mtybilly/Instruct2DS huggingface dataset:
Detection- and Trajectory-Level Exclusion in Multiple Object Tracking
Authors: Konrad Schindler, Stefan Roth Anton Milan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: When tracking multiple targets in crowded scenarios, modeling mutual exclusion between distinct targets becomes important at two levels: (1) in data association, each target observation should support at most one trajectory and each trajectory should be assigned at most one observation per frame; (2) in trajectory estimation, two trajectories should remain spatially separated at all times to avoid collisions. Yet, existing trackers often sidestep these important constraints. We address this using a mixed discrete-continuous conditional random field (CRF) that explicitly models both types of constraints: Exclusion between conflicting observations with supermodular pairwise terms, and exclusion between trajectories by generalizing global label costs to suppress the co-occurrence of incompatible labels (trajectories). We develop an expansion move-based MAP estimation scheme that handles both non-submodular constraints and pairwise global label costs. Furthermore, we perform a statistical analysis of ground-truth trajectories to derive appropriate CRF potentials for modeling data fidelity, target dynamics, and inter-target occlusion.
Real-Time Model-Based Rigid Object Pose Estimation and Tracking Combining Dense and Sparse Visual Cues
Authors: Leonardo Rubio, Javier Diaz, Eduardo Ros Karl Pauwels
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We propose a novel model-based method for estimating and tracking the six-degrees-of-freedom (6DOF) pose of rigid objects of arbitrary shapes in real-time. By combining dense motion and stereo cues with sparse keypoint correspondences, and by feeding back information from the model to the cue extraction level, the method is both highly accurate and robust to noise and occlusions. A tight integration of the graphical and computational capability of Graphics Processing Units (GPUs) results in pose updates at framerates exceeding 60 Hz. Since a benchmark dataset that enables the evaluation of stereo-vision-based pose estimators in complex scenarios is currently missing in the literature, we have introduced a novel synthetic benchmark dataset with varying objects, background motion, noise and occlusions. Using this dataset and a novel evaluation methodology, we show that the proposed method greatly outperforms state-of-the-art methods. Finally, we demonstrate excellent performance on challenging real-world sequences involving object manipulation.
Unnatural L0 Sparse Representation for Natural Image Deblurring
Authors: Shicheng Zheng, Jiaya Jia Li Xu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We show in this paper that the success of previous maximum a posterior (MAP) based blur removal methods partly stems from their respective intermediate steps, which implicitly or explicitly create an unnatural representation containing salient image structures. We propose a generalized and mathematically sound L 0 sparse expression, together with a new effective method, for motion deblurring. Our system does not require extra filtering during optimization and demonstrates fast energy decreasing, making a small number of iterations enough for convergence. It also provides a unified framework for both uniform and non-uniform motion deblurring. We extensively validate our method and show comparison with other approaches with respect to convergence speed, running time, and result quality.
Decoding Children's Social Behavior
Authors: G. Abowd, A. Rozga, M. Romero, M. Clements, S. Sclaroff, I. Essa, O. Ousley, Y. Li, C. Kim, H. Rao, J. Kim, L. Lo Presti, J. Zhang, D. Lantsman, J. Bidwell, Z. Ye J. Rehg
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We introduce a new problem domain for activity recognition: the analysis of children's social and communicative behaviors based on video and audio data. We specifically target interactions between children aged 1-2 years and an adult. Such interactions arise naturally in the diagnosis and treatment of developmental disorders such as autism. We introduce a new publicly-available dataset containing over 160 sessions of a 3-5 minute child-adult interaction. In each session, the adult examiner followed a semistructured play interaction protocol which was designed to elicit a broad range of social behaviors. We identify the key technical challenges in analyzing these behaviors, and describe methods for decoding the interactions. We present experimental results that demonstrate the potential of the dataset to drive interesting research questions, and show preliminary results for multi-modal activity recognition.
Finding Group Interactions in Social Clutter
Authors: Parker Porfilio, Todd Zickler Ruonan Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We consider the problem of finding distinctive social interactions involving groups of agents embedded in larger social gatherings. Given a pre-defined gallery of short exemplar interaction videos, and a long input video of a large gathering (with approximately-tracked agents), we identify within the gathering small sub-groups of agents exhibiting social interactions that resemble those in the exemplars. The participants of each detected group interaction are localized in space; the extent of their interaction is localized in time; and when the gallery of exemplars is annotated with group-interaction categories, each detected interaction is classified into one of the pre-defined categories. Our approach represents group behaviors by dichotomous collections of descriptors for (a) individual actions, and (b) pairwise interactions; and it includes efficient algorithms for optimally distinguishing participants from by-standers in every temporal unit and for temporally localizing the extent of the group interaction. Most importantly, the method is generic and can be applied whenever numerous interacting agents can be approximately tracked over time. We evaluate the approach using three different video collections, two that involve humans and one that involves mice.
Least Soft-Threshold Squares Tracking
Authors: Huchuan Lu, Ming-Hsuan Yang Dong Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper, we propose a generative tracking method based on a novel robust linear regression algorithm. In contrast to existing methods, the proposed Least Soft-thresold Squares (LSS) algorithm models the error term with the Gaussian-Laplacian distribution, which can be solved efficiently. Based on maximum joint likelihood of parameters, we derive a LSS distance to measure the difference between an observation sample and the dictionary. Compared with the distance derived from ordinary least squares methods, the proposed metric is more effective in dealing with outliers. In addition, we present an update scheme to capture the appearance change of the tracked target and ensure that the model is properly updated. Experimental results on several challenging image sequences demonstrate that the proposed tracker achieves more favorable performance than the state-of-the-art methods.
Online Robust Dictionary Learning
Authors: Jiaping Shi, Jiaya Jia Cewu Lu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Online dictionary learning is particularly useful for processing large-scale and dynamic data in computer vision. It, however, faces the major difficulty to incorporate robust functions, rather than the square data fitting term, to handle outliers in training data. In this paper, we propose a new online framework enabling the use of ersparse data fitting term in robust dictionary learning, notably enhancing the usability and practicality of this important technique. Extensive experiments have been carried out to validate our new framework.
Learning the Change for Automatic Image Cropping
Authors: Stephen Lin, Sing Bing Kang, Xiaoou Tang Jianzhou Yan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Image cropping is a common operation used to improve the visual quality of photographs. In this paper, we present an automatic cropping technique that accounts for the two primary considerations of people when they crop: removal of distracting content, and enhancement of overall composition. Our approach utilizes a large training set consisting of photos before and after cropping by expert photographers to learn how to evaluate these two factors in a crop. In contrast to the many methods that exist for general assessment of image quality, ours specifically examines differences between the original and cropped photo in solving for the crop parameters. To this end, several novel image features are proposed to model the changes in image content and composition when a crop is applied. Our experiments demonstrate improvements of our method over recent cropping algorithms on a broad range of images.
Multi-resolution Shape Analysis via Non-Euclidean Wavelets: Applications to Mesh Segmentation and Surface Alignment Problems
Authors: Moo K. Chung, Vikas Singh Won Hwa Kim
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: The analysis of 3-D shape meshes is a fundamental problem in computer vision, graphics, and medical imaging. Frequently, the needs of the application require that our analysis take a multi-resolution view of the shape's local and global topology, and that the solution is consistent across multiple scales. Unfortunately, the preferred mathematical construct which offers this behavior in classical image/signal processing, Wavelets, is no longer applicable in this general setting (data with non-uniform topology). In particular, the traditional definition does not allow writing out an expansion for graphs that do not correspond to the uniformly sampled lattice (e.g., images). In this paper, we adapt recent results in harmonic analysis, to derive NonEuclidean Wavelets based algorithms for a range of shape analysis problems in vision and medical imaging. We show how descriptors derived from the dual domain representation offer native multi-resolution behavior for characterizing local/global topology around vertices. With only minor modifications, the framework yields a method for extracting interest/key points from shapes, a surprisingly simple algorithm for 3-D shape segmentation (competitive with state of the art), and a method for surface alignment (without landmarks). We give an extensive set of comparison results on a large shape segmentation benchmark and derive a uniqueness theorem for the surface alignment problem.
Stochastic Deconvolution
Authors: Felix Heide, Matthias B. Hullin, Mushfiqur Rouf, Wolfgang Heidrich James Gregson
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We present a novel stochastic framework for non-blind deconvolution based on point samples obtained from random walks. Unlike previous methods that must be tailored to specific regularization strategies, the new Stochastic Deconvolution method allows arbitrary priors, including nonconvex and data-dependent regularizers, to be introduced and tested with little effort. Stochastic Deconvolution is straightforward to implement, produces state-of-the-art results and directly leads to a natural boundary condition for image boundaries and saturated pixels.
Nonparametric Scene Parsing with Adaptive Feature Relevance and Semantic Context
Authors: Jana Kosecka Gautam Singh
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: This paper presents a nonparametric approach to semantic parsing using small patches and simple gradient, color and location features. We learn the relevance of individual feature channels at test time using a locally adaptive distance metric. To further improve the accuracy of the nonparametric approach, we examine the importance of the retrieval set used to compute the nearest neighbours using a novel semantic descriptor to retrieve better candidates. The approach is validated by experiments on several datasets used for semantic parsing demonstrating the superiority of the method compared to the state of art approaches.
Social Role Discovery in Human Events
Authors: Bangpeng Yao, Li Fei-Fei Vignesh Ramanathan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We deal with the problem of recognizing social roles played by people in an event. Social roles are governed by human interactions, and form a fundamental component of human event description. We focus on a weakly supervised setting, where we are provided different videos belonging to an event class, without training role labels. Since social roles are described by the interaction between people in an event, we propose a Conditional Random Field to model the inter-role interactions, along with person specific social descriptors. We develop tractable variational inference to simultaneously infer model weights, as well as role assignment to all people in the videos. We also present a novel YouTube social roles dataset with ground truth role annotations, and introduce annotations on a subset of videos from the TRECVID-MED11 [1] event kits for evaluation purposes. The performance of the model is compared against different baseline methods on these datasets.
Learning to Estimate and Remove Non-uniform Image Blur
Authors: Jian Sun, Karteek Alahari, Jean Ponce Florent Couzinie-Devy
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: This paper addresses the problem of restoring images subjected to unknown and spatially varying blur caused by defocus or linear (say, horizontal) motion. The estimation of the global (non-uniform) image blur is cast as a multilabel energy minimization problem. The energy is the sum of unary terms corresponding to learned local blur estimators, and binary ones corresponding to blur smoothness. Its global minimum is found using Ishikawa's method by exploiting the natural order of discretized blur values for linear motions and defocus. Once the blur has been estimated, the image is restored using a robust (non-uniform) deblurring algorithm based on sparse regularization with global image statistics. The proposed algorithm outputs both a segmentation of the image into uniform-blur layers and an estimate of the corresponding sharp image. We present qualitative results on real images, and use synthetic data to quantitatively compare our approach to the publicly available implementation of Chakrabarti et al. [5].
Scene Parsing by Integrating Function, Geometry and Appearance Models
Authors: Song-Chun Zhu Yibiao Zhao
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Indoor functional objects exhibit large view and appearance variations, thus are difficult to be recognized by the traditional appearance-based classification paradigm. In this paper, we present an algorithm to parse indoor images based on two observations: i) The functionality is the most essential property to define an indoor object, e.g. "a chair to sit on"; ii) The geometry (3D shape) of an object is designed to serve its function. We formulate the nature of the object function into a stochastic grammar model. This model characterizes a joint distribution over the function-geometryappearance (FGA) hierarchy. The hierarchical structure includes a scene category, functional groups, functional objects, functional parts and 3D geometric shapes. We use a simulated annealing MCMC algorithm to find the maximum a posteriori (MAP) solution, i.e. a parse tree. We design four data-driven steps to accelerate the search in the FGA space: i) group the line segments into 3D primitive shapes, ii) assign functional labels to these 3D primitive shapes, iii) fill in missing objects/parts according to the functional labels, and iv) synthesize 2D segmentation maps and verify the current parse tree by the Metropolis-Hastings acceptance probability. The experimental results on several challenging indoor datasets demonstrate the proposed approach not only significantly widens the scope of indoor scene parsing algorithm from the segmentation and the 3D recovery to the functional object recognition, but also yields improved overall performance.
Efficient Detector Adaptation for Object Detection in a Video
Authors: Ram Nevatia Pramod Sharma
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this work, we present a novel and efficient detector adaptation method which improves the performance of an offline trained classifier (baseline classifier) by adapting it to new test datasets. We address two critical aspects of adaptation methods: generalizability and computational efficiency. We propose an adaptation method, which can be applied to various baseline classifiers and is computationally efficient also. For a given test video, we collect online samples in an unsupervised manner and train a random fern adaptive classifier . The adaptive classifier improves precision of the baseline classifier by validating the obtained detection responses from baseline classifier as correct detections or false alarms. Experiments demonstrate generalizability, computational efficiency and effectiveness of our method, as we compare our method with state of the art approaches for the problem of human detection and show good performance with high computational efficiency on two different baseline classifiers.
Evaluation of Color STIPs for Human Action Recognition
Authors: Jan C. van Gemert, Theo Gevers Ivo Everts
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: This paper is concerned with recognizing realistic human actions in videos based on spatio-temporal interest points (STIPs). Existing STIP-based action recognition approaches operate on intensity representations of the image data. Because of this, these approaches are sensitive to disturbing photometric phenomena such as highlights and shadows. Moreover, valuable information is neglected by discarding chromaticity from the photometric representation. These issues are addressed by Color STIPs. Color STIPs are multi-channel reformulations of existing intensity-based STIP detectors and descriptors, for which we consider a number of chromatic representations derived from the opponent color space. This enhanced modeling of appearance improves the quality of subsequent STIP detection and description. Color STIPs are shown to substantially outperform their intensity-based counterparts on the challenging UCF sports, UCF11 and UCF50 action recognition benchmarks. Moreover, the results show that color STIPs are currently the single best low-level feature choice for STIP-based approaches to human action recognition.
A Global Approach for the Detection of Vanishing Points and Mutually Orthogonal Vanishing Directions
Authors: Joao P. Barreto Michel Antunes
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: This article presents a new global approach for detecting vanishing points and groups of mutually orthogonal vanishing directions using lines detected in images of man-made environments. These two multi-model fitting problems are respectively cast as Uncapacited Facility Location (UFL) and Hierarchical Facility Location (HFL) instances that are efficiently solved using a message passing inference algorithm. We also propose new functions for measuring the consistency between an edge and a putative vanishing point, and for computing the vanishing point defined by a subset of edges. Extensive experiments in both synthetic and real images show that our algorithms outperform the state-ofthe-art methods while keeping computation tractable. In addition, we show for the first time results in simultaneously detecting multiple Manhattan-world configurations that can either share one vanishing direction (Atlanta world) or be completely independent.
Poselet Conditioned Pictorial Structures
Authors: Mykhaylo Andriluka, Peter Gehler, Bernt Schiele Leonid Pishchulin
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper we consider the challenging problem of articulated human pose estimation in still images. We observe that despite high variability of the body articulations, human motions and activities often simultaneously constrain the positions of multiple body parts. Modelling such higher order part dependencies seemingly comes at a cost of more expensive inference, which resulted in their limited use in state-of-the-art methods. In this paper we propose a model that incorporates higher order part dependencies while remaining efficient. We achieve this by defining a conditional model in which all body parts are connected a-priori, but which becomes a tractable tree-structured pictorial structures model once the image observations are available. In order to derive a set of conditioning variables we rely on the poselet-based features that have been shown to be effective for people detection but have so far found limited application for articulated human pose estimation. We demonstrate the effectiveness of our approach on three publicly available pose estimation benchmarks improving or being on-par with state of the art in each case.
Enriching Texture Analysis with Semantic Data
Authors: Mark S. Nixon, Mahesan Niranjan Tim Matthews
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We argue for the importance of explicit semantic modelling in human-centred texture analysis tasks such as retrieval, annotation, synthesis, and zero-shot learning. To this end, low-level attributes are selected and used to define a semantic space for texture. 319 texture classes varying in illumination and rotation are positioned within this semantic space using a pairwise relative comparison procedure. Low-level visual features used by existing texture descriptors are then assessed in terms of their correspondence to the semantic space. Textures with strong presence of attributes connoting randomness and complexity are shown to be poorly modelled by existing descriptors. In a retrieval experiment semantic descriptors are shown to outperform visual descriptors. Semantic modelling of texture is thus shown to provide considerable value in both feature selection and in analysis tasks.
Scene Text Recognition Using Part-Based Tree-Structured Character Detection
Authors: Chunheng Wang, Baihua Xiao, Yang Zhang, Song Gao, Zhong Zhang Cunzhao Shi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Scene text recognition has inspired great interests from the computer vision community in recent years. In this paper, we propose a novel scene text recognition method using part-based tree-structured character detection. Different from conventional multi-scale sliding window character detection strategy, which does not make use of the character-specific structure information, we use part-based tree-structure to model each type of character so as to detect and recognize the characters at the same time. While for word recognition, we build a Conditional Random Field model on the potential character locations to incorporate the detection scores, spatial constraints and linguistic knowledge into one framework. The final word recognition result is obtained by minimizing the cost function defined on the random field. Experimental results on a range of challenging public datasets (ICDAR 2003, ICDAR 2011, SVT) demonstrate that the proposed method outperforms stateof-the-art methods significantly both for character detection and word recognition.
MODEC: Multimodal Decomposable Models for Human Pose Estimation
Authors: Ben Taskar Ben Sapp
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We propose a multimodal, decomposable model for articulated human pose estimation in monocular images. A typical approach to this problem is to use a linear structured model, which struggles to capture the wide range of appearance present in realistic, unconstrained images. In this paper, we instead propose a model of human pose that explicitly captures a variety of pose modes. Unlike other multimodal models, our approach includes both global and local pose cues and uses a convex objective and joint training for mode selection and pose estimation. We also employ a cascaded mode selection step which controls the trade-off between speed and accuracy, yielding a 5x speedup in inference and learning. Our model outperforms state-of-theart approaches across the accuracy-speed trade-off curve for several pose datasets. This includes our newly-collected dataset of people in movies, FLIC, which contains an order of magnitude more labeled data for training and testing than existing datasets. The new dataset and code are available online. 1
Multi-task Sparse Learning with Beta Process Prior for Action Recognition
Authors: Weiming Hu, Guodong Tian, Shuang Yang, Haoran Wang Chunfeng Yuan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper, we formulate human action recognition as a novel Multi-Task Sparse Learning(MTSL) framework which aims to construct a test sample with multiple features from as few bases as possible. Learning the sparse representation under each feature modality is considered as a single task in MTSL. Since the tasks are generated from multiple features associated with the same visual input, they are not independent but inter-related. We introduce a Beta process(BP) prior to the hierarchical MTSL model, which efficiently learns a compact dictionary and infers the sparse structure shared across all the tasks. The MTSL model enforces the robustness in coefficient estimation compared with performing each task independently. Besides, the sparseness is achieved via the Beta process formulation rather than the computationally expensive l 1 norm penalty. In terms of non-informative gamma hyper-priors, the sparsity level is totally decided by the data. Finally, the learning problem is solved by Gibbs sampling inference which estimates the full posterior on the model parameters. Experimental results on the KTH and UCF sports datasets demonstrate the effectiveness of the proposed MTSL approach for action recognition.
Decoding, Calibration and Rectification for Lenselet-Based Plenoptic Cameras
Authors: Oscar Pizarro, Stefan B. Williams Donald G. Dansereau
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Plenoptic cameras are gaining attention for their unique light gathering and post-capture processing capabilities. We describe a decoding, calibration and rectification procedure for lenselet-based plenoptic cameras appropriate for a range of computer vision applications. We derive a novel physically based 4D intrinsic matrix relating each recorded pixel to its corresponding ray in 3D space. We further propose a radial distortion model and a practical objective function based on ray reprojection. Our 15-parameter camera model is of much lower dimensionality than camera array models, and more closely represents the physics of lenselet-based cameras. Results include calibration of a commercially available camera using three calibration grid sizes over five datasets. Typical RMS ray reprojection errors are 0.0628, 0.105 and 0.363 mm for 3.61, 7.22 and 35.1 mm calibration grids, respectively. Rectification examples include calibration targets and real-world imagery.
Hierarchical Video Representation with Trajectory Binary Partition Tree
Authors: Philippe Salembier Guillem Palou
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: As early stage of video processing, we introduce an iterative trajectory merging algorithm that produces a regionbased and hierarchical representation of the video sequence, called the Trajectory Binary Partition Tree (BPT). From this representation, many analysis and graph cut techniques can be used to extract partitions or objects that are useful in the context of specific applications. In order to define trajectories and to create a precise merging algorithm, color and motion cues have to be used. Both types of informations are very useful to characterize objects but present strong differences of behavior in the spatial and the temporal dimensions. On the one hand, scenes and objects are rich in their spatial color distributions, but these distributions are rather stable over time. Object motion, on the other hand, presents simple structures and low spatial variability but may change from frame to frame. The proposed algorithm takes into account this key difference and relies on different models and associated metrics to deal with color and motion information. We show that the proposed algorithm outperforms existing hierarchical video segmentation algorithms and provides more stable and precise regions.
Cloud Motion as a Calibration Cue
Authors: Mohammad T. Islam, Scott Workman Nathan Jacobs
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We propose cloud motion as a natural scene cue that enables geometric calibration of static outdoor cameras. This work introduces several new methods that use observations of an outdoor scene over days and weeks to estimate radial distortion, focal length and geo-orientation. Cloud-based cues provide strong constraints and are an important alternative to methods that require specific forms of static scene geometry or clear sky conditions. Our method makes simple assumptions about cloud motion and builds upon previous work on motion-based and line-based calibration. We show results on real scenes that highlight the effectiveness of our proposed methods.
FasT-Match: Fast Affine Template Matching
Authors: Daniel Reichman, Gilad Tsur, Shai Avidan Simon Korman
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Fast-Match is a fast algorithm for approximate template matching under 2D affine transformations that minimizes the Sum-of-Absolute-Differences (SAD) error measure. There is a huge number of transformations to consider but we prove that they can be sampled using a density that depends on the smoothness of the image. For each potential transformation, we approximate the SAD error using a sublinear algorithm that randomly examines only a small number of pixels. We further accelerate the algorithm using a branch-and-bound scheme. As images are known to be piecewise smooth, the result is a practical affine template matching algorithm with approximation guarantees, that takes a few seconds to run on a standard machine. We perform several experiments on three different datasets, and report very good results. To the best of our knowledge, this is the first template matching algorithm which is guaranteed to handle arbitrary 2D affine transformations.
Dense Non-rigid Point-Matching Using Random Projections
Authors: Dennis Decoste, Chih-Jen Lin Raffay Hamid
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We present a robust and efficient technique for matching dense sets of points undergoing non-rigid spatial transformations. Our main intuition is that the subset of points that can be matched with high confidence should be used to guide the matching procedure for the rest. We propose a novel algorithm that incorporates these high-confidence matches as a spatial prior to learn a discriminative subspace that simultaneously encodes both the feature similarity as well as their spatial arrangement. Conventional subspace learning usually requires spectral decomposition of the pair-wise distance matrix across the point-sets, which can become inefficient even for moderately sized problems. To this end, we propose the use of random projections for approximate subspace learning, which can provide significant time improvements at the cost of minimal precision loss. This efficiency gain allows us to iteratively find and remove high-confidence matches from the point sets, resulting in high recall. To show the effectiveness of our approach, we present a systematic set of experiments and results for the problem of dense non-rigid image-feature matching.
Large Displacement Optical Flow from Nearest Neighbor Fields
Authors: Hailin Jin, Zhe Lin, Scott Cohen, Ying Wu Zhuoyuan Chen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We present an optical flow algorithm for large displacement motions. Most existing optical flow methods use the standard coarse-to-fine framework to deal with large displacement motions which has intrinsic limitations. Instead, we formulate the motion estimation problem as a motion segmentation problem. We use approximate nearest neighbor fields to compute an initial motion field and use a robust algorithm to compute a set of similarity transformations as the motion candidates for segmentation. To account for deviations from similarity transformations, we add local deformations in the segmentation process. We also observe that small objects can be better recovered using translations as the motion candidates. We fuse the motion results obtained under similarity transformations and under translations together before a final refinement. Experimental validation shows that our method can successfully handle large displacement motions. Although we particularly focus on large displacement motions in this work, we make no sacrifice in terms of overall performance. In particular, our method ranks at the top of the Middlebury benchmark.
Hallucinated Humans as the Hidden Context for Labeling 3D Scenes
Authors: Hema Koppula, Ashutosh Saxena Yun Jiang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: For scene understanding, one popular approach has been to model the object-object relationships. In this paper, we hypothesize that such relationships are only an artifact of certain hidden factors, such as humans. For example, the objects, monitor and keyboard, are strongly spatially correlated only because a human types on the keyboard while watching the monitor. Our goal is to learn this hidden human context (i.e., the human-object relationships), and also use it as a cue for labeling the scenes. We present Infinite Factored Topic Model (IFTM), where we consider a scene as being generated from two types of topics: human configurations and human-object relationships. This enables our algorithm to hallucinate the possible configurations of the humans in the scene parsimoniously. Given only a dataset of scenes containing objects but not humans, we show that our algorithm can recover the human object relationships. We then test our algorithm on the task of attribute and object labeling in 3D scenes and show consistent improvements over the state-of-the-art.
Discrete MRF Inference of Marginal Densities for Non-uniformly Discretized Variable Space
Authors: Takayuki Okatani, Koichiro Deguchi Masaki Saito
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: This paper is concerned with the inference of marginal densities based on MRF models. The optimization algorithms for continuous variables are only applicable to a limited number of problems, whereas those for discrete variables are versatile. Thus, it is quite common to convert the continuous variables into discrete ones for the problems that ideally should be solved in the continuous domain, such as stereo matching and optical flow estimation. In this paper, we show a novel formulation for this continuous-discrete conversion. The key idea is to estimate the marginal densities in the continuous domain by approximating them with mixtures of rectangular densities. Based on this formulation, we derive a mean field (MF) algorithm and a belief propagation (BP) algorithm. These algorithms can correctly handle the case where the variable space is discretized in a non-uniform manner. By intentionally using such a non-uniform discretization, a higher balance between computational efficiency and accuracy of marginal density estimates could be achieved. We present a method for actually doing this, which dynamically discretizes the variable space in a coarse-to-fine manner in the course of the computation. Experimental results show the effectiveness of our approach.
Efficient Large-Scale Structured Learning
Authors: Oscar Beijbom, Serge Belongie Steve Branson
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We introduce an algorithm, SVM-IS, for structured SVM learning that is computationally scalable to very large datasets and complex structural representations. We show that structured learning is at least as fast-and often much faster-than methods based on binary classification for problems such as deformable part models, object detection, and multiclass classification, while achieving accuracies that are at least as good. Our method allows problem-specific structural knowledge to exploited for faster optimization by integrating with a user-defined importance sampling function. We demonstrate fast train times on two challenging largescale datasets for two very different problems: ImageNet for multiclass classification and CUB-200-2011 for deformable part model training. Our method is shown to be 10-50 times faster than SVMstruct for cost-sensitive multiclass classification while being about as fast as the fastest 1-vs-all methods for multiclass classification. For deformable part model training, it is shown to be 50-1000 times faster than methods based on SVMstruct, mining hard negatives, and Pegasos-style stochastic gradient descent. Source code of our method is publicly available.
Relative Volume Constraints for Single View 3D Reconstruction
Authors: Claudia Nieuwenhuis, Daniel Cremers Eno Toppe
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We introduce the concept of relative volume constraints in order to account for insufficient information in the reconstruction of 3D objects from a single image. The key idea is to formulate a variational reconstruction approach with shape priors in form of relative depth profiles or volume ratios relating object parts. Such shape priors can easily be derived either from a user sketch or from the object's shading profile in the image. They can handle textured or shadowed object regions by propagating information. We propose a convex relaxation of the constrained optimization problem which can be solved optimally in a few seconds on graphics hardware. In contrast to existing single view reconstruction algorithms, the proposed algorithm provides substantially more flexibility to recover shape details such as self-occlusions, dents and holes, which are not visible in the object silhouette.
Uncalibrated Photometric Stereo for Unknown Isotropic Reflectances
Authors: Yasuyuki Matsushita, Imari Sato, Takahiro Okabe, Yoichi Sato Feng Lu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We propose an uncalibrated photometric stereo method that works with general and unknown isotropic reflectances. Our method uses a pixel intensity profile, which is a sequence of radiance intensities recorded at a pixel across multi-illuminance images. We show that for general isotropic materials, the geodesic distance between intensity profiles is linearly related to the angular difference of their surface normals, and that the intensity distribution of an intensity profile conveys information about the reflectance properties, when the intensity profile is obtained under uniformly distributed directional lightings. Based on these observations, we show that surface normals can be estimated up to a convex/concave ambiguity. A solution method based on matrix decomposition with missing data is developed for a reliable estimation. Quantitative and qualitative evaluations of our method are performed using both synthetic and real-world scenes.
Image Understanding from Experts' Eyes by Modeling Perceptual Skill of Diagnostic Reasoning Processes
Authors: Pengcheng Shi, Anne R. Haake Rui Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Eliciting and representing experts' remarkable perceptual capability of locating, identifying and categorizing objects in images specific to their domains of expertise will benefit image understanding in terms of transferring human domain knowledge and perceptual expertise into image-based computational procedures. In this paper, we present a hierarchical probabilistic framework to summarize the stereotypical and idiosyncratic eye movement patterns shared within 11 board-certified dermatologists while they are examining and diagnosing medical images. Each inferred eye movement pattern characterizes the similar temporal and spatial properties of its corresponding segments of the experts' eye movement sequences. We further discover a subset of distinctive eye movement patterns which are commonly exhibited across multiple images. Based on the combinations of the exhibitions of these eye movement patterns, we are able to categorize the images from the perspective of experts' viewing strategies. In each category, images share similar lesion distributions and configurations. The performance of our approach shows that modeling physicians' diagnostic viewing behaviors informs about medical images' understanding to correct diagnosis.
Robust Discriminative Response Map Fitting with Constrained Local Models
Authors: Stefanos Zafeiriou, Shiyang Cheng, Maja Pantic Akshay Asthana
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We present a novel discriminative regression based approach for the Constrained Local Models (CLMs) framework, referred to as the Discriminative Response Map Fitting (DRMF) method, which shows impressive performance in the generic face fitting scenario. The motivation behind this approach is that, unlike the holistic texture based features used in the discriminative AAM approaches, the response map can be represented by a small set of parameters and these parameters can be very efficiently used for reconstructing unseen response maps. Furthermore, we show that by adopting very simple off-the-shelf regression techniques, it is possible to learn robust functions from response maps to the shape parameters updates. The experiments, conducted on Multi-PIE, XM2VTS and LFPW database, show that the proposed DRMF method outperforms stateof-the-art algorithms for the task of generic face fitting. Moreover, the DRMF method is computationally very efficient and is real-time capable. The current MATLAB implementation takes 1 second per image. To facilitate future comparisons, we release the MATLAB code acand the pretrained models for research purposes.
Unconstrained Monocular 3D Human Pose Estimation by Action Detection and Cross-Modality Regression Forest
Authors: Tae-Kyun Kim, Roberto Cipolla Tsz-Ho Yu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: This work addresses the challenging problem of unconstrained 3D human pose estimation (HPE) from a novel perspective. Existing approaches struggle to operate in realistic applications, mainly due to their scene-dependent priors, such as background segmentation and multi-camera network, which restrict their use in unconstrained environments. We therfore present a framework which applies action detection and 2D pose estimation techniques to infer 3D poses in an unconstrained video. Action detection offers spatiotemporal priors to 3D human pose estimation by both recognising and localising actions in space-time. Instead of holistic features, e.g. silhouettes, we leverage the flexibility of deformable part model to detect 2D body parts as a feature to estimate 3D poses. A new unconstrained pose dataset has been collected to justify the feasibility of our method, which demonstrated promising results, significantly outperforming the relevant state-of-the-arts.
Revisiting Depth Layers from Occlusions
Authors: Andrew Gallagher, Tsuhan Chen Adarsh Kowdle
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this work, we consider images of a scene with a moving object captured by a static camera. As the object (human or otherwise) moves about the scene, it reveals pairwise depth-ordering or occlusion cues. The goal of this work is to use these sparse occlusion cues along with monocular depth occlusion cues to densely segment the scene into depth layers. We cast the problem of depth-layer segmentation as a discrete labeling problem on a spatiotemporal Markov Random Field (MRF) that uses the motion occlusion cues along with monocular cues and a smooth motion prior for the moving object. We quantitatively show that depth ordering produced by the proposed combination of the depth cues from object motion and monocular occlusion cues are superior to using either feature independently, and using a na??ve combination of the features.
Efficient Object Detection and Segmentation for Fine-Grained Recognition
Authors: Shenghuo Zhu Anelia Angelova
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We propose a detection and segmentation algorithm for the purposes of fine-grained recognition. The algorithm first detects low-level regions that could potentially belong to the object and then performs a full-object segmentation through propagation. Apart from segmenting the object, we can also 'zoom in' on the object, i.e. center it, normalize it for scale, and thus discount the effects of the background. We then show that combining this with a state-of-the-art classification algorithm leads to significant improvements in performance especially for datasets which are considered particularly hard for recognition, e.g. birds species. The proposed algorithm is much more efficient than other known methods in similar scenarios [4, 21]. Our method is also simpler and we apply it here to different classes of objects, e.g. birds, flowers, cats and dogs. We tested the algorithm on a number of benchmark datasets for fine-grained categorization. It outperforms all the known state-of-the-art methods on these datasets, sometimes by as much as 11%. It improves the performance of our baseline algorithm by 3-4%, consistently on all datasets. We also observed more than a 4% improvement in the recognition performance on a challenging largescale flower dataset, containing 578 species of flowers and 250,000 images.