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Computer and Robot Vision, 2007. CRV '07. Fourth Canadian Conference on

Date 28-30 May 2007

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Displaying Results 1 - 25 of 70
  • Fourth Canadian Conference on Computer and Robot Vision - Cover

    Page(s): c1
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  • Fourth Canadian Conference on Computer and Robot Vision - Title page

    Page(s): i - iii
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  • Fourth Canadian Conference on Computer and Robot Vision - Copyright

    Page(s): iv
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  • Fourth Canadian Conference on Computer and Robot Vision - Table of contents

    Page(s): v - x
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  • Preface

    Page(s): xi
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  • Committees

    Page(s): xii
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  • Extrinsic Recalibration in Camera Networks

    Page(s): 3 - 10
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (252 KB) |  | HTML iconHTML  

    This work addresses the practical problem of keeping a camera network calibrated during a recording session. When dealing with real-time applications, a robust calibration of the camera network needs to be assured, without the burden of a full system recalibration at every (un)intended camera displacement. In this paper we present an efficient algorithm to detect when the extrinsic parameters of a camera are no longer valid, and reintegrate the displaced camera into the previously calibrated camera network. When the intrinsic parameters of the cameras are known, the algorithm can also be used to build ad-hoc distributed camera networks, starting from three calibrated cameras. Recalibration is done using pairs of essential matrices, based on image point correspondences. Unlike other approaches, we do not explicitly compute any 3D structure for our calibration purposes. View full abstract»

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  • Screen-Camera Calibration using a Spherical Mirror

    Page(s): 11 - 20
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (306 KB) |  | HTML iconHTML  

    Developments in the consumer market have indicated that the average user of a personal computer is likely to also own a webcam. With the emergence of this new user group will come a new set of applications, which will require a user-friendly way to calibrate the position of the camera with respect to the location of the screen. This paper presents a fully automatic method to calibrate a screen-camera setup, using a single moving spherical mirror. Unlike other methods, our algorithm needs no user intervention other then moving around a spherical mirror. In addition, if the user provides the algorithm with the exact radius of the sphere in millimeters, the scale of the computed solution is uniquely defined. View full abstract»

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  • A Simple Operator for Very Precise Estimation of Ellipses

    Page(s): 21 - 28
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (455 KB) |  | HTML iconHTML  

    This paper presents a simple linear operator that accurately estimates the position and parameters of ellipse features. Based on the dual conic model, the operator avoids the intermediate stage of precisely extracting individual edge points by exploiting directly the raw gradient information in the neighborhood of an ellipse's boundary. Moreover, under the dual representation, the dual conic can easily be constrained to a dual ellipse when minimizing the algebraic distance. The new operator is assessed and compared to other estimation approaches in simulation as well as in real situation experiments and shows better accuracy than the best approaches, including those limited to the center position. View full abstract»

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  • Training Database Adequacy Analysis for Learning-Based Super-Resolution

    Page(s): 29 - 35
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (908 KB) |  | HTML iconHTML  

    This paper explores the possibility of assessing the adequacy of a training database to be used in a learning-based super-resolution process. The Mean Euclidean Distance (MED) function is obtained by averaging the distance between each input patch and its closest candidate in the training database, for a series of blurring kernels used to construct the low-resolution database. The shape of that function is thought to indicate the level of adequacy of the database, thus indicating to the user the potential of success of a learning-based super-resolution algorithm using this database. View full abstract»

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  • Extracting Salient Objects from Operator-Framed Images

    Page(s): 36 - 43
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (335 KB) |  | HTML iconHTML  

    In images framed by human operators, as opposed to those taken under computer control, the position of objects can be an important clue to saliency. This paper uses the Berkeley image data set to show how locational and photometric information can be combined to extract a probability of saliency for all image pixels. This probability can then be thresholded and segmented to extract compact image regions with high probability of saliency. A self assessment procedure allows the algorithm to evaluate the accuracy of its results. The method can extract salient regions of non uniform color, brightness or texture against highly variable background. View full abstract»

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  • Learning Saccadic Gaze Control via Motion Prediciton

    Page(s): 44 - 54
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1328 KB) |  | HTML iconHTML  

    This paper describes a system that autonomously learns to perform saccadic gaze control on a stereo pan-tilt unit. Instead of learning a direct map from image positions to a centering action, the system first learns a forward model that predicts how image features move in the visual field as the gaze is shifted. Gaze control can then be performed by searching for the action that best centers a feature in both the left and the right image. By attacking the problem in a different way we are able to collect many training examples in each action, and thus learning converges much faster. The learning is performed using image features obtained from the scale invariant feature transform (SIFT) detected and matched before and after a saccade, and thus requires no special environment during the training stage. We demonstrate that our system stabilises already after 300 saccades, which is more than 100 times fewer than the best current approaches. View full abstract»

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  • Efficient camera motion and 3D recovery using an inertial sensor

    Page(s): 55 - 62
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2823 KB) |  | HTML iconHTML  

    This paper presents a system for 3D reconstruction using a camera combined with an inertial sensor. The system mainly exploits the orientation obtained from the inertial sensor in order to accelerate and improve the matching process between wide baseline images. The orientation further contributes to incremental 3D reconstruction of a set of feature points from linear equation systems. The processing can be performed online while using consecutive groups of three images overlapping each other. Classic or incremental bundle adjustment is applied to improve the quality of the model. Test validation has been performed on object and camera centric sequences. View full abstract»

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  • Can Lucas-Kanade be used to estimate motion parallax in 3D cluttered scenes?

    Page(s): 63 - 72
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (386 KB) |  | HTML iconHTML  

    When an observer moves in a 3D static scene, the motion field depends on the depth of the visible objects and on the observer's instantaneous translation and rotation. By computing the difference between nearby motion field vectors, the observer can estimate the direction of local motion parallax and in turn the direction of heading. It has recently been argued that, in 3D cluttered scenes such as a forest, computing local image motion using classical optical flow methods is problematic since these classical methods have problems at depth discontinuities. Hence, estimating local motion parallax from optical flow should be problematic as well. In this paper we evaluate this claim. We use the classical Lucas-Kanade method to estimate optical flow and the Rieger-Lawton method to estimate the direction of motion parallax from the estimated flow. We compare the motion parallax estimates to those of the frequency based method of Mann-Langer. We find that if the Lucas-Kanade estimates are sufficiently pruned, using both an eigenvalue condition and a mean absolute error condition, then the Lucas- Kanade/Rieger-Lawton method can perform as well as or better than the frequency-based method. View full abstract»

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  • Local Graph Matching for Object Category Recognition

    Page(s): 73 - 80
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (388 KB) |  | HTML iconHTML  

    A novel model for object category recognition in real-world scenes is proposed. Images in our model are represented by a set of triangular labelled graphs, each containing information on the appearance and geometry of a 3-tuple of distinctive image regions. In the learning stage, our model automatically learns a set of codebooks of model graphs for each object category, where each codebook contains information about which local structures may appear on which parts of the object instances of the target category. A two-stage method for optimal matching is developed, where in the first stage a Bayesian classifier based on ICA factorization is used efficiently to select the matched codebook, and in the second stage a nearest neighbourhood classifier is used to assign the test graph to one of the learned model graphs of the selected codebook. Each matched test graph casts votes for possible identity and poses of an object instance, and then a Hough transformation technique is used in the pose space to identify and localize the object instances. An extensive evaluation on several large datasets validates the robustness of our proposed model in object category recognition and localization in the presence of scale and rotation changes. View full abstract»

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  • Efficient Registration of 3D SPHARM Surfaces

    Page(s): 81 - 88
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (776 KB) |  | HTML iconHTML  

    We present SHREC, an efficient algorithm for registration of 3D SPHARM (spherical harmonic) surfaces. SHREC follows the iterative closest point (ICP) registration strategy, and alternately improves the surface correspondence and adjusts the object pose. It establishes the surface correspondence by aligning the underlying SPHARM parameterization. It employs a rotational property of the harmonic expansion to accelerate its step for parameterization rotation. It uses a hierarchical icosahedron approach to sample the rotation space and searches for the best parameterization that matches the template. Our experimental results show that SHREC can not only create more accurate registration than previous methods but also do it efficiently. SHREC is a simple, efficient and general registration method, and has a great potential to be used in many shape modeling and analysis applications. View full abstract»

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  • Computing View-normalized Body Parts Trajectories

    Page(s): 89 - 96
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (982 KB) |  | HTML iconHTML  

    This paper proposes an approach to compute view normalized body part trajectories of pedestrians from monocular video sequences. The proposed approach first extracts the 2D trajectories of both feet and of the head from tracked silhouettes. On that basis, it segments the walking trajectory into piecewise linear segments. Finally, a normalization process is applied to head and feet trajectories over each obtained straight walking segment. View normalization makes head and feet trajectories appear as if seen from a fronto-parallel viewpoint. The latter is assumed to be optimal for gait modeling and recognition purposes. The proposed approach is fully automatic as it requires neither manual initialization nor camera calibration. View full abstract»

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  • Automated Detection of Mitosis in Embryonic Tissues

    Page(s): 97 - 104
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2978 KB) |  | HTML iconHTML  

    Characterization of mitosis is important for understanding the mechanisms of development in early stage embryos. In studies of cancer, another situation in which mitosis is of interest, the tissue is stained with contrast agents before mitosis characterization; an intervention that could lead to atypical development in live embryos. A new image processing algorithm that does not rely on the use of contrast agents was developed to detect mitosis in embryonic tissue. Unlike previous approaches that uses still images, the algorithm presented here uses temporal information from time-lapse images to track the deformation of the embryonic tissue and then uses changes in intensity at tracked regions to identify the locations of mitosis. On a one hundred minute image sequence, consisting of twenty images, the algorithm successfully detected eighty-one out of the ninety-five mitosis. The performance of the algorithm is calculated using the geometric mean measure as 82%. Since no other method to count mitoses in live tissues is known, comparisons with the present results could not be made. View full abstract»

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  • Computer Assisted Detection of Polycystic Ovary Morphology in Ultrasound Images

    Page(s): 105 - 112
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (683 KB) |  | HTML iconHTML  

    Polycystic ovary syndrome (PCOS) is an endocrine abnormality with multiple diagnostic criteria due to its heterogenic manifestations. One of the diagnostic criteria includes analysis of ultrasound images of ovaries for the detection of number, size, and distribution of follicles within the ovary. This involves manual tracing and counting of follicles on the ultrasound images to determine the presence of a polycystic ovary (PCO). We describe a novel method that automates PCO detection. Our algorithm involves segmentation of follicles from ultrasound images, quantifying the attributes of the automatically segmented follicles using stereology, storing follicle attributes as feature vectors, and finally classification of the feature vector into two categories. The classification categories are: PCO present and PCO absent. An automatic PCO diagnostic tool would save considerable time spent on manual tracing of follicles and measuring the length and width of every follicle. Our procedure was able to achieve classification accuracy of 92.86% using a linear discriminant classifier. Our classifier will improve the rapidity and accuracy of PCOS diagnosis, reducing the risk of the severe complications that can arise from delayed diagnosis. View full abstract»

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  • Automatic Detection and Clustering of Actor Faces based on Spectral Clustering Techniques

    Page(s): 113 - 122
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1158 KB) |  | HTML iconHTML  

    We describe a video indexing system that aims at indexing large video files in relation to the presence of similar faces. The detection of near-frontal view faces is done with a cascade of weak classifier. Face tracking is done through a particle filter and generate trajectories. Face clusters are found based on a spectral clustering approach. We compare the performance of various spectral clustering techniques based on 2DPCA features. The system performance is evaluated against a public face database as well as on a real full-length feature movie. View full abstract»

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  • Petri Net-Based Cooperation In Multi-Agent Systems

    Page(s): 123 - 130
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (205 KB) |  | HTML iconHTML  

    We present a formal framework for robotic cooperation in which we use an extension to Petri nets, known as workflow nets, to establish a protocol among mobile agents based on the task coverage they maintain. Our choice is motivated by the fact that Petri nets handle concurrency and that goal reachability can be theoretically established. We describe the means by which cooperation is performed with Petri nets and analyze their structural and behavioral characteristics in order to show the correctness of our framework. View full abstract»

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  • Corridor Navigation and Obstacle Avoidance using Visual Potential for Mobile Robot

    Page(s): 131 - 138
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (981 KB) |  | HTML iconHTML  

    In this paper, we develop an algorithm for corridor navigation and obstacle avoidance using visual potential for visual navigation by an autonomous mobile robot. The robot is equipped with a camera system which dynamically captures the environment. The visual potential is computed from an image sequence and optical flow computed from successive images captured by the camera mounted on the robot. Our robot selects a local pathway using the visual potential observed through its vision system. Our algorithm enables mobile robots to avoid obstacles without any knowledge of a robot workspace. We demonstrate experimental results using image sequences observed with a moving camera in a simulated environment and a real environment. Our algorithm is robust against the fluctuation of displacement caused by mechanical error of the mobile robot, and the fluctuation of planar-region detection caused by a numerical error in the computation of optical flow. View full abstract»

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  • Energy Efficient Robot Rendezvous

    Page(s): 139 - 148
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    We examine the problem of finding a single meeting location for a group of heterogeneous autonomous mobile robots, such that the total system cost of traveling to the rendezvous is minimized. We propose two algorithms that solve this problem. The first method computes an approximate globally optimal meeting point using numerical simplex minimization. The second method is a computationally cheap heuristic that computes a local heading for each robot: by iterating this method, all robots arrive at the globally optimal location. We compare the performance of both methods to a naive algorithm (center of mass). Finally, we show how to extend the methods with inter-robot communication to adapt to new environmental information. View full abstract»

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  • Camera Sensor Model for Visual SLAM

    Page(s): 149 - 156
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1329 KB) |  | HTML iconHTML  

    In this paper, we present a technique for the construction of a camera sensor model for visual SLAM. The proposed method is an extension of the general camera calibration procedure and requires the camera to observe a planar checkerboard pattern shown at different orientations. By iteratively placing the pattern at different distances from the camera, we can find a relationship between the measurement noise covariance matrix and the range. We conclude that the error distribution of a camera sensor follows a Gaussian distribution, based on the Geary's test, and the magnitude of the error variance is linearly related to the range between the camera and the features being observed. Our sensor model can potentially benefit visual SLAM algorithms by varying its measurement noise covariance matrix with range. View full abstract»

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  • Quantitative Evaluation of Feature Extractors for Visual SLAM

    Page(s): 157 - 164
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (185 KB) |  | HTML iconHTML  

    We present a performance evaluation framework for visual feature extraction and matching in the visual simultaneous localization and mapping (SLAM) context. Although feature extraction is a crucial component, no qualitative study comparing different techniques from the visual SLAM perspective exists. We extend previous image pair evaluation methods to handle non-planar scenes and the multiple image sequence requirements of our application, and compare three popular feature extractors used in visual SLAM: the Harris corner detector, the Kanade-Lucas-Tomasi tracker (KLT), and the scale-invariant feature transform (SIFT). We present results from a typical indoor environment in the form of recall/precision curves, and also investigate the effect of increasing distance between image viewpoints on extractor performance. Our results show that all methods can be made to perform well, although it is possible to distinguish between the three. We conclude by presenting guidelines for selecting a feature extractor for visual SLAM based on our experiments. View full abstract»

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