OpenRaiser/Intern-Atlas huggingface dataset:
Cancelable iris biometrics and using Error Correcting Codes to reduce variability in biometric data
Authors: Sanjay Ganesh Kanade, Dijana Petrovska-Delacrétaz, Bernadette Dorizzi
Venue: CVPR
Year: 2009

With the increasing use of biometrics, more and more concerns are being raised about the privacy of the personal biometric data. Conventional biometric systems store biometric templates in a database. This may lead to the possibility of tracking personal information stored in one database by getting access to another database through cross-database matching. Moreover, biometric data are permanently associated with the user. Hence if stolen, they are lost permanently and become unusable in that system and possibly in all other systems based on that biometrics. In order to overcome this non-revocability of biometrics, we propose a two factor scheme to generate cancelable iris templates using iris-biometric and password. We employ a user specific shuffling key to shuffle the iris codes. Additionally, we introduce a novel way to use error correcting codes (ECC) to reduce the variabilities in biometric data. The shuffling scheme increases the impostor Hamming distance leaving genuine Hamming distance intact while the ECC reduce the Hamming distance for genuine comparisons by a larger amount than for the impostor comparisons. This results in better separation between genuine and impostor users which improves the verification performance. The shuffling key is protected by a password which makes the system truly revocable. The biometric data is stored in a protected form which protects the privacy. The proposed scheme reduces the equal error rate (EER) of the system by more than 90% (e.g., from 1.70% to 0.057% on the NIST-ICE database).
Unsupervised feature optimization (UFO): Simultaneous selection of multiple features with their detection parameters
Authors: Leonid Karlinsky, Michael Dinerstein, Shimon Ullman
Venue: CVPR
Year: 2009

Class learning, both supervised and unsupervised, requires feature selection, which includes two main components. The first is the selection of a discriminative subset of features from a larger pool. The second is the selection of detection parameters for each feature to optimize classification performance. In this paper we present a method for the discovery of multiple classification features, their detection parameters and their consistent configurations, in the fully unsupervised setting. This is achieved by a global optimization of joint consistency between the features as a function of the detection parameters, without assuming any prior parametric model. We demonstrate how the proposed framework can be applied for learning different types of feature parameters, such as detection thresholds and geometric relations, resulting in the unsupervised discovery of informative configurations of objects parts. We test our approach on a wide range of classes and show good results. We also demonstrate how the approach can be used to unsupervisedly separate and learn visually similar sub-classes of a single category, such as facial views or hand poses. We use the approach to compare various criteria for feature consistency, including Mutual Information, Suspicious Coincidence, L2 and Jaccard index. Finally, we compare our approach to aparametric consistency optimization technique such as pLSA and show significantly better performance.
Color estimation from a single surface color
Authors: Rei Kawakami, Katsushi Ikeuchi
Venue: CVPR
Year: 2009

This paper estimates illumination colors by using only a single surface color taken under multiple illumination colors. Past researchers have found that there is a difficulty in estimating illumination colors using a single surface color. However, the method presented here overcomes the problem. Surface color is estimated by considering four characteristics of illumination and surface color spaces. First, the outdoor-illumination colors exist in a specific color range. Second, multiple illuminations give constraints for the surface color. Third, multiple illuminations also give constraints for the color range. Fourth, each color component affects those constraints in a different manner. Based on those characteristics, a novel method can be designed. The proposed method produces consistently accurate results when multiple illumination colors are used, because the constraint (possible range of illumination colors) on illumination colors refines the estimated illumination colors, effectively.
Linear stratified approach for 3D modelling and calibration using full geometric constraints
Authors: Jae-Hean Kim
Venue: CVPR
Year: 2009

There have been many approaches to obtain 3D modeling and camera calibration simultaneously from uncalibrated images using parallelism, orthogonality and self-calibration constraints. These approaches can give more stable results with fewer images and allow us to gain the results with only linear operations in most cases. It has been proved that the estimation results are accurate enough to be used as the initial values for nonlinear optimization to refine the results. In this paper, it is shown that all the linear constraints used in the previous works performed independently up to now can be implemented easily in the proposed linear method. The proposed method uses a stratified approach, in which affine reconstruction is performed first and then metric reconstruction. In this procedure, the additional constraints newly extracted in this paper have an important role for affine reconstruction in practical situations. The study on the situations that can not be dealt with by the previous approaches is presented and it is shown that the proposed method being able to handle the cases is more flexible in use.
Adaptive image and video retargeting technique based on Fourier analysis
Authors: Jun-Seong Kim, Jin-Hwan Kim, Chang-Su Kim 0001
Venue: CVPR
Year: 2009

An adaptive image and video retargeting algorithm based on Fourier analysis is proposed in this work. We first divide an input image into several strips using the gradient information so that each strip consists of textures of similar complexities. Then, we scale each strip adaptively according to its importance measure. More specifically, the distortions, generated by the scaling procedure, are formulated in the frequency domain using the Fourier transform. Then, the objective is to determine the sizes of scaled strips to minimize the sum of distortions, subject to the constraint that the sum of their sizes should equal the size of the target output image. We solve this constrained optimization problem using the Lagrangian multiplier technique. Moreover, we extend the approach to the retargeting of video sequences. Simulation results demonstrate that the proposed algorithm provides reliable retargeting performance efficiently.
Vanishing points estimation by self-similarity
Authors: Hadas Kogan, Ron Maurer, Renato Keshet
Venue: CVPR
Year: 2009

This paper presents a novel self-similarity based approach for the problem of vanishing point estimation in man-made scenes. A vanishing point (VP) is the convergence point of a pencil (a concurrent line set), that is a perspective projection of a corresponding parallel line set in the scene. Unlike traditional VP detection that relies on extraction and grouping of individual straight lines, our approach detects entire pencils based on a property of 1D affine-similarity between parallel cross-sections of a pencil. Our approach is not limited to real pencils. Under some conditions (normally met in man-made scenes), our method can detect pencils made of virtual lines passing through similar image features, and hence can detect VPs from repeating patterns that do not contain straight edges. We demonstrate that detecting entire pencils rather than individual lines improves the detection robustness in that it improves VP detection in challenging conditions, such as very-low resolution or weak edges, and simultaneously reduces VP false-detection rate when only a small number of lines are detectable.
Continuous ratio optimization via convex relaxation with applications to multiview 3D reconstruction
Authors: Kalin Kolev, Daniel Cremers
Venue: CVPR
Year: 2009

We introduce a convex relaxation framework to optimally minimize continuous surface ratios. The key idea is to minimize the continuous surface ratio by solving a sequence of convex optimization problems. We show that such minimal ratios are superior to traditionally used minimal surface formulations in that they do not suffer from a shrinking bias and no longer require the choice of a regularity parameter. The absence of a shrinking bias in the minimal ratio model is proven analytically. Furthermore we demonstrate that continuous ratio optimization can be applied to derive a new algorithm for reconstructing three-dimensional silhouette-consistent objects from multiple views. Experimental results confirm that our approach allows to accurately reconstruct deep concavities even without the specification of tuning parameters.
On edge detection on surfaces
Authors: Michael Kolomenkin, Ilan Shimshoni, Ayellet Tal
Venue: CVPR
Year: 2009

Edge detection in images has been a fundamental problem in computer vision from its early days. Edge detection on surfaces, on the other hand, has received much less attention. The most common edges on surfaces are ridges and valleys, used for processing range images in computer vision, as well as for non-photo realistic rendering in computer graphics. We propose a new type of edges on surfaces, termed relief edges. Intuitively, the surface can be considered as an unknown smooth manifold, on top of which a local height image is placed. Relief edges are the edges of this local image. We show how to compute these edges from the local differential geometric surface properties, by fitting a local edge model to the surface. We also show how the underlying manifold and the local images can be roughly approximated and exploited in the edge detection process. Last but not least, we demonstrate the application of relief edges to artifact illustration in archaeology.
Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models
Authors: Louis Kratz, Ko Nishino
Venue: CVPR
Year: 2009

Extremely crowded scenes present unique challenges to video analysis that cannot be addressed with conventional approaches. We present a novel statistical framework for modeling the local spatio-temporal motion pattern behavior of extremely crowded scenes. Our key insight is to exploit the dense activity of the crowded scene by modeling the rich motion patterns in local areas, effectively capturing the underlying intrinsic structure they form in the video. In other words, we model the motion variation of local space-time volumes and their spatial-temporal statistical behaviors to characterize the overall behavior of the scene. We demonstrate that by capturing the steady-state motion behavior with these spatio-temporal motion pattern models, we can naturally detect unusual activity as statistical deviations. Our experiments show that local spatio-temporal motion pattern modeling offers promising results in real-world scenes with complex activities that are hard for even human observers to analyze.
Volterrafaces: Discriminant analysis using Volterra kernels
Authors: Ritwik Kumar, Arunava Banerjee, Baba C. Vemuri
Venue: CVPR
Year: 2009

In this paper we present a novel face classification system where we represent face images as a spatial arrangement of image patches, and seek a smooth nonlinear functional mapping for the corresponding patches such that in the range space, patches of the same face are close to one another, while patches from different faces are far apart, in L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> sense. We accomplish this using Volterra kernels, which can generate successively better approximations to any smooth nonlinear functional. During learning, for each set of corresponding patches we recover a Volterra kernel by minimizing a goodness functional defined over the range space of the sought functional. We show that for our definition of the goodness functional, which minimizes the ratio between intraclass distances and interclass distances, the problem of generating Volterra approximations, to any order, can be posed as a generalized eigenvalue problem. During testing, each patch from the test image that is classified independently, casts a vote towards image classification and the class with the maximum votes is chosen as the winner. We demonstrate the effectiveness of the proposed technique in recognizing faces by extensive experiments on Yale, CMU PIE and Extended Yale B benchmark face datasets and show that our technique consistently outperforms the state-of-the-art in learning based face discrimination.
Echocardiogram view classification using edge filtered scale-invariant motion features
Authors: Ritwik Kumar, Fei Wang 0002, David Beymer, Tanveer Fathima Syeda-Mahmood
Venue: CVPR
Year: 2009

In an 2D echocardiogram exam, an ultrasound probe samples the heart with 2D slices. Changing the orientation and position on the probe changes the slice viewpoint, altering the cardiac anatomy being imaged. The determination of the probe viewpoint forms an essential step in automatic cardiac echo image analysis. In this paper we present a system for automatic view classification that exploits cues from both cardiac structure and motion in echocardiogram videos. In our framework, each image from the echocardiogram video is represented by a set of novel salient features. We locate these features at scale invariant points in the edge-filtered motion magnitude images and encode them using local spatial, textural and kinetic information. Training in our system involves learning a hierarchical feature dictionary and parameters of a pyramid matching kernel based support vector machine. While testing, each image, classified independently, casts a votes towards parent video classification and the viewpoint with maximum votes wins. Through experiments on a large database of echocardiograms obtained from both diseased and control subjects, we show that our technique consistently outperforms state-of-the-art methods in the popular four-view classification test. We also present results for eight-view classification to demonstrate the scalability of our framework.
Uncalibrated synthetic aperture for defocus control
Authors: Natsumi Kusumoto, Shinsaku Hiura, Kosuke Sato
Venue: CVPR
Year: 2009

Exaggerated defocus can not be created with an ordinary compact digital camera because of its tiny sensor size, so it is hard to take pictures that attract a viewer to the main subject. On the other hand, there are many methods for controlling focus and defocus of previously taken pictures. However, most of these methods require purpose-built equipment such as a camera array to take pictures. Therefore, in this paper, we propose a method to create images focused at any depth with arbitrarily blurred background from the set of images taken by a handheld compact digital camera moved randomly. Using our method, it is possible to produce various aesthetic blurs by changing the size, shape or density of the blur kernel. In addition, we confirm the potential of our method through a subjective evaluation of blurred images created by our system.
Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive Basin Hopping Monte Carlo sampling
Authors: Junseok Kwon, Kyoung Mu Lee
Venue: CVPR
Year: 2009

We propose a novel tracking algorithm for the target of which geometric appearance changes drastically over time. To track it, we present a local patch-based appearance model and provide an efficient scheme to evolve the topology between local patches by on-line update. In the process of on-line update, the robustness of each patch in the model is estimated by a new method of measurement which analyzes the landscape of local mode of the patch. This patch can be moved, deleted or newly added, which gives more flexibility to the model. Additionally, we introduce the Basin Hopping Monte Carlo (BHMC) sampling method to our tracking problem to reduce the computational complexity and deal with the problem of getting trapped in local minima. The BHMC method makes it possible for our appearance model to consist of enough numbers of patches. Since BHMC uses the same local optimizer that is used in the appearance modeling, it can be efficiently integrated into our tracking framework. Experimental results show that our approach tracks the object whose geometric appearance is drastically changing, accurately and robustly.
Visual tracking via geometric particle filtering on the affine group with optimal importance functions
Authors: Junghyun Kwon, Kyoung Mu Lee, Frank Chongwoo Park
Venue: CVPR
Year: 2009

We propose a geometric method for visual tracking, in which the 2-D affine motion of a given object template is estimated in a video sequence by means of coordinate-invariant particle filtering on the 2-D affine group Aff(2). Tracking performance is further enhanced through a geometrically defined optimal importance function, obtained explicitly via Taylor expansion of a principal component analysis based measurement function on Aff(2). The efficiency of our approach to tracking is demonstrated via comparative experiments.
An empirical Bayes approach to contextual region classification
Authors: Svetlana Lazebnik, Maxim Raginsky
Venue: CVPR
Year: 2009

This paper presents a nonparametric approach to labeling of local image regions that is inspired by recent developments in information-theoretic denoising. The chief novelty of this approach rests in its ability to derive an unsupervised contextual prior over image classes from unlabeled test data. Labeled training data is needed only to learn a local appearance model for image patches (although additional supervisory information can optionally be incorporated when it is available). Instead of assuming a parametric prior such as a Markov random field for the class labels, the proposed approach uses the empirical Bayes technique of statistical inversion to recover a contextual model directly from the test data, either as a spatially varying or as a globally constant prior distribution over the classes in the image. Results on two challenging datasets convincingly demonstrate that useful contextual information can indeed be learned from unlabeled data.
Histogram-based interest point detectors
Authors: Wei-Ting Lee, Hwann-Tzong Chen
Venue: CVPR
Year: 2009

We present a new method for detecting interest points using histogram information. Unlike existing interest point detectors, which measure pixel-wise differences in image intensity, our detectors incorporate histogram-based representations, and thus can find image regions that present a distinct distribution in the neighborhood. The proposed detectors are able to capture large-scale structures and distinctive textured patterns, and exhibit strong invariance to rotation, illumination variation, and blur. The experimental results show that the proposed histogram-based interest point detectors perform particularly well for the tasks of matching textured scenes under blur and illumination changes, in terms of repeatability and distinctiveness. An extension of our method to space-time interest point detection for action classification is also presented.
Face verification and identification using Facial Trait Code
Authors: Ping-Han Lee, Gee-Sern Hsu, Yi-Ping Hung
Venue: CVPR
Year: 2009

We propose the Facial Trait Code (FTC) to encode human facial images. The proposed FTC is motivated by the discovery of some basic patterns existing in certain local facial features. We call these basic patterns Distinctive Trait Patterns (DTP), which can be extracted from a large number of faces. We have also found that the fusion of these DTP's can accurately capture the appearance of a face. The extraction of DTP involves clustering and boosting for maximizing the discrimination between human faces. The extracted DTP's can be symbolized and used to make up the n-ary facial trait codes. A given face can be encoded at some prescribed facial traits to render an n-ary facial trait code with each symbol in its codeword corresponding to the closest DTP. We applied FTC to a face identification and verification problems with 3575 facial images from 840 people under different illumination conditions, and it yielded satisfactory results.
Learning similarity measure for multi-modal 3D image registration
Authors: Daewon Lee, Matthias Hofmann, Florian Steinke, Yasemin Altun, Nathan D. Cahill, Bernhard Schölkopf
Venue: CVPR
Year: 2009

Multi-modal image registration is a challenging problem in medical imaging. The goal is to align anatomically identical structures; however, their appearance in images acquired with different imaging devices, such as CT or MR, may be very different. Registration algorithms generally deform one image, the floating image, such that it matches with a second, the reference image, by maximizing some similarity score between the deformed and the reference image. Instead of using a universal, but a priori fixed similarity criterion such as mutual information, we propose learning a similarity measure in a discriminative manner such that the reference and correctly deformed floating images receive high similarity scores. To this end, we develop an algorithm derived from max-margin structured output learning, and employ the learned similarity measure within a standard rigid registration algorithm. Compared to other approaches, our method adapts to the specific registration problem at hand and exploits correlations between neighboring pixels in the reference and the floating image. Empirical evaluation on CT-MR/PET-MR rigid registration tasks demonstrates that our approach yields robust performance and outperforms the state of the art methods for multi-modal medical image registration.
Curved glide-reflection symmetry detection
Authors: Seungkyu Lee 0001, Yanxi Liu 0001
Venue: CVPR
Year: 2009

We generalize reflection symmetry detection to a curved glide reflection symmetry detection problem. We propose a unifying, local feature based approach for curved glide reflection symmetry detection from real, unsegmented images, where the classic reflection symmetry becomes one of four special cases. Our method detects and groups statistically dominant local reflection axes in a 3D parameter space. A curved glid reflection symmetry axis is estimated by a set of contiguous local straight reflection axes. Experimental results of the proposed algorithm on 40 real world images demonstrate promising performance.
Compensation of motion artifacts in MRI via graph-based optimization
Authors: Tung-Ying Lee, Hong-Ren Su, Shang-Hong Lai, Ti-Chiun Chang
Venue: CVPR
Year: 2009

In two-dimensional Fourier transform magnetic resonance imaging (2DFT-MRI), patient/object motion during the image acquisition results in ghosting and blurring. These motion artifacts are commonly considered as a major limitation in the MRI community. To correct these artifacts without resorting to additional navigator echoes, most existing methods perform image quality measure to estimate motion; but they may easily fail when the motion is large. Viewed as a blind image restoration problem where the motion point spread function (PSF) is unknown, state-of-the-art restoration algorithms can not be easily applied because they cannot handle a complex PSF kernel that has the same size as the image. To overcome these challenges, we propose a novel approach that exploits the image structure to segment the kernel into several fragments. Based on this kernel representation, determining a kernel fragment can be formulated as a binary optimization problem, where each binary variable represents whether a segment in MR signals is corrupted by a certain motion or not. We establish a graphical model for these variables and estimate the kernel by minimizing an energy functional associated with the model. Experimental results show that the proposed method can provide satisfactory compensation of motion artifacts even when large motions are involved in the MR images.