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Computer Vision and Pattern Recognition Workshop, 2004. CVPRW '04. Conference on

Date June 27 2004-July 2 2004

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  • Table of contents

    Publication Year: 2004 , Page(s): i
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  • Towards Recovery of 3D Chromosome Structure

    Publication Year: 2004 , Page(s): 1
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (640 KB)  

    The objectives of this work include automatic recovery and visualization of a 3D chromosome structure from a sequence of 2D tomographic reconstruction images taken through the nucleus of a cell. Structure is very important for biologists as it affects chromosome functions, behavior of the cell and its state. Chromosome analysis is significant in the detection of diseases and in monitoring environmental gene mutations. The algorithm incorporates thresholding based on a histogram analysis with a polyline splitting algorithm, contour extraction via active contours, and detection of the 3D chromosome structure by establishing corresponding regions throughout the slices. Visualization using point cloud meshing generates a 3D surface. The 3D triangular mesh of the chromosomes provides surface detail and allows a user to interactively analyze chromosomes using visualization software. View full abstract»

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  • Object-Based Visual 3D Tracking of Articulated Objects via Kinematic Sets

    Publication Year: 2004 , Page(s): 2
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4856 KB)  

    A theoretical framework based on robotics techniques is introduced for visual tracking of parametric non-rigid multi-body objects. It is based on an a-priori model of the object including a general mechanical link description. The objective equation is defined in the object-based coordinate system and non-linear minimization relates to the movement of the object and not the camera. This results in simultaneously estimating all degrees of freedom between the object's last known position relative to its previous position as well as internal articulated parameters. A new kinematic-set formulation takes into account that articulated degrees of freedom are directly observable from the camera and therefore their estimation does not need to pass via a kinematic-chain back to the root. By doing this the tracking techniques are efficient and precise leading to real-time performance and accurate measurements. The system is locally based upon an accurate modeling of a distance criteria. A general method is derived for defining any type of mechanical link and experimental results show prismatic, rotational and helicoidal type links. A statistical M-estimation technique is applied to improve robustness. A monocular camera system was used as a real-time sensor to verify the theory. View full abstract»

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  • Integrating Region and Boundary Information for Improved Spatial Coherencein Object Tracking

    Publication Year: 2004 , Page(s): 3
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    This paper describes a novel method for performing spatially coherent motion estimation by integrating region and boundary information. The method begins with a layered, parametric flow model. Since the resulting flow estimates are typically sparse, we use the computed motion in a novel way to compare intensity values between images, thereby providing improved spatial coherence of a moving region. This dense set of intensity constraints is then used to initialize an active contour, which is influenced by both motion and intensity data to track the object's boundary. The active contour, in turn, provides additional spatial coherence by identifying motion constraints within the object boundary and using them exclusively in subsequent motion estimation for that object. The active contour is therefore automatically initialized once and, in subsequent frames, is warped forward based on the motion model. The spatial coherence constraints provided by both the motion and the boundary information act together to overcome their individual limitations. Furthermore, the approach is general, and makes no assumptions about a static background and/or a static camera. We apply the method to image sequences in which both the object and the background are moving. View full abstract»

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  • Non-Rigid Motion Estimation and Spatio-Temporal Realignment in FMRI

    Publication Year: 2004 , Page(s): 4
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    Existing approaches to the problem of subject motion artefacts in FMRI data have applied rigid-body registration techniques to what is a non-rigid problem. We propose a model which can account for the non-linear characteristics of movement effects, known to result from the acquisition methods used to form these images. The model also facilitates the proper application of temporal corrections which are needed to compensate for acquisition delays. Results of an implementation based on this model reveal that it is possible to correct for these effects, leading to accurate re-alignment and timing correction. View full abstract»

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  • Estimating the Positions and Postures of Non-Rigid Objects Lacking Sufficient Features based on the Stick and Ellipse Model

    Publication Year: 2004 , Page(s): 5
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    We propose a method for tracking non-rigid objects, using an object model generated automatically from a set of sample images. Our model consists of multiple sticks and ellipses which represent the skeleton and the areas of an object, respectively. Because appearance features have to be extracted, previous methods cannot estimate the whole area and posture for 2-D image of a non-rigid object lacking sufficient characteristic features (e.g., texture patterns, shape and so on) to be detected easily. With the proposed model, on the other hand, our method can work well because (1) each component of the model can fit each rigid part of a non-rigid object and (2) the reliability of each component is evaluated. To confirm the effectiveness of the proposed method, we conducted several experiments with goldfish. The tracking system automatically generated a model of the goldfish, and could then track goldfish even when they were partially occluded. View full abstract»

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  • Efficient Appearance-Based Tracking

    Publication Year: 2004 , Page(s): 6
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    One of the major challenges that visual tracking algorithms face nowadays is being able to cope with changes in the appearance of the target during tracking. Linear sub-space models have been extensively studied recently and are possibly the most popular way of modeling target appearance. Unfortunately, efficiency is one of the limitations of present linear subspace models, and this is a key feature for a good tracker. In this paper we present an efficient procedure for tracking based on a linear subspace model of target appearance (grey levels). A set of motion templates is built from the subspace base, which is used to efficiently compute target motion and appearance parameters. It differs from previous works in that we impose no restrictions on the subspace used for modeling appearance. In the experiments conducted we have built a modular PCA-based face tracker which shows that video-rate tracking performance can be achieved with a non optimized implementation of our algorithm. View full abstract»

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  • Stochastic Meta-Descent for Tracking Articulated Structures

    Publication Year: 2004 , Page(s): 7
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    Recently, an optimization approach for fast visual tracking of articulated structures based on Stochastic Meta-Descent (SMD) has been presented. SMD is a gradient descent with local step size adaptation that combines rapid convergence with excellent scalability. Stochastic sampling helps to avoid local minima in the optimization process. We have extended the SMD algorithm with new features for fast and accurate tracking by adapting the different step sizes between as well as within video frames and by introducing a robust likelihood function which incorporates both depths and surface orientations. A realistic deformable hand model reinforces the accuracy of our tracker. The advantages of the resulting tracker over state-of-the-art methods are corroborated through experiments. View full abstract»

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  • Non-Rigid Structure from Motion using non-Parametric Tracking and Non-Linear Optimization

    Publication Year: 2004 , Page(s): 8
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (576 KB)  

    In this paper we address the problem of estimating the 3D structure and motion of a deformable non-rigid object from a sequence of uncalibrated images. It has been recently shown that if the deformation is modelled as a linear combination of basis shapes both the motion and the 3D structure of the object may be recovered using an extension of Tomasi and Kanade's factorization algorithm for affine cameras. The main drawback of the existing methods is that the non-rigid factorization algorithm does not provide a correct estimate of the motion: the motion matrix has a repetitive structure which is not respected by the factorization algorithm. This also affects the estimation of the 3D shape. In this paper we present a non-linear optimization method which minimizes image reprojection error and imposes the correct structure onto the motion matrix by choosing an appropriate parameterization. In addition, we propose a novel non-rigid tracking algorithm based on the use of ranklets, a multiscale family of rank features. Finally, we show that improved motion and shape estimates are obtained on a real image sequence of a person's face which is moving and changing expression. View full abstract»

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  • Gender Recognition from Walking Movements using Adaptive Three-Mode PCA

    Publication Year: 2004 , Page(s): 9
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    We present an adaptive three-mode PCA framework for recognizing gender from walking movements. Prototype female and male walkers are initially decomposed into a sub-space of their three-mode components (posture, time, gender). We then assign an importance weight to each motion trajectory in the sub-space and have the model automatically learn the weight values (key features) from labeled training data. We present experiments of recognizing physical (actual) and perceived (from perceptual experiments) gender for 40 walkers. The model demonstrates greater than 90% recognition for both contexts and shows greater flexibility than standard PCA. View full abstract»

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  • Differential Structure in non-Linear Image Embedding Functions

    Publication Year: 2004 , Page(s): 10
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    Many natural image sets are samples of a low dimensional manifold in the space of all possible images. When the image data set is not a linear combination of a small number of basis images, then linear dimensionality reduction techniques such as PCA and ICA fail, and non-linear dimensionality reduction techniques are required to automatically determine the intrinsic structure of the image set. Recent techniques such as ISOMAP and LLE provide a mapping between the images and a low dimensional parameterization of the images. In this paper we consider how choosing different image distance metrics affects the low-dimensional parameterization. For image sets that arise from non-rigid and human motion analysis, and MRI applications, differential motions in some directions of the low-dimensional space correspond to common transformations in the image domain. Defining distance measures that are invariant to these transformations makes Isomap a powerful tool for automatic registration of large image or video data sets. View full abstract»

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  • Model-Based Multi-Object Segmentation via Distribution Matching

    Publication Year: 2004 , Page(s): 11
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    A new algorithm for the segmentation of objects from 3D images using deformable models is presented. This algorithm relies on learned shape and appearance models for the objects of interest. The main innovation over similar approaches is that there is no need to compute a pixelwise correspondence between the model and the image; instead, probability distributions are compared. This allows for a faster, more principled algorithm. Furthermore, the algorithm is not sensitive to the form of the shape model, making it quite flexible. Results of the algorithm are shown for the segmentation of the prostate and bladder from medical images. View full abstract»

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  • Semantic-level Understanding of Human Actions and Interactions using Event Hierarchy

    Publication Year: 2004 , Page(s): 12
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (544 KB)  

    Understanding human behavior in video data is essential in numerous applications including surveillance, video annotation/retrieval, and human-computer interfaces. This paper describes a framework for recognizing human actions and interactions in video by using three levels of abstraction. At low level, the poses of individual body parts including head, torso, arms and legs are recognized using individual Bayesian networks (BNs), which are then integrated to obtain an overall body pose. At mid level, the actions of a single person are modeled using a dynamic Bayesian network (DBN) with temporal links between identical states of the Bayesian network at time t and t+1. At high level, the results of mid-level descriptions for each person are juxtaposed along a common time line to identify an interaction between two persons. The linguistic 'verb argument structure' is used to represent human action in terms of <agent-motion-target> triplets. Spatial and temporal constraints are used for a decision tree to recognize specific interactions. A meaningful semantic description in terms of subject-verb-object is obtained. Our method provides a user-friendly natural-language description of several human interactions, and correctly describes positive, neutral, and negative interactions occurring between two persons. Example sequences of real persons are presented to illustrate the paradigm. View full abstract»

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  • Fusion of a Multiple Hypotheses Color Model and Deformable Contours for Figure Ground Segmentation in Dynamic Environments

    Publication Year: 2004 , Page(s): 13
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    In this paper we propose a new technique to perform figure-ground segmentation in image sequences of moving objects under varying illumination conditions. Unlike most of the algorithms that adapt color, the assumption of smooth change of the viewing conditions is no longer needed. To cope with this, in this work we introduce a technique that formulates multiple hypotheses about the next state of the color distribution (some of these hypotheses take into account small and gradual changes in the color model and others consider more abrupt and unexpected variations) and the hypothesis that generates the best object segmentation is used to remove noisy edges from the image. This simplifies considerably the final step of fitting a deformable contour to the object boundary, thus allowing a standard snake formulation to successfully track nonrigid contours. Reciprocally, the contour estimation is used to correct the color model. The integration of color and shape is done in a stage denominated 'sample concentration', that has been introduced as a final step to the well-known CONDENSATION algorithm. View full abstract»

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  • Simultaneous Pose Estimation and Camera Calibration from Multiple Views

    Publication Year: 2004 , Page(s): 14
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    We present an algorithm to estimate the body pose of a walking person given synchronized video input from multiple uncalibrated cameras. We construct an appearance model of human walking motion by generating examples from the space of body poses and camera locations, and clustering them using expectation-maximization. Given a segmented input video sequence, we find the closest matching appearance cluster for each silhouette and use the sequence of matched clusters to extrapolate the position of the camera with respect to the person's direction of motion. For each frame, the matching cluster also provides an estimate of the walking phase. We combine these estimates from all views and find the most likely sequence of walking poses using a cyclical, feed-forward hidden Markov model. Our algorithm requires no manual initialization and no prior knowledge about the locations of the cameras. View full abstract»

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  • Silhouette Lookup for Automatic Pose Tracking

    Publication Year: 2004 , Page(s): 15 - 22
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (752 KB)  

    Computers should be able to detect and track the articulated 3-D pose of a human being moving through a video sequence. Current tracking methods often prove slow and unreliable, and many must be initialized by a human operator before they can track a sequence. This paper introduces a simple yet effective algorithm for tracking articulated pose, based upon looking up observed silhouettes in a collection of known poses. The new algorithm runs quickly, can initialize itself without human intervention, and can automatically recover from critical tracking errors made while tracking previous frames in a video sequence. View full abstract»

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  • A New Joint Clustering and Diffeomorphism Estimation Algorithm for Non-Rigid Shape Matching

    Publication Year: 2004 , Page(s): 16 - 22
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    Matching shapes parameterized as unlabeled point-sets is a challenging problem since we have to solve for point correspondences in a non-rigid setting. Previous work on this problem such as modal matching, linear assignment, shape contexts etc. has focused more on the correspondence aspect and not on the non-rigid deformations. The principal motivation for the present work is to establish a distance measure between shapes on a shape manifold. A pre-requisite for achieving this goal is the diffeomorphic matching of point-sets. We show that a joint clustering and diffeomorphism estimation strategy is capable of simultaneously estimating correspondences and a diffeomorphism between unlabeled point-sets. Cluster centers for the two point-sets having the same label are always in correspondence. Essentially, as the cluster centers evolve during the iterations of an incremental EM algorithm, we estimate a diffeomorphism between the two sets of cluster centers. We apply our algorithm to 2D corpus callosum shapes. View full abstract»

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  • 3D Human Limb Detection using Space Carving and Multi-View Eigen Models

    Publication Year: 2004 , Page(s): 17
    Cited by:  Papers (1)
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    In this paper, we integrate space carving and eigen detection methods to develop a bottom-up 3D human limb detector. We model the body in terms of its constituent body parts; here we focus on the head, lower arms, upper arms and calves. For each body part, we build a multi-view eigen model that combines image views from multiple calibrated cameras. This approach is much more constraining than the conventional multiple single-view eigen models and provides coarse 3D pose information. We use ideas from space carving using multiple silhouette images to constrain the volume of our search for the body part locations. We have applied the method to detect the body parts of a subject in long test sequences. The approach provides bottom-up in-formation that supports the automatic initialization of a full 3D human body model. View full abstract»

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  • Human Gait Recognition

    Publication Year: 2004 , Page(s): 18
    Cited by:  Papers (1)
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    The reliable extraction of characteristic gait features from image sequences and their recognition are two important issues in gait recognition. In this paper, we propose a novel 2-step, model-based approach to gait recognition by employing a 5-link biped locomotion human model. We first extract the gait features from image sequences using the Metropolis-Hasting method. Hidden Markov Models are then trained based on the frequencies of these feature trajectories, from which recognition is performed. As it is entirely based on human gait, our approach is robust to different type of clothes the subjects wear. The model-based gait feature extraction step is insensitive to noise, cluttered background or even moving background. Furthermore, this approach also minimizes the size of the data required for recognition compared to model-free algorithms. We applied our method to both the USF Gait Challenge data-set and CMU MoBo data-set, and achieved recognition rate of 61% and 96%, respectively. The results suggest that the recognition rate is significantly limited by the distance of the subject to the camera. View full abstract»

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  • Outlier Rejection in Deformable Model Tracking

    Publication Year: 2004 , Page(s): 19
    Cited by:  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (336 KB)  

    Deformable model tracking is a powerful methodology that allows us to track the evolution of high-dimensional parameter vectors from uncalibrated monocular video sequences. The core of the approach consists of using low-level vision algorithms, such as edge trackers or optical flow, to collect a large number of 2D displacements, or motion measurements, at selected model points and mapping them into 3D space with the model Jacobians. However, the low-level algorithms are prone to errors and outliers, which can skew the entire tracking procedure if left unchecked. There are several known techniques in the literature, such as RANSAC, that can find and reject outliers. Unfortunately, these approaches are not easily mapped into the deformable model tracking framework, where there is no closed-form algebraic mapping from samples to the underlying parameter space. In this paper we present two simple, yet effective ways to find the outliers. We validate and compare these approaches in an 11-parameter deformable face tracking application against ground truth data. View full abstract»

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  • Articulated Motion Modeling for Activity Analysis

    Publication Year: 2004 , Page(s): 20
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (552 KB)  

    We propose an algorithm for articulated human motion segmentation that estimates parametric motions of body parts and segments images into moving regions accordingly. Our approach combines robust optical flow estimation, RANSAC, and region segmentation using color and Gaussian shape priors. This combination results in an algorithm that can robustly estimate and segment multiple motions, even for moving regions with small support and in low-resolution images. Based on the raw motion segmentation, consistent body motions are detected over time to characterize human activity. The effectiveness of this approach is demonstrated in a real scenario: characterizing dining activities of patients at a nursing home. View full abstract»

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  • Significance of Elastic Properties in Physics-Based Nonrigid Motion Modeling, A Numerical Sensitivity Analysis

    Publication Year: 2004 , Page(s): 21
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    Parameters used in the physical models for nonrigid motion simulation are often not known with high precision. It has been recognized that the commonly used assumptions about the parameters may have adverse effects on the modeling quality. We present an efficient sensitivity analysis method to assess the impact of those assumptions by examining the model’s response to the parameter perturbation. Numerical experiments with both synthetic and real models show that (1) the normalized sensitivity distribution can help determine the relative importance of different parameters; (2) the dimensional sensitivity is useful in the assessment of a particular parameter assumption; (3) the model is more sensitive at the property discontinuity (heterogeneity). The proposed sensitivity analysis method is general and can also be applied to the assessment of other types of assumptions such as nonlinearity and anisotropy. View full abstract»

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  • A Hierarchical Framework For High Resolution Facial Expression Tracking

    Publication Year: 2004 , Page(s): 22
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (840 KB)  

    We present a novel hierarchical framework for high resolution, nonrigid facial expression tracking. The high quality dense point clouds of facial geometry moving at video speeds are acquired using a phase-shifting based structured light ranging technique. To use such data for temporal study of the subtle dynamics in expressions and for face recognition, an efficient nonrigid facial tracking algorithm is needed to establish intra-frame correspondences. In this paper, we propose such an algorithmic framework that uses a multi-resolution 3D deformable face model, and a hierarchical tracking scheme. This framework can not only track global facial motion that is caused by muscle action, but fit to subtler expression details that are generated by highly local skin deformations. Tracking of global deformations is performed efficiently on the coarse level of our face model with one thousand nodes, to recover the changes in a few intuitive parameters that control the motion of several deformable regions. In order to capture the complementary highly local deformations, we use a variational algorithm for non-rigid shape registration based on the integration of an implicit shape representation and the Free Form Deformations (FFD). Due to the strong implicit and explicit smoothness constraints imposed by the algorithm, the resulting registration/deformation field is smooth, continuous and gives dense one-to-one intra-frame correspondences. User-input sparse facial feature correspondences can also be incorporated as hard constraints in the optimization process, in order to guarantee high accuracy of the established correspondences. Extensive tracking experiments using the dynamic facial scan of five different subjects demonstrate the accuracy and efficiency of our proposed framework. View full abstract»

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  • Spline-based Motion Recovery for 3D Surfaces Using Nonrigid Shape Properties

    Publication Year: 2004 , Page(s): 23
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    We present a spline-based nonrigid motion and point correspondence recovery method for 3D surfaces. This method is based on differential geometry. Shape information is used to recover the point correspondences. In contrast to the majority of shape-based methods which assume that shape (unit normal, curvature) changes are minimumafter motion, our method focuses on the nonrigid relationship between before-motion and after-motion shapes. This nonrigid shape relationship is described by modeling the underlying non-rigid motion; we model it as a spline transformation which has global control over the entire motion field along with the local deformation integrated within. This provides our method certain advantages over some pure differential geometric methods which also utilize the nonrigid shape relationship but only work on local areas without a global control. For example, motion regularity is hard to implement in these pure differential geometric methods but is not a problem when the motion field is controlled by a spline transformation. Furthermore, the small deformation constraint introduced by the previous works is relaxed in our method. Experiments on both synthetic and real motions have been conducted. The quantitive and qualitative evaluations of our method are presented in this paper. View full abstract»

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  • Discontinuous Non-Rigid Motion Analysis of Sea Ice using C-Band Synthetic Aperture Radar Satellite Imagery

    Publication Year: 2004 , Page(s): 24
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1712 KB)  

    Sea-ice motion consists of complex non-rigid motions involving continuous, piece-wise continuous and discrete particle motion. Techniques for estimating non-rigid motion of sea ice from pairs of satellite images (generally spaced three days apart) are still in the developmental stages. For interior Arctic and Antarctic pack ice, the continuum assumption begins to fail below the 5 km scale with evidence of discontinuities already revealed in models and remote sensing products in the form of abrupt changes in magnitude and direction of the differential velocity. Using a hierarchical multi-scale phase-correlation method and profiting from known limitations of cross correlation methods, we incorporate the identification of discontinuities into our motion estimation algorithm, thereby descending below the continuum threshold to examine the phenomenon of discontinuous non-rigid sea-ice motion. View full abstract»

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