OpenRaiser/Intern-Atlas huggingface dataset:
Human identity recognition in aerial images
Authors: Omar Oreifej, Ramin Mehran, Mubarak Shah
Venue: Conference CVPR 2010
Year: 2010

Human identity recognition is an important yet under-addressed problem. Previous methods were strictly limited to high quality photographs, where the principal techniques heavily rely on body details such as face detection. In this paper, we propose an algorithm to address the novel problem of human identity recognition over a set of unordered low quality aerial images. Assuming a user was able to manually locate a target in some images of the set, we find the target in each other query image by implementing a weighted voter-candidate formulation. In the framework, every manually located target is a voter, and the set of humans in a query image are candidates. In order to locate the target, we detect and align blobs of voters and candidates. Consequently, we use PageRank to extract distinguishing regions, and then match multiple regions of a voter to multiple regions of a candidate using Earth Mover Distance (EMD). This generates a robust similarity measure between every voter-candidate pair. Finally, we identify the candidate with the highest weighted vote as the target. We tested our technique over several aerial image sets that we collected, along with publicly available sets, and have obtained promising results.
Modulated phase-shifting for 3d scanning
Authors: Tongbo Chen, Hans-Peter Seidel, Hendrik P. A. Lensch
Venue: Conference CVPR 2008
Year: 2008

We present a new 3D scanning method using modulated phase-shifting. Optical scanning of complex objects or scenes with significant global light transport, such as subsurface scattering, interreflections, volumetric scattering, etc. is a difficult task since the direct surface reflection will be mixed with the global illumination. The direct and global components can be effficiently separated using high frequency illumination which to some extend is done in traditional phase-shifting for 3D scanning. In this paper we introduce the concept of modulation based separation where a high frequency signal is multiplied on top of other signal. The modulated signal inherits the good separation properties of the high frequency signal and allows for removing artifacts due to global illumination. This technique can be used to clean up arbitrary projected signals, e.g. photographs as well as the sinusoid patterns used for phase-shifting. For the modulated phase-shifting, we propose a two-pass separation method exploiting high frequency patterns in two-dimensions that can filter out the global components much more completely than traditional one-pass separation methods. We demonstrate the effectiveness of our approach on a couple of scenes with significant subsurface scattering and interreflections.
Micro phase shifting
Authors: Mohit Gupta 0001, Shree K. Nayar
Venue: Conference CVPR 2012
Year: 2012

We consider the problem of shape recovery for real world scenes, where a variety of global illumination (inter-reflections, subsurface scattering, etc.) and illumination defocus effects are present. These effects introduce systematic and often significant errors in the recovered shape. We introduce a structured light technique called Micro Phase Shifting, which overcomes these problems. The key idea is to project sinusoidal patterns with frequencies limited to a narrow, high-frequency band. These patterns produce a set of images over which global illumination and defocus effects remain constant for each point in the scene. This enables high quality reconstructions of scenes which have traditionally been considered hard, using only a small number of images. We also derive theoretical lower bounds on the number of input images needed for phase shifting and show that Micro PS achieves the bound.
Identigram/watermark removal using cross-channel correlation
Authors: Jaesik Park, Yu-Wing Tai, In-So Kweon
Venue: Conference CVPR 2012
Year: 2012

We introduce a method to repair an image which has been stamped by an identigram or a watermark. Our method is based on the cross-channel correlation which assures the co-occurrence of image discontinuities and correlation of color distributions across different color channels of an image. Using blind source separation, we find the transformation of color space which separates the structures of identigram and that of the original image into two different individual color channels. To repair the image contents in the corrupted channel, we formulate the problem using Bayes' rule where the prior and the likelihood probabilities are defined based on the cross-channel correlation assumption. We compare our results with results from inpainting and texture synthesis-based hole filling techniques. Our results are pleasable for real-world examples and have the maximum PSNR for synthetic examples.
Shape reconstruction based on similarity in radiance changes under varying illumination
Authors: Imari Sato, Takahiro Okabe, Qiong Yu, Yoichi Sato 0001
Venue: Conference ICCV 2007
Year: 2007

This paper presents a technique for determining an object's shape based on the similarity of radiance changes observed at points on its surface under varying illumination. To examine the similarity, we use an observation vector that represents a sequence of pixel intensities of a point on the surface under different lighting conditions. Assuming convex objects under distant illumination and orthographic projection, we show that the similarity between two observation vectors is closely related to the similarity between the surface normals of the corresponding points. This enables us to estimate the object's surface normals solely from the similarity of radiance changes under unknown distant lighting by using dimensionality reduction. Unlike most previous shape reconstruction methods, our technique neither assumes particular reflection models nor requires reference materials. This makes our method applicable to a wide variety of objects made of different materials.
A robust algorithm for point set registration using mixture of gaussians
Authors: Bing Jian, Baba C. Vemuri
Venue: Conference ICCV 2005
Year: 2005

This paper proposes a novel and robust approach to the point set registration problem in the presence of large amounts of noise and outliers. Each of the point sets is represented by a mixture of Gaussians and the point set registration is treated as a problem of aligning the two mixtures. We derive a closed-form expression for the L(2) distance between two Gaussian mixtures, which in turn leads to a computationally efficient registration algorithm. This new algorithm has an intuitive interpretation, is simple to implement and exhibits inherent statistical robustness. Experimental results indicate that our algorithm achieves very good performance in terms of both robustness and accuracy.
Learning dynamics for exemplar-based gesture recognition
Authors: Ahmed M. Elgammal, Vinay D. Shet, Yaser Yacoob, Larry S. Davis
Venue: Conference CVPR 2003
Year: 2003

This paper addresses the problem of capturing the dynamics for exemplar-based recognition systems. Traditional HMM provides a probabilistic tool to capture system dynamics and in exemplar paradigm, HMM states are typically coupled with the exemplars. Alternatively, we propose a non-parametric HMM approach that uses a discrete HMM with arbitrary states (decoupled from exemplars) to capture the dynamics over a large exemplar space where a nonparametric estimation approach is used to model the exemplar distribution. This reduces the need for lengthy and non-optimal training of the HMM observation model. We used the proposed approach for view-based recognition of gestures. The approach is based on representing each gesture as a sequence of learned body poses (exemplars). The gestures are recognized through a probabilistic framework for matching these body poses and for imposing temporal constraints between different poses using the proposed non-parametric HMM.
Multi-view structure computation without explicitly estimating motion
Authors: Hongdong Li
Venue: Conference CVPR 2010
Year: 2010

Most existing structure-from-motion methods follow a common two-step scheme, where relative camera motions are estimated in the first step and 3D structure is computed afterward in the second step. This paper presents a novel scheme which bypasses the motion-estimation step, and goes directly to structure computation step. By introducing graph rigidity theory to Sfm problems, we demonstrate that such a scheme is not only theoretically possible, but also technically feasible and effective. We also derive a new convex relaxation technique (based on semi-definite programming) which implements the above scheme very efficiently. Our new method provides other benefits as well, such as that it offers a new way to looking at Sfm, and that it is naturally suited for handling sparse large-scale Sfm problems.
A refractive camera for acquiring stereo and super-resolution images
Authors: Chunyu Gao, Narendra Ahuja
Venue: Conference CVPR 2006
Year: 2006

We propose a novel depth sensing system composed of a single camera, and a transparent plate which is placed in front of the camera and rotates about the optical axis of the camera. The camera takes a sequence of images as the plate rotates, which provide the equivalent of a large number of stereo pairs. Compared with conventional multi-camera stereo systems, the use of a single camera for capturing stereo pairs helps improve the accuracy of detecting correspondences. The availability of the large number of stereo pairs also reduces matching ambiguities, even for objects with low texture. By using both the images and the estimated depth map, we show that the proposed system is also capable of generating super-resolution images. Experimental results on reconstructing 3D structures and recovering highresolution images are presented.
Multi-object tracking through clutter using graph cuts
Authors: James G. Malcolm, Yogesh Rathi, Allen R. Tannenbaum
Venue: Conference ICCV 2007
Year: 2007

The standard graph cut technique is a robust method for globally optimal image segmentations. However, because of its global nature, it is prone to capture outlying areas similar to the object of interest. This paper proposes a novel method to constrain the standard graph cut technique for tracking anywhere from one to several objects in regions of interest. For each object, we introduce a pixel penalty based upon distance from a region of interest and so segmentation is biased to remain in this area. Also, we employ a filter predicting the location of the object. The distance penalty is then centered at this location and adoptively scaled based on prediction confidence. This method is capable of tracking multiple interacting objects of different intensity profiles in both gray-scale and color imagery.
The truth about cats and dogs
Authors: Omkar M. Parkhi, Andrea Vedaldi, C. V. Jawahar, Andrew Zisserman
Venue: Conference ICCV 2011
Year: 2011

Template-based object detectors such as the deformable parts model of Felzenszwalb et al. [11] achieve state-of-the-art performance for a variety of object categories, but are still outperformed by simpler bag-of-words models for highly flexible objects such as cats and dogs. In these cases we propose to use the template-based model to detect a distinctive part for the class, followed by detecting the rest of the object via segmentation on image specific information learnt from that part. This approach is motivated by two observations: (i) many object classes contain distinctive parts that can be detected very reliably by template-based detectors, whilst the entire object cannot; (ii) many classes (e.g. animals) have fairly homogeneous coloring and texture that can be used to segment the object once a sample is provided in an image. We show quantitatively that our method substantially outperforms whole-body template-based detectors for these highly deformable object categories, and indeed achieves accuracy comparable to the state-of-the-art on the PASCAL VOC competition, which includes other models such as bag-of-words.
Seeing through the blur
Authors: Hossein Mobahi, C. Lawrence Zitnick, Yi Ma 0001
Venue: Conference CVPR 2012
Year: 2012

This paper addresses the problem of image alignment using direct intensity-based methods for affine and homography transformations. Direct methods often employ scale-space smoothing (Gaussian blur) of the images to avoid local minima. Although, it is known that the isotropic blur used is not optimal for some motion models, the correct blur kernels have not been rigorously derived for motion models beyond translations. In this work, we derive blur kernels that result from smoothing the alignment objective function for some common motion models such as affine and homography. We show the derived kernels remove poor local minima and reach lower energy solutions in practice.
Digital face makeup by example
Authors: Dong Guo 0001, Terence Sim
Venue: Conference CVPR 2009
Year: 2009

This paper introduces an approach of creating face makeup upon a face image with another image as the style example. Our approach is analogous to physical makeup, as we modify the color and skin detail while preserving the face structure. More precisely, we first decompose the two images into three layers: face structure layer, skin detail layer, and color layer. Thereafter, we transfer information from each layer of one image to corresponding layer of the other image. One major advantage of the proposed method lies in that only one example image is required. This renders face makeup by example very convenient and practical. Equally, this enables some additional interesting applications, such as applying makeup by a portraiture. The experiment results demonstrate the effectiveness of the proposed approach in faithfully transferring makeup.
Inferring 3d body pose from silhouettes using activity manifold learning
Authors: Ahmed M. Elgammal, Chan-Su Lee
Venue: Conference CVPR 2004
Year: 2004

We aim to infer 3D body pose directly from human silhouettes. Given a visual input (silhouette), the objective is to recover the intrinsic body configuration, recover the viewpoint, reconstruct the input and detect any spatial or temporal outliers. In order to recover intrinsic body configuration (pose) from the visual input (silhouette), we explicitly learn view-based representations of activity manifolds as well as learn mapping functions between such central representations and both the visual input space and the 3D body pose space. The body pose can be recovered in a closed form in two steps by projecting the visual input to the learned representations of the activity manifold, i.e., finding the point on the learned manifold representation corresponding to the visual input, followed by interpolating 3D pose.
Modeling view and posture manifolds for tracking
Authors: Chan-Su Lee, Ahmed M. Elgammal
Venue: Conference ICCV 2007
Year: 2007

In this paper we consider modeling data lying on multiple continuous manifolds. In particular, we model the shape manifold of a person performing a motion observed from different view points along a view circle at fixed camera height. We introduce a model that ties together the body configuration (kinematics) manifold and the visual manifold (observations) in a way that facilitates tracking the 3D configuration with continuous relative view variability. The model exploits the low dimensionality nature of both the body configuration manifold and the view manifold where each of them are represented separately.
Polarization and phase-shifting for 3d scanning of translucent objects
Authors: Tongbo Chen, Hendrik P. A. Lensch, Christian Fuchs 0004, Hans-Peter Seidel
Venue: Conference CVPR 2007
Year: 2007

Translucent objects pose a difficult problem for traditional structured light 3D scanning techniques. Subsurface scattering corrupts the range estimation in two ways: by drastically reducing the signal-to-noise ratio and by shifting the intensity peak beneath the surface to a point which does not coincide with the point of incidence. In this paper we analyze and compare two descattering methods in order to obtain reliable 3D coordinates for translucent objects. By using polarization-difference imaging, subsurface scattering can be filtered out because multiple scattering randomizes the polarization direction of light while the surface reflectance partially keeps the polarization direction of the illumination. The descattered reflectance can be used for reliable 3D reconstruction using traditional optical 3D scanning techniques, such as structured light. Phase-shifting is another effective descattering technique if the frequency of the projected pattern is sufficiently high. We demonstrate the performance of these two techniques and the combination of them on scanning real-world translucent objects.
Minimizing algebraic error in geometric estimation problems
Authors: Richard I. Hartley
Venue: Conference ICCV 1998
Year: 1998

This paper gives a widely applicable technique for solving many of the parameter estimation problems encountered in geometric computer vision. A commonly used approach is to minimize an algebraic error function instead of a possibly preferable geometric error function. It is claimed in this paper that minimizing algebraic error will usually give excellent results, and in fact the main problem with most algorithms minimizing algebraic distance is that they do not take account of mathematical constraints that should be imposed on the quantity being estimated. This paper gives an efficient method of minimizing algebraic distance while taking account of the constraints. This provides new algorithms for the problems of resectioning a pinhole camera, computing the fundamental matrix, and computing the tri-focal tensor. Evaluation results are given for the resectioning and tri-focal tensor estimation algorithms.
Real-time plane-sweeping stereo with multiple sweeping directions
Authors: David Gallup, Jan-Michael Frahm, Philippos Mordohai, Qingxiong Yang, Marc Pollefeys
Venue: Conference CVPR 2007
Year: 2007

Recent research has focused on systems for obtaining automatic 3D reconstructions of urban environments from video acquired at street level. These systems record enormous amounts of video; therefore a key component is a stereo matcher which can process this data at speeds comparable to the recording frame rate. Furthermore, urban environments are unique in that they exhibit mostly planar surfaces. These surfaces, which are often imaged at oblique angles, pose a challenge for many window-based stereo matchers which suffer in the presence of slanted surfaces. We present a multi-view plane-sweep-based stereo algorithm which correctly handles slanted surfaces and runs in real-time using the graphics processing unit (GPU). Our algorithm consists of (1) identifying the scene's principle plane orientations, (2) estimating depth by performing a plane-sweep for each direction, (3) combining the results of each sweep. The latter can optionally be performed using graph cuts. Additionally, by incorporating priors on the locations of planes in the scene, we can increase the quality of the reconstruction and reduce computation time, especially for uniform textureless surfaces. We demonstrate our algorithm on a variety of scenes and show the improved accuracy obtained by accounting for slanted surfaces.
Light fall-off stereo
Authors: Miao Liao, Liang Wang 0002, Ruigang Yang, Minglun Gong
Venue: Conference CVPR 2007
Year: 2007

We present light fall-off stereo-LFS-a new method for computing depth from scenes beyond lambertian reflectance and texture. LFS takes a number of images from a stationary camera as the illumination source moves away from the scene. Based on the inverse square law for light intensity, the ratio images are directly related to scene depth from the perspective of the light source. Using this as the invariant, we developed both local and global methods for depth recovery. Compared to previous reconstruction methods for non-lamebrain scenes, LFS needs as few as two images, does not require calibrated camera or light sources, or reference objects in the scene. We demonstrated the effectiveness of LFS with a variety of real-world scenes.
Graph cut based image segmentation with connectivity priors
Authors: Sara Vicente, Vladimir Kolmogorov, Carsten Rother
Venue: Conference CVPR 2008
Year: 2008

Graph cut is a popular technique for interactive image segmentation. However, it has certain shortcomings. In particular, graph cut has problems with segmenting thin elongated objects due to the ldquoshrinking biasrdquo. To overcome this problem, we propose to impose an additional connectivity prior, which is a very natural assumption about objects. We formulate several versions of the connectivity constraint and show that the corresponding optimization problems are all NP-hard. For some of these versions we propose two optimization algorithms: (i) a practical heuristic technique which we call DijkstraGC, and (ii) a slow method based on problem decomposition which provides a lower bound on the problem. We use the second technique to verify that for some practical examples DijkstraGC is able to find the global minimum.
Image hallucination with feature enhancement
Authors: Zhiwei Xiong, Xiaoyan Sun 0001, Feng Wu 0001
Venue: Conference CVPR 2009
Year: 2009

Example-based super-resolution recovers missing high frequencies in a magnified image by learning the correspondence between co-occurrence examples at two different resolution levels. As high-resolution examples usually contain more details and are of higher dimensionality in comparison with low-resolution ones, the mapping from low-resolution to high-resolution is an ill-posed problem. Rather than imposing more complicated mapping constraints, we propose to improve the mapping accuracy by enhancing low-resolution examples in terms of mapped features, e.g., derivatives and primitives. A feature enhancement method is presented through a combination of interpolation with prefiltering and non-blind sparse prior deblurring. By enhancing low-resolution examples, unique feature information carried by high-resolution examples is decreased. This regularization reduces the intrinsic dimensionality disparity between two different resolution examples and thus improves the feature mapping accuracy. Experiments demonstrate our super-resolution scheme with feature enhancement produces high quality results both perceptually and quantitatively.
Shared parts for deformable part-based models
Authors: Patrick Ott, Mark Everingham
Venue: Conference CVPR 2011
Year: 2011

The deformable part-based model (DPM) proposed by Felzenszwalb et al. has demonstrated state-of-the-art results in object localization. The model offers a high degree of learnt invariance by utilizing viewpoint-dependent mixture components and movable parts in each mixture component. One might hope to increase the accuracy of the DPM by increasing the number of mixture components and parts to give a more faithful model, but limited training data prevents this from being effective. We propose an extension to the DPM which allows for sharing of object part models among multiple mixture components as well as object classes. This results in more compact models and allows training examples to be shared by multiple components, ameliorating the effect of a limited size training set. We (i) reformulate the DPM to incorporate part sharing, and (ii) propose a novel energy function allowing for coupled training of mixture components and object classes. We report state-of-the-art results on the PASCAL VOC dataset.