OpenRaiser/Intern-Atlas huggingface dataset:
Catadioptric Camera Calibration Using Geometric Invariants
Authors: Xianghua Ying, Zhanyi Hu
Venue: ICCV
Year: 2003

Central catadioptric cameras are imaging devices that use mirrors to enhance the field of view while preserving a single effective viewpoint. In this paper, we propose a novel method for the calibration of central catadioptric cameras using geometric invariants. Lines in space are projected into conics in the catadioptric image plane as well as spheres in space. We proved that the projection of a line can provide three invariants whereas the projection of a sphere can provide two. From these invariants, constraint equations for the intrinsic parameters of catadioptric camera are derived. Therefore, there are two variants of this novel method. The first one uses the projections of lines and the second one uses the projections of spheres. In general, the projections of two lines or three spheres are sufficient to achieve the catadioptric camera calibration. One important observation in this paper is that the method based on the projections of spheres is more robust and has higher accuracy than that using the projections of lines. The performances of our method are demonstrated by the results of simulations and experiments with real images.
Conditional Feature Sensitivity: A Unifying View on Active Recognition and Feature Selection
Authors: Xiang Sean Zhou, Dorin Comaniciu, Arun Krishnan
Venue: ICCV
Year: 2003

The objective of active recognition is to iteratively collect the next "best" measurements (e.g., camera angles or viewpoints), to maximally reduce ambiguities in recognition. However, existing work largely overlooked feature interaction issues. Feature selection, on the other hand, focuses on the selection of a subset of measurements for a given classification task, but is not context sensitive (i.e., the decision does not depend on the current input). This paper proposes a unified perspective through conditional feature sensitivity analysis, taking into account both current context and feature interactions. Based on different representations of the contextual uncertainties, we present three treatment models and exploit their joint power for dealing with complex feature interactions. Synthetic examples are used to systematically test the validity of the proposed models. A practical application in medical domain is illustrated using an echocardiography database with more than 2000 video segments with both subjective (from experts) and objective validations.
Binocular Helmholtz Stereopsis
Authors: Todd E. Zickler, Jeffrey Ho, David J. Kriegman, Jean Ponce, Peter N. Belhumeur
Venue: ICCV
Year: 2003

Helmholtz stereopsis has been introduced recently as a surface reconstruction technique that does not assume a model of surface reflectance. In the reported formulation, correspondence was established using a rank constraint, necessitating at least three viewpoints and three pairs of images. Here, it is revealed that the fundamental Helmholtz stereopsis constraint defines a nonlinear partial differential equation, which can be solved using only two images. It is shown that, unlike conventional stereo, binocular Helmholtz stereopsis is able to establish correspondence (and thereby recover surface depth) for objects having an arbitrary and unknown BRDF and in textureless regions (i.e., regions of constant or slowly varying BRDF). An implementation and experimental results validate the method for specular surfaces with and without texture.
Face Recognition in the Presence of Multiple Illumination Sources
Authors: Gaurav Aggarwal, Rama Chellappa
Venue: ICCV
Year: 2005

Most existing face recognition algorithms work well for controlled images but are quite susceptible to changes in illumination and pose. This has led to the rise of analysis-by-synthesis approaches due to their inherent potential to handle these external factors. Though these approaches work quite well, most of them assume that the face is illuminated by a single light source which is usually not true in realistic conditions. In this paper, we propose an algorithm to recognize faces illuminated by arbitrarily placed, multiple light sources. The algorithm does not need to know the number of light sources and works extremely well even while recognizing faces illuminated by different number of light sources. Results using this algorithm are reported on multiple-illumination datasets generated from PIE by T. Sim, et al. (2003) and Yale Face Database B by W. Zhao, et al. (2003). We also highlight the importance of the hard non-linearity in the Lambert's law which is often ignored, probably to linearize the estimation process
A Supervised Learning Framework for Generic Object Detection in Images
Authors: Saad Ali, Mubarak Shah
Venue: ICCV
Year: 2005

In recent years kernel principal component analysis (kernel PCA) has gained much attention because of its ability to capture nonlinear image features, which are particularly important for encoding image structure. Boosting has been established as a powerful learning algorithm that can be used for feature selection. In this paper we present a novel framework for object class detection that combines the feature reduction and feature selection abilities of kernel PCA and AdaBoost respectively. The classifier obtained in this way is able to handle change in object appearance, illumination conditions, and surrounding clutter. A nonlinear subspace is learned for positive and negative object classes using Kernel PCA. Features are derived by projecting example images onto the learned subspaces. Base learners are modeled using Bayes classifier. AdaBoost is then employed to discover the features that are most relevant for the object detection task at hand. The proposed method has been successfully tested on wide range of object classes (cars, air-planes, pedestrians, motorcycles, etc) using standard data sets and has shown remarkable performance. Using a small training set, a classifier learned in this way was able to generalize the intra-class variation while still maintaining high detection rate. In most object categories we achieved detection rates of above 95% with minimal false alarm rates. We demonstrate the effectiveness of our approach in terms of absolute performance parameters and comparative performance against current state of the art approaches.
Multi-View Surface Reconstruction Using Polarization
Authors: Gary A. Atkinson, Edwin R. Hancock
Venue: ICCV
Year: 2005

A new technique for surface reconstruction is developed that uses polarization information from two views. One common problem arising from many multiple view techniques is that of finding correspondences between pixels on each image. In the new method, these correspondences are found by exploiting the spontaneous polarization of light caused by reflection to recover surface normals. These normals are then used to recover surface height. The similarity between reconstructed surface regions determines whether or not a pair of points correspond to each other. The technique is thus able to overcome the convex/concave ambiguity found in many single view techniques. Because the technique relies on smooth surface regions to detect correspondences, rather than feature detection, it is applicable to objects normally inaccessible to stereo vision. Also due to this fact, it is possible to remove noise without causing oversmoothing problems.
Shape Recovery of 3D Data Obtained from a Moving Range Sensor by Using Image Sequences
Authors: Atsuhiko Banno, Katsushi Ikeuchi
Venue: ICCV
Year: 2005

For a large object, scanning from the air is one of the most efficient methods of obtaining 3D data. In the case of large cultural heritage objects, there are some difficulties in scanning with respect to safety and efficiency. To remedy these problems, we have been developing a novel 3D measurement system, the floating laser range sensor (FLRS), in which a range sensor is suspended beneath a balloon. The obtained data, however, have some distortions due to sensor-movements during the scanning process. In this paper, we propose a method to recover 3D range data obtained by a moving laser range sensor. This method is applicable not only to our FLRS, but also to a general moving range sensor. Using image sequences from a video camera mounted on the FLRS enables us to estimate the motion of the FLRS without any physical sensors such as gyros or GPS. In the first stage, the initial values of camera motion parameters are estimated by full-perspective factorization. The next stage refines camera motion parameters using the relationships between camera images and range data distortion. Finally, by using the refined parameters, the distorted range data are recovered. In addition, our method is applicable with an uncalibrated video camera and range sensor system. We applied this method to an actual scanning project, and the results showed the effectiveness of our method.
Efficient Learning of Relational Object Class Models
Authors: Aharon Bar-Hillel, Tomer Hertz, Daphna Weinshall
Venue: ICCV
Year: 2005

We present an efficient method for learning part-based object class models. The models include location and scale relations between parts, as well as part appearance. Models are learnt from raw object and background images, represented as an unordered set of features extracted using an interest point detector. The object class is generatively modeled using a simple Bayesian network with a central hidden node containing location and scale information, and nodes describing object parts. The model's parameters, however are optimized to reduce a loss function which reflects training error as in discriminative methods. Specifically, the optimization is done using a boosting-like technique with complexity linear in the number of parts and the number of features per image. This efficiency allows our method to learn relational models with many parts and features, and leads to improved results when compared with other methods. Extensive experimental results are described, using some common bench-mark datasets and three sets of newly collected data, showing the relative advantage of our method.
Incorporating Visual Knowledge Representation in Stereo Reconstruction
Authors: Adrian Barbu, Song Chun Zhu
Venue: ICCV
Year: 2005

In this paper, we present a two-layer generative model that incorporates generic middle-level visual knowledge for dense stereo reconstruction. The visual knowledge is represented by a dictionary of surface primitives including various categories of boundary discontinuities and junctions in parametric form. Given a stereo pair, we first compute a primal sketch representation which decomposes the image into a structural part for object boundaries and high intensity contrast represented by a 2D sketch graph, and a structure less part represented by Markov random field on pixels. Then we label the sketch graph and compute the 3D sketch (like a wire-frame) by fitting the primitive dictionary to the sketch graph. The surfaces between the 3D sketches are filled in by computing the depth of the MRP model on the structureless part. These two levels interact closely since the MRF is used to propagate information between the primitives, and at the same time, the primitives act as boundary conditions for the MRF. The two processes maximize a Bayesian posterior probability jointly. We propose an MCMC algorithm that simultaneously infers the 3D primitive types and parameters and estimates the depth of the scene. Our experiments show that this representation can infer the depth map with sharp boundaries and junctions for textureless images, curve objects and free-form shapes.
Regular Polygon Detection
Authors: Nick Barnes, Gareth Loy, David Shaw, Antonio Robles-Kelly
Venue: ICCV
Year: 2005

This paper describes a new robust regular polygon detector. The regular polygon transform is posed as a mixture of regular polygons in a five dimensional space. Given the edge structure of an image, we derive the a posteriori probability for a mixture of regular polygons, and thus the probability density function for the appearance of a mixture of regular polygons. Likely regular polygons can be isolated quickly by discretising and collapsing the search space into three dimensions. The remaining dimensions may be efficiently recovered subsequently using maximum likelihood at the locations of the most likely polygons in the subspace. This leads to an efficient algorithm. Also the a posteriori formulation facilitates inclusion of additional a priori information leading to real-time application to road sign detection. The use of gradient information also reduces noise compared to existing approaches such as the generalised Hough transform. Results are presented for images with noise to show stability. The detector is also applied to two separate applications: real-time road sign detection for on-line driver assistance; and feature detection, recovering stable features in rectilinear environments.
Good Continuation of General 2D Visual Features: Dual Harmonic Models and Computational Inference
Authors: Ohad Ben-Shahar, Steven W. Zucker
Venue: ICCV
Year: 2005

Good continuation is a fundamental principle of perceptual organization that guides the grouping of parts based on how they should succeed one another within coherent wholes. Despite the general language that was used by the Gestalt psychologists in phrasing this principle, computational work has focused almost exclusively on the study of curve-like structures. Here we offer for the first time, a rigorous generalization of good continuation to arbitrary visual structures that can be abstracted as scalar functions over the image plane. The differential geometry of these structures dictates that their good continuation should be based both on their value and on the geometry of their level sets, which yield a coupled system of equations solvable for a formal model. We exhibit the resulting computation on shading and intensity functions, demonstrating how it eliminates spurious measurements while preserving both regular structure and singularities. Related implementations could be applied to color channels, motion magnitude, and disparity signals
Actions as Space-Time Shapes
Authors: Moshe Blank, Lena Gorelick, Eli Shechtman, Michal Irani, Ronen Basri
Venue: ICCV
Year: 2005

Human action in video sequences can be seen as silhouettes of a moving torso and protruding limbs undergoing articulated motion. We regard human actions as three-dimensional shapes induced by the silhouettes in the space-time volume. We adopt a recent approach by Gorelick et al. (2004) for analyzing 2D shapes and generalize it to deal with volumetric space-time action shapes. Our method utilizes properties of the solution to the Poisson equation to extract space-time features such as local space-time saliency, action dynamics, shape structure and orientation. We show that these features are useful for action recognition, detection and clustering. The method is fast, does not require video alignment and is applicable in (but not limited to) many scenarios where the background is known. Moreover, we demonstrate the robustness of our method to partial occlusions, non-rigid deformations, significant changes in scale and viewpoint, high irregularities in the performance of an action and low quality video
Detecting Irregularities in Images and in Video
Authors: Oren Boiman, Michal Irani
Venue: ICCV
Year: 2005

We address the problem of detecting irregularities in visual data, e.g., detecting suspicious behaviors in video sequences, or identifying salient patterns in images. The term "irregular" depends on the context in which the "regular" or "valid" are defined. Yet, it is not realistic to expect explicit definition of all possible valid configurations for a given context. We pose the problem of determining the validity of visual data as a process of constructing a puzzle: We try to compose a new observed image region or a new video segment ("the query") using chunks of data ("pieces of puzzle") extracted from previous visual examples ("the database "). Regions in the observed data which can be composed using large contiguous chunks of data from the database are considered very likely, whereas regions in the observed data which cannot be composed from the database (or can be composed, but only using small fragmented pieces) are regarded as unlikely/suspicious. The problem is posed as an inference process in a probabilistic graphical model. We show applications of this approach to identifying saliency in images and video, and for suspicious behavior recognition.
A Theoretical Limit on the Number of Effective Pixels that can be Optically Resolved on a Non-Planar Subject
Authors: Robert Boman
Venue: ICCV
Year: 2005

In normal imaging systems, the depth of field is inversely-proportional to the lens aperture. If we assume the system is diffraction limited, then the maximum resolution (i.e. pixels per mm) is proportional to the lens aperture. Thus there is a tradeoff between depth of field and resolution. This tradeoff creates an upper limit on the number of pixels that can be resolved on a nonplanar subject. This paper presents the theoretical limit on the number of pixels. The derivations of the limit show that the limit is only a function of the subject size and depth. The subject distance, focal length, and sensor size do not matter. For small subjects, the limit is well below the capabilities of modern imaging systems. For example, a subject 15 cm wide and 10cm deep can only be imaged with 300,000 pixels even though sensors with 10 times that many pixels are readily available. The resulting limit has obvious applications in machine vision, particularly when specifying optics and imaging sensors. Experimental results are provided to validate the main result of the paper.
Integrated Spatial and Frequency Domain 2D Motion Segmentation and Estimation
Authors: Alexia Briassouli, Narendra Ahuja
Venue: ICCV
Year: 2005

A video containing multiple objects in rotational and translational motion is analyzed through a combination of spatial and frequency domain representations. It is argued that the combined analysis can take advantage of the strengths of both representations. Initial estimates of constant, as well as time-varying, translation and rotation velocities are obtained from frequency analysis. Improved motion estimates and motion segmentation for the case of translation are achieved by integrating spatial and Fourier domain information. For combined rotational and translational motions, the frequency representation is used for motion estimation, but only spatial information can be used to separate and extract the independently moving objects. The proposed algorithms are tested on synthetic and real videos.
3D Object Reconstruction from a Single 2D Line Drawing without Hidden Lines
Authors: Liangliang Cao, Jianzhuang Liu, Xiaoou Tang
Venue: ICCV
Year: 2005

The human vision system can interpret a single 2D line drawing as a 3D object without much difficulty even if the hidden lines of the object are invisible. Several reconstruction approaches have tried to emulate this ability, but they cannot recover the complete object if the hidden lines of the object are not shown. This paper proposes a novel approach for reconstructing complete 3D objects from line drawings without hidden lines. First, we develop some constraints and properties for the inference of the topology of the invisible edges and vertices of an object. Then we present a reconstruction method based on perceptual symmetry and planarity of the object. We give a number of examples to demonstrate the ability of our approach.
Large Deformation Diffeomorphic Metric Mapping of Fiber Orientations
Authors: Yan Cao, Michael I. Miller, Raimond L. Winslow, Laurent Younes
Venue: ICCV
Year: 2005

This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRI) through the large deformation diffeomorphic metric mapping of vector fields, focusing on the fiber orientations, considered as unit vector fields on the image volume. We study a suitable action of diffeomorphisms on such vector fields, and provide an extension of the large deformation diffeomorphic metric-mapping framework to this type of dataset, resulting in optimizing for geodesies on the space of diffeomorphisms connecting two images. Two different distance function of vector fields are considered. Existence of the minimizers under smoothness assumptions on the compared vector fields is proved, and coarse to fine hierarchical strategies are detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI heart and brain images
Robust Path-Based Spectral Clustering with Application to Image Segmentation
Authors: Hong Chang 0001, Dit-Yan Yeung
Venue: ICCV
Year: 2005

Spectral clustering and path-based clustering are two recently developed clustering approaches that have delivered impressive results in a number of challenging clustering tasks. However, they are not robust enough against noise and outliers in the data. In this paper, based on M-estimation from robust statistics, we develop a robust path-based spectral clustering method by defining a robust path-based similarity measure for spectral clustering. Our method is significantly more robust than spectral clustering and path-based clustering. We have performed experiments based on both synthetic and real-world data, comparing our method with some other methods. In particular, color images from the Berkeley segmentation dataset and benchmark are used in the image segmentation experiments. Experimental results show that our method consistently outperforms other methods due to its higher robustness.
Image Statistics Based on Diffeomorphic Matching
Authors: Guillaume Charpiat, Olivier D. Faugeras, Renaud Keriven
Venue: ICCV
Year: 2005

We propose a new approach to deal with the first and second order statistics of a set of images. These statistics take into account the images characteristic deformations and their variations in intensity. The central algorithm is based on nonsupervised diffeomorphic image matching (without landmarks or human intervention). As they convey the notion of the mean shape and colors of an object and the one of its common variations, such statistics of sets of images may be relevant in the context of object recognition, both in the segmentation of any of its representations and in the classification of them. The proposed approach has been tested on a small database of face images to compute a mean face and second order statistics. The results are very encouraging since, whereas the algorithm does not need any human intervention and is not specific to face image databases, the mean image looks like a real face and the characteristic modes of variation (deformation and intensity changes) are sensible.