OpenRaiser/Intern-Atlas huggingface dataset:
Subtly Different Facial Expression Recognition and Expression Intensity Estimation
Authors: James Jenn-Jier Lien, Takeo Kanade, Jeffrey F. Cohn, Ching-Chung Li
Venue: CVPR
Year: 1998

We have developed a computer vision system, including both facial feature extraction and recognition, that automatically discriminates among subtly different facial expressions. Expression classification is based on Facial Action Coding System (FACS) action units (AUs), and discrimination is performed using Hidden Markov Models (HMMs). Three methods are developed to extract facial expression information for automatic recognition. The first method is facial feature point tracking using a coarse-to-fine pyramid method. This method is sensitive to subtle feature motion and is capable of handling large displacements with sub-pixel accuracy. The second method is dense flow tracking together with principal component analysis (PCA) where the entire facial motion information per frame is compressed to a low-dimensional weight vector. The third method is high gradient component (i.e., furrow) analysis in the spatio-temporal domain, which exploits the transient variation associated with the facial expression. Upon extraction of the facial information, non-rigid facial expression is separated from the rigid head motion component, and the face images are automatically aligned and normalized using an affine transformation. This system also provides expression intensity estimation, which has significant effect on the actual meaning of the expression.
Probabilistic Reasoning Models for Face Recognition
Authors: Chengjun Liu, Harry Wechsler
Venue: CVPR
Year: 1998

We introduce in this paper two probabilistic reasoning models (PPM-1 and PRM-2) which combine the Principal Component Analysis (PCA) technique and the Bayes classifier and show their feasibility on the face recognition problem. The conditional probability density function for each class is modeled using the within class scatter and the Maximum A Posteriori (MAP) classification rule is implemented in the reduced PCA subspace. Experiments carried out using 1107 facial images corresponding to 369 subjects (with 169 subjects having duplicate images) from the FERET database show that the PRM approach compares favorably against the two well-known methods for face recognition-the Eigenfaces and Fisherfaces.
Creaseness Measures for CT and MR Image Registration
Authors: Antonio M. López 0001, Joan Serrat 0002
Venue: CVPR
Year: 1998

Creases are a type of ridge/valley structures that can be characterized by local conditions. Therefore, creaseness refers to local ridgeness and valleyness. The curvature K of the level curves and the mean curvature k/sub M/ of the level surfaces are good measures of creaseness for 2-d and 3-d images, respectively. However, the way they are computed gives rise to discontinuities, reducing their usefulness in many applications. We propose a new creaseness measure, based on these curvatures, that avoids the discontinuities. We demonstrate its usefulness in the registration of CT and MR brain volumes, from the same patient, by searching the maximum in the correlation of their creaseness responses (ridgeness from the CT and valleyness from the MR). Due to the high dimensionality of the space of transforms, the search is performed by a hierarchical approach combined with an optimization method at each level of the hierarchy.
Dense Shape and Motion from Region Correspondences by Factorization
Authors: Jianbo Ma, Narendra Ahuja
Venue: CVPR
Year: 1998

In this paper, we propose an algorithm for estimating dense shape and motion of dynamic piecewise planar scenes from region correspondences using factorization. Region correspondences are used since they are easier to establish and more reliable than either line or point correspondences. The image measurements required are the centroid and area for each region. Singular value decomposition is employed to find the basis of range space of the motion, shape, and surface normal matrices. By imposing model constraints, motion, shape, and surface normal can be recovered only from region correspondences.
Towards the Computational Perception of Action
Authors: Richard Mann, Allan D. Jepson
Venue: CVPR
Year: 1998

Understanding observations of interacting objects requires one to reason about qualitative scene dynamics. For example, on observing a hand lifting a can, we may infer that an 'active' hand is applying an upwards force (by grasping) to lift a 'passive' can. Previously we presented a system that infers qualitative scene dynamics from the instantaneous motion of objects. However; since that analysis only considered single frames in isolation, there were often multiple interpretations for each frame. In this work we show how the dynamic information inferred at each frame can be integrated over time to reduce ambiguity. Our approach to integrating information is to extend our representation to describe objects by a set of properties or capabilities that are assumed to persist over time. Given this extended representation we find interpretations that require the smallest set(s) of properties over the whole image sequence.
Soccer Image Sequence Computed by a Virtual Camera
Authors: Kentaro Matsui, Masaki Iwase, Masato Agata, Toshimitsu Tanaka, Noboru Ohnishi
Venue: CVPR
Year: 1998

To create a more realistic soccer game derived from TV images, we developed an image synthesis system that generates an image sequence from the viewpoint of a player on the field. This system is based on the camera calibration theory. The system first determines the camera parameters of a TV image by using the intersection points of the white lines drawn on the soccer field. It then extracts players from each image and estimates their positons in the world coordinate system. Finally it applies a running motion to the players in their respective positions and generates computer graphics animation from the viewpoint of any player selected by a user. The system was tested over seven sequences of TV images and demonstrated satisfactory results.
2D Fluid Motion Analysis from a Single Image
Authors: Mariette Maurizot, Patrick Bouthemy, Bernard Delyon
Venue: CVPR
Year: 1998

This paper is concerned with the analysis of 2D fluid motion from numerical images. The interpretation of such deformable flow fields can be derived from the characterization of linear motion models provided that first order approximations are considered in an adequate neighborhood of so-called singular points where the velocity becomes null. However, locating such points, delimiting this neighborhood, and estimating the associated 2D affine motion model, are intricate difficult problems. We explicitly address these three joint problems according to a statistical adaptive approach. In the fluid mechanics images we are dealing with, the motion model can be directly inferred from a single image, since the visualized form accounts for the underlying motion. We have developed an original method which relies on an orthogonality constraint between the spatial image gradient field and the motion model velocity field, while explicitly formalizing and handling both model and measurement noises. This method has been validated on several real fluid flow images.
Singularity Analysis for Articulated Object Tracking
Authors: Daniel D. Morris, James M. Rehg
Venue: CVPR
Year: 1998

We analyze the use of kinematic constraints for articulated object tracking. Conditions for the occurrence of singularities in 3-D models are presented and their effects on tracking are characterized We describe a novel 2-D Scaled Prismatic Model (SPM) for figure registration. In contrast to 3-D kinematic models, the SPM has fewer singularity problems and does not require detailed knowledge of the 3-D kinematics. We fully characterize the singularities in the SPM and illustrate tracking through singularities using synthetic and real examples with 3-D and 2-D models. Our results demonstrate the significant benefits of the SPM in tracking with a single source of video.
Edge/Region-Based Segmentation and Reconstruction of Underwater Acoustic Images by Markov Random Fields
Authors: Vittorio Murino, Andrea Trucco
Venue: CVPR
Year: 1998

This paper describes a technique for the reconstruction and segmentation of three-dimensional acoustical images using a coupled Random Fields able to actively integrate confidence information associated with acquired data. Beamforming, a method widely applied in acoustic imaging, is used to build a three-dimensional image, associated point by point with another kind of information representing the reliability (i.e. "confidence") of such an image. Unfortunately, this kind of images is plagued by several problems due to the nature of the signal and to the related sensing system, thus heavily affecting data quality. Specifically, speckle noise and the broad directivity characteristic of the sensor lead to very degraded images. In the proposed algorithm, range and confidence images are modelled as Markov Random Fields whose associated probability distributions are specified by a single energy functional. A three-fold process has been applied able to reconstruct, segment, and restore the involved acoustic images exploiting both types of data. Our approach showed better performances with respect to other MRF-based methods as well as classical methods disregarding reliability information. Optimal (in the Maximum A-Posteriori probability sense) estimates of the 3D and confidence images are obtained by minimizing the energy functional by using simulated annealing.
Weak Orthogonalization of Face and Perturbation for Recognition
Authors: Kenji Nagao, Masaki Sohma
Venue: CVPR
Year: 1998

This paper describes a new method for face recognition under drastic changes of the imaging processes through which the facial images are acquired. Unlike the conventional methods that use only the face features, the present method exploits the statistical information of the variations between the face image sets being compared, in addition to the features of the faces themselves. To incorporate both of the face and perturbation features for recognition, we develop a technique called weak orthogonalization. Of the two subspaces that transforms the given two overlapped subspaces so that the volume of the intersection of the resulting two subspaces is minimized. Matching operations are performed in the transformed face space that has thus been weakly orthogonalized against perturbation space. Experimental results on real pictures of the frontal faces from drivers' licenses show that the new algorithm improves the recognition performance over the conventional methods. We also demonstrate the effectiveness of our method on image sets with changes in viewing geometry.
Efficient Query Refinement for Image Retrieval
Authors: Chahab Nastar, Matthias Mitschke, Christophe Meilhac
Venue: CVPR
Year: 1998

Although powerful image representations have been proposed for content-based image retrieval, most of the current systems are "rigid", i.e. they retrieve a fixed set of images as response to a given query and an image feature. In this paper, our goal is to introduce tools for making image retrieval systems more flexible. More precisely, we use multiple image features, and present in details a new relevance feedback technique that integrates the positive and negative examples provided by the user. Experimental results on various large databases show that the proposed technique is more performant than the standard relevance feedback approach.
Exhaustive Detection of Manufacturing Flaws as Abnormalities
Authors: Van-Duc Nguyen, J. Alison Noble, Joseph L. Mundy, John Janning, Joseph Ross
Venue: CVPR
Year: 1998

Manufacturing flaws of all types, shapes, and sizes can be exhaustively detected as abnormal pixels, if process and noise variations can be learned at every pixel in the inspection area. This statistical template approach to automated visual inspection is extremely fast, effective, and flexible, while achieving false negative rate <10/sup -6/. Critical to this approach are the following novel features: 1) represent both geometry and process information in a model template; 2) align 3D surfaces with subpixel accuracy; compensate for local deformation and texture; 4) estimate bimodal distribution robustly. This novel paradigm was applied to the automatic screening of X-ray images of turbine blades. It has been validated with over 50,000 images and shown to outperform regular inspectors looking at high-pass filtered images.
Computing the Camera Heading from Multiple Frames
Authors: John Oliensis
Venue: CVPR
Year: 1998

We present a new algorithm for computing the heading from multiple frames which improves on previous approaches. It exploits our discovery and analysis of a new structure-from-motion ambiguity. Allowing for this ambiguity also makes two-frame reconstruction more robust. We show experimentally that the error landscape for planar scenes has few significant local minima.
Variable-Scale Smoothing and Edge Detection Guided by Stereoscopy
Authors: Clark F. Olson
Venue: CVPR
Year: 1998

It is typical in edge detection applications to examine a single scale or to consider some space of scales in the image without knowing which scale is appropriate for each location in the image. However, many images contain a wide variation in the distance to the scene points, and thus objects of the same size can appear at greatly differing scales in the image. We present a method where the scale of the smoothing and edge detection is varied locally according to the distance to the scene point, which we estimate through stereoscopy. The edges that are detected are thus at the same scale in the world, rather than at the same scale in the image. This method has been implemented efficiently by smoothing the image at a discrete set of scales and performing interpolation to estimate the response at the correct scale for each pixel. The application of this technique to an ordnance recognition problem has resulted in a considerable improvement in performance. 1 Introduction Image smoothing ...
Recognizing Abruptly Changing Facial Expressions from Time-Sequential Face Images
Authors: Takahiro Otsuka, Jun Ohya
Venue: CVPR
Year: 1998

This paper proposes a method that can spot and recognize each facial expression. From time-sequential images that contain multiple facial expressions that could abruptly change from one expression to another expression. Previously, the authors have proposed an HMM (Hidden Markov Models) based method for recognizing a spotted facial expression. In this paper to HMM, we add states corresponding to the simultaneous motion of two different facial expressions: i.e. a muscle relaxation for one expression and a muscle contraction for another expression. Then, the added states are each lined from the HMM apex state of one expression and are linked to that of another expression. Experimental results showed that for most pairs of expressions the change in expression can be recognized accurately. In addition, recognition rate for very fast change of expressions improved significantly. The proposed method was applied to regenerate facial expressions on a synthesized character to show the method's effectiveness in obtaining facial motion information.
Camera Calibration and Euclidean Reconstruction from Known Observer Translations
Authors: Tomás Pajdla, Václav Hlavác
Venue: CVPR
Year: 1998

We present a technique for camera calibration and Euclidean reconstruction from multiple images of the same scene. Unlike standard Tsai's camera calibration from a known scene, we exploited controlled known motions of the camera to obtain its calibration and Euclidean reconstruction without any knowledge about the scene. We consider three linearly independent translations of an uncalibrated camera mounted on a robot arm that provides us with four views of the scene. The translations of the robot arm are measured in a robot coordinate system. This special, but still realistic, arrangement allowed us to find a linear algorithm for recovering all intrinsic camera calibration parameters, the rotation of the camera with respect to the robot coordinate system, and proper scaling factors for all points allowing their Euclidean reconstruction. The experiments showed that an efficient and robust algorithm was obtained by exploiting Total Least Squares in combination with careful normalization of image coordinates.
A Content-Based Search Engine for VRML Databases
Authors: Eric Paquet, Marc Rioux
Venue: CVPR
Year: 1998

This paper presents a new approach for the classification and retrieval of three-dimensional images and models from databases. A set of retrieval algorithms is introduced. These algorithms are content-based, meaning that the input is, not made out of keywords but of three-dimensional models. Tensor of inertia, distribution of normals and distribution of cords are used to describe each model. The database can be searched by scale, shape or color or any combination of these parameters. A user friendly interface makes the retrieval operation simple and intuitive and allows to edit reference models according to the specifications of the user. Experimental results using a database of more than 400 range images and 1000 VRML models are presented.
Real-Time Tracking for Enhanced Tennis Broadcasts
Authors: Gopal Sarma Pingali, Yves Jean, Ingrid Carlbom
Venue: CVPR
Year: 1998

This paper develops real time tracking technology for sports broadcast applications. The specific sport chosen here is the game of tennis. The outputs of the tennis tracking system are spatio-temporal trajectories of motion of the players and the ball which can in turn provide a number of statistics about the game. For instance, the distance travelled by a player, the speed and the acceleration at any instant, as well as court coverage patterns can be obtained from the trajectories. The statistics so obtained can be visualized in compelling ways to enhance the appreciation of the athleticism and strategy involved in the sport. We present techniques for tracking the players and the ball in video obtained from stationary cameras. The problem is challenging as the tracking needs to be performed outdoors, players are fast-moving non-rigid objects, and the ball is a small object that can move at speeds in the range of 150 miles an hour. Player trajectories are obtained by dynamically clustering tracks of local features. Ball segmentation and tracking is based on shape and color features of the ball. Real time tracking results are presented on video recorded live by the authors in an international tennis tournament.
Joint Probabilistic Techniques for Tracking Multi-Part Objects
Authors: Christopher Rasmussen, Gregory D. Hager
Venue: CVPR
Year: 1998

Common objects such as people and cars comprise many visual parts and attributes, yet image-based tracking algorithms are often keyed to only one of a target's identifying characteristics. In this paper, we present a framework for combining and sharing information among several state estimation processes operating on the same underlying visual object. Well-known techniques for joint probabilistic data association are adapted to yield increased robustness when multiple trackers attuned to disparate visual cues are deployed simultaneously. We also formulate a measure of tracker confidence, based on distinctiveness and occlusion probability, which permits the deactivation of trackers before erroneous state estimates adversely affect the ensemble. We discuss experiments focusing on color-region- and snake-based tracking that demonstrate the efficacy of this approach.
Object Detection and Localization by Dynamic Template Warping
Authors: Aparna Lakshmi Ratan, W. Eric L. Grimson, William M. Wells III
Venue: CVPR
Year: 1998

A simple method is presented for detecting, localizing and recognizing classes of objects, that accommodates wide variation in an object's pose. The method utilizes a small two-dimensional template that is warped into an image, and converts localization to a one-dimensional sub-problem, with the search for a match between image and template executed by dynamic programming. The method recovers three of the six degrees of freedom of motion (2 translation, 1 rotation), and accommodates two more DOF in the search process (1 rotation, 1 translation), and is extensible to the final DOF. Experiments demonstrate that the method provides an efficient search strategy that outperforms normalized correlation. This is demonstrated in the example domain of face detection and localization, and is extended to more general detection tasks. An additional technique recovers a rough object pose from the match results, and is used in a two stage recognition experiment using maximization of mutual information.
A Verification Protocol and Statistical Performance Analysis for Face Recognition Algorithms
Authors: Syed A. Rizvi, P. Jonathon Phillips, Hyeonjoon Moon
Venue: CVPR
Year: 1998

Two key performance characterization of biometric algorithms (face recognition in particular) are (1) verification performance and (2) and performance as a function of database size and composition. This characterization is required for developing robust face recognition algorithms and for successfully transitioning algorithms from the laboratory to real world. In this paper we (1) present a general verification protocol and apply it to the results from the Sep96 FERET test, and (2) discuss and present results on the effects of database size and variability on identification and verification performance.
Exploitation of Natural Image Statistics by Biological Vision Systems: 1/f2 Power Spectra and Self-Similar Bandpass Decompositions
Authors: Florian Röhrbein, Christoph Zetzsche
Venue: CVPR
Year: 1998

The second-order statistics of natural images can be well characterized by a "self-similar" 1/F/sup 2/ power spectrum and the bandpass decomposition in biological vision systems is characterized by a self-similar, wavelet-like structuring of the "frequency channels". It has thus often been suggested that there might exist a systematic interrelationship between these two properties, but a complete formal derivation of this relation has not yet been provided. Using rate-distortion arguments and a complexity measure, we first show that a self-similar bandpass decomposition can achieve a desired level of distortion with a less complex system structure than required for a decomposition in bands of equal linear bandwidth. A closer analysis reveals that the true optimum decomposition is approximately self-similar but shows a systematic decrease of the log-bandwidths with increasing center frequency of the subbands. Since this effect has also been observed in neurophysiological experiments, we conclude that the typical properties of visual neurons may in fact result from an optimized exploitation of the statistical redundancies of the natural environment.
Varying Focal Length Self-Calibration and Pose Estimation from Two Images
Authors: Benny Rousso, Erez Shilat
Venue: CVPR
Year: 1998

This paper presents a self-calibration and pose estimation method that uses two cameras which only differ by focal length. The estimation of the rotation and focal lengths is independent of the translation recovery. Unlike most methods, we do not initialize our recovery with the projective camera. Instead we estimate the ego-motion and calibration from 3 homographies. These homographies can be easily obtained from a fundamental matrix or a trifocal tensor.