OpenRaiser/Intern-Atlas huggingface dataset:
Recovering Photometric Properties of Multiple Strongly-Reflective, Partially-Transparent Surfaces from a Single Image
Authors: José P. Queiroz-Neto, Rodrigo L. Carceroni, Lara C. R. Coelho
Venue: ICCV
Year: 2005

This paper introduces a method to recover photometric parameters of a set of 3D surfaces from a single image with significant global-illumination effects such as inter-reflections and transparencies. Since this problem is ambiguous for arbitrary unknown scenes, our formulation assumes that the scene consists of a small set of photometrically homogeneous surfaces with known 3D shapes, illuminated by known light sources. We show that under these conditions, the system of nonlinear equations that defines how the image is formed may be factorized into a vector composed only of products of some photometric parameters, and a matrix, whose elements depend non-linearly on both the known illumination, the known 3D shapes and the remaining photometric parameters. This factorization leads to an efficient optimization-based algorithm to compute all unknown photometric parameters from a single input image. Experiments with real data show that this algorithm is more stable and efficient than simpler alternatives
Segmentation of Hybrid Motions via Hybrid Quadratic Surface Analysis
Authors: Shankar R. Rao, Allen Y. Yang, Andrew Wagner, Yi Ma 0001
Venue: ICCV
Year: 2005

In this paper, we investigate the mathematical problem underlying segmentation of hybrid motions: given a series of tracked feature correspondences between two (perspective) images, we seek to segment and estimate multiple motions, possibly of different types (e.g., affine, epipolar, and homography). In order to accomplish this task, we cast the problem into a more general mathematical framework of segmenting data samples drawn from a mixture of linear subspaces and quadratic surfaces. The result is a novel algorithm called hybrid quadratic surface analysis (HQSA). HQSA uses both the derivatives and Hessians of fitting polynomials for the data to separate linear data samples from quadratic data samples. These derivatives and Hessians also lead to important necessary conditions, based on the so-called mutual contraction subspace, to separate data samples on different quadratic surfaces. The algebraic solution we derive is non-iterative and numerically stable. It tolerates moderate noise and can be used in conjunction with outlier removal techniques. We show how to solve the hybrid motion segmentation problem using HQSA, and demonstrate its performance on simulated data with noise and on real perspective images.
Recovering Human Body Configurations Using Pairwise Constraints between Parts
Authors: Xiaofeng Ren, Alexander C. Berg, Jitendra Malik
Venue: ICCV
Year: 2005

The goal of this work is to recover human body configurations from static images. Without assuming a priori knowledge of scale, pose or appearance, this problem is extremely challenging and demands the use of all possible sources of information. We develop a framework which can incorporate arbitrary pairwise constraints between body parts, such as scale compatibility, relative position, symmetry of clothing and smooth contour connections between parts. We detect candidate body parts from bottom-up using parallelism, and use various pairwise configuration constraints to assemble them together into body configurations. To find the most probable configuration, we solve an integer quadratic programming problem with a standard technique using linear approximations. Approximate IQP allows us to incorporate much more information than the traditional dynamic programming and remains computationally efficient. 15 hand-labeled images are used to train the low-level part detector and learn the pairwise constraints. We show test results on a variety of images.
Prior-Based Segmentation by Projective Registration and Level Sets
Authors: Tammy Riklin-Raviv, Nahum Kiryati, Nir A. Sochen
Venue: ICCV
Year: 2005

Object detection and segmentation can be facilitated by the availability of a reference object. However, accounting for possible transformations between the different object views, as part of the segmentation process, remains a challenge. Recent works address this problem by using comprehensive training data. Other approaches are applicable only to limited object classes or can only accommodate similarity transformations. We suggest a novel variational approach to prior-based segmentation, which accounts for planar projective transformation, using a single reference object. The prior shape is registered concurrently with the segmentation process, without point correspondence. The algorithm detects the object of interest and correctly extracts its boundaries. The homography between the two object views is accurately recovered as well. Extending the Chan-Vese level set framework, we propose a region-based segmentation functional that includes explicit representation of the projective homography between the prior shape and the shape to segment. The formulation is derived from two-view geometry. Segmentation of a variety of objects is demonstrated and the recovered transformation is verified.
Phase Field Models and Higher-Order Active Contours
Authors: Marie Rochery, Ian Jermyn, Josiane Zerubia
Venue: ICCV
Year: 2005

The representation and modelling of regions is an important topic in computer vision. In this paper, we represent a region via a level set of a 'phase field' function. The function is not constrained, e.g. to be a distance function; nevertheless, phase field energies equivalent to classical active contour energies can be defined. They represent an advantageous alternative to other methods: a linear representation space; ease of implementation (a PDE with no reinitialization); neutral initialization; greater topological freedom. We extend the basic phase field model with terms that reproduce 'higher-order active contour' energies, a powerful way of including prior geometric knowledge in the active contour framework via nonlocal interactions between contour points, in addition to the above advantages, the phase field greatly simplifies the analysis and implementation of the higher-order terms. We define a phase field model that favours regions composed of thin arms meeting at junctions, combine this with image terms, and apply the model to the extraction of line networks from remote sensing images
On the Spatial Statistics of Optical Flow
Authors: Stefan Roth 0001, Michael J. Black
Venue: ICCV
Year: 2005

We develop a method for learning the spatial statistics of optical flow fields from a novel training database. Training flow fields are constructed using range images of natural scenes and 3D camera motions recovered from handheld and car-mounted video sequences. A detailed analysis of optical flow statistics in natural scenes is presented and machine learning methods are developed to learn a Markov random field model of optical flow. The prior probability of a flow field is formulated as a field-of-experts model that captures the higher order spatial statistics in overlapping patches and is trained using contrastive divergence. This new optical flow prior is compared with previous robust priors and is incorporated into a recent, accurate algorithm for dense optical flow computation. Experiments with natural and synthetic sequences illustrate how the learned optical flow prior quantitatively improves flow accuracy and how it captures the rich spatial structure found in natural scene motion.
Fitting Globally Stabilized Algebraic Surfaces to Range Data
Authors: H. Türker Sahin, Mustafa Unel
Venue: ICCV
Year: 2005

Linear fitting of implicit algebraic models to data usually suffers from global stability problems. Complicated object structures can accurately be modeled by closed-bounded surfaces of higher degrees using ridge regression. This paper derives an explicit formula for computing a Euclidean invariant 3D ridge regression matrix and applies it for the global stabilization of a particular linear fitting method. Experiments show that the proposed approach improves global stability of resulting surfaces significantly
Using Extended Light Sources for Modeling Object Appearance under Varying Illumination
Authors: Imari Sato, Takahiro Okabe, Yoichi Sato 0001, Katsushi Ikeuchi
Venue: ICCV
Year: 2005

In this study, we demonstrate the effectiveness of using extended light sources for modeling the appearance of an object for varying illumination. Extended light sources have a radiance distribution that is similar to that of the Gaussian function and have the potential of functioning as a low-pass filter when the appearance of an object is sampled under them. This enables us to obtain a set of basis images of an object for variable illumination from input images of the object taken under those light sources without suffering aliasing caused by insufficient sampling of its appearance. Furthermore, extended light sources are useful in terms of reducing high contrast in image intensities due to specular and diffuse reflection components. This helps us observe both specular and diffuse reflection components of an object in the same image taken with a single shutter speed. We have tested our proposed approach based on extended light sources with objects of complex appearance that are generally difficult to model using image-based modeling techniques.
A Theory of Inverse Light Transport
Authors: Steven M. Seitz, Yasuyuki Matsushita, Kiriakos N. Kutulakos
Venue: ICCV
Year: 2005

In this paper we consider the problem of computing and removing interreflections in photographs of real scenes. Towards this end, we introduce the problem of inverse light transport - given a photograph of an unknown scene, decompose it into a sum of n-bounce images, where each image records the contribution of light that bounces exactly n times before reaching the camera. We prove the existence of a set of interreflection cancelation operators that enable computing each n-bounce image by multiplying the photograph by a matrix. This matrix is derived from a set of "impulse images" obtained by probing the scene with a narrow beam of light. The operators work under unknown and arbitrary illumination, and exist for scenes that have arbitrary spatially-varying BRDFs. We derive a closed-form expression for these operators in the Lambertian case and present experiments with textured and untextured Lambertian scenes that confirm our theory's predictions.
Vehicle Identification between Non-Overlapping Cameras without Direct Feature Matching
Authors: Ying Shan, Harpreet S. Sawhney, Rakesh Kumar 0001
Venue: ICCV
Year: 2005

We propose a novel method for identifying road vehicles between two nonoverlapping cameras. The problem is formulated as a same-different classification problem: probability of two vehicle images from two distinct cameras being from the same vehicle or from different vehicles. The key idea is to compute the probability without matching the two vehicle images directly, which is a process vulnerable to drastic appearance and aspect changes. We represent each vehicle image as an embedding amongst representative exemplars of vehicles within the same camera. The embedding is computed as a vector each of whose components is a nonmetric distance for a vehicle to an exemplar. The nonmetric distances are computed using robust matching of oriented edge images. A set of truthed training examples of same-different vehicle pairings across the two cameras is used to learn a classifier that encodes the probability distributions. A pair of the embeddings representing two vehicles across two cameras is then used to compute the same-different probability. In order for the vehicle exemplars to be representative for both cameras, we also propose a method for jointly selection of corresponding exemplars using the training data. Experiments on observations of over 400 vehicles under drastically illumination and camera conditions demonstrate promising results.
Object Tracking across Multiple Independently Moving Aerial Cameras
Authors: Yaser Sheikh, Mubarak Shah
Venue: ICCV
Year: 2005

A camera mounted on an aerial vehicle provides an excellent means for monitoring large areas of a scene. Utilizing several such cameras on different aerial vehicles allows further flexibility, in terms of increased visual scope and in the pursuit of multiple targets. In this paper, we address the problem of tracking objects across multiple moving airborne cameras. Since the cameras are moving and often widely separated, direct appearance-based or proximity-based constraints cannot be used. Instead, we exploit geometric constraints on the relationship between the motions of each object across cameras, to test multiple correspondence hypotheses, without assuming any prior calibration information. We propose a statistically and geometrically meaningful means of evaluating a hypothesized correspondence between two observations in different cameras. Second, since multiple cameras exist, ensuring coherency in correspondence, i.e. transitive closure is maintained between more than two cameras, is an essential requirement. To ensure such coherency we pose the problem of object tracking across cameras as a k-dimensional matching and use an approximation to find the maximum likelihood assignment of correspondence. Third, we show that as a result of tracking objects across the cameras, a concurrent visualization of multiple aerial video streams is possible. Results are shown on a number of real and controlled scenarios with multiple objects observed by multiple cameras, validating our qualitative models.
Exploring the Space of a Human Action
Authors: Yaser Sheikh, Mumtaz Sheikh, Mubarak Shah
Venue: ICCV
Year: 2005

One of the fundamental challenges of recognizing actions is accounting for the variability that arises when arbitrary cameras capture humans performing actions. In this paper, we explicitly identify three important sources of variability: (1) viewpoint, (2) execution rate, and (3) anthropometry of actors, and propose a model of human actions that allows us to investigate all three. Our hypothesis is that the variability associated with the execution of an action can be closely approximated by a linear combination of action bases in joint spatio-temporal space. We demonstrate that such a model bounds the rank of a matrix of image measurements and that this bound can be used to achieve recognition of actions based only on imaged data. A test employing principal angles between subspaces that is robust to statistical fluctuations in measurement data is presented to find the membership of an instance of an action. The algorithm is applied to recognize several actions, and promising results have been obtained.
Fast Global Kernel Density Mode Seeking with Application to Localisation and Tracking
Authors: Chunhua Shen, Michael J. Brooks, Anton van den Hengel
Venue: ICCV
Year: 2005

We address the problem of seeking the global mode of a density function using the mean shift algorithm. Mean shift, like other gradient ascent optimization methods, is susceptible to local maxima, and hence often fails to find the desired global maximum. In this work, we propose a multi-bandwidth mean shift procedure that alleviates this problem, which we term annealed mean shift, as it shares similarities with the annealed importance sampling procedure. The bandwidth of the algorithm plays the same role as the temperature in annealing. We observe that the over-smoothed density function with a sufficiently large bandwidth is uni-modal. Using a continuation principle, the influence of the global peak in the density function is introduced gradually. In this way the global maximum is more reliably located. Generally, the price of this annealing-like procedure is that more iteration is required since it is imperative that the computation complexity is minimal in real-time applications such as visual tracking. We propose an accelerated version of the mean shift algorithm. Compared with the conventional mean shift algorithm, the accelerated mean shift can significantly decrease the number of iterations required for convergence. The proposed algorithm is applied to the problems of visual tracking and object localization. We empirically show on various data sets that the proposed algorithm can reliably find the true object location when the starting position of mean shift is far away from the global maximum, in contrast with the conventional mean shift algorithm that will usually get trapped in a spurious local maximum.
Learning Non-Generative Grammatical Models for Document Analysis
Authors: Michael Shilman, Percy Liang, Paul A. Viola
Venue: ICCV
Year: 2005

We present a general approach for the hierarchical segmentation and labeling of document layout structures. This approach models document layout as a grammar and performs a global search for the optimal parse based on a grammatical cost function. Our contribution is to utilize machine learning to discriminatively select features and set all parameters in the parsing process. Therefore, and unlike many other approaches for layout analysis, ours can easily adapt itself to a variety of document analysis problems. One need only specify the page grammar and provide a set of correctly labeled pages. We apply this technique to two document image analysis tasks: page layout structure extraction and mathematical expression interpretation. Experiments demonstrate that the learned grammars can be used to extract the document structure in 57 files from the UWIII document image database. We also show that the same framework can be used to automatically interpret printed mathematical expressions so as to recreate the original LaTeX
Non-Negative Lighting and Specular Object Recognition
Authors: Sameer Shirdhonkar, David W. Jacobs
Venue: ICCV
Year: 2005

Recognition of specular objects is particularly difficult because their appearance is much more sensitive to lighting changes than that of Lambertian objects. We consider an approach in which we use a 3D model to deduce the lighting that best matches the model to the image. In this case, an important constraint is that incident lighting should be non-negative everywhere. In this paper, we propose a new method to enforce this constraint and explore its usefulness in specular object recognition, using the spherical harmonic representation of lighting. The method follows from a novel extension of Szego's eigenvalue distribution theorem to spherical harmonics, and uses semidefinite programming to perform a constrained optimization. The new method is faster as well as more accurate than previous methods. Experiments on both synthetic and real data indicate that the constraint can improve recognition of specular objects by better separating the correct and incorrect models
Contour-Based Learning for Object Detection
Authors: Jamie Shotton, Andrew Blake 0001, Roberto Cipolla
Venue: ICCV
Year: 2005

We present a novel categorical object detection scheme that uses only local contour-based features. A two-stage, partially supervised learning architecture is proposed: a rudimentary detector is learned from a very small set of segmented images and applied to a larger training set of un-segmented images; the second stage bootstraps these detections to learn an improved classifier while explicitly training against clutter. The detectors are learned with a boosting algorithm which creates a location-sensitive classifier using a discriminative set of features from a randomly chosen dictionary of contour fragments. We present results that are very competitive with other state-of-the-art object detection schemes and show robustness to object articulations, clutter, and occlusion. Our major contributions are the application of boosted local contour-based features for object detection in a partially supervised learning framework, and an efficient new boosting procedure for simultaneously selecting features and estimating per-feature parameters.
Conditional Random Fields for Contextual Human Motion Recognition
Authors: Cristian Sminchisescu, Atul Kanaujia, Zhiguo Li, Dimitris N. Metaxas
Venue: ICCV
Year: 2005

We present algorithms for recognizing human motion in monocular video sequences, based on discriminative conditional random field (CRF) and maximum entropy Markov models (MEMM). Existing approaches to this problem typically use generative (joint) structures like the hidden Markov model (HMM). Therefore they have to make simplifying, often unrealistic assumptions on the conditional independence of observations given the motion class labels and cannot accommodate overlapping features or long term contextual dependencies in the observation sequence. In contrast, conditional models like the CRFs seamlessly represent contextual dependencies, support efficient, exact inference using dynamic programming, and their parameters can be trained using convex optimization. We introduce conditional graphical models as complementary tools for human motion recognition and present an extensive set of experiments that show how these typically outperform HMMs in classifying not only diverse human activities like walking, jumping. running, picking or dancing, but also for discriminating among subtle motion styles like normal walk and wander walk
Recovering Facial Shape and Albedo Using a Statistical Model of Surface Normal Direction
Authors: William A. P. Smith, Edwin R. Hancock
Venue: ICCV
Year: 2005

This paper describes how facial shape can be modelled using a statistical model that captures variations in surface normal direction. To construct this model, we make use of the azimuthal equidistant projection to map surface normals from the unit sphere to points on a local tangent plane. The variations in surface normal direction are captured using the covariance matrix for the projected point positions. This allows us to model variations in face shape using a standard point distribution model. We train the model on fields of surface normals extracted from range data and show how to fit the model to intensity data using constraints on the surface normal direction provided by Lambert's law. We demonstrate that this process yields accurate facial shape recovery and allows an estimate of the albedo map to be made from single, real world face images.
TemporalBoost for Event Recognition
Authors: Paul Smith 0003, Niels da Vitoria Lobo, Mubarak Shah
Venue: ICCV
Year: 2005

This paper contributes a new boosting paradigm to achieve detection of events in video. Previous boosting paradigms in vision focus on single frame detection and do not scale to video events. Thus new concepts need to be introduced to address questions such as determining if an event has occurred, localizing the event, handling same action performed at different speeds, incorporating previous classifier responses into current decision, using temporal consistency of data to aid detection and recognition. The proposed method has the capability to improve weak classifiers by allowing them to use previous history in evaluating the current frame. A learning mechanism built into the boosting paradigm is also given which allows event level decisions to be made. This is contrasted with previous work in boosting which uses limited higher level temporal reasoning and essentially makes object detection decisions at the frame level. Our approach makes extensive use of temporal continuity of video at the classifier and detector levels. We also introduce a relevant set of activity features. Features are evaluated at multiple zoom levels to improve detection. We show results for a system that is able to recognize 11 actions.