OpenRaiser/Intern-Atlas huggingface dataset:
A linear formulation of shape from specular flow
Authors: Guillermo D. Cañas, Yuriy Vasilyev, Yair Adato, Todd E. Zickler, Steven J. Gortler, Ohad Ben-Shahar
Venue: ICCV
Year: 2009

When a curved mirror-like surface moves relative to its environment, it induces a motion field-or specular flow- on the image plane that observes it. This specular flow is related to the mirror's shape through a non-linear partial differential equation, and there is interest in understanding when and how this equation can be solved for surface shape. Existing analyses of this `shape from specular flow equation' have focused on closed-form solutions, and while they have yielded insight, their critical reliance on externally-provided initial conditions and/or specific motions makes them difficult to apply in practice. This paper resolves these issues. We show that a suitable reparameterization leads to a linear formulation of the shape from specular flow equation. This formulation radically simplifies the reconstruction process and allows, for example, both motion and shape to be recovered from as few as two specular flows even when no externally-provided initial conditions are available. Our analysis moves us closer to a practical method for recovering shape from specular flow that operates under arbitrary, unknown motions in unknown illumination environments and does not require additional shape information from other sources.
Heterogeneous feature machines for visual recognition
Authors: Liangliang Cao, Jiebo Luo 0001, Feng Liang 0002, Thomas S. Huang
Venue: ICCV
Year: 2009

With the recent efforts made by computer vision researchers, more and more types of features have been designed to describe various aspects of visual characteristics. Modeling such heterogeneous features has become an increasingly critical issue. In this paper, we propose a machinery called the Heterogeneous Feature Machine (HFM) to effectively solve visual recognition tasks in need of multiple types of features. Our HFM builds a kernel logistic regression model based on similarities that combine different features and distance metrics. Different from existing approaches that use a linear weighting scheme to combine different features, HFM does not require the weights to remain the same across different samples, and therefore can effectively handle features of different types with different metrics. To prevent the model from overfitting, we employ the so-called group LASSO constraints to reduce model complexity. In addition, we propose a fast algorithm based on co-ordinate gradient descent to efficiently train a HFM. The power of the proposed scheme is demonstrated across a wide variety of visual recognition tasks including scene, event and action recognition.
Minimizing energy functions on 4-connected lattices using elimination
Authors: Peter Carr 0001, Richard I. Hartley
Venue: ICCV
Year: 2009

We describe an energy minimization algorithm for functions defined on 4-connected lattices, of the type usually encountered in problems involving images. Such functions are often minimized using graph-cuts/max-flow, but this method is only applicable to submodular problems. In this paper, we describe an algorithm that will solve any binary problem, irrespective of whether it is submodular or not, and for multilabel problems we use alpha-expansion. The method is based on the elimination algorithm, which eliminates nodes from the graph until the remaining function is submodular. It can then be solved using max-flow. Values of eliminated variables are recovered using back-substitution. We compare the algorithm's performance against alternative methods for solving non-submodular problems, with favourable results.
Detection and removal of chromatic moving shadows in surveillance scenarios
Authors: Ivan Huerta Casado, Michael B. Holte, Thomas B. Moeslund, Jordi Gonzàlez 0001
Venue: ICCV
Year: 2009

Segmentation in the surveillance domain has to deal with shadows to avoid distortions when detecting moving objects. Most segmentation approaches dealing with shadow detection are typically restricted to penumbra shadows. Therefore, such techniques cannot cope well with umbra shadows. Consequently, umbra shadows are usually detected as part of moving objects. In this paper we present a novel technique based on gradient and colour models for separating chromatic moving cast shadows from detected moving objects. Firstly, both a chromatic invariant colour cone model and an invariant gradient model are built to perform automatic segmentation while detecting potential shadows. In a second step, regions corresponding to potential shadows are grouped by considering “a bluish effect” and an edge partitioning. Lastly, (i) temporal similarities between textures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for all potential shadow regions in order to finally identify umbra shadows. Unlike other approaches, our method does not make any a-priori assumptions about camera location, surface geometries, surface textures, shapes and types of shadows, objects, and background. Experimental results show the performance and accuracy of our approach in different shadowed materials and illumination conditions.
Efficient, high-quality image contour detection
Authors: Bryan Catanzaro, Bor-Yiing Su, Narayanan Sundaram, Yunsup Lee, Mark Murphy, Kurt Keutzer
Venue: ICCV
Year: 2009

Image contour detection is fundamental to many image analysis applications, including image segmentation, object recognition and classification. However, highly accurate image contour detection algorithms are also very computationally intensive, which limits their applicability, even for offline batch processing. In this work, we examine efficient parallel algorithms for performing image contour detection, with particular attention paid to local image analysis as well as the generalized eigensolver used in Normalized Cuts. Combining these algorithms into a contour detector, along with careful implementation on highly parallel, commodity processors from Nvidia, our contour detector provides uncompromised contour accuracy, with an F-metric of 0.70 on the Berkeley Segmentation Dataset. Runtime is reduced from 4 minutes to 1.8 seconds. The efficiency gains we realize enable high-quality image contour detection on much larger images than previously practical, and the algorithms we propose are applicable to several image segmentation approaches. Efficient, scalable, yet highly accurate image contour detection will facilitate increased performance in many computer vision applications.
Analysis of orientation and scale in smoothly varying textures
Authors: Jason Chang 0001, John W. Fisher III
Venue: ICCV
Year: 2009

We present a novel representation for modeling textured regions subject to smooth variations in orientation and scale. Utilizing the steerable pyramid of Simoncelli and Freeman as a basis, we decompose textured regions of natural images into explicit local attributes of contrast, bias, scale, and orientation. Additionally, we impose smoothness on these attributes via Markov random fields. The combination allows for demonstrable improvements in common scene analysis applications including unsupervised segmentation, reflectance and shading estimation, and estimation of the radiometric response function from a single image.
The infinite Hidden Markov random field model
Authors: Sotirios P. Chatzis, Gabriel Tsechpenakis
Venue: ICCV
Year: 2009

Dirichlet process (DP) mixture models have recently emerged in the cornerstone of nonparametric Bayesian statistics as promising candidates for clustering applications where the number of clusters is unknown a priori. Hidden Markov random field (HMRF) models are parametric statistical models widely used for image segmentation, as they appear naturally in problems where a spatially-constrained clustering scheme is asked for. A major limitation of HMRF models concerns the automatic selection of the proper number of their states, i.e. the number of segments derived by the image segmentation procedure. Typically, for this purpose, various likelihood based criteria are employed. Nevertheless, such methods often fail to yield satisfactory results, exhibiting significant overfitting proneness. Recently, higher order conditional random field models using potentials defined on superpixels have been considered as alternatives tackling these issues. Still, these models are in general computationally inefficient, a fact that limits their widespread adoption in practical applications. To resolve these issues, in this paper we introduce a novel, nonparametric Bayesian formulation for the HMRF model, the infinite HMRF model. We describe an efficient variational Bayesian inference algorithm for the proposed model, and we apply it to a series of image segmentation problems, demonstrating its advantages over existing methodologies.
Gradient domain layer separation under independent motion
Authors: Yunqiang Chen, Ti-Chiun Chang, Chunxiao Zhou, Tong Fang
Venue: ICCV
Year: 2009

Multi-exposure X-ray imaging can see through objects and separate different material into transparent layers. However, layer motion makes the separation task under-determined. Instead of aligning the non-rigid motion, we address the layer separation problem in gradient domain and propose an energy optimization framework to regularize it by explicitly enforcing independence constraint. It is shown that gradient domain allows more accurate and robust independence analysis between non-stationary signal using mutual information (MI) and hence achieves better separation. Furthermore, gradient fields contain sufficient information for full reconstruction of separated layers by solving the Poisson Equation. For efficient regularization of the gradient separation, energy terms based on the Taylor expansion of MI is further derived. Evaluation on both synthesized and real datasets proves the effectiveness of our algorithm and its robustness to complex tissue motion.
Level set segmentation with both shape and intensity priors
Authors: Siqi Chen, Richard J. Radke
Venue: ICCV
Year: 2009

We present a new variational level-set-based segmentation formulation that uses both shape and intensity prior information learned from a training set. By applying Bayes' rule to the segmentation problem, the cost function decomposes into shape and image energy parts. The shape energy is based on recently proposed nonparametric shape distributions, and we propose a new image energy model that incorporates learned intensity information from both foreground and background objects. The proposed variational level set segmentation framework has two main advantages. First, by characterizing image information with regional intensity distributions, there is no need to balance image energy and shape energy using a heuristic weighting factor. Second, by incorporating learned intensity information into the image model using a nonparametric density estimation method and an appropriate distance measure, our segmentation framework can handle problems where the interior/exterior of the shape has a highly inhomogeneous intensity distribution. We demonstrate our segmentation algorithm using challenging pelvis CT scans.
Automatic ovarian follicle quantification from 3D ultrasound data using global/local context with database guided segmentation
Authors: Terrence Chen, Wei Zhang 0018, Sara Good, Shaohua Kevin Zhou, Dorin Comaniciu
Venue: ICCV
Year: 2009

In this paper, we present a novel probabilistic framework for automatic follicle quantification in 3D ultrasound data. The proposed framework robustly estimates size and location of each individual ovarian follicle by fusing the information from both global and local context. Follicle candidates at detected locations are then segmented by a novel database guided segmentation method. To efficiently search hypothesis in a high dimensional space for multiple object detection, a clustered marginal space learning approach is introduced. Extensive evaluations conducted on 501 volumes containing 8108 follicles showed that our method is able to detect and segment ovarian follicles with high robustness and accuracy. It is also much faster than the current ultrasound manual workflow. The proposed method is able to streamline the clinical workflow and improve the accuracy of existing follicular measurements.
Realtime background subtraction from dynamic scenes
Authors: Li Cheng 0001, Minglun Gong
Venue: ICCV
Year: 2009

This paper examines the problem of moving object detection. More precisely, it addresses the difficult scenarios where background scene textures in the video might change over time. In this paper, we formulate the problem mathematically as minimizing a constrained risk functional motivated from the large margin principle. It is a generalization of the one class support vector machines (1-SVMs) to accommodate spatial interactions, which is further incorporated into an online learning framework to track temporal changes. As a result it yields a closed-form update formula, a central component of the proposed algorithm to enable prompt adaptation to spatio-temporal changes. We also analyze the mistake bound and discuss issues such as dealing with non-stationary distributions, making use of kernels and efficient inference by a variant of dynamic programming. By exploiting the inherently concurrent structure, the proposed approach is designed to work with the highly parallel graphics processors (GPUs) to facilitate realtime analysis. Our empirical study demonstrates that the proposed approach works in realtime (over 80 frames per second) and at the same time performs competitively against state-of-the-art offline and quasi-realtime methods.
Non-negative matrix factorization of partial track data for motion segmentation
Authors: Anil M. Cheriyadat, Richard J. Radke
Venue: ICCV
Year: 2009

This paper addresses the problem of segmenting low-level partial feature point tracks belonging to multiple motions. We show that the local velocity vectors at each instant of the trajectory are an effective basis for motion segmentation. We decompose the velocity profiles of point tracks into different motion components and corresponding non-negative weights using non-negative matrix factorization (NNMF). We then segment the different motions using spectral clustering on the derived weights. We test our algorithm on the Hopkins 155 benchmarking database and several new sequences, demonstrating that the proposed algorithm can accurately segment multiple motions at a speed of a few seconds per frame. We show that our algorithm is particularly successful on low-level tracks from real-world video that are fragmented, noisy and inaccurate.
Building recognition using sketch-based representations and spectral graph matching
Authors: Yu-Chia Chung, Tony X. Han, Zhihai He
Venue: ICCV
Year: 2009

In this work, we address the problem of building recognition across two camera views with large changes in scales and viewpoints. The main idea is to construct a semantically rich sketch-based representation for buildings which is invariant under large scale and perspective changes. After multi-scale maximally stable extremal regions (MSER) detection, the proposed approach finds repeated structural components of buildings, such as window, doors, and facades, and extracts semantically rich features, which are organized into a sketch-based representation of buildings. These descriptors are then clustered in association with different planes of the building and matched across video frames using spectral graph analysis. Our experiments demonstrate that the proposed approach outperforms SIFT-based matching schemes, especially for images with large viewpoint changes.
Component analysis approach to estimation of tissue intensity distributions of 3D images
Authors: Arridhana Ciptadi, Cheng Chen, Vitali Zagorodnov
Venue: ICCV
Year: 2009

Many segmentation problems in medical imaging rely on accurate modeling and estimation of tissue intensity probability density functions. Gaussian mixture modeling, currently the most common approach, has several drawbacks, such as reliance on a specific model and iterative optimization. It also does not take advantage of substantially larger amount of data provided by 3D acquisitions, which are becoming standard in clinical environment. We propose a novel completely non-parametric algorithm to estimate the tissue intensity probabilities in 3D images. Instead of relying on traditional framework of iterating between classification and estimation, we pose the problem as an instance of a blind source separation problem, where the unknown distributions are treated as sources and histograms of image subvolumes as mixtures. The new approach performed well on synthetic data and real magnetic resonance (MR) scans, robustly capturing intensity distributions of even small image structures and partial volume voxels.
A new minimal solution to the relative pose of a calibrated stereo camera with small field of view overlap
Authors: Brian Clipp, Christopher Zach, Jan-Michael Frahm, Marc Pollefeys
Venue: ICCV
Year: 2009

In this paper we present a new minimal solver for the relative pose of a calibrated stereo camera (i.e. a pair of rigidly mounted cameras). Our method is based on the fact that a feature visible in all four images (two image pairs acquired at two points in time) constrains the relative pose of the second stereo camera to lie on a sphere around this feature, which has a known, triangulated position in the first stereo camera coordinate frame. This constraint leaves three degrees of freedom; two for the location of the second camera on the sphere, and the third for the rotation in the respective tangent plane. We use three 2D correspondences, in particular two correspondences from the left (or right) camera and one correspondence from the other camera, to solve for these three remaining degrees of freedom. This approach is amenable to stereo cameras having a small overlap in their views. We present an efficient solution for this novel relative pose problem, describe the incorporation of our proposed solver into the RANSAC framework, evaluate its performance given noise and outliers, and demonstrate its use in a real-time structure from motion system.
Power watersheds: A new image segmentation framework extending graph cuts, random walker and optimal spanning forest
Authors: Camille Couprie, Leo J. Grady, Laurent Najman, Hugues Talbot
Venue: ICCV
Year: 2009

In this work, we extend a common framework for seeded image segmentation that includes the graph cuts, random walker, and shortest path optimization algorithms. Viewing an image as a weighted graph, these algorithms can be expressed by means of a common energy function with differing choices of a parameter q acting as an exponent on the differences between neighboring nodes. Introducing a new parameter p that fixes a power for the edge weights allows us to also include the optimal spanning forest algorithm for watersheds in this same framework. We then propose a new family of segmentation algorithms that fixes p to produce an optimal spanning forest but varies the power q beyond the usual watershed algorithm, which we term power watersheds. Placing the watershed algorithm in this energy minimization framework also opens new possibilities for using unary terms in traditional watershed segmentation and using watersheds to optimize more general models of use in application beyond image segmentation.
3D open-surface shape correspondence for statistical shape modeling: Identifying topologically consistent landmarks
Authors: Pahal Dalal, Lili Ju, Michael McLaughlin, Xiangrong Zhou, Hiroshi Fujita 0001, Song Wang 0002
Venue: ICCV
Year: 2009

Shape correspondence, which aims at accurately identifying corresponding landmarks from a given population of shape instances, is a very challenging step in constructing a statistical shape model such as the Point Distribution Model. The state-of-the-art methods such as MDL and SPHARM are primarily focused on closed-surface shape correspondence. In this paper we develop a novel method aimed at identifying accurately corresponding landmarks on 3D open-surfaces with a closed boundary. In particular, we enforce explicit topology consistency on the identified landmarks to ensure that they form a simple, consistent triangle mesh to more accurately model the correspondence of the underlying continuous shape instances. The proposed method also ensures the correspondence of the boundary of the open surfaces. For our experiments, we test the proposed method by constructing a statistical shape model of the human diaphragm from 26 shape instances.
Automatic learning and extraction of multi-local features
Authors: Oscar M. Danielsson, Stefan Carlsson, Josephine Sullivan
Venue: ICCV
Year: 2009

In this paper we introduce a new kind of feature - the multi-local feature, so named as each one is a collection of local features, such as oriented edgels, in a very specific spatial arrangement. A multi-local feature has the ability to capture underlying constant shape properties of exemplars from an object class. Thus it is particularly suited to representing and detecting visual classes that lack distinctive local structures and are mainly defined by their global shape. We present algorithms to automatically learn an ensemble of these features to represent an object class from weakly labelled training images of that class, as well as procedures to detect these features efficiently in novel images. The power of multi-local features is demonstrated by using the ensemble in a simple voting scheme to perform object category detection on a standard database. Despite its simplicity, this scheme yields detection rates matching state-of-the-art object detection systems.
Convex multi-region segmentation on manifolds
Authors: Amaël Delaunoy, Ketut Fundana, Emmanuel Prados, Anders Heyden
Venue: ICCV
Year: 2009

In this paper, we address the problem of segmenting data defined on a manifold into a set of regions with uniform properties. In particular, we propose a numerical method when the manifold is represented by a triangular mesh. Based on recent image segmentation models, our method minimizes a convex energy and then enjoys significant favorable properties: it is robust to initialization and avoid the problem of the existence of local minima present in many variational models. The contributions of this paper are threefold: firstly we adapt the convex image labeling model to manifolds; in particular the total variation formulation. Secondly we show how to implement the proposed method on triangular meshes, and finally we show how to use and combine the method in other computer vision problems, such as 3D reconstruction. We demonstrate the efficiency of our method by testing it on various data.
Globally optimal segmentation of multi-region objects
Authors: Andrew Delong, Yuri Boykov
Venue: ICCV
Year: 2009

Many objects contain spatially distinct regions, each with a unique colour/texture model. Mixture models ignore the spatial distribution of colours within an object, and thus cannot distinguish between coherent parts versus randomly distributed colours. We show how to encode geometric interactions between distinct region+boundary models, such as regions being interior/exterior to each other along with preferred distances between their boundaries. With a single graph cut, our method extracts only those multi-region objects that satisfy such a combined model. We show applications in medical segmentation and scene layout estimation. Unlike Li et al. we do not need “domain unwrapping” nor do we have topological limits on shapes.