and spatial-layout properties are samples from an unknown pdf. If the image contains texture, by definition, the image will also contain a large number of statistically similar texels. This, in turn, will give rise to modes in the pdf of region properties. Texture segmentation can thus be formulated as identifying modes of this pdf. To this end, first, we use a low-level, multiscale segmentation to extract image regions at all scales present. Then, we use the meanshift with a new, variable-bandwidth, hierarchical kernel to identify modes of the pdf defined over the extracted hierarchy of image regions. The hierarchical kernel is aimed at capturing texel substructure. Experiments demonstrate that accounting for the structural properties of texels is critical for texture segmentation, leading to competitive performance vs. the state of the art.
Coarse registration of 3D surface triangulations based on moment invariants with applications to object alignment and identification
Authors: Michael Trummer, Herbert Suesse, Joachim Denzler
Venue: ICCV
Year: 2009

We present a new, direct way to register three-dimensional (3D) surfaces given the respective 3D points and surface triangulations. Our method is non-iterative and does not require any initial solution. The idea is to compute 3D invariants based on local surface moments. The resulting local surface descriptors are invariant with respect to Euclidean or to similarity transformations, by choice. In the final step we use the Hungarian method to find a minimum cost assignment of the computed descriptors. The method is robust against different point densities, noise and partial overlap. Our experiments with real data also show that the method can serve as automatic initialization of the iterative-closest-point (ICP) algorithm and, hence, extends the field of applications for this standard registration method.
On optimizing subspaces for face recognition
Authors: Jilin Tu, Xiaoming Liu 0002, Peter Henry Tu
Venue: ICCV
Year: 2009

We propose a subspace learning algorithm for face recognition by directly optimizing recognition performance scores. Our approach is motivated by the following observations: 1) Different face recognition tasks (i.e., face identification and verification) have different performance metrics, which implies that there exist distinguished subspaces that optimize these scores, respectively. Most prior work focused on optimizing various discriminative or locality criteria and neglect such distinctions. 2) As the gallery (target) and the probe (query) data are collected in different settings in many real-world applications, there could exist consistent appearance incoherences between the gallery and the probe data for the same subject. Knowledge regarding these incoherences could be used to guide the algorithm design, resulting in performance gain. Prior efforts have not focused on these facts. In this paper, we rigorously formulate performance scores for both the face identification and the face verification tasks, provide a theoretical analysis on how the optimal subspaces for the two tasks are related, and derive gradient descent algorithms for optimizing these subspaces. Our extensive experiments on a number of public databases and a real-world face database demonstrate that our algorithm can improve the performance of given subspace based face recognition algorithms targeted at a specific face recognition task.
Kernel methods for weakly supervised mean shift clustering
Authors: Oncel Tuzel, Fatih Porikli, Peter Meer
Venue: ICCV
Year: 2009

Mean shift clustering is a powerful unsupervised data analysis technique which does not require prior knowledge of the number of clusters, and does not constrain the shape of the clusters. The data association criteria is based on the underlying probability distribution of the data points which is defined in advance via the employed distance metric. In many problem domains, the initially designed distance metric fails to resolve the ambiguities in the clustering process. We present a novel semi-supervised kernel mean shift algorithm where the inherent structure of the data points is learned with a few user supplied constraints in addition to the original metric. The constraints we consider are the pairs of points that should be clustered together. The data points are implicitly mapped to a higher dimensional space induced by the kernel function where the constraints can be effectively enforced. The mode seeking is then performed on the embedded space and the approach preserves all the advantages of the original mean shift algorithm. Experiments on challenging synthetic and real data clearly demonstrate that significant improvements in clustering accuracy can be achieved by employing only a few constraints.
Efficient privacy preserving video surveillance
Authors: Maneesh Upmanyu, Anoop M. Namboodiri, Kannan Srinathan, C. V. Jawahar
Venue: ICCV
Year: 2009

Widespread use of surveillance cameras in offices and other business establishments, pose a significant threat to the privacy of the employees and visitors. The challenge of introducing privacy and security in such a practical surveillance system has been stifled by the enormous computational and communication overhead required by the solutions. In this paper, we propose an efficient framework to carry out privacy preserving surveillance. We split each frame into a set of random images. Each image by itself does not convey any meaningful information about the original frame, while collectively, they retain all the information. Our solution is derived from a secret sharing scheme based on the Chinese Remainder Theorem, suitably adapted to image data. Our method enables distributed secure processing and storage, while retaining the ability to reconstruct the original data in case of a legal requirement. The system installed in an office like environment can effectively detect and track people, or solve similar surveillance tasks. Our proposed paradigm is highly efficient compared to Secure Multiparty Computation, making privacy preserving surveillance, practical.
Quasi-periodic event analysis for social game retrieval
Authors: Ping Wang 0012, Gregory D. Abowd, James M. Rehg
Venue: ICCV
Year: 2009

A new problem of retrieving social games from unstructured videos is proposed. Social games are characterized by repetitions (with variations) of alternating turns between two players. We define games as quasi-periodic motion patterns in video based on their repetitiveness property. We have developed an algorithm to extract such patterns from video. The patterns extracted by our method, from video clips of social games taken from YouTube, are shown to correspond to meaningful stages of the games. We demonstrate promising results in retrieving social games from unstructured, lab-recorded footage of children's play, and identifying social interactions in a dataset of approximately 3.75 hours of home movies.
Shape analysis with multivariate tensor-based morphometry and holomorphic differentials
Authors: Yalin Wang 0001, Tony F. Chan, Arthur W. Toga, Paul M. Thompson
Venue: ICCV
Year: 2009

In this paper, we propose multivariate tensor-based surface morphometry, a new method for surface analysis, using holomorphic differentials; we also apply it to study brain anatomy. Differential forms provide a natural way to parameterize 3D surfaces, but the multivariate statistics of the resulting surface metrics have not previously been investigated. We computed new statistics from the Riemannian metric tensors that retain the full information in the deformation tensor fields. We present the canonical holomorphic one-forms with improved numerical accuracy and computational efficiency. We applied this framework to 3D MRI data to analyze hippocampal surface morphometry in Alzheimer’s Disease (AD; 12 subjects), lateral ventricular surface morphometry in HIV/AIDS (11 subjects) and biomarkers in lateral ventricles in HIV/AIDS (11 subjects). Experimental results demonstrated that our method powerfully detected brain surface abnormalities. Multivariate statistics on the local tensors outperformed other TBM methods including analysis of the Jacobian determinant, the largest eigenvalue, or the pair of eigenvalues, of the surface Jacobian matrix. 1.
Studying brain morphometry using conformal equivalence class
Authors: Yalin Wang 0001, Wei Dai, Yi-Yu Chou, Xianfeng Gu, Tony F. Chan, Arthur W. Toga, Paul M. Thompson
Venue: ICCV
Year: 2009

Two surfaces are conformally equivalent if there exists a bijective angle-preserving map between them. The Teichmüller space for surfaces with the same topology is a finite-dimensional manifold, where each point represents a conformal equivalence class, and the conformal map is homotopic to the identity map. In this paper, we propose a novel method to apply conformal equivalence based shape index to study brain morphometry. The shape index is defined based on Teichmüller space coordinates. It is intrinsic, and invariant under conformal transformations, rigid motions and scaling. It is also simple to compute; no registration of surfaces is needed. Using the Yamabe flow method, we can conformally map a genus-zero open boundary surface to the Poincaré disk. The shape indices that we compute are the lengths of a special set of geodesics under hyperbolic metric. By computing and studying this shape index and its statistical behavior, we can analyze differences in anatomical morphometry due to disease or development. Study on twin lateral ventricular surface data shows it may help detect generic influence on lateral ventricular shapes. In leave-one-out validation tests, we achieved 100% accurate classification (versus only 68% accuracy for volume measures) in distinguishing 11 HIV/AIDS individuals from 8 healthy control subjects, based on Teichmüller coordinates for lateral ventricular surfaces extracted from their 3D MRI scans.Our conformal invariants, the Teichmüller coordinates, successfully classified all lateral ventricular surfaces, showing their promise for analyzing anatomical surface morphometry.
Joint learning of visual attributes, object classes and visual saliency
Authors: Gang Wang 0012, David A. Forsyth
Venue: ICCV
Year: 2009

We present a method to learn visual attributes (eg.“red”, “metal”, “spotted”) and object classes (eg. “car”, “dress”, “umbrella”) together. We assume images are labeled with category, but not location, of an instance. We estimate models with an iterative procedure: the current model is used to produce a saliency score, which, together with a homogeneity cue, identifies likely locations for the object (resp. attribute); then those locations are used to produce better models with multiple instance learning. Crucially, the object and attribute models must agree on the potential locations of an object. This means that the more accurate of the two models can guide the improvement of the less accurate model. Our method is evaluated on two data sets of images of real scenes, one in which the attribute is color and the other in which it is material. We show that our joint learning produces improved detectors. We demonstrate generalization by detecting attribute-object pairs which do not appear in our training data. The iteration gives significant improvement in performance.
Segmentation, ordering and multi-object tracking using graphical models
Authors: Chaohui Wang, Martin de La Gorce, Nikos Paragios
Venue: ICCV
Year: 2009

In this paper, we propose a unified graphical-model framework to interpret a scene composed of multiple objects in monocular video sequences. Using a single pairwise Markov random field (MRF), all the observed and hidden variables of interest such as image intensities, pixels' states (associated object's index and relative depth), objects' states (model motion parameters and relative depth) are jointly considered. Particular attention is given to occlusion handling by introducing a rigorous visibility modeling within the MRF formulation. Through minimizing the MRF's energy, we simultaneously segment, track and sort by depth the objects. Promising experimental results demonstrate the potential of this framework and its robustness to image noise, cluttered background, moving camera and background, and even complete occlusions.
Image annotation using multi-label correlated Green's function
Authors: Hua Wang 0007, Heng Huang 0001, Chris H. Q. Ding
Venue: ICCV
Year: 2009

Image annotation has been an active research topic in the recent years due to its potentially large impact on both image understanding and web/database image search. In this paper, we target at solving the automatic image annotation problem in a novel semi-supervised learning framework. A novel multi-label correlated Green's function approach is proposed to annotate images over a graph. The correlations among labels are integrated into the objective function which improves the performance significantly. We also propose a new adaptive decision boundary method for multi-label assignment to deal with the difficulty of label assignment in most of the existing rank-based multi-label classification algorithms. Instead of setting the threshold heuristically or by experience, our method principally compute it upon the prior knowledge in the training data. We perform our methods on three commonly used image annotation testing data sets. Experimental results show significant improvements on classification performance over four other state-of-the-art methods. As a general semi-supervised learning framework, other local feature based image annotation methods could be easily incorporated into our framework to improve the performance.
Learning image similarity from Flickr groups using Stochastic Intersection Kernel MAchines
Authors: Gang Wang 0012, Derek Hoiem, David A. Forsyth
Venue: ICCV
Year: 2009

Measuring image similarity is a central topic in computer vision. In this paper, we learn similarity from Flickr groups and use it to organize photos. Two images are similar if they are likely to belong to the same Flickr groups. Our approach is enabled by a fast Stochastic Intersection Kernel MAchine (SIKMA) training algorithm, which we propose. This proposed training method will be useful for many vision problems, as it can produce a classifier that is more accurate than a linear classifier, trained on tens of thousands of examples in two minutes. The experimental results show our approach performs better on image matching, retrieval, and classification than using conventional visual features.
High-resolution shape reconstruction from multiple range images based on simultaneous estimation of surface and motion
Authors: Yoshihiro Watanabe, Takashi Komuro, Masatoshi Ishikawa
Venue: ICCV
Year: 2009

Recognition of dynamic scenes based on shape information could be useful for various applications. In this study, we aimed at improving the resolution of three-dimensional (3D) data obtained from moving targets. We present a simple clean and robust method that jointly estimates motion parameters and a high-resolution 3D shape. Experimental results are provided to illustrate the performance of the proposed algorithm.
Structure- and motion-adaptive regularization for high accuracy optic flow
Authors: Andreas Wedel, Daniel Cremers, Thomas Pock, Horst Bischof
Venue: ICCV
Year: 2009

The accurate estimation of motion in image sequences is of central importance to numerous computer vision applications. Most competitive algorithms compute flow fields by minimizing an energy made of a data and a regularity term. To date, the best performing methods rely on rather simple purely geometric regularizes favoring smooth motion. In this paper, we revisit regularization and show that appropriate adaptive regularization substantially improves the accuracy of estimated motion fields. In particular, we systematically evaluate regularizes which adoptively favor rigid body motion (if supported by the image data) and motion field discontinuities that coincide with discontinuities of the image structure. The proposed algorithm relies on sequential convex optimization, is real-time capable and outperforms all previously published algorithms by more than one average rank on the Middlebury optic flow benchmark.