OpenRaiser/Intern-Atlas huggingface dataset:
Invariant Features of Local Texturesa rotation invariant local texture descriptor
Authors: Pranam Janney, Zhenghua Yu
Venue: CVPR
Year: 2007

In this paper, we present a new rotation-invariant texture descriptor algorithm called invariant features of local textures (IFLT). The proposed algorithm extracts rotation invariant features from a small neighbourhood of pixels around a centre pixel or a texture patch. Intensity vector which is derived from a texture patch is normalized and Haar wavelet filtered to derive rotation-invariant features. Texture classification experiments on the Brodatz album and Outex databases have shown that the proposed algorithm has a high rate of correct classification.
Scene-space Feature Detectors
Authors: Yves Jean
Venue: CVPR
Year: 2007

Derivative, spectral, and other operators implemented in filter kernels and applied with convolution have been well studied in image recognition problems. We explore in this paper a novel system that applies such filters in the actual 3D scene. The system projects images of filters into the scene as controlled illumination. The projected filters are subjected to object surface reflectance (BRDF) and scene composition; and the filter response is captured by camera pixels. Our approach utilizes a pixel-based convolution formulation, exploiting the intrinsic integration performed by individual pixel sensors and diffraction-limited optics. For example, we project a grid of Gaussian derivative filters as images and simply retrieve a filter response as a pixel value. This is a local (pixel-only) computation that is not limited by camera resolution. Our system can serve as a front-end to existing techniques for surface or geometry estimation from texture analysis. A projector and camera pair is the basic equipment requirement.
Using Genetic Algorithms to Improve Matching Performance of Changeable biometrics from Combining PCA and ICA Methods
Authors: MinYi Jeong, Jeung-Yoon Choi, Jaihie Kim
Venue: CVPR
Year: 2007

Biometrics is personal authentication which uses an individual's information. In terms of user authentication, biometric systems have many advantages. However, despite its advantages, they also have some disadvantages in the area of privacy problems. Changeable biometrics is solution to problem of privacy protection. In this paper we propose a changeable face biometrics system to overcome this problem. The proposed method uses the PCA and ICA methods and genetic algorithms. PCA and ICA coefficient vectors extracted from an input face image were normalized using their norm. The two normalized vectors were transformed using a weighting matrix which is derived using genetic algorithms and then scrambled randomly. A new transformed face coefficient vector was generated by addition of the two weighted normalized vectors. Through experiments, we see that we can achieve performance accuracy that is better than conventional methods. And, it is also shown that the changeable templates are non-invertible and provide sufficient reproducibility.
A Real-Time ProCam System for Interaction with Chinese Ink-and-Wash Cartoons
Authors: Ming Jin, Hui Zhang, Xubo Yang, Shuangjiu Xiao
Venue: CVPR
Year: 2007

This poster describes our recently developed real-time projector-camera system for interaction with Chinese Ink-and-Wash Cartoons. We implement a real-time interactive water simulation under the acceleration of GPU, together with a sensing subsystem using computer vision techniques. Combined with Chinese stylized fish rendered in process, the system provides real-time interactions with traditional Chinese paintings. By stirring up the still water, fish and other essential elements of Chinese paintings, we hope to present new interaction techniques and more lively Chinese painting sceneries compared to those in traditional static settings.
Real-Time Projector Tracking on Complex Geometry Using Ordinary Imagery
Authors: Tyler Johnson, Henry Fuchs
Venue: CVPR
Year: 2007

Using cameras to geometrically calibrate projector-based displays has been widely reported in the literature over the last decade. Most systems project structured-light patterns during a setup phase before starting the application in order to evaluate the geometric mapping from projector pixels to locations on the display surface. If this mapping changes e.g. the projector is moved, the application must be stopped and calibration re-initiated. Our pose estimation technique is based on detecting a set of image correspondences between the moving projector and a static camera using only the imagery displayed by the projector. We use an up-front calibration process to establish the geometry of the display surface, the pose of the camera, and the initial pose of the projector.
Real-Time Projector Tracking on Complex Geometry Using Ordinary Imagery
Authors: Tyler Johnson, Henry Fuchs
Venue: CVPR
Year: 2007

This DOI is not currently attached to any metadata records. DOIs can’t actually ever be deleted (they’re persistent), but sometimes our members create DOIs in error. We do have a process to approximate deletion which we follow only in rare cases where the DOI has been genuinely created in error, and most crucially, if the DOI has never been published anywhere online or in print and never otherwise distributed to or communicated with anyone (authors, readers, reviewers, etc.
Euclidean Path Modeling from Ground and Aerial Views
Authors: Imran N. Junejo, Hassan Foroosh
Venue: CVPR
Year: 2007

We address the issue of Euclidean path modeling in a single camera for activity monitoring in a multi-camera video surveillance system. The paper proposes a novel linear solution to auto-calibrate any camera observing pedestrians and uses these calibrated cameras to detect unusual object behavior. The input trajectories are metric rectified and the input sequences are registered to the satellite imagery and prototype path models are constructed. During the testing phase, using our simple yet efficient similarity measures, we seek a relation between the input trajectories derived from a sequence and the prototype path models. Real-world pedestrian sequences are used to demonstrate the practicality of the proposed method.
Robust Face Alignment for Illumination and Pose Invariant Face Recognition
Authors: Fatih Kahraman, Binnur Kurt, Muhittin Gökmen
Venue: CVPR
Year: 2007

In building a face recognition system for real-life scenarios, one usually faces the problem that is the selection of a feature-space and preprocessing methods such as alignment under varying illumination conditions and poses. In this study, we developed a robust face alignment approach based on Active Appearance Model (AAM) by inserting an illumination normalization module into the standard AAM searching procedure and inserting different poses of the same identity into the training set. The modified AAM search can now handle both illumination and pose variations in the same epoch, hence it provides better convergence in both point-to-point and point-to-curve senses. We also investigate how face recognition performance is affected by the selection of feature space as well as the proposed alignment method. The experimental results show that the combined pose alignment and illumination normalization methods increase the recognition rates considerably for all feature-spaces.
Sensor and Data Systems, Audio-Assisted Cameras and Acoustic Doppler Sensors
Authors: Kaustubh Kalgaonkar, Paris Smaragdis, Bhiksha Raj
Venue: CVPR
Year: 2007

In this chapter we present two technologies for sensing and surveillance -audio-assisted cameras and acoustic Doppler sensors for gait recognition.
Quasi-Dense Wide Baseline Matching Using Match Propagation
Authors: Juho Kannala, Sami S. Brandt
Venue: CVPR
Year: 2007

In this paper we propose extensions to the match propagation algorithm which is a technique for computing quasi-dense point correspondences between two views. The extensions make the match propagation applicable for wide baseline matching, i.e., for cases where the camera pose can vary a lot between the views. Our first extension is to use a local affine model for the geometric transformation between the images. The estimate of the local transformation is obtained from affine covariant interest regions which are used as seed matches. The second extension is to use the second order intensity moments to adapt the current estimate of the local affine transformation during the propagation. This allows a single seed match to propagate into regions where the local transformation between the views differs from the initial one. The experiments with real data show that the proposed techniques improve both the quality and coverage of the quasi-dense disparity map.
Robust Bayesian Estimation and Normalized Convolution for Super-resolution Image Reconstruction
Authors: Antonis Katartzis, Maria Petrou
Venue: CVPR
Year: 2007

We investigate new ways of improving the performance of Bayesian-based super-resolution image reconstruction by using a discontinuity adaptive image prior distribution based on robust statistics and a fast and efficient way of initializing the optimization process. The latter is an adapted normalized convolution (NC) technique that incorporates the uncertainty induced by registration errors. We present both qualitative and quantitative results on real video sequences and demonstrate the advantages of the proposed method compared to conventional methodologies.
Spatio-temporal Shape and Flow Correlation for Action Recognition
Authors: Yan Ke, Rahul Sukthankar, Martial Hebert
Venue: CVPR
Year: 2007

This paper explores the use of volumetric features for action recognition. First, we propose a novel method to correlate spatio-temporal shapes to video clips that have been automatically segmented. Our method works on over-segmented videos, which means that we do not require background subtraction for reliable object segmentation. Next, we discuss and demonstrate the complementary nature of shape- and flow-based features for action recognition. Our method, when combined with a recent flow-based correlation technique, can detect a wide range of actions in video, as demonstrated by results on a long tennis video. Although not specifically designed for whole-video classification, we also show that our method's performance is competitive with current action classification techniques on a standard video classification dataset.
Keypoint Descriptors for Matching Across Multiple Image Modalities and Non-linear Intensity Variations
Authors: Avi Kelman, Michal Sofka, Charles V. Stewart
Venue: CVPR
Year: 2007

In this paper, we investigate the effect of substantial inter-image intensity changes and changes in modality on the performance of keypoint detection, description, and matching algorithms in the context of image registration. In doing so, we modify widely-used keypoint descriptors such as SIFT and shape contexts, attempting to capture the insight that some structural information is indeed preserved between images despite dramatic appearance changes. These extensions include (a) pairing opposite-direction gradients in the formation of orientation histograms and (b) focusing on edge structures only. We also compare the stability of MSER, Laplacian-of-Gaussian, and Harris corner keypoint location detection and the impact of detection errors on matching results. Our experiments on multimodal image pairs and on image pairs with significant intensity differences show that indexing based on our modified descriptors produces more correct matches on difficult pairs than current techniques at the cost of a small decrease in performance on easier pairs. This extends the applicability of image registration algorithms such as the Dual-Bootstrap which rely on correctly matching only a small number of keypoints.
Manifold Learning Techniques in Image Analysis of High-dimensional Diffusion Tensor Magnetic Resonance Images
Authors: Parmeshwar Khurd, Sajjad Baloch, Ruben C. Gur, Christos Davatzikos, Ragini Verma
Venue: CVPR
Year: 2007

Diffusion tensor magnetic resonance imaging (DT-MRI) provides a comprehensive characterization of white matter (WM) in the brain and therefore, plays a crucial role in the investigation of diseases in which WM is suspected to be compromised such as multiple sclerosis and neuropsychiatric disorders like schizophrenia. However changes induced by pathology may be subtle and affected regions of the brain can only be revealed by a group-based analysis of patients in comparison with healthy controls. This in turn requires voxel-based statistical analysis of spatially normalized brain DT images, as in the case of conventional MR images. However this process is rendered extremely challenging in DT-MRI due to the high dimensionality of the data and its inherent non-linearity that causes linear component analysis methods to be inapplicable. We therefore propose a novel framework for the statistical analysis of DT-MRI data using manifold-based techniques such as isomap and kernel PCA that determine the underlying manifold structure of the data, embed it to a manifold and help perform high dimensional statistics on the manifold to determine regions of difference between the groups of patients and controls. The framework has been successfully applied to DT-MRI data from patients with schizophrenia, as well as to study developmental changes in small animals, both of which identify regional changes, indicating the need for manifold-based methods for the statistical analysis of DTI.
An Embodied User Interface for Increasing Physical Activities in Game
Authors: Si-Jung Kim, Woodrow W. Winchester III, Yun-Bum Choi, Juck-Sik Lee
Venue: CVPR
Year: 2007

The rate of obese has been increasing and obesity has emerged as a significant threat not only to the health but also in society. Obesity has adverse effects such as physical appearance, psychosocial consequences and metabolic disturbances. One of reasons causing these phenomena is most games have static and stationary user interfaces as input devices. These kinds of interfaces hold users at their computers and cause not only decreases of the strength of their health, but also blocks communications between family members. In this paper, we propose physical activity based interactive exercise called Punch Punch, which is played with virtual objects displaying on a large screen. The informal study revealed that the Punch Punch enhanced physical and social activities while playing games. The goal of this study is finding embodied user interfaces to increase physical and social activities.
Incremental Linear Discriminant Analysis Using Sufficient Spanning Set Approximations
Authors: Tae-Kyun Kim 0001, Shu-Fai Wong, Björn Stenger, Josef Kittler, Roberto Cipolla
Venue: CVPR
Year: 2007

This paper presents a new incremental learning solution for linear discriminant analysis (LDA). We apply the concept of the sufficient spanning set approximation in each update step, i.e. for the between-class scatter matrix, the projected data matrix as well as the total scatter matrix. The algorithm yields a more general and efficient solution to incremental LDA than previous methods. It also significantly reduces the computational complexity while providing a solution which closely agrees with the batch LDA result. The proposed algorithm has a time complexity of O(Nd <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and requires O(Nd) space, where d is the reduced subspace dimension and N the data dimension. We show two applications of incremental LDA: First, the method is applied to semi-supervised learning by integrating it into an EM framework. Secondly, we apply it to the task of merging large databases which were collected during MPEG standardization for face image retrieval.
Projector Calibration using Arbitrary Planes and Calibrated Camera
Authors: Makoto Kimura, Masaaki Mochimaru, Takeo Kanade
Venue: CVPR
Year: 2007

In this paper, an easy calibration method for projector is proposed. The calibration handled in this paper is projective relation between 3D space and 2D pattern, and is not correction of trapezoid distortion in projected pattern. In projector-camera systems, especially for 3D measurement, such calibration is the basis of process. The projection from projector can be modeled as inverse projection of the pinhole camera, which is generally considered as perspective projection. In the existing systems, some special objects or devices are often used to calibrate projector, so that 3D-2D projection map can be measured for typical camera calibration methods. The proposed method utilizes projective geometry between camera and projector, so that it requires only pre-calibrated camera and a plane. It is easy to practice, easy to calculate, and reasonably accurate.
A Robust Warping Method for Fingerprint Matching
Authors: Dongjin Kwon, Il Dong Yun, Sang Uk Lee
Venue: CVPR
Year: 2007

This paper presents a robust warping method for minutiae based fingerprint matching approaches. In this method, a deformable fingerprint surface is described using a triangular mesh model. For given two extracted minutiae sets and their correspondences, the proposed method constructs an energy function using a robust correspondence energy estimator and smoothness measuring of the mesh model. We obtain a convergent deformation pattern using an efficient gradient based energy optimization method. This energy optimization approach deals successfully with deformation errors caused by outliers, which are more difficult problems for the thin-plate spline (TPS) model. The proposed method is fast and the run-time performance is comparable with the method based on the TPS model. In the experiments, we provide a visual inspection of warping results on given correspondences and quantitative results using database.
Towards Automatic Photometric Correction of Casually Illuminated Documents
Authors: George V. Landon, Yun Lin 0011, W. Brent Seales
Venue: CVPR
Year: 2007

Creating uniform lighting for archival-quality document acquisition remains a non-trivial problem. We propose a novel method for automatic photometric correction of non-planar documents by estimating a single, point light-source using a simple light probe. By adding a simple piece of folded white paper with a known 3D surface to a scene, we are able to extract the 3D position of a light source, automatically perform white balance correction, and determine areas of poor illumination. Furthermore, this method is designed with the purpose of adding it to an already implemented document digitization pipeline. To justify our claims, we provide an accuracy analysis of our correction technique using simulated ground-truth data which allows individual sources of error to be determined and compared. These techniques are then applied on real documents that have been acquired using a 3D scanner.
Hybrid learning of large jigsaws
Authors: Julia A. Lasserre, Anitha Kannan, John M. Winn
Venue: CVPR
Year: 2007

A jigsaw is a recently proposed generative model that describes an image as a composition of non-overlapping patches of varying shape, extracted from a latent image. By learning the latent jigsaw image which best explains a set of images, it is possible to discover the shape, size and appearance of repeated structures in the images. A challenge when learning this model is the very large space of possible jigsaw pixels which can potentially be used to explain each image pixel. The previous method of inference for this model scales linearly with the number of jigsaw pixels, making it unusable for learning the large jigsaws needed for many practical applications. In this paper, we make three contributions that enable the learning of large jigsaws -a novel sparse belief propagation algorithm, a hybrid method which significantly improves the sparseness of this algorithm, and a method that uses these techniques to make learning of large jigsaws feasible. We provide detailed analysis of how our hybrid inference method leads to significant savings in memory and computation time. To demonstrate the success of our method, we present experimental results applying large jigsaws to an object recognition task.
Dynamic 3D Scene Analysis from a Moving Vehicle
Authors: Bastian Leibe, Nico Cornelis, Kurt Cornelis, Luc Van Gool
Venue: CVPR
Year: 2007

In this paper, we present a system that integrates fully automatic scene geometry estimation, 2D object detection, 3D localization, trajectory estimation, and tracking for dynamic scene interpretation from a moving vehicle. Our sole input are two video streams from a calibrated stereo rig on top of a car. From these streams, we estimate structure-from-motion (SfM) and scene geometry in real-time. In parallel, we perform multi-view/multi-category object recognition to detect cars and pedestrians in both camera images. Using the SfM self-localization, 2D object detections are converted to 3D observations, which are accumulated in a world coordinate frame. A subsequent tracking module analyzes the resulting 3D observations to find physically plausible spacetime trajectories. Finally, a global optimization criterion takes object-object interactions into account to arrive at accurate 3D localization and trajectory estimates for both cars and pedestrians. We demonstrate the performance of our integrated system on challenging real-world data showing car passages through crowded city areas.
The Hyperbolic Geometry of Illumination-Induced Chromaticity Changes
Authors: Reiner Lenz, Pedro Latorre-Carmona, Peter Meer
Venue: CVPR
Year: 2007

The non-negativity of color signals implies that they span a conical space with a hyperbolic geometry. We use perspective projections to separate intensity from chromaticity, and for 3-D color descriptors the chromatic properties are represented by points on the unit disk. Descriptors derived from the same object point but under different imaging conditions can be joined by a hyperbolic geodesic. The properties of this model are investigated using multichannel images of natural scenes and black body illuminants of different temperatures. We show, over a series of static scenes with different illuminants, how illumination changes influence the hyperbolic distances and the geodesics. Descriptors derived from conventional RGB images are also addressed.
Beyond Local Appearance: Category Recognition from Pairwise Interactions of Simple Features
Authors: Marius Leordeanu, Martial Hebert, Rahul Sukthankar
Venue: CVPR
Year: 2007

We present a discriminative shape-based algorithm for object category localization and recognition. Our method learns object models in a weakly-supervised fashion, without requiring the specification of object locations nor pixel masks in the training data. We represent object models as cliques of fully-interconnected parts, exploiting only the pairwise geometric relationships between them. The use of pairwise relationships enables our algorithm to successfully overcome several problems that are common to previously-published methods. Even though our algorithm can easily incorporate local appearance information from richer features, we purposefully do not use them in order to demonstrate that simple geometric relationships can match (or exceed) the performance of state-of-the-art object recognition algorithms.