pwc-archive/papers-with-abstracts huggingface dataset:
Superpixel-Based Video Object Segmentation Using Perceptual Organization and Location Prior
Authors: Daniela Giordano, Francesca Murabito, Simone Palazzo, Concetto Spampinato
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Foreground Segmentation, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation

In this paper we present an approach for segmenting objects in videos taken in complex scenes with multiple and different targets. The method does not make any specific assumptions about the videos and relies on how objects are perceived by humans according to Gestalt laws. Initially, we rapidly generate a coarse foreground segmentation, which provides predictions about motion regions by analyzing how superpixel segmentation changes in consecutive frames. We then exploit these location priors to refine the initial segmentation by optimizing an energy function based on appearance and perceptual organization, only on regions where motion is observed.  We evaluated our method on complex and challenging video sequences and it showed significant performance improvements over recent state-of-the-art methods, being also fast enough to be used for "on-the-fly" processing.
Robust Image Filtering Using Joint Static and Dynamic Guidance
Authors: Bumsub Ham, Minsu Cho, Jean Ponce
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Denoising, Super-Resolution

Regularizing images under a guidance signal has been used in various tasks in computer vision and computational photography, particularly for noise reduction and joint upsampling. The aim is to transfer fine structures of guidance signals to input images, restoring noisy or altered structures. One of main drawbacks in such a data-dependent framework is that it does not handle differences in structure between guidance and input images. We address this problem by jointly leveraging structural information of guidance and input images. Image filtering is formulated as a nonconvex optimization problem, which is solved by the majorization-minimization algorithm. The proposed algorithm converges quickly while guaranteeing a local mini- mum. It effectively controls image structures at different scales and can handle a variety of types of data from different sensors. We demonstrate the flexibility and effectiveness of our model in several applications including depth super- resolution, scale-space filtering, texture removal, flash/non- flash denoising, and RGB/NIR denoising.
Solving Multiple Square Jigsaw Puzzles With Missing Pieces
Authors: Genady Paikin, Ayellet Tal
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Jigsaw-puzzle solving is necessary in many applications, including biology, archaeology, and every-day life. In this paper we consider the square jigsaw puzzle problem, where the goal is to reconstruct the image from a set of non-overlapping, unordered, square puzzle parts. Our key contribution is a fast, fully-automatic, and general solver, which assumes no prior knowledge about the original image. It is general in the sense that it can handle puzzles of unknown size, with pieces of unknown orientation, and even puzzles with missing pieces. Moreover, it can handle all the above, given pieces from multiple puzzles. Through an extensive evaluation we show that our approach outperforms state-of-the-art methods on commonly-used datasets.
A Dynamic Convolutional Layer for Short Range Weather Prediction
Authors: Benjamin Klein, Lior Wolf, Yehuda Afek
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Prediction

We present a new deep network layer called ``Dynamic Convolutional Layer" which is a generalization of the convolutional layer. The conventional convolutional layer uses filters that are learned during training and are held constant during testing. In contrast, the dynamic convolutional layer uses filters that will vary from input to input during testing. This is achieved by learning a function that maps the input to the filters. We apply the dynamic convolutional layer to the application of short range weather prediction and show performance improvements compared to other baselines.
SWIFT: Sparse Withdrawal of Inliers in a First Trial
Authors: Maryam Jaberi, Marianna Pensky, Hassan Foroosh
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Clustering

We study the simultaneous detection of multiple structures in the presence of overwhelming number of outliers in a large population of points. Our approach reduces the problem to sampling an extremely sparse subset of the original population of data in one grab, followed by an unsupervised clustering of the population based on a set of instantiated models from this sparse subset. We show that the problem can be modeled using a multivariate hypergeometric distribution, and derive accurate mathematical bounds to determine a tight approximation to the sample size, leading thus to a sparse sampling strategy. We evaluate the method thoroughly in terms of accuracy, its behavior against varying input parameters, and comparison against existing methods, including the state of the art. The key features of the proposed approach are: (i) sparseness of the sampled set, where the level of sparseness is independent of the population size and the distribution of data, (ii) robustness in the presence of overwhelming number of outliers, and (iii) unsupervised detection of all model instances, i.e. without requiring any prior knowledge of the number of embedded structures. To demonstrate the generic nature of the proposed method, we show experimental results on different computer vision problems, such as detection of physical structures e.g. lines, planes, etc., as well as more abstract structures such as fundamental matrices, and homographies in multi-body structure from motion.
Dataset Fingerprints: Exploring Image Collections Through Data Mining
Authors: Konstantinos Rematas, Basura Fernando, Frank Dellaert, Tinne Tuytelaars
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

As the amount of visual data increases, so does the need for summarization tools that can be used to explore large image collections and to quickly get familiar with their content. In this paper, we propose dataset fingerprints, a new and powerful method based on data mining that extracts meaningful patterns from a set of images. The discovered patterns are compositions of discriminative mid-level features that co-occur in several images. Compared to earlier work, ours stands out because i) it's fully unsupervised, ii) discovered patterns cover large parts of the images,often corresponding to full objects or meaningful parts thereof, and iii) different patterns are connected based on co-occurrence, allowing a user to ``browse'' / ``surf'' the images from one pattern to the next and to group patterns in a semantically meaningful manner.
Transport-Based Single Frame Super Resolution of Very Low Resolution Face Images
Authors: Soheil Kolouri, Gustavo K. Rohde
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Super-Resolution

Extracting high-resolution information from highly degraded facial images is an important problem with several applications in science and technology. Here we describe a single frame super resolution technique that uses a transport-based formulation of the problem. The method consists of a training and a testing phase. In the training phase, a nonlinear Lagrangian model of high-resolution facial appearance is constructed fully automatically. In the testing phase,  the resolution of a degraded image is enhanced by finding the model parameters that best fit the given low resolution data. We test the approach on two face datasets, namely the extended Yale Face Database B and the AR face datasets, and compare it to state of the art methods. The proposed method  outperforms existing solutions in problems related to enhancing images of very low resolution.
3D Reconstruction in the Presence of Glasses by Acoustic and Stereo Fusion
Authors: Mao Ye, Yu Zhang, Ruigang Yang, Dinesh Manocha
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: 3D Reconstruction, Sensor Fusion, Transparent objects

We present a practical and inexpensive method to reconstruct 3D scenes that include piece-wise planar transparent objects. Our work is motivated by the need for automatically generating 3D models of interior scenes, in which glass structures are common. These large structures are often invisible to cameras or even our human visual system. Existing 3D reconstruction methods for transparent objects are usually not applicable in such a room-size reconstruction setting. Our approach augments a regular depth camera (e.g., the Microsoft Kinect camera) with a single ultrasonic sensor, which is able to measure distance to any objects, including transparent surfaces. We present a novel sensor fusion algorithm that first segments the depth map into different categories such as opaque/transparent/infinity (e.g., too far to measure) and then updates the depth map based on the segmentation outcome. Our current hardware setup can generate only one additional point measurement per frame, yet our fusion algorithm is able to generate satisfactory reconstruction results based on our probabilistic model. We highlight the performance in many challenging indoor benchmarks.
Deep Sparse Representation for Robust Image Registration
Authors: Yeqing Li, Chen Chen, Fei Yang, Junzhou Huang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Registration

The definition of the similarity measure is an essential component in image registration. In this paper, we propose a novel similarity measure for registration of two or more images. The proposed method is motivated by that the optimally registered images can be deeply sparsified in the gradient domain and frequency domain, with the separation of a sparse tensor of errors. One of the key advantages of the proposed similarity measure is its robustness to severe intensity distortions, which widely exist on medical images, remotely sensed images and natural photos due to the difference of acquisition modalities or illumination conditions. Two efficient algorithms are proposed to solve the batch image registration and pair registration problems in a unified framework. We validate our method on extensive challenging datasets. The experimental results demonstrate the robustness, accuracy and efficiency of our method over 9 traditional and state-of-the-art algorithms on synthetic images and a wide range of real-world applications.
Real-Time Part-Based Visual Tracking via Adaptive Correlation Filters
Authors: Ting Liu, Gang Wang, Qingxiong Yang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Bayesian Inference, Object Tracking, Visual Tracking

Robust object tracking is a challenging task in computer vision. To better solve the partial occlusion issue, part-based methods are widely used in visual object trackers. However, due to the complicated online training and updating process, most of these part-based trackers cannot run in real-time. Correlation filters have been used in tracking tasks recently because of the high efficiency. However, the conventional correlation filter based trackers cannot deal with occlusion. Furthermore, most correlation filter based trackers fix the scale and rotation of the target which makes the trackers unreliable in long-term tracking tasks. In this paper, we propose a novel tracking method which track objects based on parts with multiple correlation filters. Our method can run in real-time. Additionally, the Bayesian inference framework and a structural constraint mask are adopted to enable our tracker to be robust to various appearance changes. Extensive experiments have been done to prove the effectiveness of our method.
Beyond Spatial Pooling: Fine-Grained Representation Learning in Multiple Domains
Authors: Chi Li, Austin Reiter, Gregory D. Hager
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, Object Recognition, Representation Learning

Object recognition systems have shown great progress over recent years. However, creating object representations that are robust to changes in viewpoint while capturing local visual details continues to be a challenge. In particular, recent convolutional architectures employ spatial pooling to achieve scale and shift invariances, but they are still sensitive to out-of-plane rotations. In this paper, we formulate a probabilistic framework for analyzing the performance of pooling. This framework suggests two directions for improvement. First, we apply multiple scales of filters coupled with different pooling granularities, and second we make use of color as an additional pooling domain, thereby reducing the sensitivity to spatial deformations. We evaluate our algorithm on the object instance recognition task using two independent publicly available RGB-D datasets, and demonstrate significant improvements over the current state-of-the-art. In addition, we present a new dataset for industrial objects to further validate the effectiveness of our approach versus other state-of-the-art approaches for object recognition using RGB-D data.
HC-Search for Structured Prediction in Computer Vision
Authors: Michael Lam, Janardhan Rao Doppa, Sinisa Todorovic, Thomas G. Dietterich
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Depth Estimation, Monocular Depth Estimation, object-detection, Object Detection, Prediction, Scene Labeling, Structured Prediction

The mainstream approach to structured prediction problems in computer vision is to learn an energy function such that the solution minimizes that function.  At prediction time, this approach must solve an often-challenging optimization problem.  Search-based methods provide an alternative that has the potential to achieve higher performance.  These methods learn to control a search procedure that constructs and evaluates candidate solutions.  The recently-developed HC-Search method has been shown to achieve state-of-the-art results in natural language processing, but mixed success when applied to vision problems.  This paper studies whether HC-Search can achieve similarly competitive performance on basic vision tasks such as object detection, scene labeling, and monocular depth estimation, where the leading paradigm is energy minimization.  To this end, we introduce a search operator suited to the vision domain that improves a candidate solution by probabilistically sampling likely object configurations in the scene from the hierarchical Berkeley segmentation.  We complement this search operator by applying the DAgger algorithm to robustly train the search heuristic so it learns from its previous mistakes.  Our evaluation shows that these improvements reduce the branching factor and search depth, and thus give a significant performance boost.  Our state-of-the-art results on scene labeling and depth estimation suggest that HC-Search provides a suitable tool for learning and inference in vision.
High-Speed Hyperspectral Video Acquisition With a Dual-Camera Architecture
Authors: Lizhi Wang, Zhiwei Xiong, Dahua Gao, Guangming Shi, Wen-Jun Zeng, Feng Wu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Vocal Bursts Intensity Prediction

We propose a novel dual-camera design to acquire 4D high-speed hyperspectral (HSHS) videos with high spatial and spectral resolution. Our work has two key technical contributions. First, we build a dual-camera system that simultaneously captures a panchromatic video at a high frame rate and a hyperspectral video at a low frame rate, which jointly provide reliable projections for the underlying HSHS video. Second, we exploit the panchromatic video to learn an over-complete 3D dictionary to represent each band-wise video sparsely, and a robust computational reconstruction is then employed to recover the HSHS video based on the joint videos and the self-learned dictionary. Experimental results demonstrate that, for the first time to our knowledge, the hyperspectral video frame rate reaches up to 100fps with decent quality, even when the incident light is not strong.
More About VLAD: A Leap From Euclidean to Riemannian Manifolds
Authors: Masoud Faraki, Mehrtash T. Harandi, Fatih Porikli
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Classification, Face Recognition, General Classification, Scene Recognition

This paper takes a step forward in image and video coding by extending the well-known Vector of Locally Aggregated Descriptors (VLAD) onto an extensive space of curved Riemannian manifolds. We provide a comprehensive mathematical framework that formulates the aggregation problem of such manifold data into an elegant solution. In particular, we consider structured descriptors from visual data, namely Region Covariance Descriptors and linear subspaces that reside on the manifold of Symmetric Positive Definite matrices and the Grassmannian manifolds, respectively. Through rigorous experimental validation, we demonstrate the superior performance of this novel Riemannian VLAD descriptor on several visual classification tasks including video-based face recognition, dynamic scene recognition, and head pose classification.
Camera Intrinsic Blur Kernel Estimation: A Reliable Framework
Authors: Ali Mosleh, Paul Green, Emmanuel Onzon, Isabelle Begin, J. M. Pierre Langlois
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Homography Estimation

This paper presents a reliable non-blind method to measure intrinsic lens blur. We first introduce an accurate camera-scene alignment framework that avoids erroneous homography estimation and camera tone curve estimation. This alignment is used to generate a sharp correspondence of a target pattern captured by the camera. Second, we introduce a Point Spread Function (PSF) estimation approach where information about the frequency spectrum of the target image is taken into account. As a result of these steps and the ability to use multiple target images in this framework, we achieve a PSF estimation method robust against noise and suitable for mobile devices. Experimental results show that the proposed method results in PSFs with more than 10 dB higher accuracy in noisy conditions compared with the PSFs generated using state-of-the-art techniques.
Classifier Learning With Hidden Information
Authors: Ziheng Wang, Qiang Ji
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Traditional data-driven classifier learning approaches become limited when the training data is inadequate either in quantity or quality. To address this issue, in this paper we propose to combine hidden information and data to enhance classifier learning. Hidden information represents information that is only available during training but not available during testing. It often exists in many applications yet has not been thoroughly exploited, and existing methods to utilize hidden information are still limited. To this end, we propose two general approaches to exploit different types of hidden information to improve different classifiers. We also extend the proposed methods to deal with incomplete hidden information. Experimental results on different applications demonstrate the effectiveness of the proposed methods for exploiting hidden information and their superior performance to existing methods.
Single Target Tracking Using Adaptive Clustered Decision Trees and Dynamic Multi-Level Appearance Models
Authors: Jingjing Xiao, Rustam Stolkin, Ales Leonardis
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Computational Efficiency, Superpixels

This paper presents a method for single target tracking of arbitrary objects in challenging video sequences. Targets are modeled at three different levels of granularity (pixel level, parts-based level and bounding box level), which are cross-constrained to enable robust model relearning. The main contribution is an adaptive clustered decision tree method which dynamically selects the minimum combination of features necessary to sufficiently represent each target part at each frame, thereby providing robustness with computational efficiency. The adaptive clustered decision tree is implemented in two separate parts of the tracking algorithm: firstly to enable robust matching at the parts-based level between successive frames; and secondly to select the best superpixels for learning new parts of the target. We have tested the tracker using two different tracking benchmarks (VOT2013-2014 and CVPR2013 tracking challenges), based on two different test methodologies, and show it to be significantly more robust than the best state-of-the-art methods from both of those tracking challenges, while also offering competitive tracking precision.
Simultaneous Video Defogging and Stereo Reconstruction
Authors: Zhuwen Li, Ping Tan, Robby T. Tan, Danping Zou, Steven Zhiying Zhou, Loong-Fah Cheong
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Matting, Stereo Matching, Stereo Matching Hand

We present a method to jointly estimate scene depth and recover the clear latent image from a foggy video sequence. In our formulation, the depth cues from stereo matching and fog information reinforce each other, and produce superior results than conventional stereo or defogging algorithms. We first improve the photo-consistency term to explicitly model the appearance change due to the scattering effects. The prior matting Laplacian constraint on fog transmission imposes a detail-preserving smoothness constraint on the scene depth. We further enforce the ordering consistency between scene depth and fog transmission at neighboring points. These novel constraints are formulated together in an MRF framework, which is optimized iteratively by introducing auxiliary variables. The experiment results on real videos demonstrate the strength of our method.
Learning Deep Representations for Ground-to-Aerial Geolocalization
Authors: Tsung-Yi Lin, Yin Cui, Serge Belongie, James Hays
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Face Verification

The recent availability of geo-tagged images and rich geospatial data has inspired a number of algorithms for image based geolocalization. Most approaches predict the location of a query image by matching to ground-level images with known locations (e.g., street-view data). However, most of the Earth does not have ground-level reference photos available. Fortunately, more complete coverage is provided by oblique aerial or "bird's eye" imagery. In this work, we localize a ground-level query image by matching it to a reference database of aerial imagery. We use publicly available data to build a dataset of 78K aligned cross- view image pairs. The primary challenge for this task is that traditional computer vision approaches cannot handle the wide baseline and appearance variation of these cross- view pairs. We use our dataset to learn a feature representation in which matching views are near one another and mismatched views are far apart. Our proposed approach, Where-CNN, is inspired by deep learning success in face verification and achieves significant improvements over traditional hand-crafted features and existing deep features learned from other large-scale databases. We show the effectiveness of Where-CNN in finding matches between street view and aerial view imagery and demonstrate the ability of our learned features to generalize to novel locations.