OpenRaiser/Intern-Atlas huggingface dataset:
Designing Spatially Coherent Minimizing Flows for Variational Problems Based on Active Contours
Authors: Guillaume Charpiat, Renaud Keriven, Jean-Philippe Pons, Olivier D. Faugeras
Venue: ICCV
Year: 2005

This paper tackles an important aspect of the variational problems involving active contours, which has been largely overlooked so far: the optimization by gradient flows. Classically, the definition of a gradient depends directly on the choice of an inner product structure. This consideration is largely absent from the active contours literature. Most authors, overtly or covertly, assume that the space of admissible deformations is ruled by the canonical L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> inner product. The classical gradient flows reported in the literature are relative to this particular choice. In this paper, we investigate the relevance of using other inner products, yielding other gradient descents, and some other minimizing flows not deriving from any inner product. In particular we, show how to induce different degrees of spatial coherence into the minimizing flow, in order to decrease the probability of getting trapped into irrelevant local minima. We show with some numerical experiments that the sensitivity of the active contours method to initial conditions, which seriously limits its applicability and its efficiency, is alleviated by our application-specific spatially coherent minimizing flows
Learning Effective Image Metrics from Few Pairwise Examples
Authors: Hwann-Tzong Chen, Tyng-Luh Liu, Chiou-Shann Fuh
Venue: ICCV
Year: 2005

We present a new approach to learning image metrics. The main advantage of our method lies in a formulation that requires only a few pairwise examples. Apparently, based on the little amount of side-information, it would take a very effective learning scheme to yield a useful image metric. Our algorithm achieves this goal by addressing two key issues. First, we establish a global-local (glocal) image representation that induces two structure-meaningful vector spaces to respectively describe the global and the local image properties. Second, we develop a metric optimization framework that finds an optimal bilinear transform to best explain the given side-information. We emphasize it is the glocal image representation that makes the use of bilinear transform more powerful. Experimental results on classifications of face images and visual tracking are included to demonstrate the contributions of the proposed method.
Mutual Information Regularized Bayesian Framework for Multiple Image Restoration
Authors: Yunqiang Chen, Hongcheng Wang, Tong Fang, Jason Tyan
Venue: ICCV
Year: 2005

Bayesian methods have been extensively used in various applications. However, there are two intrinsic issues rarely addressed, namely generalization and validity, in the context of multiple image restoration, we show that traditional Bayesian methods are sensitive to model errors and cannot guarantee valid results satisfying the underlying prior knowledge, e.g. independent noise property. To improve the Bayesian framework's generalization, we propose to explicitly enforce the validity of the result. Independent noise prior is very important but largely under-utilized in previous literature. In this paper, we use mutual information (MI) to explicitly enforce the independence. Efficient approximations based on Taylor expansion are proposed to adapt MI into standard energy forms to regularize the Bayesian methods. The new regularized Bayesian framework effectively utilizes the traditional generative signal/noise models but is much more robust to various model errors, as demonstrated in experiments on some demanding imaging applications.
Registration of Multimodal Fluorescein Images Sequence of the Retina
Authors: Tae Eun Choe, Isaac Cohen
Venue: ICCV
Year: 2005

In this study, we present a Y-feature extraction method for registering color and fluorescein angiograms of the retina. The registration of multimodal fluorescein imagery requires the identification of strong geometric features in the retinal images that are invariant across modalities and to temporal grey level variations due to the propagation of the dye in the vessels. The most informative features, invariant across the considered modalities, are the locations of vessels' ramification: the so-called Y-features. We propose a Y-feature extraction method based on the local classification of image gradient information and an articulated model. An appropriate cost function is proposed for fitting the model using a gradient based approach. The fitted Y-features are subsequently matched across the images for registering the color and fluorescein images. Experimental results obtained on a large database validate the proposed method.
Object Recognition in High Clutter Images Using Line Features
Authors: Philip David, Daniel DeMenthon
Venue: ICCV
Year: 2005

We present an object recognition algorithm that uses model and image line features to locate complex objects in high clutter environments. Finding correspondences between model and image features is the main challenge in most object recognition systems. In our approach, corresponding line features are determined by a three-stage process. The first stage generates a large number of approximate pose hypotheses from correspondences of one or two lines in the model and image. Next, the pose hypotheses from the previous stage are quickly ranked by comparing local image neighborhoods to the corresponding local model neighborhoods. Fast nearest neighbor and range search algorithms are used to implement a distance measure that is unaffected by clutter and partial occlusion. The ranking of pose hypotheses is invariant to changes in image scale, orientation, and partially invariant to affine distortion. Finally, a robust pose estimation algorithm is applied for refinement and verification, starting from the few best approximate poses produced by the previous stages. Experiments on real images demonstrate robust recognition of partially occluded objects in very high clutter environments
BRDF Invariant Stereo Using Light Transport Constancy
Authors: James E. Davis, Ruigang Yang, Liang Wang 0002
Venue: ICCV
Year: 2005

Nearly all existing methods for stereo reconstruction assume that scene reflectance is Lambertian, and make use of color constancy as a matching invariant. We introduce a new invariant for stereo reconstruction called light transport constancy, which allows completely arbitrary scene reflectance (BRDFs). This invariant can be used to formulate a rank constraint on multiview stereo matching when the scene is observed in several lighting configurations. In addition, we show that this multiview constraint can be used with as few as two cameras and two lighting configurations. Unlikely previous methods for BRDF invariant stereo, light transport constancy does not require precisely configured or calibrated light sources, nor calibration objects in the scene. Importantly, the new constraint can be used to provide BRDF invariance to any existing stereo method, whenever appropriate lighting variation is available.
Can Two Specular Pixels Calibrate Photometric Stereo?
Authors: Ondrej Drbohlav, Mike J. Chantler
Venue: ICCV
Year: 2005

Lambertian photometric stereo with unknown light source parameters is ambiguous. Provided that the object imaged constitutes a surface, the ambiguity is represented by the group of generalised bas-relief (GBR) transformations. We show that this ambiguity is resolved when specular reflection is present in two images taken under two different light source directions. We identify all configurations of the two directional lights which are singular and show that they can easily be tested for. While previous work used optimisation algorithms to apply the constraints implied by the specular reflectance component, we have developed a linear algorithm to achieve this goal. Our theory can be utilised to construct fast algorithms for automatic reconstruction of smooth glossy surfaces.
Incremental Discovery of Object Parts in Video Sequences
Authors: Stéphane Drouin, Patrick Hébert, Marc Parizeau
Venue: ICCV
Year: 2005

This paper addresses the fundamental problem of automatically discovering an unknown moving deformable object in a monocular video sequence. No prior model of the object is used; it is only assumed that the object is composed of a set of apparently rigid parts that are not necessarily visible simultaneously, making it possible to circumvent the typical constraint of model initialization. A set of rigid parts describing the object is incrementally extracted in a modeling-tracking loop with reinforced memory. In this framework, low-level segmentation is considered as a necessary but non reliable process that helps initiating hypotheses. Motion-based layer segmentation from feature points and edges is applied only when and where no modeled parts can be tracked. Using the quantity of motion measure, it is further shown how to deal with temporal scale. The interest for this approach in applications such as human tracking is demonstrated for a set of various sequences including a rapidly evolving shape
Variational-Based Method to Extract Parametric Shapes from Images
Authors: Moumen T. El-Melegy, Nagi H. Al-Ashwal, Aly A. Farag
Venue: ICCV
Year: 2005

In this paper, we propose a variational method to segment image objects, which have a given parametric shape based on a level-set formulation of the Mumford-Shah functional, and the shape parameters. We define an energy functional composed by two complementary terms. The first one detects object boundaries using a Chan-Vese-like method. The second term constrains the contour to find a shape compatible with the parametric shape. The segmentation of the object of interest is given by the minimum of our energy functional. This minimum is computed with the calculus of variation and the gradient descent method that provide a system of evolution equations solved with the well-known level set method. We focus in this paper on the parametric category of image linear objects. Applications of the proposed model are presented on synthetic and real images.
Identifying Individuals in Video by Combining "Generative" and Discriminative Head Models
Authors: Mark Everingham, Andrew Zisserman
Venue: ICCV
Year: 2005

The objective of this work is automatic detection and identification of individuals in unconstrained consumer video, given a minimal number of labelled faces as training data. Whilst much work has been done on (mainly frontal) face detection and recognition, current methods are not sufficiently robust to deal with the wide variations in pose and appearance found in such video. These include variations in scale, illumination, expression, partial occlusion, motion blur, etc. We describe two areas of innovation: the first is to capture the 3-D appearance of the entire head, rather than just the face region, so that visual features such as the hairline can be exploited. The second is to combine discriminative and 'generative' approaches for detection and recognition. Images rendered using the head model are used to train a discriminative tree-structured classifier giving efficient detection and pose estimates over a very wide pose range with three degrees of freedom. Subsequent verification of the identity is obtained using the head model in a 'generative' framework. We demonstrate excellent performance in detecting and identifying three characters and their poses in a TV situation comedy
Probabilistic Contour Extraction Using Hierarchical Shape Representation
Authors: Xin Fan, Chun Qi, Dequn Liang, Hua Huang 0001
Venue: ICCV
Year: 2005

In this paper, we address the issue of extracting contour of the object with a specific shape. A hierarchical graphical model is proposed to represent shape variations. A complex shape is decomposed into several components which are described as principal component analysis (PCA) based models in various levels. The hierarchical representation allows for chain-like conditional dependency within a single level and bidirectional communication between different levels. Additionally, a sequential Monte-Carlo (SMC) based inference algorithm that can explore the graphical structure is proposed to estimate the contour. The experiments performed on real-world hand and face images show that the proposed method is effective in combating occlusion and cluttered background. Moreover, it is possible to isolate the localization error to an individual component of a shape attributed to the hierarchical representation.
Realtime IBR with Omnidirectional Crossed-Slits Projection
Authors: Doron Feldman, Daphna Weinshall
Venue: ICCV
Year: 2005

The crossed-slits (X-Slits) projection can be used to generate new views of a scene from a sequence of perspective images. Compared with other image-based rendering (IBR) techniques, X-Slits image generation is simple and requires a relatively small number of input images, which makes it suitable for realtime IBR. In this paper we extend this model to omnidirectional cameras and a circular slit. We show how it can be used for realtime image-based rendering of omnidirectional images, and how to optimize it for speed and quality. We analyze the inherent geometric distortions of the circular X-Slits projection, and describe a normalization mechanism to reduce distortions, creating a realistic virtual environment. Essentially the same mechanism is used to augment the X-Slits images with artificial objects, when using standard graphics tools which assume perspective projection.
Bayesian Structural Content Abstraction for Region-Level Image Authentication
Authors: Wei Feng 0005, Zhi-Qiang Liu
Venue: ICCV
Year: 2005

We present a hierarchical representation of image structure and use it for image content authentication. Firstly, we model the image with the Markov pixon random field. Within the Bayesian framework, the optimal label map and regional pixon map can be obtained, based on which we define an undirected graph, namely Bayesian structural content abstraction (BaSCA). This representation captures the spatial topology information of homogeneous regions as well as their finest scale and interactions. Then, an efficient optimization scheme has been proposed to iteratively minimize the distance (or learning error) to all content-identical image samples generated by an acceptable operation set defined by the user. In addition, we use the regional pixon map to remove spurious vertices and thus to establish a BaSCA hierarchy naturally The BaSCA itself and its features can act as the signature of the protected image. Our experimental results show that the proposed approach has much less false positive and comparable false negative probability compared with the existing methods.
Building a Classification Cascade for Visual Identification from One Example
Authors: Andras Ferencz, Erik G. Learned-Miller, Jitendra Malik
Venue: ICCV
Year: 2005

Object identification (OID) is specialized recognition where the category is known (e.g. cars) and the algorithm recognizes an object's exact identity (e.g. Bob's BMW). Two special challenges characterize OID. (1) Interclass variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. (2) There may be many classes but few or just one positive "training" examples per class. Due to (1), a solution must locate possibly subtle object-specific salient features (a door handle) while avoiding distracting ones (a specular highlight). However, (2) rules out direct techniques of feature selection. We describe an online algorithm that takes one model image from a known category and builds an efficient "same" vs. "different" classification cascade by predicting the most discriminative feature set for that object. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered feature sequence unique to a specific model image, maximizing cumulative information content. Learned stopping thresholds make the classifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category, without prior knowledge about the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods.
Discontinuity Preserving Stereo with Small Baseline Multi-Flash Illumination
Authors: Rogério Schmidt Feris, Ramesh Raskar, Longbin Chen, Kar-Han Tan, Matthew Turk 0001
Venue: ICCV
Year: 2005

Currently, sharp discontinuities in depth and partial occlusions in multiview imaging systems pose serious challenges for many dense correspondence algorithms. However, it is important for 3D reconstruction methods to preserve depth edges as they correspond to important shape features like silhouettes which are critical for understanding the structure of a scene. In this paper, we show how active illumination algorithms can produce a rich set of feature maps that are useful in dense 3D reconstruction. We start by showing a method to compute a qualitative depth map from a single camera, which encodes object relative distances and can be used as a prior for stereo. In a multiview setup, we show that along with depth edges, binocular half-occluded pixels can also be explicitly and reliably labeled. To demonstrate the usefulness of these feature maps, we show how they can be used in two different algorithms for dense stereo correspondence. Our experimental results show that our enhanced stereo algorithms are able to extract high quality, discontinuity preserving correspondence maps from scenes that are extremely challenging for conventional stereo methods.
Robust Structure from Motion and Identified Dynamics
Authors: Benjamin R. Fransen, Octavia I. Camps, Mario Sznaier
Venue: ICCV
Year: 2005

This paper addresses robust recovery of structure and motion for rigid bodies in video sequences. For small inter-frame motion, feature appearance is commonly estimated according to the previous frame. However, in the case of occlusion or corrupt frames, the small interframe motion model fails. In this paper, we propose to use robust identification techniques to estimate the motion dynamics based on a set of previous frames. These dynamics are then recursively used to robustly estimate object structure and motion and to predict the object appearance in future frames. Results are tested for 3D reconstructions of rigid bodies in real and synthetic image sequences.
Efficient Block Noise Removal Based on Nonlinear Manifolds
Authors: Haoying Fu, Hongyuan Zha, Jesse L. Barlow
Venue: ICCV
Year: 2005

The problem of block noise removal is considered. It is assumed that the original image is on or close to a sub-space of admissible images in the form of a low dimensional nonlinear manifold. We propose to use a close variant of the total variation regularizer for measuring block noise. Based on this noise measure, we present an effective approach that reconstructs the original image in the presence of block noise. Our main computational task is the solution of a quadratic programming problem, for which we propose a very efficient interior point method. The effectiveness and efficiency of our approach is demonstrated by an example.
Vignette and Exposure Calibration and Compensation
Authors: Dan B. Goldman, Jiun-Hung Chen
Venue: ICCV
Year: 2005

We discuss calibration and removal of "vignetting" (radial falloff) and exposure (gain) variations from sequences of images. Unique solutions for vignetting, exposure and scene radiances are possible when the response curve is known. When the response curve is unknown, an exponential ambiguity prevents us from recovering these parameters uniquely. However, the vignetting and exposure variations can nonetheless be removed from the images without resolving this ambiguity. Applications include panoramic image mosaics, photometry for material reconstruction, image-based rendering, and preprocessing for correlation-based vision algorithms.
Shape and Spatially-Varying BRDFs from Photometric Stereo
Authors: Dan B. Goldman, Brian Curless, Aaron Hertzmann, Steven M. Seitz
Venue: ICCV
Year: 2005

This paper describes a photometric stereo method designed for surfaces with spatially-varying BRDFs, including surfaces with both varying diffuse and specular properties. Our method builds on the observation that most objects are composed of a small number of fundamental materials. This approach recovers not only the shape but also material BRDFs and weight maps, yielding compelling results for a wide variety of objects. We also show examples of interactive lighting and editing operations made possible by our method.
A Semi-Supervised Framework for Mapping Data to the Intrinsic Manifold
Authors: Haifeng Gong, Chunhong Pan, Qing Yang 0002, Hanqing Lu, Songde Ma
Venue: ICCV
Year: 2005

This paper presents a novel scheme for manifold learning. Different from the previous work reducing data to Euclidean space which cannot handle the looped manifold well, we map the scattered data to its intrinsic parameter manifold by semisupervised learning. Given a set of partially labeled points, the map to a specified parameter manifold is computed by an iterative neighborhood average method called anchor points diffusion procedure (APD). We explore this idea on the most frequently used close formed manifolds, Stiefel manifolds whose special cases include hyper sphere and orthogonal group. The experiments show that APD can recover the underlying intrinsic parameters of points on scattered data manifold successfully.
On Optimizing Template Matching via Performance Characterization
Authors: Tony X. Han, Visvanathan Ramesh, Ying Zhu 0006, Thomas S. Huang
Venue: ICCV
Year: 2005

Template matching is a fundamental operator in computer vision and is widely used in feature tracking, motion estimation, image alignment, and mosaicing. Under a certain parameterized warping model, the traditional template matching algorithm estimates the geometric warp parameters that minimize the SSD between the target and a warped template. The performance of the template matching can be characterized by deriving the distribution of warp parameter estimate as a function of the ideal template, the ideal warp parameters, and a given noise or perturbation model. In this paper, we assume a discretization of the warp parameter space and derive the theoretical expression for the probability mass function (PMF) of the parameter estimate. As the PMF is also a function of the template size, we can optimize the choice of the template or block size by determining the template/block size that gives the estimate with minimum entropy. Experimental results illustrate the correctness of the theory. An experiment involving feature point tracking in face video is shown to illustrate the robustness of the algorithm in a real-world problem.
Multiple Light Sources and Reflectance Property Estimation Based on a Mixture of Spherical Distributions
Authors: Kenji Hara, Ko Nishino, Katsushi Ikeuchi
Venue: ICCV
Year: 2005

In tins paper we propose a new method for simultaneously estimating the illumination of the scene and the reflectance property of an object from a single image. We assume that the illumination consists of multiple point sources and the shape of the object is known. Unlike previous methods, we will recover not only the direction and intensity of the light sources, but also the number of light sources and the specular reflection parameter of the object. First, we represent the illumination on the surface of a unit sphere as a finite mixture of von Mises-Fisher distributions by deriving a spherical specular reflection model. Next, we estimate this mixture and the number of distributions. Finally, using this result as initial estimates, we refine the estimates using the original specular reflection model. We can use the results to render the object under novel lighting conditions