mtybilly/Instruct2DS huggingface dataset:
Globally Consistent Multi-label Assignment on the Ray Space of 4D Light Fields
Authors: Christoph Straehle, Bastian Goldluecke Sven Wanner
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We present the first variational framework for multi-label segmentation on the ray space of 4D light fields. For traditional segmentation of single images, features need to be extracted from the 2D projection of a three-dimensional scene. The associated loss of geometry information can cause severe problems, for example if different objects have a very similar visual appearance. In this work, we show that using a light field instead of an image not only enables to train classifiers which can overcome many of these problems, but also provides an optimal data structure for label optimization by implicitly providing scene geometry information. It is thus possible to consistently optimize label assignment over all views simultaneously. As a further contribution, we make all light fields available online with complete depth and segmentation ground truth data where available, and thus establish the first benchmark data set for light field analysis to facilitate competitive further development of algorithms.
Top-Down Segmentation of Non-rigid Visual Objects Using Derivative-Based Search on Sparse Manifolds
Authors: Gustavo Carneiro Jacinto C. Nascimento
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: The solution for the top-down segmentation of non-rigid visual objects using machine learning techniques is generally regarded as too complex to be solved in its full generality given the large dimensionality of the search space of the explicit representation of the segmentation contour. In order to reduce this complexity, the problem is usually divided into two stages: rigid detection and non-rigid segmentation. The rationale is based on the fact that the rigid detection can be run in a lower dimensionality space (i.e., less complex and faster) than the original contour space, and its result is then used to constrain the non-rigid segmentation. In this paper, we propose the use of sparse manifolds to reduce the dimensionality of the rigid detection search space of current stateof-the-art top-down segmentation methodologies. The main goals targeted by this smaller dimensionality search space are the decrease of the search running time complexity and the reduction of the training complexity of the rigid detector. These goals are attainable given that both the search and training complexities are function of the dimensionality of the rigid search space. We test our approach in the segmentation of the left ventricle from ultrasound images and lips from frontal face images. Compared to the performance of state-of-the-art non-rigid segmentation system, our experiments show that the use of sparse manifolds for the rigid detection leads to the two goals mentioned above.
Harry Potter's Marauder's Map: Localizing and Tracking Multiple Persons-of-Interest by Nonnegative Discretization
Authors: Yi Yang, Alexander Hauptmann Shoou-I Yu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: A device just like Harry Potter's Marauder's Map, which pinpoints the location of each person-of-interest at all times, provides invaluable information for analysis of surveillance videos. To make this device real, a system would be required to perform robust person localization and tracking in real world surveillance scenarios, especially for complex indoor environments with many walls causing occlusion and long corridors with sparse surveillance camera coverage. We propose a tracking-by-detection approach with nonnegative discretization to tackle this problem. Given a set of person detection outputs, our framework takes advantage of all important cues such as color, person detection, face recognition and non-background information to perform tracking. Local learning approaches are used to uncover the manifold structure in the appearance space with spatio-temporal constraints. Nonnegative discretization is used to enforce the mutual exclusion constraint, which guarantees a person detection output to only belong to exactly one individual. Experiments show that our algorithm performs robust localization and tracking of persons-of-interest not only in outdoor scenes, but also in a complex indoor real-world nursing home environment.
Fast Image Super-Resolution Based on In-Place Example Regression
Authors: Zhe Lin, Scott Cohen Jianchao Yang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We propose a fast regression model for practical single image super-resolution based on in-place examples, by leveraging two fundamental super-resolution approaches-learning from an external database and learning from selfexamples. Our in-place self-similarity refines the recently proposed local self-similarity by proving that a patch in the upper scale image have good matches around its origin location in the lower scale image. Based on the in-place examples, a first-order approximation of the nonlinear mapping function from lowto high-resolution image patches is learned. Extensive experiments on benchmark and realworld images demonstrate that our algorithm can produce natural-looking results with sharp edges and preserved fine details, while the current state-of-the-art algorithms are prone to visual artifacts. Furthermore, our model can easily extend to deal with noise by combining the regression results on multiple in-place examples for robust estimation. The algorithm runs fast and is particularly useful for practical applications, where the input images typically contain diverse textures and they are potentially contaminated by noise or compression artifacts.
Query Adaptive Similarity for Large Scale Object Retrieval
Authors: Christian Wengert, Luc Van Gool Danfeng Qin
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Many recent object retrieval systems rely on local features for describing an image. The similarity between a pair of images is measured by aggregating the similarity between their corresponding local features. In this paper we present a probabilistic framework for modeling the feature to feature similarity measure. We then derive a query adaptive distance which is appropriate for global similarity evaluation. Furthermore, we propose a function to score the individual contributions into an image to image similarity within the probabilistic framework. Experimental results show that our method improves the retrieval accuracy significantly and consistently. Moreover, our result compares favorably to the state-of-the-art.
Winding Number for Region-Boundary Consistent Salient Contour Extraction
Authors: Hongdong Li, Xuming He Yansheng Ming
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: This paper aims to extract salient closed contours from an image. For this vision task, both region segmentation cues (e.g. color/texture homogeneity) and boundary detection cues (e.g. local contrast, edge continuity and contour closure) play important and complementary roles. In this paper we show how to combine both cues in a unified framework. The main focus is given to how to maintain the consistency (compatibility) between the region cues and the boundary cues. To this ends, we introduce the use of winding number-a well-known concept in topology-as a powerful mathematical device. By this device, the region-boundary consistency is represented as a set of simple linear relationships. Our method is applied to the figure-ground segmentation problem. The experiments show clearly improved results.
Analytic Bilinear Appearance Subspace Construction for Modeling Image Irradiance under Natural Illumination and Non-Lambertian Reflectance
Authors: Aly A. Farag Shireen Y. Elhabian
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Conventional subspace construction approaches suffer from the need of "large-enough" image ensemble rendering numerical methods intractable. In this paper, we propose an analytic formulation for low-dimensional subspace construction in which shading cues lie while preserving the natural structure of an image sample. Using the frequencyspace representation of the image irradiance equation, the process of finding such subspace is cast as establishing a relation between its principal components and that of a deterministic set of basis functions, termed as irradiance harmonics. Representing images as matrices further lessen the number of parameters to be estimated to define a bilinear projection which maps the image sample to a lowerdimensional bilinear subspace. Results show significant impact on dimensionality reduction with minimal loss of information as well as robustness against noise.
A Fully-Connected Layered Model of Foreground and Background Flow
Authors: Jonas Wulff, Erik B. Sudderth, Hanspeter Pfister, Michael J. Black Deqing Sun
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Layered models allow scene segmentation and motion estimation to be formulated together and to inform one another. Traditional layered motion methods, however, employ fairly weak models of scene structure, relying on locally connected Ising/Potts models which have limited ability to capture long-range correlations in natural scenes. To address this, we formulate a fully-connected layered model that enables global reasoning about the complicated segmentations of real objects. Optimization with fully-connected graphical models is challenging, and our inference algorithm leverages recent work on efficient mean field updates for fully-connected conditional random fields. These methods can be implemented efficiently using high-dimensional Gaussian filtering. We combine these ideas with a layered flow model, and find that the long-range connections greatly improve segmentation into figure-ground layers when compared with locally connected MRF models. Experiments on several benchmark datasets show that the method can recover fine structures and large occlusion regions, with good flow accuracy and much lower computational cost than previous locally-connected layered models.
Bilinear Programming for Human Activity Recognition with Unknown MRF Graphs
Authors: Qinfeng Shi, Chunhua Shen, Anton van den Hengel Zhenhua Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Markov Random Fields (MRFs) have been successfully applied to human activity modelling, largely due to their ability to model complex dependencies and deal with local uncertainty. However, the underlying graph structure is often manually specified, or automatically constructed by heuristics. We show, instead, that learning an MRF graph and performing MAP inference can be achieved simultaneously by solving a bilinear program. Equipped with the bilinear program based MAP inference for an unknown graph, we show how to estimate parameters efficiently and effectively with a latent structural SVM. We apply our techniques to predict sport moves (such as serve, volley in tennis) and human activity in TV episodes (such as kiss, hug and Hi-Five). Experimental results show the proposed method outperforms the state-of-the-art.
What Object Motion Reveals about Shape with Unknown BRDF and Lighting
Authors: Dikpal Reddy, Yizhou Wang, Ravi Ramamoorthi Manmohan Chandraker
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We present a theory that addresses the problem of determining shape from the (small or differential) motion of an object with unknown isotropic reflectance, under arbitrary unknown distant illumination, for both orthographic and perpsective projection. Our theory imposes fundamental limits on the hardness of surface reconstruction, independent of the method involved. Under orthographic projection, we prove that three differential motions suffice to yield an invariant that relates shape to image derivatives, regardless of BRDF and illumination. Under perspective projection, we show that four differential motions suffice to yield depth and a linear constraint on the surface gradient, with unknown BRDF and lighting. Further, we delineate the topological classes up to which reconstruction may be achieved using the invariants. Finally, we derive a general stratification that relates hardness of shape recovery to scene complexity. Qualitatively, our invariants are homogeneous partial differential equations for simple lighting and inhomogeneous for complex illumination. Quantitatively, our framework shows that the minimal number of motions required to resolve shape is greater for more complex scenes. Prior works that assume brightness constancy, Lambertian BRDF or a known directional light source follow as special cases of our stratification. We illustrate with synthetic and real data how potential reconstruction methods may exploit our framework.
Learning by Associating Ambiguously Labeled Images
Authors: Shijie Xiao, Kui Jia, Tsung-Han Chan, Shenghua Gao, Dong Xu, Yi Ma Zinan Zeng
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We study in this paper the problem of learning classifiers from ambiguously labeled images. For instance, in the collection of new images, each image contains some samples of interest (e.g., human faces), and its associated caption has labels with the true ones included, while the samplelabel association is unknown. The task is to learn classifiers from these ambiguously labeled images and generalize to new images. An essential consideration here is how to make use of the information embedded in the relations between samples and labels, both within each image and across the image set. To this end, we propose a novel framework to address this problem. Our framework is motivated by the observation that samples from the same class repetitively appear in the collection of ambiguously labeled training images, while they are just ambiguously labeled in each image. If we can identify samples of the same class from each image and associate them across the image set, the matrix formed by the samples from the same class would be ideally low-rank. By leveraging such a low-rank assumption, we can simultaneously optimize a partial permutation matrix (PPM) for each image, which is formulated in order to exploit all information between samples and labels in a principled way. The obtained PPMs can be readily used to assign labels to samples in training images, and then a standard SVM classifier can be trained and used for unseen data. Experiments on benchmark datasets show the effectiveness of our proposed method.
As-Projective-As-Possible Image Stitching with Moving DLT
Authors: Tat-Jun Chin, Michael S. Brown, David Suter Julio Zaragoza
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We investigate projective estimation under model inadequacies, i.e., when the underpinning assumptions of the projective model are not fully satisfied by the data. We focus on the task of image stitching which is customarily solved by estimating a projective warp -a model that is justified when the scene is planar or when the views differ purely by rotation. Such conditions are easily violated in practice, and this yields stitching results with ghosting artefacts that necessitate the usage of deghosting algorithms. To this end we propose as-projective-as-possible warps, i.e., warps that aim to be globally projective, yet allow local non-projective deviations to account for violations to the assumed imaging conditions. Based on a novel estimation technique called Moving Direct Linear Transformation (Moving DLT), our method seamlessly bridges image regions that are inconsistent with the projective model. The result is highly accurate image stitching, with significantly reduced ghosting effects, thus lowering the dependency on post hoc deghosting.
Light Field Distortion Feature for Transparent Object Recognition
Authors: Hajime Nagahara, Atsushi Shimada, Rin-Ichiro Taniguchi Kazuki Maeno
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Current object-recognition algorithms use local features, such as scale-invariant feature transform (SIFT) and speeded-up robust features (SURF), for visually learning to recognize objects. These approaches though cannot apply to transparent objects made of glass or plastic, as such objects take on the visual features of background objects, and the appearance of such objects dramatically varies with changes in scene background. Indeed, in transmitting light, transparent objects have the unique characteristic of distorting the background by refraction. In this paper, we use a single-shot light Aeld image as an input and model the distortion of the light Aeld caused by the refractive property of a transparent object. We propose a new feature, called the light Aeld distortion (LFD) feature, for identifying a transparent object. The proposal incorporates this LFD feature into the bag-of-features approach for recognizing transparent objects. We evaluated its performance in laboratory and real settings.
Ensemble Learning for Confidence Measures in Stereo Vision
Authors: Rahul Nair, Daniel Kondermann Ralf Haeusler
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: With the aim to improve accuracy of stereo confidence measures, we apply the random decision forest framework to a large set of diverse stereo confidence measures. Learning and testing sets were drawn from the recently introduced KITTI dataset, which currently poses higher challenges to stereo solvers than other benchmarks with ground truth for stereo evaluation. We experiment with semi global matching stereo (SGM) and a census dataterm, which is the best performing realtime capable stereo method known to date. On KITTI images, SGM still produces a significant amount of error. We obtain consistently improved area under curve values of sparsification measures in comparison to best performing single stereo confidence measures where numbers of stereo errors are large. More specifically, our method performs best in all but one out of 194 frames of the KITTI dataset.
Mirror Surface Reconstruction from a Single Image
Authors: Richard Hartley, Mathieu Salzmann Miaomiao Liu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: This paper tackles the problem of reconstructing the shape of a smooth mirror surface from a single image. In particular, we consider the case where the camera is observing the reflection of a static reference target in the unknown mirror. We first study the reconstruction problem given dense correspondences between 3D points on the reference target and image locations. In such conditions, our differential geometry analysis provides a theoretical proof that the shape of the mirror surface can be uniquely recovered if the pose of the reference target is known. We then relax our assumptions by considering the case where only sparse correspondences are available. In this scenario, we formulate reconstruction as an optimization problem, which can be solved using a nonlinear least-squares method. We demonstrate the effectiveness of our method on both synthetic and real images.
Handling Noise in Single Image Deblurring Using Directional Filters
Authors: Sunghyun Cho, Dimitris Metaxas, Sylvain Paris, Jue Wang Lin Zhong
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: State-of-the-art single image deblurring techniques are sensitive to image noise. Even a small amount of noise, which is inevitable in low-light conditions, can degrade the quality of blur kernel estimation dramatically. The recent approach of Tai and Lin [17] tries to iteratively denoise and deblur a blurry and noisy image. However, as we show in this work, directly applying image denoising methods often partially damages the blur information that is extracted from the input image, leading to biased kernel estimation. We propose a new method for handling noise in blind image deconvolution based on new theoretical and practical insights. Our key observation is that applying a directional low-pass filter to the input image greatly reduces the noise level, while preserving the blur information in the orthogonal direction to the filter. Based on this observation, our method applies a series of directional filters at different orientations to the input image, and estimates an accurate Radon transform of the blur kernel from each filtered image. Finally, we reconstruct the blur kernel using inverse Radon transform. Experimental results on synthetic and real data show that our algorithm achieves higher quality results than previous approaches on blurry and noisy images. 1
Joint Spectral Correspondence for Disparate Image Matching
Authors: Kostas Daniilidis Mayank Bansal
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We address the problem of matching images with disparate appearance arising from factors like dramatic illumination (day vs. night), age (historic vs. new) and rendering style differences. The lack of local intensity or gradient patterns in these images makes the application of pixellevel descriptors like SIFT infeasible. We propose a novel formulation for detecting and matching persistent features between such images by analyzing the eigen-spectrum of the joint image graph constructed from all the pixels in the two images. We show experimental results of our approach on a public dataset of challenging image pairs and demonstrate significant performance improvements over state-of-the-art.
From Local Similarity to Global Coding: An Application to Image Classification
Authors: Hamid R. Rabiee, Mehrdad Farajtabar, Marjan Ghazvininejad Amirreza Shaban
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Bag of words models for feature extraction have demonstrated top-notch performance in image classification. These representations are usually accompanied by a coding method. Recently, methods that code a descriptor giving regard to its nearby bases have proved efficacious. These methods take into account the nonlinear structure of descriptors, since local similarities are a good approximation of global similarities. However, they confine their usage of the global similarities to nearby bases. In this paper, we propose a coding scheme that brings into focus the manifold structure of descriptors, and devise a method to compute the global similarities of descriptors to the bases. Given a local similarity measure between bases, a global measure is computed. Exploiting the local similarity of a descriptor and its nearby bases, a global measure of association of a descriptor to all the bases is computed. Unlike the locality-based and sparse coding methods, the proposed coding varies smoothly with respect to the underlying manifold. Experiments on benchmark image classification datasets substantiate the superiority of the proposed method over its locality and sparsity based rivals.
Probabilistic Elastic Matching for Pose Variant Face Verification
Authors: Gang Hua, Zhe Lin, Jonathan Brandt, Jianchao Yang Haoxiang Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Pose variation remains to be a major challenge for realworld face recognition. We approach this problem through a probabilistic elastic matching method. We take a part based representation by extracting local features (e.g., LBP or SIFT) from densely sampled multi-scale image patches. By augmenting each feature with its location, a Gaussian mixture model (GMM) is trained to capture the spatialappearance distribution of all face images in the training corpus. Each mixture component of the GMM is confined to be a spherical Gaussian to balance the influence of the appearance and the location terms. Each Gaussian component builds correspondence of a pair of features to be matched between two faces/face tracks. For face verification, we train an SVM on the vector concatenating the difference vectors of all the feature pairs to decide if a pair of faces/face tracks is matched or not. We further propose a joint Bayesian adaptation algorithm to adapt the universally trained GMM to better model the pose variations between the target pair of faces/face tracks, which consistently improves face verification accuracy. Our experiments show that our method outperforms the state-ofthe-art in the most restricted protocol on Labeled Face in the Wild (LFW) and the YouTube video face database by a significant margin.
Story-Driven Summarization for Egocentric Video
Authors: Kristen Grauman Zheng Lu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We present a video summarization approach that discovers the story of an egocentric video. Given a long input video, our method selects a short chain of video subshots depicting the essential events. Inspired by work in text analysis that links news articles over time, we define a randomwalk based metric of influence between subshots that reflects how visual objects contribute to the progression of events. Using this influence metric, we define an objective for the optimal k-subshot summary. Whereas traditional methods optimize a summary's diversity or representativeness, ours explicitly accounts for how one sub-event "leads to" another--which, critically, captures event connectivity beyond simple object co-occurrence. As a result, our summaries provide a better sense of story. We apply our approach to over 12 hours of daily activity video taken from 23 unique camera wearers, and systematically evaluate its quality compared to multiple baselines with 34 human subjects.
Towards Pose Robust Face Recognition
Authors: Zhen Lei, Stan Z. Li Dong Yi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Most existing pose robust methods are too computational complex to meet practical applications and their performance under unconstrained environments are rarely evaluated. In this paper, we propose a novel method for pose robust face recognition towards practical applications, which is fast, pose robust and can work well under unconstrained environments. Firstly, a 3D deformable model is built and a fast 3D model fitting algorithm is proposed to estimate the pose of face image. Secondly, a group of Gabor filters are transformed according to the pose and shape of face image for feature extraction. Finally, PCA is applied on the pose adaptive Gabor features to remove the redundances and Cosine metric is used to evaluate the similarity. The proposed method has three advantages: (1) The pose correction is applied in the filter space rather than image space, which makes our method less affected by the precision of the 3D model; (2) By combining the holistic pose transformation and local Gabor filtering, the final feature is robust to pose and other negative factors in face recognition; (3) The 3D structure and facial symmetry are successfully used to deal with self-occlusion. Extensive experiments on FERET and PIE show the proposed method outperforms state-ofthe-art methods significantly, meanwhile, the method works well on LFW.
All About VLAD
Authors: Andrew Zisserman Relja Arandjelovic
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: The objective of this paper is large scale object instance retrieval, given a query image. A starting point of such systems is feature detection and description, for example using SIFT. The focus of this paper, however, is towards very large scale retrieval where, due to storage requirements, very compact image descriptors are required and no information about the original SIFT descriptors can be accessed directly at run time. We start from VLAD, the state-of-the art compact descriptor introduced by J??gou et al. [8] for this purpose, and make three novel contributions: first, we show that a simple change to the normalization method significantly improves retrieval performance; second, we show that vocabulary adaptation can substantially alleviate problems caused when images are added to the dataset after initial vocabulary learning. These two methods set a new stateof-the-art over all benchmarks investigated here for both mid-dimensional (20k-D to 30k-D) and small (128-D) descriptors. Our third contribution is a multiple spatial VLAD representation, MultiVLAD, that allows the retrieval and localization of objects that only extend over a small part of an image (again without requiring use of the original image SIFT descriptors).
Graph-Based Discriminative Learning for Location Recognition
Authors: Noah Snavely Song Cao
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Recognizing the location of a query image by matching it to a database is an important problem in computer vision, and one for which the representation of the database is a key issue. We explore new ways for exploiting the structure of a database by representing it as a graph, and show how the rich information embedded in a graph can improve a bagof-words-based location recognition method. In particular, starting from a graph on a set of images based on visual connectivity, we propose a method for selecting a set of subgraphs and learning a local distance function for each using discriminative techniques. For a query image, each database image is ranked according to these local distance functions in order to place the image in the right part of the graph. In addition, we propose a probabilistic method for increasing the diversity of these ranked database images, again based on the structure of the image graph. We demonstrate that our methods improve performance over standard bag-of-words methods on several existing location recognition datasets.
Calibrating Photometric Stereo by Holistic Reflectance Symmetry Analysis
Authors: Ping Tan Zhe Wu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Under unknown directional lighting, the uncalibrated Lambertian photometric stereo algorithm recovers the shape of a smooth surface up to the generalized bas-relief (GBR) ambiguity. We resolve this ambiguity from the halfvector symmetry, which is observed in many isotropic materials. Under this symmetry, a 2D BRDF slice with low-rank structure can be obtained from an image, if the surface normals and light directions are correctly recovered. In general, this structure is destroyed by the GBR ambiguity. As a result, we can resolve the ambiguity by restoring this structure. We develop a simple algorithm of auto-calibration from separable homogeneous specular reflection of real images. Compared with previous methods, this method takes a holistic approach to exploiting reflectance symmetry and produces superior results.
Relative Hidden Markov Models for Evaluating Motion Skill
Authors: Baoxin Li Qiang Zhang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: This paper is concerned with a novel problem: learning temporal models using only relative information. Such a problem arises naturally in many applications involving motion or video data. Our focus in this paper is on videobased surgical training, in which a key task is to rate the performance of a trainee based on a video capturing his motion. Compared with the conventional method of relying on ratings from senior surgeons, an automatic approach to this problem is desirable for its potential lower cost, better objectiveness, and real-time availability. To this end, we propose a novel formulation termed Relative Hidden Markov Model and develop an algorithm for obtaining a solution under this model. The proposed method utilizes only a relative ranking (based on an attribute of interest) between pairs of the inputs, which is easier to obtain and often more consistent, especially for the chosen application domain. The proposed algorithm effectively learns a model from the training data so that the attribute under consideration is linked to the likelihood of the inputs under the learned model. Hence the model can be used to compare new sequences. Synthetic data is first used to systematically evaluate the model and the algorithm, and then we experiment with real data from a surgical training system. The experimental results suggest that the proposed approach provides a promising solution to the real-world problem of motion skill evaluation from video.
Classification of Tumor Histology via Morphometric Context
Authors: Alexander Borowsky, Paul Spellman, Bahram Parvin Hang Chang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Image-based classification of tissue histology, in terms of different components (e.g., normal signature, categories of aberrant signatures), provides a series of indices for tumor composition. Subsequently, aggregation of these indices in each whole slide image (WSI) from a large cohort can provide predictive models of clinical outcome. However, the performance of the existing techniques is hindered as a result of large technical and biological variations that are always present in a large cohort. In this paper, we propose two algorithms for classification of tissue histology based on robust representations of morphometric context, which are built upon nuclear level morphometric features at various locations and scales within the spatial pyramid matching (SPM) framework. These methods have been evaluated on two distinct datasets of different tumor types collected from The Cancer Genome Atlas (TCGA), and the experimental results indicate that our methods are (i) extensible to different tumor types; (ii) robust in the presence of wide technical and biological variations; (iii) invariant to different nuclear segmentation strategies; and (iv) scalable with varying training sample size. In addition, our experiments suggest that enforcing sparsity, during the construction of morphometric context, further improves the performance of the system.
Five Shades of Grey for Fast and Reliable Camera Pose Estimation
Authors: Istvan Szentandrasi, Michal Zacharias, Marketa Dubska, Rudolf Kajan Adam Herout
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We introduce here an improved design of the Uniform Marker Fields and an algorithm for their fast and reliable detection. Our concept of the marker field is designed so that it can be detected and recognized for camera pose estimation: in various lighting conditions, under a severe perspective, while heavily occluded, and under a strong motion blur. Our marker field detection harnesses the fact that the edges within the marker field meet at two vanishing points and that the projected planar grid of squares can be defined by a detectable mathematical formalism. The modules of the grid are greyscale and the locations within the marker field are defined by the edges between the modules. The assumption that the marker field is planar allows for a very cheap and reliable camera pose estimation in the captured scene. The detection rates and accuracy are slightly better compared to state-of-the-art marker-based solutions. At the same time, and more importantly, our detector of the marker field is several times faster and the reliable real-time detection can be thus achieved on mobile and low-power devices. We show three targeted applications where the planarity is assured and where the presented marker field design and detection algorithm provide a reliable and extremely fast solution.
Fast Patch-Based Denoising Using Approximated Patch Geodesic Paths
Authors: Sing Bing Kang, Jie Yang, Jingyi Yu Xiaogang Chen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Patch-based methods such as Non-Local Means (NLM) and BM3D have become the de facto gold standard for image denoising. The core of these approaches is to use similar patches within the image as cues for denoising. The operation usually requires expensive pair-wise patch comparisons. In this paper, we present a novel fast patch-based denoising technique based on Patch Geodesic Paths (PatchGP). PatchGPs treat image patches as nodes and patch differences as edge weights for computing the shortest (geodesic) paths. The path lengths can then be used as weights of the smoothing/denoising kernel. We first show that, for natural images, PatchGPs can be effectively approximated by minimum hop paths (MHPs) that generally correspond to Euclidean line paths connecting two patch nodes. To construct the denoising kernel, we further discretize the MHP search directions and use only patches along the search directions. Along each MHP, we apply a weight propagation scheme to robustly and efficiently compute the path distance. To handle noise at multiple scales, we conduct wavelet image decomposition and apply PatchGP scheme at each scale. Comprehensive experiments show that our approach achieves comparable quality as the state-of-the-art methods such as NLM and BM3D but is a few orders of magnitude faster.
Boosting Binary Keypoint Descriptors
Authors: Mario Christoudias, Pascal Fua, Vincent Lepetit Tomasz Trzcinski
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Binary keypoint descriptors provide an efficient alternative to their floating-point competitors as they enable faster processing while requiring less memory. In this paper, we propose a novel framework to learn an extremely compact binary descriptor we call BinBoost that is very robust to illumination and viewpoint changes. Each bit of our descriptor is computed with a boosted binary hash function, and we show how to efficiently optimize the different hash functions so that they complement each other, which is key to compactness and robustness. The hash functions rely on weak learners that are applied directly to the image patches, which frees us from any intermediate representation and lets us automatically learn the image gradient pooling configuration of the final descriptor. Our resulting descriptor significantly outperforms the state-of-the-art binary descriptors and performs similarly to the best floating-point descriptors at a fraction of the matching time and memory footprint.
Structured Face Hallucination
Authors: Sifei Liu, Ming-Hsuan Yang Chih-Yuan Yang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: The goal of face hallucination is to generate highresolution images with fidelity from low-resolution ones. In contrast to existing methods based on patch similarity or holistic constraints in the image space, we propose to exploit local image structures for face hallucination. Each face image is represented in terms of facial components, contours and smooth regions. The image structure is maintained via matching gradients in the reconstructed highresolution output. For facial components, we align input images to generate accurate exemplars and transfer the high-frequency details for preserving structural consistency. For contours, we learn statistical priors to generate salient structures in the high-resolution images. A patch matching method is utilized on the smooth regions where the image gradients are preserved. Experimental results demonstrate that the proposed algorithm generates hallucinated face images with favorable quality and adaptability.
Adaptive Active Learning for Image Classification
Authors: Yuhong Guo Xin Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Recently active learning has attracted a lot of attention in computer vision field, as it is time and cost consuming to prepare a good set of labeled images for vision data analysis. Most existing active learning approaches employed in computer vision adopt most uncertainty measures as instance selection criteria. Although most uncertainty query selection strategies are very effective in many circumstances, they fail to take information in the large amount of unlabeled instances into account and are prone to querying outliers. In this paper, we present a novel adaptive active learning approach that combines an information density measure and a most uncertainty measure together to select critical instances to label for image classifications. Our experiments on two essential tasks of computer vision, object recognition and scene recognition, demonstrate the efficacy of the proposed approach.
Improving an Object Detector and Extracting Regions Using Superpixels
Authors: Afshin Dehghan, Mubarak Shah Guang Shu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We propose an approach to improve the detection performance of a generic detector when it is applied to a particular video. The performance of offline-trained objects detectors are usually degraded in unconstrained video environments due to variant illuminations, backgrounds and camera viewpoints. Moreover, most object detectors are trained using Haar-like features or gradient features but ignore video specific features like consistent color patterns. In our approach, we apply a Superpixel-based Bag-of-Words (BoW) model to iteratively refine the output of a generic detector. Compared to other related work, our method builds a video-specific detector using superpixels, hence it can handle the problem of appearance variation. Most importantly, using Conditional Random Field (CRF) along with our super pixel-based BoW model, we develop and algorithm to segment the object from the background . Therefore our method generates an output of the exact object regions instead of the bounding boxes generated by most detectors. In general, our method takes detection bounding boxes of a generic detector as input and generates the detection output with higher average precision and precise object regions. The experiments on four recent datasets demonstrate the effectiveness of our approach and significantly improves the state-of-art detector by 5-16% in average precision.
HDR Deghosting: How to Deal with Saturation?
Authors: Orazio Gallo, Kari Pulli, Xiaobai Sun Jun Hu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We present a novel method for aligning images in an HDR (high-dynamic-range) image stack to produce a new exposure stack where all the images are aligned and appear as if they were taken simultaneously, even in the case of highly dynamic scenes. Our method produces plausible results even where the image used as a reference is either too dark or bright to allow for an accurate registration.
Transfer Sparse Coding for Robust Image Representation
Authors: Guiguang Ding, Jianmin Wang, Jiaguang Sun, Yuchen Guo, Philip S. Yu Mingsheng Long
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Sparse coding learns a set of basis functions such that each input signal can be well approximated by a linear combination of just a few of the bases. It has attracted increasing interest due to its state-of-the-art performance in BoW based image representation. However, when labeled and unlabeled images are sampled from different distributions, they may be quantized into different visual words of the codebook and encoded with different representations, which may severely degrade classification performance. In this paper, we propose a Transfer Sparse Coding (TSC) approach to construct robust sparse representations for classifying cross-distribution images accurately. Specifically, we aim to minimize the distribution divergence between the labeled and unlabeled images, and incorporate this criterion into the objective function of sparse coding to make the new representations robust to the distribution difference. Experiments show that TSC can significantly outperform state-ofthe-art methods on three types of computer vision datasets.
Semi-supervised Learning of Feature Hierarchies for Object Detection in a Video
Authors: Guang Shu, Mubarak Shah Yang Yang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We propose a novel approach to boost the performance of generic object detectors on videos by learning videospecific features using a deep neural network. The insight behind our proposed approach is that an object appearing in different frames of a video clip should share similar features, which can be learned to build better detectors. Unlike many supervised detector adaptation or detection-bytracking methods, our method does not require any extra annotations or utilize temporal correspondence. We start with the high-confidence detections from a generic detector, then iteratively learn new video-specific features and refine the detection scores. In order to learn discriminative and compact features, we propose a new feature learning method using a deep neural network based on auto encoders. It differs from the existing unsupervised feature learning methods in two ways: first it optimizes both discriminative and generative properties of the features simultaneously, which gives our features better discriminative ability; second, our learned features are more compact, while the unsupervised feature learning methods usually learn a redundant set of over-complete features. Extensive experimental results on person and horse detection show that significant performance improvement can be achieved with our proposed method.
Computationally Efficient Regression on a Dependency Graph for Human Pose Estimation
Authors: Rama Chellappa Kota Hara
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We present a hierarchical method for human pose estimation from a single still image. In our approach, a dependency graph representing relationships between reference points such as body joints is constructed and the positions of these reference points are sequentially estimated by a successive application of multidimensional output regressions along the dependency paths, starting from the root node. Each regressor takes image features computed from an image patch centered on the current node's position estimated by the previous regressor and is specialized for estimating its child nodes' positions. The use of the dependency graph allows us to decompose a complex pose estimation problem into a set of local pose estimation problems that are less complex. We design a dependency graph for two commonly used human pose estimation datasets, the Buffy Stickmen dataset and the ETHZ PASCAL Stickmen dataset, and demonstrate that our method achieves comparable accuracy to state-of-the-art results on both datasets with significantly lower computation time than existing methods. Furthermore, we propose an importance weighted boosted regression trees method for transductive learning settings and demonstrate the resulting improved performance for pose estimation tasks.