OpenRaiser/Intern-Atlas huggingface dataset:
Limits of Learning-Based Superresolution Algorithms
Authors: Zhouchen Lin, Junfeng He, Xiaoou Tang, Chi-Keung Tang
Venue: ICCV
Year: 2007

Learning-based superresolution (SR) are popular SR techniques that use application dependent priors to infer the missing details in low resolution images (LRIs). However, their performance still deteriorates quickly when the magnification factor is moderately large. This leads us to an important problem: "Do limits of learning-based SR algorithms exist?" In this paper, we attempt to shed some light on this problem when the SR algorithms are designed for general natural images (GNIs). We first define an expected risk for the SR algorithms that is based on the root mean squared error between the superresolved images and the ground truth images. Then utilizing the statistics of GNIs, we derive a closed form estimate of the lower bound of the expected risk. The lower bound can be computed by sampling real images. By computing the curve of the lower bound w.r.t. the magnification factor, we can estimate the limits of learning-based SR algorithms, at which the lower bound of expected risk exceeds a relatively large threshold. We also investigate the sufficient number of samples to guarantee an accurate estimation of the lower bound.
Proximity Distribution Kernels for Geometric Context in Category Recognition
Authors: Haibin Ling, Stefano Soatto
Venue: ICCV
Year: 2007

We propose using the proximity distribution of vector- quantized local feature descriptors for object and category recognition. To this end, we introduce a novel "proximity distribution kernel" that naturally combines local geometric as well as photometric information from images. It satisfies Mercer's condition and can therefore be readily combined with a support vector machine to perform visual categorization in a way that is insensitive to photometric and geometric variations, while retaining significant discriminative power. In particular, it improves on the results obtained both with geometrically unconstrained "bags of features" approaches, as well as with over-constrained "affine procrustes." Indeed, we test this approach on several challenging data sets, including Graz-01, Graz-02, and the PASCAL challenge. We registered the average performance at 91.5% on Graz-01, 82.7% on Graz-02, and 74.5% on PASCAL. Our approach is designed to enforce and exploit geometric consistency among objects in the same category; therefore, it does not improve the performance of existing algorithms on datasets where the data is already roughly aligned and scaled. Our method has the potential to be extended to more complex geometric relationships among local features, as we illustrate in the experiments.
A Study of Face Recognition as People Age
Authors: Haibin Ling, Stefano Soatto, Narayanan Ramanathan, David W. Jacobs
Venue: ICCV
Year: 2007

In this paper we study face recognition across ages within a real passport photo verification task. First, we propose using the gradient orientation pyramid for this task. Discarding the gradient magnitude and utilizing hierarchical techniques, we found that the new descriptor yields a robust and discriminative representation. With the proposed descriptor, we model face verification as a two-class problem and use a support vector machine as a classifier. The approach is applied to two passport data sets containing more than 1,800 image pairs from each person with large age differences. Although simple, our approach outperforms previously tested Bayesian technique and other descriptors, including the intensity difference and gradient with magnitude. In addition, it works as well as two commercial systems. Second, for the first time, we empirically study how age differences affect recognition performance. Our experiments show that, although the aging process adds difficulty to the recognition task, it does not surpass illumination or expression as a confounding factor.
Unsupervised Image Categorization and Object Localization using Topic Models and Correspondences between Images
Authors: David Liu 0001, Tsuhan Chen
Venue: ICCV
Year: 2007

Topic models from the text understanding literature have shown promising results in unsupervised image categorization and object localization. Categories are treated as topics, and words are formed by vector quantizing local descriptors of image patches. Limitations of topic models include their weakness in localizing objects, and the requirement of a fairly large proportion of words coming from the object. We present a new approach that employs correspondences between images to provide information about object configuration, which in turn enhances the reliability of object localization and categorization. This approach is efficient, as it requires only a small number of correspondences. We show improved categorization and localization performance on real and synthetic data. Moreover, we can push the limits of topic models when the proportion of words coming from the object is very low.
A systematic approach for 2D-image to 3D-range registration in urban environments
Authors: Lingyun Liu, Ioannis Stamos
Venue: ICCV
Year: 2007

The photorealistic modeling of large-scale objects, such as urban scenes, requires the combination of range sensing technology and digital photography. In this paper, we attack the key problem of camera pose estimation, in an automatic and efficient way. First, the camera orientation is recovered by matching vanishing points (extracted from 2D images) with 3D directions (derived from a 3D range model). Then, a hypothesis-and-test algorithm computes the camera positions with respect to the 3D range model by matching corresponding 2D and 3D linear features. The camera positions are further optimized by minimizing a line-to-line distance. The advantage of our method over earlier work has to do with the fact we do not need to rely on extracted planar facades, or other higher-order features; we are utilizing low- level linear features. That makes this method more general, robust, and efficient. Our method can also be enhanced by the incorporation of traditional structure-from-motion algorithms. We have also developed a user-interface for allowing users to accurately texture-map 2D images onto 3D range models at interactive rates. We have tested our system in a large variety of urban scenes.
Hierarchical Model-Based Human Motion Tracking Via Unscented Kalman Filter
Authors: GuoJun Liu, Xianglong Tang, Jianhua Huang 0002, Jiafeng Liu, Da Sun
Venue: ICCV
Year: 2007

This paper presents a computer vision system for tracking high-speed non-rigid skaters over a large playing area in short track speeding skating competitions. The outputs of the tracking system are spatio-temporal trajectories of the players which can be further processed and analyzed by sport experts. Given very fast and non-smooth camera motions to capture highly complex and dynamic scenes of skating, tracking amorphous skaters should be a challenging task. We propose a new method of (1) automatically computing the transformation matrices to map each frame of the imagery to the globally consistent model of the rink and (2) incorporating the hierarchical model based on the contextual knowledge and multiple cues into the unscented Kalman filter to improve the tracking performance when occlusion occurs. Experimental results show that the proposed algorithm is very efficient and effective on video recorded live by the authors in the World Short Track Speed Skating Championships.
Gradient Feature Selection for Online Boosting
Authors: Xiaoming Liu 0002, Ting Yu 0003
Venue: ICCV
Year: 2007

Boosting has been widely applied in computer vision, especially after Viola and Jones's seminal work. The marriage of rectangular features and integral-image- enabled fast computation makes boosting attractive for many vision applications. However, this popular way of applying boosting normally employs an exhaustive feature selection scheme from a very large hypothesis pool, which results in a less-efficient learning process. Furthermore, this poses additional constraint on applying boosting in an onine fashion, where feature re-selection is often necessary because of varying data characteristic, but yet impractical due to the huge hypothesis pool. This paper proposes a gradient-based feature selection approach. Assuming a generally trained feature set and labeled samples are given, our approach iteratively updates each feature using the gradient descent, by minimizing the weighted least square error between the estimated feature response and the true label. In addition, we integrate the gradient-based feature selection with an online boosting framework. This new online boosting algorithm not only provides an efficient way of updating the discriminative feature set, but also presents a unified objective for both feature selection and weak classifier updating. Experiments on the person detection and tracking applications demonstrate the effectiveness of our proposal.
Deformable Image Mosaicing for Optical Biopsy
Authors: Kevin E. Loewke, David B. Camarillo, Kenneth Salisbury, Sebastian Thrun
Venue: ICCV
Year: 2007

Traditional image mosaicing usually relies on rigid image transformations. In many medical applications, however, tissue deformation during image acquisition or 3D parallax effects may require nonrigid transformations in the mosaicing process. This paper presents a new method that integrates deformable surface models into the image mosaicing algorithms. Our approach has two main contributions. First, we present a global alignment algorithm to efficiently deal with accumulated image registration errors. Second, we introduce a local alignment algorithm to accommodate local scene deformations. These two problems are integrated into a single optimization problem that simultaneously recovers the motion of the camera as well as the structure of the scene. Our approach is demonstrated on simulations, images from a hand-held digital camera, and microscopic images acquired with a micro-endoscope.
USSR: A Unified Framework for Simultaneous Smoothing, Segmentation, and Registration of Multiple Images
Authors: Nicholas A. Lord, Jeffrey Ho, Baba C. Vemuri
Venue: ICCV
Year: 2007

Image smoothing, segmentation and registration are three key processing steps in many computer vision applications. In this paper, we present a novel framework for achieving all three seemingly disparate goals simultaneously across multiple images in a unified framework via a single variational principle. The proposed method ensures that the estimated registration is unbiased and all compositions of registration maps are compatible. The solution to the variational problem is achieved efficiently by solving a coupled system of partial differential equations over the common domain on which the registration maps are defined. The effectiveness of the proposed framework is demonstrated on sets of real images.
High Dynamic Range Camera using Reflective Liquid Crystal
Authors: Hidetoshi Mannami, Ryusuke Sagawa, Yasuhiro Mukaigawa, Tomio Echigo, Yasushi Yagi
Venue: ICCV
Year: 2007

High dynamic range images (HDRIs) are needed for capturing scenes that include drastic lighting changes. This paper presents a method to improve the dynamic range of a camera by using a reflective liquid crystal. The system consists of a camera and a reflective liquid crystal placed in front of the camera. By controlling the attenuation rate of the liquid crystal, the scene radiance for each pixel is adaptively controlled. After the control, the original scene radiance is derived from the attenuation rate of the liquid crystal and the radiance obtained by the camera. A prototype system has been developed and tested for a scene that includes drastic lighting changes. The radiance of each pixel was independently controlled and the HDRIs were obtained by calculating the original scene radiance from these results.
Automatic Camera Network Localization using Object Image Tracks
Authors: Marci Meingast, Songhwai Oh, Shankar Sastry 0001
Venue: ICCV
Year: 2007

Camera networks are being used in more applications as different types of sensor networks are used to instrument large spaces. Here we show a method for localizing the cameras in a camera network to recover the orientation and position up to scale of each camera, even when cameras are wide-baseline or have different photometric properties. Using moving objects in the scene, we use an intra-camera step and an inter-camera step in order to localize. The intra-camera step compares frames from a single camera to build the tracks of the objects in the image plane of the camera. The inter-camera step uses these object image tracks from each camera as features for correspondence between cameras. We demonstrate this idea on both simulated and real data.
Locally-Constrained Region-Based Methods for DW-MRI Segmentation
Authors: John Melonakos, Marc Niethammer, Vandana Mohan, Marek Kubicki, James V. Miller, Allen R. Tannenbaum
Venue: ICCV
Year: 2007

In this paper, we describe a method for segmenting fiber bundles from diffusion-weighted magnetic resonance images using a locally-constrained region based approach. From a pre-computed optimal path, the algorithm propagates outward capturing only those voxels which are locally connected to the fiber bundle. Rather than attempting to find large numbers of open curves or single fibers, which individually have questionable meaning, this method segments the full fiber bundle region. The strengths of this approach include its ease-of-use, computational speed, and applicability to a wide range of fiber bundles. In this work, we show results for segmenting the cingulum bundle. Finally, we explain how this approach and extensions thereto overcome a major problem that typical region-based flows experience when attempting to segment neural fiber bundles.
Evaluation of Large Scale Scene Reconstruction
Authors: Paul Merrell, Philippos Mordohai, Jan-Michael Frahm, Marc Pollefeys
Venue: ICCV
Year: 2007

We present an evaluation methodology and data for large scale video-based 3D reconstruction. We evaluate the effects of several parameters and draw conclusions that can be useful for practical systems operating in uncontrolled environments. Unlike the benchmark datasets used for the binocular stereo and multi-view reconstruction evaluations, which were collected under well-controlled conditions, our datasets are captured outdoors using video cameras mounted on a moving vehicle. As a result, the videos are much more realistic and include phenomena such as exposure changes from viewing both bright and dim surfaces, objects at varying distances from the camera, and objects of varying size and degrees of texture. The dataset includes ground truth models and precise camera pose information. We also present an evaluation methodology applicable to reconstructions of large scale environments. We evaluate the accuracy and completeness of reconstructions obtained by two fast, visibility-based depth map fusion algorithms as parameters vary.
Separating Parts from 2D Shapes using Relatability
Authors: Xiaofeng Mi, Douglas DeCarlo
Venue: ICCV
Year: 2007

It's often important to analyze shapes as made up of parts. But there are two ways to think of how parts fit together. We can characterize the remainder of a shape after apart is removed; here we want to cut the shape so what remains has the simplest possible structure. Alternatively, we can cut out the part so that the part itself takes on a simple shape. These cuts do not directly give rise to a segmentation of the shape; a point inside the shape may associate with the part, the remainder, neither, or both. We present a new model for reconstructing these cuts based on the differential geometry of smoothed local symmetries. The model takes into account relatability (which characterizes clean cuts) to determine part boundaries. Our approach complements and unifies existing work on part- based segmentation of shape, and can be used to construct interesting simplifications of shapes.
Localizing Unordered Panoramic Images Using the Levenshtein Distance
Authors: Damien Michel, Antonis A. Argyros, Manolis I. A. Lourakis
Venue: ICCV
Year: 2007

This paper proposes a feature-based method for recovering the relative positions of the viewpoints of a set of panoramic images for which no a priori order information is available, along with certain structure information regarding the imaged environment. The proposed approach operates incrementally, employing the Levenshtein distance to deduce the spatial proximity of image viewpoints and thus determine the order in which images should be processed. The Levenshtein distance also provides matches between images, from which their underlying environment points can be recovered. Recovered points that are visible in multiple views permit the localization of more views which in turn allow the recovery of more points. The process repeats until all views have been localized. Periodic refinement of the reconstruction with the aid of bundle adjustment, distributes the reconstruction errors among images. The method is demonstrated on several unordered sets of panoramic images obtained in an indoor environment.
Circle-Marker Detection Method for Omnidirectional Images and its Application to Robot Positioning
Authors: Yoshihiko Mochizuki, Atsushi Imiya, Akihiko Torii
Venue: ICCV
Year: 2007

Introduction In this paper, we develop an algorithm for the detection of circles from an image captured by a monocular omnidirectional camera system. We assume that an image captured by an omnidirectional camera system is normalised to a spherical image, the image on the unit sphere. Using this geometrical property of the omnidirectional images, we introduce a method for marker-based positioning and navigation of autonomous mobile robots which mounts a monocular omnidirectional camera system. We first clarify the geometric properties of the spherical image of a circle marker placed on the ground plane, and we show that, for the detection of a plane from circle markers, we are required to capture at least two coplanar circle markers. We prove that the image of a circle on the spherical image is a fourth-order algebraic curve, which is the intersection of a sphere and an oblique elliptic cone. For the detection of marker images on the spherical images, we introduce a method for transforming the detection of this fourth-order algebraic curve to the detection of a spatial conic, which is quadric. Second, we develop a voting method for the extraction of images of planar circle markers on a spherical image, using the spatial-quadric detection strategy. Finally, using the assumption for the geometrical configuration of the camera system and the circle markers on the plane on which the robot moves, we derive a positioning algorithm. This positioning method for the robot mounting a monocular omnidirectional camera system allows us to navigate a robot using our circle-detection algorithm. We show some numerical examples both for synthetic and real images.
A new Omnidirectional Stereovision Sensor
Authors: El Mustapha Mouaddib, Gilles Dequen, Laure Devendeville
Venue: ICCV
Year: 2007

This paper describes a new compact omnidirectional stereovision sensor that combines a single orthographic camera and four paraboloidal mirrors. Its geometry has been designed with the help of a stochastic optimization approach in order to minimize the 3D reconstruction error In comparison with state-of-the-art sensors described in the literature, better results are obtained for this sensor during simulations. We will especially compare it with a classical configuration with two mirrors. Two criteria will be used. the 3D reconstruction accuracy and the field of view. We illustrate the advantages of our sensor within a framework of a simulation using a realistic environment and a ray-tracing software.
Generalized Median Graphs: Theory and Applications
Authors: Lopamudra Mukherjee, Vikas Singh, Jiming Peng, Jinhui Xu 0001, Michael J. Zeitz, Ronald Berezney
Venue: ICCV
Year: 2007

We study the so-called Generalized Median graph problem where the task is to to construct a prototype (i.e., a 'model') from an input set of graphs. The problem finds applications in many vision (e.g., object recognition) and learning problems where graphs are increasingly being adopted as a representation tool. Existing techniques for this problem are evolutionary search based; in this paper, we propose a polynomial time algorithm based on a linear programming formulation. We present an additional bi-level method to obtain solutions arbitrarily close to the optimal in non-polynomial time (in worst case). Within this new framework, one can optimize edit distance functions that capture similarity by considering vertex labels as well as the graph structure simultaneously. In context of our motivating application, we discuss experiments on molecular image analysis problems - the methods will provide the basis for building a topological map of all pairs of the human chromosome.
An Omnidirectional Vision Sensor with Single View and Constant Resolution
Authors: Hajime Nagahara, Koji Yoshida, Masahiko Yachida
Venue: ICCV
Year: 2007

Many omnidirectional vision sensors based on convex mirrors have been proposed for observing a 360 degrees field of view. Some have a single viewpoint using a hyperboloidal or parabolic mirror, others have a constant resolution property. Until now, there have not been any that have had both these properties. In this paper, we propose an omnidirectional vision sensor that has both properties; that is, a single viewpoint and a constant resolution. The proposed omnidirectional sensor uses two mirrors, which improves the degree of freedom of the design for satisfying each property. We discuss optimization of the design in terms of geometry and optics.
Depth Information by Stage Classification
Authors: Vladimir Nedovic, Arnold W. M. Smeulders, André Redert, Jan-Mark Geusebroek
Venue: ICCV
Year: 2007

Recently, methods for estimating 3D scene geometry or absolute scene depth information from 2D image content have been proposed. However, general applicability of these methods in depth estimation may not be realizable, as inconsistencies may be introduced due to a large variety of possible pictorial content. We identify scene categorization as the first step towards efficient and robust depth estimation from single images. To that end, we describe a limited number of typical 3D scene geometries, called stages, each having a unique depth pattern and thus providing a specific context for stage objects. This type of scene information narrows down the possibilities with respect to individual objects' locations, scales and identities. We show how these stage types can be efficiently learned and how they can lead to robust extraction of depth information. Our results indicate that stages without much variation and object clutter can be detected robustly, with up to 60% success rate.
Surface-from-Gradients with Incomplete Data for Single View Modeling
Authors: Heung-Sun Ng, Tai-Pang Wu, Chi-Keung Tang
Venue: ICCV
Year: 2007

Surface gradients are useful to surface reconstruction in single view modeling, shape-from-shading, and photometric stereo. Previous algorithms minimize a complex, nonlinear energy functional, or require dense surface gradients to perform integration to generate 3D locations, or require user-input heights to constrain the solution space, or produce severe distortion and smooth out surface details. Most single-view algorithms output a Monge patch (height-field), which may introduce further surface distortion along object silhouettes and surface orientation discontinuities. Our proposed algorithm operates on a single view of complete or incomplete data. The data can be gradients without 3D locations, or 3D locations without gradients. The output surface, which is not necessarily a height-field, preserves salient depth and orientation discontinuities. Experimental comparisons on both simple and complex data show that our method produces better surfaces with significantly less distortion and more details preserved. The implementation of our closed-form solution is very straightforward.