pwc-archive/papers-with-abstracts huggingface dataset:
A New Perspective on Uncalibrated Photometric Stereo
Authors: Thoma Papadhimitri, Paolo Favaro
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

We investigate the problem of reconstructing normals, albedo and lights of Lambertian surfaces in uncalibrated photometric stereo under the perspective projection model. Our analysis is based on establishing the integrability constraint. In the orthographic projection case, it is well-known that when such constraint is imposed, a solution can be identified only up to 3 parameters, the so-called generalized bas-relief (GBR) ambiguity. We show that in the perspective projection case the solution is unique. We also propose a closed-form solution which is simple, efficient and robust. We test our algorithm on synthetic data and publicly available real data. Our quantitative tests show that our method outperforms all prior work of uncalibrated photometric stereo under orthographic projection.
A Joint Model for 2D and 3D Pose Estimation from a Single Image
Authors: Edgar Simo-Serra, Ariadna Quattoni, Carme Torras, Francesc Moreno-Noguer
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Evolutionary Algorithms, Pose Estimation

We introduce a novel approach to automatically recover 3D human pose from a single image. Most previous work follows a pipelined approach: initially, a set of 2D features such as edges, joints or silhouettes are detected in the image, and then these observations are used to infer the 3D pose. Solving these two problems separately may lead to erroneous 3D poses when the feature detector has performed poorly. In this paper, we address this issue by jointly solving both the 2D detection and the 3D inference problems. For this purpose, we propose a Bayesian framework that integrates a generative model based on latent variables and discriminative 2D part detectors based on HOGs, and perform inference using evolutionary algorithms. Real experimentation demonstrates competitive results, and the ability of our methodology to provide accurate 2D and 3D pose estimations even when the 2D detectors are inaccurate.
A Statistical Model for Recreational Trails in Aerial Images
Authors: Andrew Predoehl, Scott Morris, Kobus Barnard
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

We present a statistical model of aerial images of recreational trails, and a method to infer trail routes in such images. We learn a set of textons describing the images, and use them to divide the image into super-pixels represented by their texton. We then learn, for each texton, the frequency of generating on-trail and off-trail pixels, and the direction of trail through on-trail pixels. From these, we derive an image likelihood function. We combine that with a prior model of trail length and smoothness, yielding a posterior distribution for trails, given an image. We search for good values of this posterior using a novel stochastic variation of Dijkstra's algorithm. Our experiments, on trail images and groundtruth collected in the western continental USA, show substantial improvement over those of the previous best trail-finding method.
Learning Video Saliency from Human Gaze Using Candidate Selection
Authors: Dmitry Rudoy, Dan B. Goldman, Eli Shechtman, Lihi Zelnik-Manor
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Saliency Prediction

During recent years remarkable progress has been made in visual saliency modeling. Our interest is in video saliency. Since videos are fundamentally different from still images, they are viewed differently by human observers. For example, the time each video frame is observed is a fraction of a second, while a still image can be viewed leisurely. Therefore, video saliency estimation methods should differ substantially from image saliency methods. In this paper we propose a novel method for video saliency estimation, which is inspired by the way people watch videos. We explicitly model the continuity of the video by predicting the saliency map of a given frame, conditioned on the map from the previous frame. Furthermore, accuracy and computation speed are improved by restricting the salient locations to a carefully selected candidate set. We validate our method using two gaze-tracked video datasets and show we outperform the state-of-the-art.
Designing Category-Level Attributes for Discriminative Visual Recognition
Authors: Felix X. Yu, Liangliang Cao, Rogerio S. Feris, John R. Smith, Shih-Fu Chang
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Attribute, Transfer Learning, Zero-Shot Learning

Attribute-based representation has shown great promises for visual recognition due to its intuitive interpretation and cross-category generalization property. However, human efforts are usually involved in the attribute designing process, making the representation costly to obtain. In this paper, we propose a novel formulation to automatically design discriminative "category-level attributes", which can be efficiently encoded by a compact category-attribute matrix. The formulation allows us to achieve intuitive and critical design criteria (category-separability, learnability) in a principled way. The designed attributes can be used for tasks of cross-category knowledge transfer, achieving superior performance over well-known attribute dataset Animals with Attributes (AwA) and a large-scale ILSVRC2010 dataset (1.2M images). This approach also leads to state-ofthe-art performance on the zero-shot learning task on AwA.
Dense Segmentation-Aware Descriptors
Authors: Eduard Trulls, Iasonas Kokkinos, Alberto Sanfeliu, Francesc Moreno-Noguer
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Motion Estimation, Segmentation

In this work we exploit segmentation to construct appearance descriptors that can robustly deal with occlusion and background changes. For this, we downplay measurements coming from areas that are unlikely to belong to the same region as the descriptor's center, as suggested by soft segmentation masks. Our treatment is applicable to any image point, i.e. dense, and its computational overhead is in the order of a few seconds. We integrate this idea with Dense SIFT, and also with Dense Scale and Rotation Invariant Descriptors (SID), delivering descriptors that are densely computable, invariant to scaling and rotation, and robust to background changes. We apply our approach to standard benchmarks on large displacement motion estimation using SIFT-flow and widebaseline stereo, systematically demonstrating that the introduction of segmentation yields clear improvements.
Modeling Mutual Visibility Relationship in Pedestrian Detection
Authors: Wanli Ouyang, Xingyu Zeng, Xiaogang Wang
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Pedestrian Detection

Detecting pedestrians in cluttered scenes is a challenging problem in computer vision. The difficulty is added when several pedestrians overlap in images and occlude each other. We observe, however, that the occlusion/visibility statuses of overlapping pedestrians provide useful mutual relationship for visibility estimation the visibility estimation of one pedestrian facilitates the visibility estimation of another. In this paper, we propose a mutual visibility deep model that jointly estimates the visibility statuses of overlapping pedestrians. The visibility relationship among pedestrians is learned from the deep model for recognizing co-existing pedestrians. Experimental results show that the mutual visibility deep model effectively improves the pedestrian detection results. Compared with existing image-based pedestrian detection approaches, our approach has the lowest average miss rate on the CaltechTrain dataset, the Caltech-Test dataset and the ETH dataset. Including mutual visibility leads to 4% 8% improvements on multiple benchmark datasets.
Discriminatively Trained And-Or Tree Models for Object Detection
Authors: Xi Song, Tianfu Wu, Yunde Jia, Song-Chun Zhu
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Object, object-detection, Object Detection

This paper presents a method of learning reconfigurable And-Or Tree (AOT) models discriminatively from weakly annotated data for object detection. To explore the appearance and geometry space of latent structures effectively, we first quantize the image lattice using an overcomplete set of shape primitives, and then organize them into a directed acyclic And-Or Graph (AOG) by exploiting their compositional relations. We allow overlaps between child nodes when combining them into a parent node, which is equivalent to introducing an appearance Or-node implicitly for the overlapped portion. The learning of an AOT model consists of three components: (i) Unsupervised sub-category learning (i.e., branches of an object Or-node) with the latent structures in AOG being integrated out. (ii) Weaklysupervised part configuration learning (i.e., seeking the globally optimal parse trees in AOG for each sub-category). To search the globally optimal parse tree in AOG efficiently, we propose a dynamic programming (DP) algorithm. (iii) Joint appearance and structural parameters training under latent structural SVM framework. In experiments, our method is tested on PASCAL VOC 2007 and 2010 detection benchmarks of 20 object classes and outperforms comparable state-of-the-art methods.
Intrinsic Scene Properties from a Single RGB-D Image
Authors: Jonathan T. Barron, Jitendra Malik
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

In this paper we extend the "shape, illumination and reflectance from shading" (SIRFS) model [3, 4], which recovers intrinsic scene properties from a single image. Though SIRFS performs well on images of segmented objects, it performs poorly on images of natural scenes, which contain occlusion and spatially-varying illumination. We therefore present Scene-SIRFS, a generalization of SIRFS in which we have a mixture of shapes and a mixture of illuminations, and those mixture components are embedded in a "soft" segmentation of the input image. We additionally use the noisy depth maps provided by RGB-D sensors (in this case, the Kinect) to improve shape estimation. Our model takes as input a single RGB-D image and produces as output an improved depth map, a set of surface normals, a reflectance image, a shading image, and a spatially varying model of illumination. The output of our model can be used for graphics applications, or for any application involving RGB-D images.
Cross-View Image Geolocalization
Authors: Tsung-Yi Lin, Serge Belongie, James Hays
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

The recent availability of large amounts of geotagged imagery has inspired a number of data driven solutions to the image geolocalization problem. Existing approaches predict the location of a query image by matching it to a database of georeferenced photographs. While there are many geotagged images available on photo sharing and street view sites, most are clustered around landmarks and urban areas. The vast majority of the Earth's land area has no ground level reference photos available, which limits the applicability of all existing image geolocalization methods. On the other hand, there is no shortage of visual and geographic data that densely covers the Earth we examine overhead imagery and land cover survey data but the relationship between this data and ground level query photographs is complex. In this paper, we introduce a cross-view feature translation approach to greatly extend the reach of image geolocalization methods. We can often localize a query even if it has no corresponding groundlevel images in the database. A key idea is to learn the relationship between ground level appearance and overhead appearance and land cover attributes from sparsely available geotagged ground-level images. We perform experiments over a 1600 km d-region containing a variety of scenes and land cover types. For each query, our algorithm produces a probability density over the region of interest.
Learning Cross-Domain Information Transfer for Location Recognition and Clustering
Authors: Raghuraman Gopalan
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Clustering

Estimating geographic location from images is a challenging problem that is receiving recent attention. In contrast to many existing methods that primarily model discriminative information corresponding to different locations, we propose joint learning of information that images across locations share and vary upon. Starting with generative and discriminative subspaces pertaining to domains, which are obtained by a hierarchical grouping of images from adjacent locations, we present a top-down approach that first models cross-domain information transfer by utilizing the geometry of these subspaces, and then encodes the model results onto individual images to infer their location. We report competitive results for location recognition and clustering on two public datasets, im2GPS and San Francisco, and empirically validate the utility of various design choices involved in the approach.
Statistical Textural Distinctiveness for Salient Region Detection in Natural Images
Authors: Christian Scharfenberger, Alexander Wong, Khalil Fergani, John S. Zelek, David A. Clausi
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Saliency Detection

A novel statistical textural distinctiveness approach for robustly detecting salient regions in natural images is proposed. Rotational-invariant neighborhood-based textural representations are extracted and used to learn a set of representative texture atoms for defining a sparse texture model for the image. Based on the learnt sparse texture model, a weighted graphical model is constructed to characterize the statistical textural distinctiveness between all representative texture atom pairs. Finally, the saliency of each pixel in the image is computed based on the probability of occurrence of the representative texture atoms, their respective statistical textural distinctiveness based on the constructed graphical model, and general visual attentive constraints. Experimental results using a public natural image dataset and a variety of performance evaluation metrics show that the proposed approach provides interesting and promising results when compared to existing saliency detection methods.
Robust Multi-resolution Pedestrian Detection in Traffic Scenes
Authors: Junjie Yan, Xucong Zhang, Zhen Lei, Shengcai Liao, Stan Z. Li
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Pedestrian Detection

The serious performance decline with decreasing resolution is the major bottleneck for current pedestrian detection techniques [14, 23]. In this paper, we take pedestrian detection in different resolutions as different but related problems, and propose a Multi-Task model to jointly consider their commonness and differences. The model contains resolution aware transformations to map pedestrians in different resolutions to a common space, where a shared detector is constructed to distinguish pedestrians from background. For model learning, we present a coordinate descent procedure to learn the resolution aware transformations and deformable part model (DPM) based detector iteratively. In traffic scenes, there are many false positives located around vehicles, therefore, we further build a context model to suppress them according to the pedestrian-vehicle relationship. The context model can be learned automatically even when the vehicle annotations are not available. Our method reduces the mean miss rate to 60% for pedestrians taller than 30 pixels on the Caltech Pedestrian Benchmark, which noticeably outperforms previous state-of-the-art (71%).
Hypergraphs for Joint Multi-view Reconstruction and Multi-object Tracking
Authors: Martin Hofmann, Daniel Wolf, Gerhard Rigoll
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Multi-Object Tracking, Object Tracking

We generalize the network flow formulation for multiobject tracking to multi-camera setups. In the past, reconstruction of multi-camera data was done as a separate extension. In this work, we present a combined maximum a posteriori (MAP) formulation, which jointly models multicamera reconstruction as well as global temporal data association. A flow graph is constructed, which tracks objects in 3D world space. The multi-camera reconstruction can be efficiently incorporated as additional constraints on the flow graph without making the graph unnecessarily large. The final graph is efficiently solved using binary linear programming. On the PETS 2009 dataset we achieve results that significantly exceed the current state of the art.
Recognizing Activities via Bag of Words for Attribute Dynamics
Authors: Weixin Li, Qian Yu, Harpreet Sawhney, Nuno Vasconcelos
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Activity Recognition, Attribute

In this work, we propose a novel video representation for activity recognition that models video dynamics with attributes of activities. A video sequence is decomposed into short-term segments, which are characterized by the dynamics of their attributes. These segments are modeled by a dictionary of attribute dynamics templates, which are implemented by a recently introduced generative model, the binary dynamic system (BDS). We propose methods for learning a dictionary of BDSs from a training corpus, and for quantizing attribute sequences extracted from videos into these BDS codewords. This procedure produces a representation of the video as a histogram of BDS codewords, which is denoted the bag-of-words for attribute dynamics (BoWAD). An extensive experimental evaluation reveals that this representation outperforms other state-of-the-art approaches in temporal structure modeling for complex activity recognition.
Towards Fast and Accurate Segmentation
Authors: Camillo J. Taylor
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Edge Detection, Segmentation

In this paper we explore approaches to accelerating segmentation and edge detection algorithms based on the gPb framework. The paper characterizes the performance of a simple but effective edge detection scheme which can be computed rapidly and offers performance that is competitive with the pB detector. The paper also describes an approach for computing a reduced order normalized cut that captures the essential features of the original problem but can be computed in less than half a second on a standard computing platform.
Fast, Accurate Detection of 100,000 Object Classes on a Single Machine
Authors: Thomas Dean, Mark A. Ruzon, Mark Segal, Jonathon Shlens, Sudheendra Vijayanarasimhan, Jay Yagnik
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: object-detection, Object Detection

Many object detection systems are constrained by the time required to convolve a target image with a bank of filters that code for different aspects of an object's appearance, such as the presence of component parts. We exploit locality-sensitive hashing to replace the dot-product kernel operator in the convolution with a fixed number of hash-table probes that effectively sample all of the filter responses in time independent of the size of the filter bank. To show the effectiveness of the technique, we apply it to evaluate 100,000 deformable-part models requiring over a million (part) filters on multiple scales of a target image in less than 20 seconds using a single multi-core processor with 20GB of RAM. This represents a speed-up of approximately 20,000 times-four orders of magnitude-when compared with performing the convolutions explicitly on the same hardware. While mean average precision over the full set of 100,000 object classes is around 0.16 due in large part to the challenges in gathering training data and collecting ground truth for so many classes, we achieve a mAP of at least 0.20 on a third of the classes and 0.30 or better on about 20% of the classes.
Robust Object Co-detection
Authors: Xin Guo, Dong Liu, Brendan Jou, Mojun Zhu, Anni Cai, Shih-Fu Chang
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Clustering, Object, object-detection, Object Detection

Object co-detection aims at simultaneous detection of objects of the same category from a pool of related images by exploiting consistent visual patterns present in candidate objects in the images. The related image set may contain a mixture of annotated objects and candidate objects generated by automatic detectors. Co-detection differs from the conventional object detection paradigm in which detection over each test image is determined one-by-one independently without taking advantage of common patterns in the data pool. In this paper, we propose a novel, robust approach to dramatically enhance co-detection by extracting a shared low-rank representation of the object instances in multiple feature spaces. The idea is analogous to that of the well-known Robust PCA [28], but has not been explored in object co-detection so far. The representation is based on a linear reconstruction over the entire data set and the low-rank approach enables effective removal of noisy and outlier samples. The extracted low-rank representation can be used to detect the target objects by spectral clustering. Extensive experiments over diverse benchmark datasets demonstrate consistent and significant performance gains of the proposed method over the state-of-the-art object codetection method and the generic object detection methods without co-detection formulations.
Supervised Kernel Descriptors for Visual Recognition
Authors: Peng Wang, Jingdong Wang, Gang Zeng, Weiwei Xu, Hongbin Zha, Shipeng Li
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: General Classification, image-classification, Image Classification

In visual recognition tasks, the design of low level image feature representation is fundamental. The advent of local patch features from pixel attributes such as SIFT and LBP, has precipitated dramatic progresses. Recently, a kernel view of these features, called kernel descriptors (KDES) [1], generalizes the feature design in an unsupervised fashion and yields impressive results. In this paper, we present a supervised framework to embed the image level label information into the design of patch level kernel descriptors, which we call supervised kernel descriptors (SKDES). Specifically, we adopt the broadly applied bag-of-words (BOW) image classification pipeline and a large margin criterion to learn the lowlevel patch representation, which makes the patch features much more compact and achieve better discriminative ability than KDES. With this method, we achieve competitive results over several public datasets comparing with stateof-the-art methods.
Shape from Silhouette Probability Maps: Reconstruction of Thin Objects in the Presence of Silhouette Extraction and Calibration Error
Authors: Amy Tabb
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Camera Calibration

This paper considers the problem of reconstructing the shape of thin, texture-less objects such as leafless trees when there is noise or deterministic error in the silhouette extraction step or there are small errors in camera calibration. Traditional intersection-based techniques such as the visual hull are not robust to error because they penalize false negative and false positive error unequally. We provide a voxel-based formalism that penalizes false negative and positive error equally, by casting the reconstruction problem as a pseudo-Boolean minimization problem, where voxels are the variables of a pseudo-Boolean function and are labeled occupied or empty. Since the pseudo-Boolean minimization problem is NP-Hard for nonsubmodular functions, we developed an algorithm for an approximate solution using local minimum search. Our algorithm treats input binary probability maps (in other words, silhouettes) or continuously-valued probability maps identically, and places no constraints on camera placement. The algorithm was tested on three different leafless trees and one metal object where the number of voxels is 54.4 million (voxel sides measure 3.6 mm). Results show that our approach reconstructs the complicated branching structure of thin, texture-less objects in the presence of error where intersection-based approaches currently fail. 1