Showing papers by "Takeo Kanade published in 2000"

Comprehensive database for facial expression analysis

Abstract: Within the past decade, significant effort has occurred in developing methods of facial expression analysis. Because most investigators have used relatively limited data sets, the generalizability of these various methods remains unknown. We describe the problem space for facial expression analysis, which includes level of description, transitions among expressions, eliciting conditions, reliability and validity of training and test data, individual differences in subjects, head orientation and scene complexity image characteristics, and relation to non-verbal behavior. We then present the CMU-Pittsburgh AU-Coded Face Expression Image Database, which currently includes 2105 digitized image sequences from 182 adult subjects of varying ethnicity, performing multiple tokens of most primary FACS action units. This database is the most comprehensive testbed to date for comparative studies of facial expression analysis.

A System for Video Surveillance and Monitoring

Abstract: Under the three-year Video Surveillance and Monitoring (VSAM) project (1997‐1999), the Robotics Institute at Carnegie Mellon University (CMU) and the Sarnoff Corporation developed a system for autonomous Video Surveillance and Monitoring. The technical approach uses multiple, cooperative video sensors to provide continuous coverage of people and vehicles in a cluttered environment. This final report presents an overview of the system, and of the technical accomplishments that have been achieved.

A statistical method for 3D object detection applied to faces and cars

Abstract: In this paper, we describe a statistical method for 3D object detection. We represent the statistics of both object appearance and "non-object" appearance using a product of histograms. Each histogram represents the joint statistics of a subset of wavelet coefficients and their position on the object. Our approach is to use many such histograms representing a wide variety of visual attributes. Using this method, we have developed the first algorithm that can reliably detect human faces with out-of-plane rotation and the first algorithm that can reliably detect passenger cars over a wide range of viewpoints.

Limits on super-resolution and how to break them

Abstract: We analyze the super-resolution reconstruction constraints. In particular we derive a sequence of results which all show that the constraints provide far less useful information as the magnification factor increases. It is well established that the use of a smoothness prior may help somewhat, however for large enough magnification factors any smoothness prior leads to overly smooth results. We therefore propose an algorithm that learns recognition-based priors for specific classes of scenes, the use of which gives far better super-resolution results for both faces and text.

Hallucinating faces

Abstract: Faces often appear very small in surveillance imagery because of the wide fields of view that are typically used and the relatively large distance between the cameras and the scene. For tasks such as face recognition, resolution enhancement techniques are therefore generally needed. Although numerous resolution enhancement algorithms have been proposed in the literature, most of them are limited by the fact that they make weak, if any, assumptions about the scene. We propose an algorithm to learn a prior on the spatial distribution of the image gradient for frontal images of faces. We proceed to show how such a prior can be incorporated into a resolution enhancement algorithm to yield 4- to 8-fold improvements in resolution (i.e., 16 to 64 times as many pixels). The additional pixels are, in effect, hallucinated.

A cooperative algorithm for stereo matching and occlusion detection

Abstract: Presents a stereo algorithm for obtaining disparity maps with occlusion explicitly detected. To produce smooth and detailed disparity maps, two assumptions that were originally proposed by Marr and Poggio (1976, 1979) are adopted: uniqueness and continuity. That is, the disparity maps have a unique value per pixel and are continuous almost everywhere. These assumptions are enforced within a three-dimensional array of match values in disparity space. Each match value corresponds to a pixel in an image and a disparity relative to another image. An iterative algorithm updates the match values by diffusing support among neighboring values and inhibiting others along similar lines of sight. By applying the uniqueness assumption, occluded regions can be explicitly identified. To demonstrate the effectiveness of the algorithm, we present the processing results from synthetic and real image pairs, including ones with ground-truth values for quantitative comparison with other methods.

Introduction to the special section on video surveillance

Abstract: UTOMATED video surveillance addresses real-time observation of people and vehicles within a busy environment, leading to a description of their actions and interactions. The technical issues include moving object detection and tracking, object classification, human motion analysis, and activity understanding, touching on many of the core topics of computer vision, pattern analysis, and aritificial intelligence. Video surveillance has spawned large research projects in the United States, Europe, and Japan, and has been the topic of several international conferences and workshops in recent years. There are immediate needs for automated surveillance systems in commercial, law enforcement, and military applications. Mounting video cameras is cheap, but finding available human resources to observe the output is expensive. Although surveillance cameras are already prevalent in banks, stores, and parking lots, video data currently is used only “after the fact” as a forensic tool, thus losing its primary benefit as an active, real-time medium. What is needed is continuous 24-hour monitoring of surveillance video to alert security officers to a burglary in progress or to a suspicious individual loitering in the parking lot, while there is still time to prevent the crime. In addition to the obvious security applications, video surveillance technology has been proposed to measure traffic flow, detect accidents on highways, monitor pedestrian congestion in public spaces, compile consumer demographics in shopping malls and amusement parks, log routine maintainence tasks at nuclear facilities, and count endangered species. The numerous military applications include patrolling national borders, measuring the flow of refugees in troubled areas, monitoring peace treaties, and providing secure perimeters around bases and embassies. The 11 papers in this special section illustrate topics and techniques at the forefront of video surveillance research. These papers can be loosely organized into three categories. Detection and tracking involves real-time extraction of moving objects from video and continuous tracking over time to form persistent object trajectories. C. Stauffer and W.E.L. Grimson introduce unsupervised statistical learning techniques to cluster object trajectories produced by adaptive background subtraction into descriptions of normal scene activity. Viewpoint-specific trajectory descriptions from multiple cameras are combined into a common scene coordinate system using a calibration technique described by L. Lee, R. Romano, and G. Stein, who automatically determine the relative exterior orientation of overlapping camera views by observing a sparse set of moving objects on flat terrain. Two papers address the accumulation of noisy motion evidence over time. R. Pless, T. Brodský, and Y. Aloimonos detect and track small objects in aerial video sequences by first compensating for the self-motion of the aircraft, then accumulating residual normal flow to acquire evidence of independent object motion. L. Wixson notes that motion in the image does not always signify purposeful travel by an independently moving object (examples of such “motion clutter” are wind-blown tree branches and sun reflections off rippling water) and devises a flow-based salience measure to highlight objects that tend to move in a consistent direction over time. Human motion analysis is concerned with detecting periodic motion signifying a human gait and acquiring descriptions of human body pose over time. R. Cutler and L.S. Davis plot an object’s self-similarity across all pairs of frames to form distinctive patterns that classify bipedal, quadripedal, and rigid object motion. Y. Ricquebourg and P. Bouthemy track apparent contours in XT slices of an XYT sequence volume to robustly delineate and track articulated human body structure. I. Haritaoglu, D. Harwood, and L.S. Davis present W4, a surveillance system specialized to the task of looking at people. The W4 system can locate people and segment their body parts, build simple appearance models for tracking, disambiguate between and separately track multiple individuals in a group, and detect carried objects such as boxes and backpacks. Activity analysis deals with parsing temporal sequences of object observations to produce high-level descriptions of agent actions and multiagent interactions. In our opinion, this will be the most important area of future research in video surveillance. N.M. Oliver, B. Rosario, and A.P. Pentland introduce Coupled Hidden Markov Models (CHMMs) to detect and classify interactions consisting of two interleaved agent action streams and present a training method based on synthetic agents to address the problem of parameter estimation from limited real-world training examples. M. Brand and V. Kettnaker present an entropyminimization approach to estimating HMM topology and

A real time system for robust 3D voxel reconstruction of human motions

Abstract: We present a multi-PC/camera system that can perform 3D reconstruction and ellipsoid fitting of moving humans in real time. The system consists of five cameras. Each camera is connected to a PC which locally extracts the silhouettes of the moving person in the image captured by the camera. The five silhouette images are then sent, via local network, to a host computer to perform 3D voxel-based reconstruction by an algorithm called SPOT. Ellipsoids are then used to fit the reconstructed data. By using a simple and user-friendly interface, the user can display and observe, in real time and from any view-point, the 3D models of the moving human body. With a rate of higher than 15 frames per second, the system is able to capture non-intrusively, a sequence of human motions.

A statistical approach to 3d object detection applied to faces and cars

Abstract: In this thesis, we describe a statistical method for 3D object detection. In this method, we decompose the 3D geometry of each object into a small number of viewpoints. For each viewpoint, we construct a decision rule that determines if the object is present at that specific orientation. Each decision rule uses the statistics of both object appearance and “non-object” visual appearance. We represent each set of statistics using a product of histograms. Each histogram represents the joint statistics of a subset of wavelet coefficients and their position on the object. Our approach is to use many such histograms representing a wide variety of visual attributes. Using this method, we have developed the first algorithm that can reliably detect faces that vary from frontal view to full profile view and the first algorithm that can reliably detect cars over a wide range of viewpoints.

Dual-state parametric eye tracking

Abstract: Most eye trackers work well for open eyes. However blinking is a physiological necessity for humans. More over, for applications such as facial expression analysis and driver awareness systems, we need to do more than tracking of the locations of the person's eyes but obtain their detailed description. We need to recover the state of the eyes (i.e., whether they are open or closed), and the parameters of an eye model (e.g., the location and radius of the iris, and the corners and height of the eye opening). We develop a dual-state model-based system for tracking eye features that uses convergent tracking techniques and show how it can be used to detect whether the eyes are open or closed, and to recover the parameters of the eye model. Processing speed on a Pentium II 400 MHz PC is approximately 3 frames/second. In experimental tests on 500 image sequences from child and adult subjects with varying colors of skin and eye, accurate tracking results are obtained in 98% of image sequences.

Modifying Soft Tissue Models: Progressive Cutting with Minimal New Element Creation

Abstract: Surgical simulation is a promising technology for training medical students and planning procedures One major requirement for these simulation systems is a method to generate realistic cuts through soft tissue models This paper describes methods for cutting through tetrahedral models of soft tissue The cutting surface follows the free form path of the user’s motion, and generates a minimal set of new elements to replace intersected tetrahedra Intersected elements are progressively cut to minimize the lag between the user’s motion and model modification A linear finite element model is used to model deformation of the soft tissue These cutting techniques coupled with a physically based deformation model increases the accuracy and applicability of a surgical simulation system

Shape and motion carving in 6D

Abstract: The motion of a non-rigid scene over time imposes more constraints on its structure than those derived from images at a single time instant alone. An algorithm is presented for simultaneously recovering dense scene shape and scene flow (i.e. the instantaneous 3D motion at every point in the scene). The algorithm operates by carving away hexels, or points in the 6D space of all possible shapes and flows that are inconsistent with the images captures at either time instant, or across time. The recovered shape is demonstrated to be more accurate than that recovered using images at a single time instant. Applications of the combined scene shape and flow include motion capture for animation, retiming of videos, and non-rigid motion analysis.

Image-consistent surface triangulation

Abstract: Given a set of 3D points that we know lie on the surface of an object, we can define many possible surfaces that pass through all of these points. Even when we consider only surface triangulations, there are still an exponential number of valid triangulations that all fit the data. Each triangulation will produce a different faceted surface connecting the points. Our goal is to overcome this ambiguity and find the particular surface that is closest to the true object surface. We do not know the true surface but instead we assume that we have a set of images of the object. We propose selecting a triangulation based on its consistency with this set of images of the object. We present an algorithm that starts with an initial rough triangulation and refines the triangulation until it obtains a surface that best accounts for the images of the object. Our method is thus able to overcome the surface ambiguity problem and at the same time capture sharp corners and handle concave regions and occlusions. We show results for a few real objects.

Recognizing lower face action units for facial expression analysis

Abstract: Most automatic expression analysis systems attempt to recognize a small set of prototypic expressions (e.g., happiness and anger). Such prototypic expressions, however, occur infrequently. Human emotions and intentions are communicated more often by changes in one or two discrete facial features. We develop an automatic system to analyze subtle changes in facial expressions based on both permanent (e.g., mouth, eye, and brow) and transient (e.g., furrows and wrinkles) facial features in a nearly frontal image sequence. Multi-state facial component models are proposed for tracking and modeling different facial features. Based on these multi-state models, and without artificial enhancement, we detect and track the facial features, including mouth, eyes, brow, cheeks, and their related wrinkles and facial furrows. Moreover we recover detailed parametric descriptions of the facial features. With these features as the inputs, 11 individual action units or action unit combinations are recognized by a neural network algorithm. A recognition rate of 96.7% is obtained. The recognition results indicate that our system can identify action units regardless of whether they occur singly or in combinations.

Reconstruction of a scene with multiple linearly moving objects

Abstract: We describe an algorithm for reconstructing a scene containing multiple moving objects. Given a monocular image sequence, we recover the scene structure, the trajectories of the moving objects and the camera motion simultaneously. The number of the moving objects is automatically detected without prior motion segmentation. Assuming that the objects are moving linearly with constant speeds, we propose a unified geometrical representation of the static scene and the moving objects. This representation enables the embedding of the motion constraints into the scene structure, which leads to a factorization-based algorithm. Experimental results on synthetic and real images are presented.

Image Registration Using Wavelet-Based Motion Model

Abstract: An image registration algorithm is developed to estimate dense motion vectors between two images using the coarse-to-fine wavelet-based motion model. This motion model is described by a linear combination of hierarchical basis functions proposed by Cai and Wang (SIAM Numer. Anal., 33(3):937–970, 1996). The coarser-scale basis function has larger support while the finer-scale basis function has smaller support. With these variable supports in full resolution, the basis functions serve as large-to-small windows so that the global and local information can be incorporated concurrently for image matching, especially for recovering motion vectors containing large displacements. To evaluate the accuracy of the wavelet-based method, two sets of test images were experimented using both the wavelet-based method and a leading pyramid spline-based method by Szeliski et al. (International Journal of Computer Vision, 22(3):199–218, 1996). One set of test images, taken from Barron et al. (International Journal of Computer Vision, 12:43–77, 1994), contains small displacements. The other set exhibits low texture or spatial aliasing after image blurring and contains large displacements. The experimental results showed that our wavelet-based method produced better motion estimates with error distributions having a smaller mean and smaller standard deviation.

Attitude control optimization for a small-scale unmanned helicopter

Abstract: This paper presents results from the attitude control optimization for a small-scale helicopter by using an identified model of the vehicle dynamics that explicitly accounts for the coupled rotor/stabilizer/fuselage (r/s/f) dynamics. The accuracy of the model is verified by showing that it successfully predicts the performance of the control system currently used for Carnegie Mellon's autonomous helicopter (baseline controller). Elementary stability analysis shows that the light damping in the coupled r/s/f mode, which is due to the stabilizer bar, limits the performance of the baseline control system. This limitation is compensated by a second order notch filter. The control system is subsequently optimized using the CONDUIT control design framework with a frequency response envelope specification, which allows the attitude control performance to be accurately specified while insuring that the lightly damped r/s/f mode is adequately compensated.

Creating 3D models with uncalibrated cameras

Abstract: We describe a factorization-based method to recover 3D models from multiple perspective views with uncalibrated cameras. The method first performs a projective reconstruction using a bilinear factorization algorithm, and then converts the projective solution to a Euclidean one by enforcing metric constraints. We present three factorization-based normalization algorithms to generate the Euclidean reconstruction and the intrinsic parameters, assuming zero skews. The first two algorithms are linear, one for dealing with the case that only the focal lengths are unknown, and another for the case that the focal lengths and the constant principal point are unknown. The third algorithm is bilinear dealing with the case that the focal lengths, the principal points and the aspect ratios are all unknown. We present the results of applying this method to building modeling, terrain recovery and multi-camera calibration.

Eye-State Action Unit Detection by Gabor Wavelets

Abstract: Eyes play important roles in emotion and paralinguistic communications. Detection of eye state is necessaryfor applications such as driver awareness systems. In this paper, we develop an automatic system to detect eye-state action units (AU) based on Facial Action Coding System (FACS) by use of Gabor wavelets in a nearly frontal-viewed image sequence. Three eye-state AU (AU 41, AU42, and AU43) are detected. After tracking the eye corners in the whole sequence, the eye appearance information is extracted at three points of each eye (i.e., inner corner, outer corner, and the point between the inner corner and the outer corner) as a set of multi-scale and multi-orientation Gabor coefficients. Then, the normalized Gabor coefficients are fed into a neural-network-based eye-state AU detector. An average recognition rate of 83% is obtained for 112 images from 17 image sequences of 12 subjects.

Transgraph: interactive intensity-based 2D/3D registration of X-ray and CT data

Abstract: This paper presents work towards a system for intra-operative registration of 3D CT data to 2D X-ray radiographs. The registration procedure involves iterative comparison of Digitally Reconstructed Radiographs (DRRs) with X-ray images acquired during surgery. A new data structure called a Transgraph permits rapid generation of DRRS, and greatly speeds up the registration process. The underlying data structures are described, and the registration algorithm is evaluated for application to an existing image guided radiosurgery system.

Post-Operative Measurement of Acetabular Cup Position Using X-Ray/CT Registration

Abstract: This paper describes a system for measuring acetabular implant orientation following total hip replacement surgery. After a manual initialization procedure, the position of the pelvis is established relative to a pair of nearly orthogonal radiographs by automatically registering to pre-operative pelvic CT data. The pose of the cup is then recovered by projecting a 3D surface model into the two images. A phantom study is presented in which this pose is expressed relative to well defined anatomical landmarks and compared to measurements obtained using an image-guided surgery system.

A histogram-based method for detection of faces and cars

Abstract: We describe a statistical method for 3D object detection. We represent the statistics of both object appearance and "non-object" appearance using a product of histograms. Each histogram represents the joint statistics of a subset of wavelet coefficients and their position on the object. Our approach is to use many such histograms representing a wide variety of visual attributes. Using this method, we have developed the first algorithm that can reliably detect human faces that vary from frontal view to full profile view and the first algorithm that can reliably detect cars over a wide range of viewpoints.

Reconstructing specimens using DIC microscope images

Abstract: Differential Interference Contrast (DIC) microscopy is a powerful visualization tool used to study live biological cells. Its use, however, has been limited to qualitative observations. The inherent nonlinear relationship between the object properties and the image intensity makes quantitative analysis difficult. Towards quantitatively measuring optical properties of objects from DIC images, the authors develop a method to reconstruct the specimen's optical properties over a three-dimensional volume. The method is a nonlinear optimization which uses hierarchical representations of the specimen and data. As a necessary tool, the authors have developed and validated a computational model for the DIC image formation process. They test their algorithm by reconstructing the optical properties of known specimens.

Precision 3-D Modeling for Autonomous Helicopter Flight

Abstract: We present our primary research goals, which motivate the need for 3-D modeling as a key element for helicopter state estimation and situational awareness. We present a research system architecture, which integrates 3-D modeling and other on-board sensing to isolate the issues and requirements for our on-going research. Finally, we conclude by presenting preliminary modeling results and future research plans.

Appearance-Based Virtual-View Generation for Fly Through in a Real Dynamic Scene

Abstract: We present appearance-based Virtual view generation which allows viewers to fly through a real dynamic scene. The scene is captured by synchronized multiple cameras. Arbitrary views are generated by interpolating two original camera-view images near the given viewpoint. The quality of the generated synthetic view is determined by the precision, consistency and density of correspondences between the two images. All or most of previous work that uses interpolation extracts the correspondences from these two images. However, not only is it difficult to do so reliably (the task requires a good stereo algorithm), but also the two images alone sometimes do not have enough information, due to problems such as occlusion. Instead, we take advantage of the fact that we have many views, from which we can extract much more reliable and comprehensive 3D geometry of the scene as a 3D model. The dense and precise correspondences between the two images, to be used for interpolation, are derived from this constructed 3D model. Our method of 3D modeling from multiple images uses the Multiple Baseline Stereo method and Shape from Silhouette method.

Bootstrap a statistical brain atlas

Abstract: Registration of medical images enables quantitative study of anatomical differences between populations, as well as detection of abnormal variations indicative of pathologies. However inherent anatomical variabilities between individuals and possible pathologies make registration difficult. This paper presents a bootstrap strategy for characterizing non-pathological variations in human brain anatomy, as well its application to achieve accurate 3-D deformable registration. Inherent anatomical variations are initially extracted by deformably registering training data with an expert-segmented 3-D image, a digital brain atlas. Statistical properties of the density and geometric variations in brain anatomy are extracted and encoded into the atlas to build a statistical atlas. These statistics are then used as prior knowledge to guide the deformation process. A bootstrap loop is formed by registering the statistical atlas to larger training sets as more data becomes available, so as to ensure more robust knowledge extraction, and to achieve more precise registration. Compared to an algorithm with no knowledge guidance, registration using the statistical atlas reduces the overall error by 34%.

Computational model of DIC microscopy for reconstructing 3-D specimens.

Abstract: Differential Interference Contrast (DIC) microscopy is a powerful visualization tool used to study live biological cells. Its use, however, has been limited to qualitative observations. The inherent non-linear relation between the object properties and the image intensity makes quantitative analysis difficult. As a first step towards measuring optical properties of objects from DIC images, we develop a model for the image formation process using methods consistent with energy conservation laws. We verify our model by comparing real image data of manufactured specimens to simulated images of virtual objects. As the next step, we plan to use this model to reconstruct the three-dimensional properties of unknown specimens.