OpenRaiser/Intern-Atlas huggingface dataset:
Good continuations in digital image level lines
Authors: Frédéric Cao
Venue: Conference ICCV 2003
Year: 2003

We propose a probabilistic algorithm able to detect the curves that are unexpectedly smooth in a set of digital curves. The only parameter is a false alarm rate, influencing the detection only by its logarithm. We experiment the good continuation criterion on image level lines. One of the conclusion is that, accordingly to Gestalt theory, one can detect edges in a way that is widely independent of contrast. We also use the same kind of method to detect corners and junctions.
How hard is 3-view triangulation really?
Authors: Henrik Stewénius, Frederik Schaffalitzky, David Nistér
Venue: Conference ICCV 2005
Year: 2005

We present a solution for optimal triangulation in three views. The solution is guaranteed to find the optimal solution because it computes all the stationary points of the (maximum likelihood) objective function. Internally, the solution is found by computing roots of multivariate polynomial equations, directly solving the conditions for stationarity. The solver makes use of standard methods from computational commutative algebra to convert the root-finding problem into a 47 /spl times/ 47 nonsymmetric eigenproblem. Although there are in general 47 roots, counting both real and complex ones, the number of real roots is usually much smaller. We also show experimentally that the number of stationary points that are local minima and lie in front of each camera is small but does depend on the scene geometry.
Card: Compact and real-time descriptors
Authors: Mitsuru Ambai, Yuichi Yoshida
Venue: Conference ICCV 2011
Year: 2011

We propose Compact And Real-time Descriptors (CARD) which can be computed very rapidly and be expressed by short binary codes. An efficient algorithm based on lookup tables is presented for extracting histograms of oriented gradients, which results in approximately 16 times faster computation time per descriptor than that of SIFT. Our lookup-table-based approach can handle arbitrary layouts of bins, such as the grid binning of SIFT and the log-polar binning of GLOH, thus yielding sufficient discrimination power. In addition, we introduce learning-based sparse hashing to convert the extracted descriptors to short binary codes. This conversion is achieved very rapidly by multiplying a very sparse integer weight matrix by the descriptors and aggregating signs of their multiplications. The weight matrix is optimized in a training phase so as to make Hamming distances between encoded training pairs reflect visual dissimilarities between them. Experimental results demonstrate that CARD outperforms previous methods in terms of both computation time and memory usage.
Surface stereo with soft segmentation
Authors: Michael Bleyer, Carsten Rother, Pushmeet Kohli
Venue: Conference CVPR 2010
Year: 2010

This paper proposes a new stereo model which encodes the simple assumption that the scene is composed of a few, smooth surfaces. A key feature of our model is the surface-based representation, where each pixel is assigned to a 3D surface (planes or B-splines). This representation enables several important contributions: Firstly, we formulate a higher-order prior which states that pixels of similar appearance are likely to belong to the same 3D surface. This enables to incorporate the very popular color segmentation constraint in a soft and principled way. Secondly, we use a global MDL prior to penalize the number of surfaces. Thirdly, we are able to incorporate, in a simple way, a prior which favors low curvature surfaces. Fourthly, we improve the asymmetric occlusion model by disallowing pixels of the same surface to occlude each other. Finally, we use the known fusion move approach which enables a powerful optimization of our model, despite the infinite number of possible labelings (surfaces).
Single-shot high dynamic range imaging with conventional camera hardware
Authors: Keigo Hirakawa, Paul M. Simon
Venue: Conference ICCV 2011
Year: 2011

A combination of photographic filter placed over the lens and the color filter array on image sensor induces differences in red, green, and blue channel sensitivities. Spectrally selective single-shot HDR (S4HDR) imaging treats this as an exposure bracketing. Optimally exposed regions of low dynamic range red/green/blue color components are merged in a principled manner to yield a single HDR color image. Though not expected to yield results superior to the traditional time multiplexing counterparts, the single-shot HDR solution we propose is a robust alternative that can be realized with conventional camera hardware.
Visual recognition and detection under bounded computational resources
Authors: Sudheendra Vijayanarasimhan, Ashish Kapoor
Venue: Conference CVPR 2010
Year: 2010

Visual recognition and detection are computationally intensive tasks and current research efforts primarily focus on solving them without considering the computational capability of the devices they run on. In this paper we explore the challenge of deriving methods that consider constraints on computation, appropriately schedule the next best computation to perform and finally have the capability of producing reasonable results at any time when a solution is required. We specifically derive an approach for the task of object category localization and classification in cluttered, natural scenes that can not only produce anytime results but also utilize the principle of value-of-information in order to provide the most recognition bang for the computational buck. Experiments on two standard object detection challenges show that the proposed framework can triage computation effectively and attain state-of-the-art results when allowed to run till completion. Additionally, the real benefit of the proposed framework is highlighted in the experiments where we demonstrate that the method can provide reasonable recognition results even if the procedure needs to terminate before completion.
Surface Shape Reconstruction of an Undulating Transparent Object
Authors: Hiroshi Murase
Venue: Conference ICCV 1990
Year: 1990

An algorithm is described for reconstructing the surface shape of a nonrigid transparent object, such as water, from the apparent motion of the observed pattern. This algorithm is based on the optical and statistical analysis of the distortions. It consists of the following parts: extraction of optical flow, averaging of each point trajectory obtained from the optical flow sequence, calculation of the surface normal using optical characteristics, and reconstruction of the surface. The algorithm is applied to synthetic and real images to demonstrate its performance.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
A globally optimal data-driven approach for image distortion estimation
Authors: Yuandong Tian, Srinivasa G. Narasimhan
Venue: Conference CVPR 2010
Year: 2010

Image alignment in the presence of non-rigid distortions is a challenging task. Typically, this involves estimating the parameters of a dense deformation field that warps a distorted image back to its undistorted template. Generative approaches based on parameter optimization such as Lucas-Kanade can get trapped within local minima. On the other hand, discriminative approaches like Nearest-Neighbor require a large number of training samples that grows exponentially with the desired accuracy. In this work, we develop a novel data-driven iterative algorithm that combines the best of both generative and discriminative approaches. For this, we introduce the notion of a “pull-back” operation that enables us to predict the parameters of the test image using training samples that are not in its neighborhood (not ϵ-close) in parameter space. We prove that our algorithm converges to the global optimum using a significantly lower number of training samples that grows only logarithmically with the desired accuracy. We analyze the behavior of our algorithm extensively using synthetic data and demonstrate successful results on experiments with complex deformations due to water and clothing.
Locality-sensitive support vector machine by exploring local correlation and global regularization
Authors: Guojun Qi, Qi Tian 0001, Thomas S. Huang
Venue: Conference CVPR 2011
Year: 2011

Local classifiers have obtained great success in classification task due to its powerful discriminating ability on local regions. However, most of them still have restricted generalization in twofold: (1) each local classifier is sensitive to noise in local regions which leads to overfitting phenomenon in local classifiers; (2) the local classifiers also ignore the local correlation determined by the sample distribution in each local region. To overcome the above two problems, we present a novel locality-sensitive support vector machine (LSSVM) in this paper for image retrieval problem. This classifier applies locality-sensitive hashing (LSH) to divide the whole feature space into a number of local regions, on each of them a local model can be better constructed due to smaller within-class variation on it. To avoid these local models from overfitting into locality-sensitive structures, it imposes a global regularizer across local regions so that local classifiers are smoothly glued together to form a regularized overall classifier. local correlation is modeled to capture the sample distribution that determines the locality structure of each local region, which can increase the discriminating ability of the algorithm. To evaluate the performance, we apply the proposed algorithm into image retrieval task and competitive results are obtained on the real-world web image data set.
Image retrieval with geometry-preserving visual phrases
Authors: Yimeng Zhang, Zhaoyin Jia, Tsuhan Chen
Venue: Conference CVPR 2011
Year: 2011

The most popular approach to large scale image retrieval is based on the bag-of-visual-word (BoV) representation of images. The spatial information is usually re-introduced as a post-processing step to re-rank the retrieved images, through a spatial verification like RANSAC. Since the spatial verification techniques are computationally expensive, they can be applied only to the top images in the initial ranking. In this paper, we propose an approach that can encode more spatial information into BoV representation and that is efficient enough to be applied to large-scale databases. Other works pursuing the same purpose have proposed exploring the word co-occurrences in the neighborhood areas. Our approach encodes more spatial information through the geometry-preserving visual phrases (GVP). In addition to co-occurrences, the GVP method also captures the local and long-range spatial layouts of the words. Our GVP based searching algorithm increases little memory usage or computational time compared to the BoV method. Moreover, we show that our approach can also be integrated to the min-hash method to improve its retrieval accuracy. The experiment results on Oxford 5K and Flicker 1M dataset show that our approach outperforms the BoV method even following a RANSAC verification.
Continuously tracking and see-through occlusion based on a new hybrid synthetic aperture imaging model
Authors: Tao Yang 0006, Yanning Zhang 0001, Xiaomin Tong, Xiaoqiang Zhang 0002, Rui Yu
Venue: Conference CVPR 2011
Year: 2011

Robust detection and tracking of multiple people in cluttered and crowded scenes with severe occlusion is a significant challenging task for many computer vision applications. In this paper, we present a novel hybrid synthetic aperture imaging model to solve this problem. The main characteristics of this approach include: (1) To the best of our knowledge, this algorithm is the first time to solve the occluded people imaging and tracking problem in a joint multiple camera synthetic aperture imaging domain. (2) A multiple model framework is designed to achieve seamless interaction among the detection, imaging and tracking modules. (3)In the object detection module, a multiple constraints based approach is presented for people localizing and ghost objects removal in a 3D foreground silhouette synthetic aperture imaging volume. (4) In the synthetic imaging module, a novel occluder removal based synthetic imaging approach is proposed to continuously obtain object clear image even under severe occlusion. (5) In the object tracking module, a camera array is used for robust people tracking in color synthetic aperture images. A network camera based hybrid synthetic aperture imaging system has been set up, and experimental results with qualitative and quantitative analysis demonstrate that the method can reliably locate and see people in challenge scene.
Extraction of surface normal and index of refraction using a pair of passive infrared polarimetric sensors
Authors: Firooz Sadjadi, Farzad Sadjadi
Venue: Conference CVPR 2007
Year: 2007

This paper summarizes a method for extracting 3D information and indices of refraction from a scene by means of a pair of polarimetric passive imaging sensors. Each sensor provides the Stokes vector at each sensor pixel location, from which, degree and angle of linear polarization are computed. Angle of linear polarization provides the azimuth angle of the surface normal vector. Two cases are considered. For the special case when the two sensors have a common azimuth plane the index of refraction can be find analytically in terms of the degrees of polarization and the angle between the lines of sight from the two sensors, from which the depression angle of the surface normal can be computed. For the second and more general case the surface normal is estimated from the cross-product of the azimuth vectors from the two sensors and the inner product of the line of sight vectors and surface normal. Once the de-presion angles are estimated the index of refraction can be computed.
A revisit to cost aggregation in stereo matching: How far can we reduce its computational redundancy?
Authors: Dongbo Min, Jiangbo Lu, Minh N. Do
Venue: Conference ICCV 2011
Year: 2011

This paper presents a novel method for performing an efficient cost aggregation in stereo matching. The cost aggregation problem is re-formulated with a perspective of a histogram, and it gives us a potential to reduce the complexity of the cost aggregation significantly. Different from the previous methods which have tried to reduce the complexity in terms of the size of an image and a matching window, our approach focuses on reducing the computational redundancy which exists among the search range, caused by a repeated filtering for all disparity hypotheses. Moreover, we also reduce the complexity of the window-based filtering through an efficient sampling scheme inside the matching window. The trade-off between accuracy and complexity is extensively investigated into parameters used in the proposed method. Experimental results show that the proposed method provides high-quality disparity maps with low complexity. This work provides new insights into complexity-constrained stereo matching algorithm design.
A closed-form solution to uncalibrated photometric stereo via diffuse maxima
Authors: Paolo Favaro, Thoma Papadhimitri
Venue: Conference CVPR 2012
Year: 2012

In this paper we propose a novel solution to uncalibrated photometric stereo. Our approach is to eliminate the so-called generalized bas relief (GBR) ambiguity by exploiting points where the Lambertian reflection is maximal. We demonstrate several noteworthy properties of these maxima: 1) Closed-form solution: A single diffuse maximum constrains the GBR ambiguity to a semi-circle in 3D space; 2) Efficiency: As few as two diffuse maxima in different images identify a unique solution; 3) GBR-invariance: The estimation error of the GBR parameters is completely independent of the true parameters. Furthermore, our algorithm is remarkably robust: It can obtain an accurate estimate of the GBR parameters even with extremely high levels of outliers in the detected maxima (up to 80% of the observations). The method is validated on real data and achieves state-of-the-art results.
Bayesian deblurring with integrated noise estimation
Authors: Uwe Schmidt 0001, Kevin Schelten, Stefan Roth 0001
Venue: Conference CVPR 2011
Year: 2011

Conventional non-blind image deblurring algorithms involve natural image priors and maximum a-posteriori (MAP) estimation. As a consequence of MAP estimation, separate pre-processing steps such as noise estimation and training of the regularization parameter are necessary to avoid user interaction. Moreover, MAP estimates involving standard natural image priors have been found lacking in terms of restoration performance. To address these issues we introduce an integrated Bayesian framework that unifies non-blind deblurring and noise estimation, thus freeing the user of tediously pre-determining a noise level. A sampling-based technique allows to integrate out the unknown noise level and to perform deblurring using the Bayesian minimum mean squared error estimate (MMSE), which requires no regularization parameter and yields higher performance than MAP estimates when combined with a learned high-order image prior. A quantitative evaluation demonstrates state-of-the-art results for both non-blind deblurring and noise estimation.
Image annotation using bi-relational graph of images and semantic labels
Authors: Hua Wang 0007, Heng Huang 0001, Chris H. Q. Ding
Venue: Conference CVPR 2011
Year: 2011

Image annotation is usually formulated as a multi-label semi-supervised learning problem. Traditional graph-based methods only utilize the data (images) graph induced from image similarities, while ignore the label (semantic terms) graph induced from label correlations of a multi-label image data set. In this paper, we propose a novel Bi-relational Graph (BG) model that comprises both the data graph and the label graph as subgraphs, and connect them by an additional bipartite graph induced from label assignments. By considering each class and its labeled images as a semantic group, we perform random walk on the BG to produce group-to-vertex relevance, including class-to-image and class-to-class relevances. The former can be used to predict labels for unannotated images, while the latter are new class relationships, called as Causal Relationships (CR), which are asymmetric. CR is learned from input data and has better semantic meaning to enhance the label prediction for unannotated images. We apply the proposed approaches to automatic image annotation and semantic image retrieval tasks on four benchmark multi-label image data sets. The superior performance of our approaches compared to state-of-the-art multi-label classification methods demonstrate their effectiveness.
A rank minimization approach to video inpainting
Authors: Tao Ding, Mario Sznaier, Octavia I. Camps
Venue: Conference ICCV 2007
Year: 2007

This paper addresses the problem of video inpainting, that is seamlessly reconstructing missing portions in a set of video frames. We propose to solve this problem proceeding as follows: (i) finding a set of descriptors that encapsulate the information necessary to reconstruct a frame, (ii) finding an optimal estimate of the value of these descriptors for the missing/corrupted frames, and (iii) using the estimated values to reconstruct the frames. The main result of the paper shows that the optimal descriptor estimates can be efficiently obtained by minimizing the rank of a matrix directly constructed from the available data, leading to a simple, computationally attractive, dynamic inpainting algorithm that optimizes the use of spatio/temporal information. Moreover, contrary to most currently available techniques, the method can handle non-periodic target motions, non-stationary backgrounds and moving cameras. These results are illustrated with several examples, including reconstructing dynamic textures and object disocclusion in cases involving both moving targets and camera.
Relevance feedback retrieval of time series data
Authors: Eamonn J. Keogh, Michael J. Pazzani
Venue: Conference SIGIR 1999
Year: 1999

There has been much recent interest in retrieval of time series data. Earlier work has used a fixed similarity metric (e.g., Euclidean distance) to determine the similarity between a userspecified query and items in the database. Here, we describe a novel approach to retrieval of time series data by using relevance feedback from the user to adjust the similarity metric. This is important because the Euclidean distance metric does not capture many notions of similarity between time series. In particular, Euclidean distance is sensitive to various “distortions” such as offset translation, amplitude scaling, etc. Depending on the domain and the user, one may wish a query to be sensitive or insensitive to these distortions to varying degrees. This paper addresses this problem by introducing a profile that encodes the user's subjective notion of similarity in a domain. These profiles can be learned continuously from interaction with the user. We further show how the user profile may be embedded in a system that uses relevance feedback to modify the query in a manner analogous to the familiar text retrieval algorithms.
Vector boosting for rotation invariant multi-view face detection
Authors: Chang Huang, Haizhou Ai, Yuan Li 0022, Shihong Lao
Venue: Conference ICCV 2005
Year: 2005

In this paper, we propose a novel tree-structured multiview face detector (MVFD), which adopts the coarse-to-fine strategy to divide the entire face space into smaller and smaller subspaces. For this purpose, a newly extended boosting algorithm named vector boosting is developed to train the predictors for the branching nodes of the tree that have multicomponents outputs as vectors. Our MVFD covers a large range of the face space, say, +/-45/spl deg/ rotation in plane (RIP) and +/-90/spl deg/ rotation off plane (ROP), and achieves high accuracy and amazing speed (about 40 ms per frame on a 320 /spl times/ 240 video sequence) compared with previous published works. As a result, by simply rotating the detector 90/spl deg/, 180/spl deg/ and 270/spl deg/, a rotation invariant (360/spl deg/ RIP) MVFD is implemented that achieves real time performance (11 fps on a 320 /spl times/ 240 video sequence) with high accuracy.
Collaborative filtering with preferences inferred from brain signals
Authors: Keith M. Davis III, Michiel M. A. Spapé, Tuukka Ruotsalo
Venue: Conference WWW 2021
Year: 2021

Collaborative filtering is a common technique in which interaction data from a large number of users are used to recommend items to an individual that the individual may prefer but has not interacted with. Previous approaches have achieved this using a variety of behavioral signals, from dwell time and clickthrough rates to self-reported ratings. However, such signals are mere estimations of the real underlying preferences of the users. Here, we use brain-computer interfacing to infer preferences directly from the human brain. We then utilize these preferences in a collaborative filtering setting and report results from an experiment where brain inferred preferences are used in a neural collaborative filtering framework. Our results demonstrate, for the first time, that brain-computer interfacing can provide a viable alternative for behavioral and self-reported preferences in realistic recommendation scenarios. We also discuss the broader implications of our findings for personalization systems and user privacy.