pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Boost Filamentary Structure Segmentation
Authors: Lin Gu, Li Cheng
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Matting, Segmentation

The challenging problem of filamentary structure segmentation has a broad range of applications in biological and medical fields. A critical yet challenging issue remains on how to detect and restore the small filamentary fragments from backgrounds: The small fragments are of diverse shapes and appearances, meanwhile the backgrounds could be cluttered and ambiguous. Focusing on this issue, this paper proposes an iterative two-step learning-based approach to boost the performance based on a base segmenter arbitrarily chosen from a number of existing segmenters: We start with an initial partial segmentation where the filamentary structure obtained is of high confidence based on this existing segmenter. We also define a scanning horizon as epsilon balls centred around the partial segmentation result. Step one of our approach centers on a data-driven latent classification tree model to detect the filamentary fragments. This model is learned via a training process, where a large number of distinct local figure/background separation scenarios are established and geometrically organized into a tree structure. Step two spatially restores the isolated fragments back to the current partial segmentation, which is accomplished by means of completion fields and matting. Both steps are then alternated with the growth of partial segmentation result, until the input image space is entirely explored. Our approach is rather generic and can be easily augmented to a wide range of existing supervised/unsupervised segmenters to produce an improved result. This has been empirically verified on specific filamentary structure segmentation tasks: retinal blood vessel segmentation as well as neuronal segmentations, where noticeable improvement has been shown over the original state-of-the-arts.
Weakly-Supervised Structured Output Learning With Flexible and Latent Graphs Using High-Order Loss Functions
Authors: Gustavo Carneiro, Tingying Peng, Christine Bayer, Nassir Navab
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

We introduce two new structured output models that use a latent graph, which is flexible in terms of the number of nodes and structure, where the training process minimises a high-order loss function using a weakly annotated training set. These models are developed in the context of microscopy imaging of malignant tumours, where the estimation of the number and proportion of classes of microcirculatory supply units (MCSU) is important in the assessment of the efficacy of common cancer treatments (an MCSU is a region of the tumour tissue supplied by a microvessel).  The proposed methodologies take as input multimodal microscopy images of a tumour, and estimate the number and proportion of MCSU classes. This estimation is facilitated by the use of an underlying latent graph (not present in the manual annotations), where each MCSU is represented by a node in this graph, labelled with the MCSU class and image location. The training process uses the manual weak annotations available, consisting of the number of MCSU classes per training image, where the training objective is the minimisation of a high-order loss function based on the norm of the error between the manual and estimated  annotations.  One of the models proposed is based on a new flexible latent structure support vector machine (FLSSVM) and the other is based on a deep convolutional neural network (DCNN) model. Using a dataset of 89 weakly annotated pairs of multimodal images from eight tumours, we show that the quantitative results from DCNN are superior, but the qualitative results from FLSSVM are better and both display high correlation values regarding the number and proportion of MCSU classes compared to the manual annotations.
Efficient Classifier Training to Minimize False Merges in Electron Microscopy Segmentation
Authors: Toufiq Parag, Dan C. Ciresan, Alessandro Giusti
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Active Learning, Segmentation, Superpixels

The prospect of neural reconstruction from Electron Microscopy (EM) images has been elucidated by the automatic segmentation algorithms. Although segmentation algorithms eliminate the necessity of tracing the neurons by hand, significant manual effort is still essential for correcting the mistakes they make. A considerable amount of human labor is also required for annotating groundtruth volumes for training the classifiers of a segmentation framework. It is critically important to diminish the dependence on human interaction in the overall reconstruction system.  This study proposes a novel classifier training algorithm for EM segmentation aimed to reduce the amount of manual effort demanded by the groundtruth annotation and error refinement tasks. Instead of using an exhaustive pixel level groundtruth, an active learning algorithm is proposed for sparse labeling of pixel and boundaries of superpixels. Because over-segmentation errors are in general more tolerable and easier to correct than the under-segmentation errors, our algorithm is designed to prioritize minimization of false-merges over false-split mistakes. Our experiments on both 2D and 3D data  suggest that the proposed method yields segmentation outputs that are more amenable to neural reconstruction than those of existing methods.
On Statistical Analysis of Neuroimages With Imperfect Registration
Authors: Won Hwa Kim, Sathya N. Ravi, Sterling C. Johnson, Ozioma C. Okonkwo, Vikas Singh
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

A variety of studies in neuroscience/neuroimaging seek to perform statistical inference on the acquired brain image scans for diagnosis as well as understanding the pathological manifestation of diseases. To do so, an important first step is to register (or co-register) all of the image data into a common coordinate system. This permits meaningful comparison of the intensities at each voxel across groups (e.g., diseased versus healthy) to evaluate the effects of the disease and/or use machine learning algorithms in a subsequent step. But errors in the underlying registration make this problematic, they either decrease the statistical power or make the follow-up inference tasks less effective/accurate. In this paper, we derive a novel algorithm which offers immunity to local errors in the underlying deformation field obtained from registration procedures. By deriving a deformation invariant representation of the image, the downstream analysis can be made more robust as if one had access to a (hypothetical) far superior registration procedure. Our algorithm is based on recent work on Scattering coefficients. Using this as a starting point, we show how results from harmonic analysis (especially, non-Euclidean wavelets) yields strategies for designing deformation and additive noise invariant representations of large 3-D brain image volumes. We present a set of results on synthetic and real brain images where we achieve robust statistical analysis even in the presence of substantial deformation errors; here, standard analysis procedures significantly under-perform and fail to identify the true signal.
Convex Optimization With Abstract Linear Operators
Authors: Steven Diamond, Stephen Boyd
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

We introduce a convex optimization modeling framework that transforms a convex optimization problem expressed in a form natural and convenient for the user into an equivalent cone program in a way that preserves fast linear transforms in the original problem. By representing linear functions in the transformation process not as matrices, but as graphs that encode composition of abstract linear operators, we arrive at a matrix-free cone program, i.e., one whose data matrix is represented by an abstract linear operator and its adjoint. This cone program can then be solved by a matrix-free cone solver. By combining the matrix-free modeling framework and cone solver, we obtain a general method for efficiently solving convex optimization problems involving fast linear transforms.
Building Dynamic Cloud Maps From the Ground Up
Authors: Calvin Murdock, Nathan Jacobs, Robert Pless
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

Satellite imagery of cloud cover is extremely important for understanding and predicting weather. We demonstrate how this imagery can be constructed "from the ground up" without requiring expensive geo-stationary satellites. This is accomplished through a novel approach to approximate continental-scale cloud maps using only ground-level imagery from publicly-available webcams. We collected a year's worth of satellite data and simultaneously-captured, geo-located outdoor webcam images from 4388 sparsely distributed cameras across the continental USA. The satellite data is used to train a dynamic model of cloud motion alongside 4388 regression models (one for each camera) to relate ground-level webcam data to the satellite data at the camera's location. This novel application of large-scale computer vision to meteorology and remote sensing is enabled by a smoothed, hierarchically-regularized dynamic texture model whose system dynamics are driven to remain consistent with measurements from the geo-located webcams. We show that our hierarchical model is better able to incorporate sparse webcam measurements resulting in more accurate cloud maps in comparison to a standard dynamic textures implementation. Finally, we demonstrate that our model can be successfully applied to other natural image sequences from the DynTex database, suggesting a broader applicability of our method.
A Versatile Learning-Based 3D Temporal Tracker: Scalable, Robust, Online
Authors: David Joseph Tan, Federico Tombari, Slobodan Ilic, Nassir Navab
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: CPU, Occlusion Handling

This paper proposes a temporal tracking algorithm based on Random Forest that uses depth images to estimate and track the 3D pose of a rigid object in real-time. Compared to the state of the art aimed at the same goal, our algorithm holds important attributes such as high robustness against holes and occlusion, low computational cost of both learning and tracking stages, and low memory consumption. These are obtained (a) by a novel formulation of the learning strategy, based on a dense sampling of the camera viewpoints and learning independent trees from a single image for each camera view; as well as, (b) by an insightful occlusion handling strategy that enforces the forest to recognize the object's local and global structures. Due to these attributes, we report state-of-the-art tracking accuracy on benchmark datasets, and accomplish remarkable scalability with the number of targets, being able to simultaneously track the pose of over a hundred objects at 30 fps with an off-the-shelf CPU. In addition, the fast learning time enables us to extend our algorithm as a robust online tracker for model-free 3D objects under different viewpoints and appearance changes as demonstrated by the experiments.
Realtime Edge-Based Visual Odometry for a Monocular Camera
Authors: Juan Jose Tarrio, Sol Pedre
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Attribute, Visual Odometry

In this work we present a novel algorithm for realtime visual odometry for a monocular camera. The main idea is to develop an approach between classical feature-based visual odometry systems and modern direct dense/semi-dense methods, trying to benefit from the best attributes of both. Similar to feature-based systems, we extract information from the images, instead of working with raw image intensities as direct methods. In particular, the information extracted are the edges present in the image, while the rest of the algorithm is designed to take advantage of the structural information provided when pixels are treated as edges. Edge extraction is an efficient and higly parallelizable operation. The edge depth information extracted is dense enough to allow acceptable surface fitting, similar to modern semi-dense methods. This is a valuable attribute that feature-based odometry lacks. Experimental results show that the proposed method has similar drift than state of the art feature-based and direct methods, and is a simple algorithm that runs at realtime and can be parallelized. Finally, we have also developed an inertial aided version that successfully stabilizes an unmanned air vehicle in complex indoor environments using only a frontal camera, while running the complete solution in the embedded hardware on board the vehicle.
Fill and Transfer: A Simple Physics-Based Approach for Containability Reasoning
Authors: Lap-Fai Yu, Noah Duncan, Sai-Kit Yeung
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

The visual perception of object affordances has emerged as a useful ingredient for building powerful computer vision and robotic applications. In this paper we introduce a novel approach to reason about liquid containability - the affordance of containing liquid. Our approach analyzes container objects based on two simple physical processes: the Fill and Transfer of liquid. First, it reasons about whether a given 3D object is a liquid container and its best filling direction. Second, it proposes directions to transfer its contained liquid to the outside while avoiding spillage. We compare our simplified model with a common fluid dynamics simulation and demonstrate that our algorithm makes human-like choices about the best directions to fill containers and transfer liquid from them. We apply our approach to reason about the containability of several real-world objects acquired using a consumer-grade depth camera.
On Linear Structure From Motion for Light Field Cameras
Authors: Ole Johannsen, Antonin Sulc, Bastian Goldluecke
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Point cloud reconstruction, Pose Estimation

We present a novel approach to relative pose estimation which is tailored to 4D light field cameras. From the relationships between scene geometry and light field structure and an analysis of the light field projection in terms of Pluecker ray coordinates, we deduce a set of linear constraints on ray space correspondences between a light field camera pair. These can be applied to infer relative pose of the light field cameras and thus obtain a point cloud reconstruction of the scene. While the proposed method has interesting relationships to pose estimation for generalized cameras based on ray-to-ray correspondence, our experiments demonstrate that our approach is both more accurate and computationally more efficient. It also compares favourably to direct linear pose estimation based on aligning the 3D point clouds obtained by reconstructing depth for each individual light field. To further validate the method, we employ the pose estimates to merge light fields captured with hand-held consumer light field cameras into refocusable panoramas.
Minimal Solvers for 3D Geometry From Satellite Imagery
Authors: Enliang Zheng, Ke Wang, Enrique Dunn, Jan-Michael Frahm
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: 3D geometry

We propose two novel minimal solvers which advance the state of the art in satellite imagery processing. Our methods are efficient and do not rely on the prior existence of complex inverse mapping functions to correlate 2D image coordinates and 3D terrain. Our first solver improves on the stereo correspondence problem for satellite imagery, in that we provide an exact image-to-object space mapping (where prior methods were inaccurate). Our second solver provides a novel mechanism for 3D point triangulation, which has improved robustness and accuracy over prior techniques. Given the usefulness and ubiquity of satellite imagery, our proposed methods allow for improved results in a variety of existing and future applications.
An Efficient Minimal Solution for Multi-Camera Motion
Authors: Jonathan Ventura, Clemens Arth, Vincent Lepetit
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Motion Estimation

We propose an efficient method for estimating the motion of a multi-camera rig from a minimal set of feature correspondences.  Existing methods for solving the multi-camera relative pose problem require extra correspondences, are slow to compute, and/or produce a multitude of solutions.  Our solution uses a first-order approximation to relative pose in order to simplify the problem and produce an accurate estimate quickly.  The solver is applicable to sequential multi-camera motion estimation and is fast enough for real-time implementation in a random sampling framework.  Our experiments show that our approach is both stable and efficient on challenging test sequences.
Learning Shape, Motion and Elastic Models in Force Space
Authors: Antonio Agudo, Francesc Moreno-Noguer
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

In this paper, we address the problem of simultaneously recovering the 3D shape and pose of a deformable and potentially elastic object from 2D motion. This is a highly ambiguous problem typically tackled by using  low-rank shape and trajectory constraints.  We show that formulating the problem in terms of a low-rank force space that induces the deformation, allows for a better physical interpretation of the resulting priors and a more accurate representation of the actual object's behavior. However, this comes at the price of, besides force and pose, having to estimate the elastic model of the object. For this, we use an Expectation Maximization strategy, where each of these parameters are successively learned within partial M-steps, while robustly dealing with missing observations. We thoroughly validate the approach on both mocap and real sequences, showing more accurate 3D reconstructions than state-of-the-art, and additionally providing an estimate of the full elastic model with no a priori information.
Exploiting High Level Scene Cues in Stereo Reconstruction
Authors: Simon Hadfield, Richard Bowden
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: 3D Reconstruction, Scene Understanding, Vocal Bursts Intensity Prediction

We present a novel approach to 3D reconstruction which is inspired by the human visual system. This system unifies standard appearance matching and triangulation techniques with higher level reasoning and scene understanding, in order to resolve ambiguities between different interpretations of the scene. The types of reasoning integrated in the approach includes recognising common configurations of surface normals and semantic edges (e.g. convex, concave and occlusion boundaries). We also recognise the coplanar, collinear and symmetric structures which are especially common in man made environments.
Point Triangulation Through Polyhedron Collapse Using the l[?] Norm
Authors: Simon Donne, Bart Goossens, Wilfried Philips
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

Multi-camera triangulation of feature points based on a minimisation of the overall L2 reprojection error can get stuck in suboptimal local minima or require slow global optimisation. For this reason, researchers have proposed optimising the L-infinity norm of the L2 single view reprojection errors, which avoids the problem of local minima entirely. In this paper we present a novel method for L-infinity triangulation that minimizes the L-infinity norm of the L-infinity reprojection errors: this apparently small difference leads to a much faster but equally accurate solution which is related to the MLE under the assumption of uniform noise. The proposed method adopts a new optimisation strategy based on solving simple quadratic equations. This stands in contrast with the fastest existing methods, which solve a sequence of more complex auxiliary Linear Programming or Second Order Cone Problems. The proposed algorithm performs well: for triangulation, it achieves the same accuracy as existing techniques while executing faster and being straightforward to implement.
Optimizing the Viewing Graph for Structure-From-Motion
Authors: Chris Sweeney, Torsten Sattler, Tobias Hollerer, Matthew Turk, Marc Pollefeys
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

The viewing graph represents a set of views that are related by pairwise relative geometries. In the context of Structure-from-Motion (SfM), the viewing graph is the input to the incremental or global estimation pipeline. Much effort has been put towards developing robust algorithms to overcome potentially inaccurate relative geometries in the viewing graph during SfM. In this paper, we take a fundamentally different approach to SfM and instead focus on improving the quality of the viewing graph before applying SfM. Our main contribution is a novel optimization that improves the quality of the relative geometries in the viewing graph by enforcing loop consistency constraints with the epipolar point transfer. We show that this optimization greatly improves the accuracy of relative poses in the viewing graph and removes the need for filtering steps or robust algorithms typically used in global SfM methods. In addition, the optimized viewing graph can be used to efficiently calibrate cameras at scale. We combine our viewing graph optimization and focal length calibration into a global SfM pipeline that is more efficient than existing approaches. To our knowledge, ours is the first global SfM pipeline capable of handling uncalibrated image sets.
Intrinsic Scene Decomposition From RGB-D images
Authors: Mohammed Hachama, Bernard Ghanem, Peter Wonka
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Intrinsic Image Decomposition

In this paper, we address the problem of computing an intrinsic decomposition of the colors of a surface into an albedo and a shading term. The surface is reconstructed from a single or multiple RGB-D images of a static scene obtained from different views. We thereby extend and improve existing works in the area of intrinsic image decomposition. In a variational framework, we formulate the problem as a minimization of an energy composed of two terms: a data term and a regularity term. The first term is related to the image formation process and expresses the relation between the albedo, the surface normals, and the incident illumination. We use an affine shading model, a combination of a Lambertian model, and an ambient lighting term. This model is relevant for Lambertian surfaces. When available, multiple views can be used to handle view-dependent non-Lambertian reflections. The second term contains an efficient combination of l2 and l1-regularizers on the illumination vector field and albedo respectively. Unlike most previous approaches, especially Retinex-like techniques, these terms do not depend on the image gradient or texture, thus reducing the mixing shading/reflectance artifacts and leading to better results. The obtained non-linear optimization problem is efficiently solved using a cyclic block coordinate descent algorithm. Our method outperforms a range of state-of-the-art algorithms on a popular benchmark dataset.