pwc-archive/papers-with-abstracts huggingface dataset:
Recognition of Complex Events: Exploiting Temporal Dynamics between Underlying Concepts
Authors: Subhabrata Bhattacharya, Mahdi M. Kalayeh, Rahul Sukthankar, Mubarak Shah
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Action Classification, Event Detection, General Classification, Time Series, Time Series Analysis

While approaches based on bags of features excel at low-level action classification, they are ill-suited for recognizing complex events in video, where concept-based temporal representations currently dominate. This paper proposes a novel representation that captures the temporal dynamics of windowed mid-level concept detectors in order to improve complex event recognition. We first express each video as an ordered vector time series, where each time step consists of the vector formed from the concatenated confidences of the pre-trained concept detectors. We hypothesize that the dynamics of time series for different instances from the same event class, as captured by simple linear dynamical system (LDS) models, are likely to be  similar even if the instances differ in terms of low-level visual features. We propose a two-part representation composed of fusing: (1) a singular value decomposition of block Hankel matrices (SSID-S) and (2) a harmonic signature (HS) computed from the corresponding eigen-dynamics matrix. The proposed method offers several benefits over alternate approaches: our approach is straightforward to implement, directly employs existing concept detectors and can be plugged into linear classification frameworks. Results on standard datasets such as NIST's TRECVID Multimedia Event Detection task demonstrate the improved accuracy of the proposed method.
Video Event Detection by Inferring Temporal Instance Labels
Authors: Kuan-Ting Lai, Felix X. Yu, Ming-Syan Chen, Shih-Fu Chang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Event Detection

Video event detection allows intelligent indexing of video content based on events. Traditional approaches extract features from video frames or shots, then quantize and pool the features to form a single vector representation for the entire video. Though simple and efficient, the final pooling step may lead to loss of temporally local information, which is important in indicating which part in a long video signifies presence of the event. In this work, we propose a novel instance-based video event detection approach. We represent each video as multiple "instances", defined as video segments of different temporal intervals. The objective is to learn an instance-level event detection model based on only video-level labels. To solve this problem, we propose a large-margin formulation which treats the instance labels as hidden latent variables, and simultaneously infers the instance labels as well as the instance-level classification model. Our framework infers optimal solutions that assume positive videos have a large number of positive instances while negative videos have the fewest ones. Extensive experiments on large-scale video event datasets demonstrate significant performance gains. The proposed method is also useful in explaining the detection results by localizing the temporal segments in a video which is responsible for the positive detection.
Backscatter Compensated Photometric Stereo with 3 Sources
Authors: Chourmouzios Tsiotsios, Maria E. Angelopoulou, Tae-Kyun Kim, Andrew J. Davison
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

Photometric stereo offers the possibility of object shape reconstruction via reasoning about the amount of light reflected from oriented surfaces. However, in murky media such as sea water, the illuminating light interacts with the medium and some of it is backscattered towards the camera. Due to this additive light component, the standard Photometric Stereo equations lead to poor quality shape estimation. Previous authors have attempted to reformulate the approach but have either neglected backscatter entirely or disregarded its non-uniformity on the sensor when camera and lights are close to each other. We show that by compensating effectively for the backscatter component, a linear formulation of Photometric Stereo is allowed which recovers an accurate normal map using only 3 lights. Our backscatter compensation method for point-sources can be used for estimating the uneven backscatter directly from single images without any prior knowledge about the characteristics of the medium or the scene. We compare our method with previous approaches through extensive experimental results, where a variety of objects are imaged in a big water tank whose turbidity is systematically increased, and show reconstruction quality which degrades little relative to clean water results even with a very significant scattering level.
Calibrating a Non-isotropic Near Point Light Source using a Plane
Authors: Jaesik Park, Sudipta N. Sinha, Yasuyuki Matsushita, Yu-Wing Tai, In So Kweon
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Position

We show that a non-isotropic near point light source rigidly attached to a camera can be calibrated using multiple images of a weakly textured planar scene. We prove that if the radiant intensity distribution (RID) of a light source is radially symmetric with respect to its dominant direction, then the shading observed on a Lambertian scene plane is bilaterally symmetric with respect to a 2D line on the plane. The symmetry axis detected in an image provides a linear constraint for estimating the dominant light axis. The light position and RID parameters can then be estimated using a linear method. Specular highlights if available can also be used for light position estimation. We also extend our method to handle non-Lambertian reflectances which we model using a biquadratic BRDF. We have evaluated our method on synthetic data quantitavely. Our experiments on real scenes show that our method works well in practice and enables light calibration without the need of a specialized hardware.
A New Perspective on Material Classification and Ink Identification
Authors: Rakesh Shiradkar, Li Shen, George Landon, Sim Heng Ong, Ping Tan
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Classification, General Classification, Material Classification

The surface bi-directional reflectance distribution function (BRDF) can be used to distinguish different materials. The BRDFs of many real materials are near isotropic and can be approximated well by a 2D function. When the camera principal axis is coincident with the surface normal of the material sample, the captured BRDF slice is nearly 1D, which suffers from significant information loss. Thus, improvement in classification performance can be achieved by simply setting the camera at a slanted view to capture a larger portion of the BRDF domain. We further use a handheld flashlight camera to capture a 1D BRDF slice for material classification. This 1D slice captures important reflectance properties such as specular reflection and retro-reflectance. We apply these results on ink classification, which can be used in forensics and analyzing historical manuscripts. For the first time, we show that most of the inks on the market can be well distinguished by their reflectance properties.
High Quality Photometric Reconstruction using a Depth Camera
Authors: Sk. Mohammadul Haque, Avishek Chatterjee, Venu Madhav Govindu
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Reconstruction, Vocal Bursts Intensity Prediction

In this paper we present a depth-guided photometric 3D reconstruction method that works solely with a depth camera like the Kinect. Existing methods that fuse depth with normal estimates use an external RGB camera to obtain photometric information and treat the depth camera as a black box that provides a low quality depth estimate. Our contribution to such methods are two fold. Firstly, instead of using an extra RGB camera, we use the infra-red (IR) camera of the depth camera system itself to directly obtain high resolution photometric information. We believe that ours is the first method to use an IR depth camera system in this manner. Secondly, photometric methods applied to complex objects result in numerous holes in the reconstructed surface due to shadows and self-occlusions. To mitigate this problem, we develop a simple and effective multiview reconstruction approach that fuses depth and normal information from multiple viewpoints to build a complete, consistent and accurate 3D surface representation. We demonstrate the efficacy of our method to generate high quality 3D surface reconstructions for some complex 3D figurines.
Robust Surface Reconstruction via Triple Sparsity
Authors: Hicham Badri, Hussein Yahia, Driss Aboutajdine
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Surface Reconstruction

Reconstructing a surface/image from corrupted gradient fields is a crucial step in many imaging applications where a gradient field  is subject to both noise and unlocalized outliers, resulting typically in a non-integrable field.  We present in this paper a new optimization method for robust surface reconstruction. The proposed formulation is based on a triple sparsity prior : a sparse prior on the residual gradient field and a double  sparse prior on the surface gradients. We develop an efficient alternate minimization strategy to solve the proposed optimization problem. The method is able to recover a good quality surface from severely corrupted gradients thanks to its ability to handle both noise and outliers. We demonstrate the performance of the proposed method on synthetic and real data. Experiments show that the proposed solution outperforms  some existing methods in the three possible cases : noise only, outliers only and mixed noise/outliers.
Scattering Parameters and Surface Normals from Homogeneous Translucent Materials using Photometric Stereo
Authors: Bo Dong, Kathleen D. Moore, Weiyi Zhang, Pieter Peers
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Inverse Rendering, Object

This paper proposes a novel photometric stereo solution to jointly estimate surface normals and scattering parameters from a globally planar, homogeneous, translucent object.  Similar to classic photometric stereo, our method only requires as few as three observations of the translucent object under directional lighting. Naively applying classic photometric stereo results in blurred photometric normals.  We develop a novel blind deconvolution algorithm based on inverse rendering for recovering the sharp surface normals and the material properties.  We demonstrate our method on a variety of translucent objects.
Better Shading for Better Shape Recovery
Authors: Moumen T. El-Melegy, Aly S. Abdelrahim, Aly A. Farag
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

The basic idea of shape from shading is to infer the shape of a surface from its shading information in a single image. Since this problem is ill-posed, a number of simplifying assumptions have been often used. However they rarely hold in practice. This paper presents a simple shading-correction algorithm that transforms the image to a new image that better satisfies the assumptions typically needed by existing algorithms, thus improving the accuracy of shape recovery. The algorithm takes advantage of some local shading measures that have been driven under these assumptions. The method is successfully evaluated on real data of human teeth with ground-truth 3D shapes.
Deformable Object Matching via Deformation Decomposition based 2D Label MRF
Authors: Kangwei Liu, Junge Zhang, Kaiqi Huang, Tieniu Tan
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

Deformable object matching, which is also called elastic matching or deformation matching, is an important and challenging problem in computer vision. Although numerous deformation models have been proposed in different matching tasks, not many of them investigate the intrinsic physics underlying deformation. Due to the lack of physical analysis, these models cannot describe the structure changes of deformable objects very well. Motivated by this, we analyze the deformation physically and propose a novel deformation decomposition model to represent various deformations. Based on the physical model, we formulate the matching problem as a two-mensional label Markov Random Field. The MRF energy function is derived from the deformation decomposition model. Furthermore, we propose a two-stage method to optimize the MRF energy function. To provide a quantitative benchmark, we build a deformation matching database with an evaluation criterion. Experimental results show that our method outperforms previous approaches especially on complex deformations.
Locally Optimized Product Quantization for Approximate Nearest Neighbor Search
Authors: Yannis Kalantidis, Yannis Avrithis
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Quantization

We present a simple vector quantizer that combines low distortion with fast search and apply it to approximate nearest neighbor (ANN) search in high dimensional spaces. Leveraging the very same data structure that is used to provide non-exhaustive search, i.e., inverted lists or a multi-index, the idea is to locally optimize an individual product quantizer (PQ) per cell and use it to encode residuals. Local optimization is over rotation and space decomposition; interestingly, we apply a parametric solution that assumes a normal distribution and is extremely fast to train. With a reasonable space and time overhead that is constant in the data size, we set a new state-of-the-art on several public datasets, including a billion-scale one.
Multi-source Deep Learning for Human Pose Estimation
Authors: Wanli Ouyang, Xiao Chu, Xiaogang Wang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Deep Learning, Human Detection, Pose Estimation

Visual appearance score, appearance mixture type and deformation are three important information sources for human pose estimation. This paper proposes to build a multi-source deep model  in order to extract non-linear representation from these different aspects of information sources. With the deep model, the global, high-order human body articulation patterns in these information sources are extracted for pose estimation. The task for estimating body locations and the task for human detection are jointly learned using a unified deep model. The proposed approach can be viewed as a post-processing of pose estimation results and can flexibly integrate with existing methods by taking their information sources as input. By extracting the non-linear representation from multiple information sources, the deep model outperforms  state-of-the-art by up to 8.6 percent on three public benchmark datasets.
Posebits for Monocular Human Pose Estimation
Authors: Gerard Pons-Moll, David J. Fleet, Bodo Rosenhahn
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Pose Estimation, Image Retrieval, Pose Estimation, Retrieval

We advocate the inference of qualitative information about 3D human  pose, called posebits, from images.  Posebits represent boolean  geometric relationships between body parts  (e.g., left-leg in front of right-leg or hands close to each other). The advantages of posebits as a mid-level representation are 1)  for many tasks of interest, such qualitative pose information may be sufficient (e.g., semantic image retrieval),  2) it is relatively easy to annotate large image corpora with posebits, as  it simply requires answers to yes/no questions; and 3) they help  resolve challenging pose ambiguities and therefore facilitate the difficult talk of image-based 3D pose estimation. We introduce posebits, a posebit database, a method for selecting useful  posebits for pose estimation and a structural SVM model for posebit inference.   Experiments show the use of posebits for semantic image  retrieval and for improving 3D pose estimation.
Real-time Simultaneous Pose and Shape Estimation for Articulated Objects Using a Single Depth Camera
Authors: Mao Ye, Ruigang Yang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Pose Estimation

In this paper we present a novel real-time algorithm for simultaneous pose and shape estimation for articulated objects, such as human beings and animals. The key of our pose estimation component is to embed the articulated deformation model with exponential-maps-based parametrization into a Gaussian Mixture Model. Benefiting from the probabilistic measurement model, our algorithm requires no explicit point correspondences as opposed to most existing methods. Consequently, our approach is less sensitive to local minimum and well handles fast and complex motions. Extensive evaluations on publicly available datasets demonstrate that our method outperforms most state-of-art pose estimation algorithms with large margin, especially in the case of challenging motions. Moreover, our novel shape adaptation algorithm based on the same probabilistic model automatically captures the shape of the subjects during the dynamic pose estimation process. Experiments show that our shape estimation method achieves comparable accuracy with state of the arts, yet requires neither parametric model nor extra calibration procedure.
Mixing Body-Part Sequences for Human Pose Estimation
Authors: Anoop Cherian, Julien Mairal, Karteek Alahari, Cordelia Schmid
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Pose Estimation

In this paper, we present a method for estimating articulated human poses in videos. We cast this as an optimization problem defined on body parts with spatio-temporal links between them. The resulting formulation is unfortunately intractable and previous approaches only provide approximate solutions. Although such methods perform well on certain body parts, e.g., head, their performance on lower arms, i.e., elbows and wrists, remains poor. We present a new approximate scheme with two steps dedicated to pose estimation. First, our approach takes into account temporal links with subsequent frames for the less-certain parts, namely elbows and wrists.  Second, our method decomposes poses into limbs, generates limb sequences across time, and recomposes poses by mixing these body part sequences.    We introduce a new dataset "Poses in the Wild", which is more challenging than the existing ones, with sequences containing background clutter, occlusions, and severe camera motion. We experimentally compare our method with recent approaches on this new dataset as well as on two other benchmark datasets, and show significant improvement.
Fisher and VLAD with FLAIR
Authors: Koen E. A. van de Sande, Cees G. M. Snoek, Arnold W. M. Smeulders
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

A major computational bottleneck in many current algorithms is the evaluation of arbitrary boxes. Dense local analysis and powerful bag-of-word encodings, such as Fisher vectors and VLAD, lead to improved accuracy at the expense of increased computation time. Where a simplification in the representation is tempting, we exploit novel representations while maintaining accuracy. We start from state-of-the-art, fast selective search, but our method will apply to any initial box-partitioning. By representing the picture as sparse integral images, one per codeword, we achieve a Fast Local Area Independent Representation. FLAIR allows for very fast evaluation of any box encoding and still enables spatial pooling. In FLAIR we achieve exact VLAD's difference coding, even with L2 and power-norms. Finally, by multiple codeword assignments, we achieve exact and approximate Fisher vectors with FLAIR. The results are a 18x speedup, which enables us to set a new state-of-the-art on the challenging 2010 PASCAL VOC objects and the fine-grained categorization of the CUB-2011 200 bird species. Plus, we rank number one in the official ImageNet 2013 detection challenge.
Immediate, Scalable Object Category Detection
Authors: Yusuf Aytar, Andrew Zisserman
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object, Reranking, Retrieval

The objective of this work is object category detection in large scale image datasets in the manner of Video Google  an object category is specified by a HOG classifier template, and retrieval is immediate at run time. We make the following three contributions: (i) a new image representation based on mid-level discriminative patches, that is designed to be suited to immediate object category detection and inverted file indexing; (ii) a sparse representation of a HOG classifier using a set of mid-level discriminative classifier patches; and (iii) a fast method for spatial reranking images on their detections. We evaluate the detection method on the standard PASCAL VOC 2007 dataset, together with a 100K image subset of ImageNet, and demonstrate near state of the art detection performance at low ranks whilst maintaining immediate retrieval speeds. Applications are also demonstrated using an exemplar-SVM for pose matched retrieval.
Occlusion Coherence: Localizing Occluded Faces with a Hierarchical Deformable Part Model
Authors: Golnaz Ghiasi, Charless C. Fowlkes
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Face Detection, Object Recognition

The presence of occluders significantly impacts performance of systems for object recognition.  However, occlusion is typically treated as an unstructured source of noise and explicit models for occluders have lagged behind those for object appearance and shape.  In this paper we describe a hierarchical deformable part model for face detection and keypoint localization that explicitly models occlusions of parts.  The proposed model structure makes it possible to augment positive training data with large numbers of synthetically occluded instances.  This allows us to easily incorporate the statistics of occlusion patterns in a discriminatively trained model.  We test the model on several benchmarks for keypoint localization including challenging sets featuring significant occlusion. We find that the addition of an explicit model of occlusion yields a system that outperforms existing approaches in keypoint localization accuracy.
Word Channel Based Multiscale Pedestrian Detection Without Image Resizing and Using Only One Classifier
Authors: Arthur Daniel Costea, Sergiu Nedevschi
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: General Classification, GPU, Pedestrian Detection

Most pedestrian detection approaches that achieve high accuracy and precision rate and that can be used for real-time applications are based on histograms of gradient orientations. Usually multiscale detection is attained by resizing the image several times and by recomputing the image features or using multiple classifiers for different scales.  In this paper we present a pedestrian detection approach that uses the same classifier for all pedestrian scales based on image features computed for a single scale. We go beyond the low level pixel-wise gradient orientation bins and use higher level visual words organized into Word Channels. Boosting is used to learn classification features from the integral Word Channels.  The proposed approach is evaluated on multiple datasets and achieves outstanding results on the INRIA and Caltech-USA benchmarks. By using a GPU implementation we achieve a classification rate of over 10 million bounding boxes per second and a 16 FPS rate for multiscale detection in a 640×480 image.