WO2000033253A1 - Visualiseur pour flux optique par une sequence temporelle tridimensionnelle - Google Patents

Visualiseur pour flux optique par une sequence temporelle tridimensionnelle Download PDF

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Publication number
WO2000033253A1
WO2000033253A1 PCT/US1999/028063 US9928063W WO0033253A1 WO 2000033253 A1 WO2000033253 A1 WO 2000033253A1 US 9928063 W US9928063 W US 9928063W WO 0033253 A1 WO0033253 A1 WO 0033253A1
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WO
WIPO (PCT)
Prior art keywords
feature
image
track
feature point
images
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PCT/US1999/028063
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English (en)
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WO2000033253A8 (fr
Inventor
Philip R. Moorby
John S. Robotham
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Synapix, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Synapix, Inc. filed Critical Synapix, Inc.
Priority to AU16340/00A priority Critical patent/AU1634000A/en
Publication of WO2000033253A1 publication Critical patent/WO2000033253A1/fr
Publication of WO2000033253A8 publication Critical patent/WO2000033253A8/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T13/00—Animation
    • G06T13/20—3D [Three Dimensional] animation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/24—Aligning, centring, orientation detection or correction of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/98—Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns
    • G06V10/987—Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns with the intervention of an operator
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/62—Extraction of image or video features relating to a temporal dimension, e.g. time-based feature extraction; Pattern tracking

Definitions

  • the present invention relates to computer image processing and in particular to a technique for visualizing feature tracks and identifying errors and anomalies therein prior to subsequent processing.
  • An image processing function called feature tracking is the process of selecting features from an initial scene and then tracking these features across a related series of images of the same scene.
  • Each image is typically represented as an array of pixel values, and a feature point in such an image is typically identified as a region of one or more pixels (or sub-pixels) .
  • Feature tracking is the basis for several techniques whereby multiple feature points are simultaneously tracked across related image frames to develop further information about this scene. These include techniques for tracking two-dimensional shapes across frames, for estimating three-dimensional paths of selected feature points, for estimating three-dimensional camera paths, and for recovering estimated three-dimensional scene structure (including estimated depths of object surfaces) .
  • the use of feature tracking techniques in these applications can be very powerful, because they transform an image processing problem into a domain where geometric constraints can be applied.
  • Most feature tracking methods are highly sensitive to the initial selection of each feature point. Automated feature point selection is typically done using criteria applied solely to the initial frame (such as choosing an area of high contrast) . This selection can easily prove to be a poor choice for tracking in successive frames. Like-wise, a manual selection made by a human operator may not be well suited for tracking over multiple frames.
  • selection sensitivity becomes critical. Even when multiple features can be correlated and tracked as a group, reducing selection sensitivity depends on tracking all the features across multiple image frames while maintaining the correlation between them.
  • a feature can be "lost" due to imaging artifacts such as noise or transient lighting conditions. These artifacts can make it difficult or impossible to distinguish the feature identified in one frame from its surroundings in another frame.
  • a feature can also be lost when it is visible in one frame but occluded (or partially occluded) in another. Feature occlusion may be due to changing camera orientation, and/or movement of one or more object (s) in the visual scene.
  • a lost feature can reappear in yet another frame, but not be recognized as a continuation of a previously identified feature. This feature might be ignored, and remain lost.
  • a broken path has two (or more) discontinuous segments such that one path ends where the feature was lost, and the next path begins where the feature reappears.
  • a single feature may therefore be erroneously tracked as multiple unrelated and independent features, each with its own unique piece of the broken path.
  • a bad match is a feature identified in one frame that is incorrectly matched to a different feature in another frame.
  • a bad match can be even more troublesome than a lost feature or broken path, since the feature tracking algorithm proceeds as if the feature were being correctly tracked.
  • the present invention is a visualization tool that displays the output of a feature tracking or optical flow algorithm in a type of three-dimensional "spaghetti graph.”
  • the spaghetti graph enables a human user to identify and eliminate outliers and other bad track matches from the results of a feature tracking algorithm performed on the original 2D image sequence.
  • the technique involves building a track in three dimensions representing the movement of a single feature through the sequence of images, and furthermore builds a track for any number of features in the sequence.
  • the display provides a representation of the tracks in a 3D coordinate system where the x and y coordinates are the coordinates of a feature within the image coordinate system, and the z coordinate is a number associated with the temporal ordering of each image frame in the sequence of images .
  • the tracks are preferably marked with an attribute of a selected pixel in the feature in the originating image in order to further allow the user to visually separate the tracks.
  • the marked track may be colored in the same color as the selected pixel in the case of a color image, or set to a corresponding grey scale value in the case of a black and white image .
  • the result is a three-dimensional display of marked tracks representing the evolution of the optical flow over time.
  • the 3D track representation may be manipulated by rotation, scaling, zooming, viewpoint modification, and other standard 3D viewer tools which permit the user to view a 3D object from various angles on a 2D computer monitor. This permits the user to identify problem areas such as broken paths or lost features indicated by places in the graph where tracks are not smooth, tracks end or begin abruptly, tracks cross one another, or have other anomalies.
  • the graph may therefore be used to evaluate the quality of different feature tracking runs and/or algorithms .
  • the invention provides further benefits in terms of producing feature tracking outputs which are of greater accuracy by eliminating the very features which cause most errors in computations. For example, once problem areas in the optical flow are identified and/or corrected, the user can rerun feature tracking algorithms or improve their results by excluding problem bad tracks or outliers from camera path or scene model analysis .
  • FIG. 1 is a block diagram of an image processing system in which a feature track visualization technique may be used according to the invention.
  • Fig. 2 is a more detailed view of a sequence of images and a feature point generation process showing their interaction with a feature tracking, scene modeling, and camera modeling process.
  • Fig. 3 is an exemplary view of a camera, its image plane, and the derivation of feature points, scene structure and camera models.
  • Fig. 4 is a flow chart of a sequence of steps performed in order to produce a feature track visualization according to the invention.
  • Fig. 5 is a set of steps that may be performed subsequent to the visualization process of Fig. 4 to identify and remove bad tracks or anomalies from subsequent processing.
  • Fig. 6 is an exemplary first image from a sequence of images .
  • Fig. 7 is an exemplary 3D feature track visualization.
  • Fig. 8 is the same feature track visualization but viewed from a second viewpoint with a higher zoom factor, illustrating a bad track having an anomaly.
  • Fig. 9 is an even closer view illustrating a broken track.
  • Fig. 1 is a block diagram of the components of a digital image processing system 10 in which a feature track visualization technique according to the invention may be implemented.
  • the system 10 includes a computer workstation 20, a computer monitor 21, and input devices such as a keyboard 22 and mouse or stylus 23.
  • the workstation 20 also includes input/output interfaces 24, storage 25, such as a disk 26 and random access memory 27, as well as one or more processors 28.
  • the workstation 20 may be a computer graphics workstation such as the 02/Octane sold by Silicon Graphics, Inc., a Windows NT type-work station, or other suitable computer or computers .
  • the computer monitor 21, keyboard 22, mouse or stylus 23, and other input devices are used to interact with various software elements of the system existing in the workstation 20 to cause programs to be run and data to be stored as described below.
  • the system 10 also includes a number of other hardware elements typical of an image processing system, such as a video monitor 30, audio monitors 31, hardware accelerator 32, and user input devices 33.
  • image capture devices such as a video cassette recorder (VCR) , video tape recorder (VTR) , and/or digital disk recorder 34 (DDR) , cameras 35, and/or film scanner/telecine 36.
  • Sensors 38 may also provide information about the scene and image capture devices .
  • One aspect of the present invention is concerned with a technique for visualizing an array of feature points derived from a sequence of images provided by one of the image capture devices.
  • a sequence 50 of images 51-1, 51-2, ..., 51-F are provided to a feature point generation process 54.
  • the images 51 may be provided at a Dl resolution of 720 by 486 pixels.
  • Each entry in the feature array 58 may actually represent a feature selected over the tiled image 51, such as over a 5x5 or a 7x7 pixel tile.
  • An output of the feature point generation process 54 a set of arrays 58-1, 58-2, ..., 58-F of feature points with typically an array 58 for each input image
  • Feature tracking 61 may, for example, estimate the path or "directional flow" of two-dimensional shapes across the image sequence 50, or estimate three- dimensional paths of selected feature points.
  • the scene structure model 62 may derive information about the relative distances or "depth" of objects in the image sequence 50.
  • the camera modeling processes 63 may estimate one or more camera paths in three dimensions from multiple feature points . Considering the scene structure modeling 62 and camera modeling 63 more particularly, the sequence 50 of images 51-1, and 51-2, ..., 51-F is typically taken from a camera that is moving relative to objects in a scene.
  • Feature points 52 are often selected to be the corners of objects in the images 51, although other selection methods may be used.
  • Each feature point 52 corresponds to a single world point, located at position s p in some fixed world coordinate system. This point will appear at varying positions in each of the following images 51-2, ..., 51-F, depending on the position and orientation of the camera in that image, and depending upon whether the point moves or remains fixed over time in world coordinates relative to the camera.
  • the observed image position of point p in frame f is written as the two-vector u fp containing its image x- and y- coordinates, which is sometimes written as (u fp ,v fp ) .
  • These image positions are measured by tracking the feature from frame to frame using known feature tracking techniques .
  • the camera position and orientation in each frame is described by a rotation matrix R f and a translation vector t f representing the transformation from world coordinates to camera coordinates in each frame. It is possible to physically interpret the rows of R f as giving the orientation of the camera axes in each frame - the first row i f , gives the orientation of the camera's x-axis, the second row, j f , gives the orientation of the camera's y-axis, and the third row, k f , gives the orientation of the camera's optical axis, which points along the camera's line of sight.
  • the vector t f indicates the position of the camera in each frame by pointing from the world origin to the camera's focal point. This formulation is illustrated in Fig. 3.
  • projection The process of projecting a three-dimensional point onto the image plane in a given frame is referred to as projection.
  • This process models the physical process by which light from a point in the world is focused on the camera's image plane, and mathematical projection models of various degrees of sophistication can be used to compute the expected or predicted image positions P(f,p) as a function of s p , R f , and t f .
  • this process depends not only on the position of a point and the position and orientation of the camera, but also on the complex lens optics and image digitization characteristics. These may include an orthographic projection model, scaled orthographic projection model, para-perspective projection model, perspective projection model, radial projection model, or other types of models.
  • the specific algorithms used to derive a scene structure 62 or camera model 63 are not of particular importance to the present invention. Rather, the present invention is concerned with a technique for developing a visual representation of the arrays of feature points 58 to better permit identification of errors and anomalies therein.
  • the feature points developed from the image sequence 50 are stored in the feature array 58 as a number of associated image feature entries 60.
  • each entry 60 in the feature array 58 contains at least (1) a grid position (GRID POS) or "(x,y) coordinate" and (2) a flow vector (FLOW) or "path.”
  • Path for the feature array 58 is developed by applying a feature tracking algorithm 60 across successive images 51.
  • the image stream 50 contains images of a rotating cube 68 against a uniform dark background.
  • the visual corners 52 of the cube 68 are what is traditionally detected and tracked as feature points .
  • the GRID POS data for each feature point in image 51-1 is thus the (x,y) position of each feature point in the first array 58-1.
  • a second image 51-2 in the sequence has the cube rotated to a different position. As shown, a corresponding movement of the feature points 52 occurs.
  • the feature points are thus stored in a second array 58-2 of the feature array 58.
  • a sub-pixel directional flow vector can be generated representing the movement of each feature point 52.
  • the vectors are generated between the first 51-1 and second image 51-2, the second 51-2 and third 51-3 image, and so on up to the F'th image 51-F.
  • a corresponding flow vector can thus be derived for each feature point pair which determines the sub-pixel location of the feature point in a next successive image.
  • Data representing the flow vector for each feature point is stored in the PATH entries in feature array 58.
  • a given directional flow vector for example, associated with the subsequent images 51, may have a different magnitude and direction as the speed and direction of the cube 68 changes.
  • Fig. 4 is a sequence of steps that can now be performed given that the feature array 58 containing sets of feature points and flow vectors for each image is available.
  • a first state 102 is entered in which the feature track algorithm is used to define feature points and paths for each frame as already described.
  • the following states 104 through state 110 are executed for each feature point array 58.
  • a loop is performed for each image, f, in the array.
  • a track segment is built in three dimensions for each feature point 52 from its data associated with each image in the feature array 58.
  • a track segment is built in three dimensions by plotting a line segment beginning at a location (x,y,f) where the x and y coordinates correspond to the relative position of the feature point in its associated image 51, and its location along the z axis is a number, f, associated with the temporal ordering or the "index" of each image 50 in the sequence 51.
  • the line segment is drawn in the direction given by the corresponding path vector.
  • the track segment is actually rendered on the display.
  • the track is rendered in a color that is the same as the feature point's color in the first frame of the sequence 51.
  • States 104 through 110 are iterated until a track is displayed representing the movement of a single feature point throughout an entire sequence of images and such a track is built for each feature point in the image.
  • the result is a set of colored tracks representing the evolution of the optical flow over time through the image sequence 51.
  • the result is then displayed to the user, and the user is permitted in state 114 to change the viewpoint via rotation, zooming, and other standard 3D viewer tools in order to evaluate the quality of the feature tracking algorithm.
  • the user may access the quality of the particular feature tracking algorithm implemented to easily identify problems areas such as places in which the tracks are not smooth, tracks begin or end abruptly, tracks cross one another, or have other anomalies.
  • Fig. 6 there is shown a view of a scene in which a woman is seated in a room next to a fireplace.
  • Fig. 7 shows one view of a feature track visualization produced from this scene according to the sequence of steps performed in Fig. 4.
  • the sequence of images was taken by panning the camera around the seated woman in the room.
  • the particular feature points can be traced more or less back to their origin points in the first image in the sequence by coordinating the color of the feature points with the colors of various regions in the first image in the sequence .
  • Fig. 8 is a viewpoint of the same set of tracks but taken from a closer viewpoint. Notice that one of the tracks 200 has an anomaly in that it has a sharp peak in a region of otherwise smooth tracks. The user knows this because the camera movement could not have possibly produced such an anomaly for only one feature of the image when other surrounding features in the same portion of the image exhibit much smoother flow.
  • Fig. 9 is an even more detailed viewpoint of a track 210 which is considered to be "bad" in that there is an obvious break or premature end point for the track 210.
  • the process of Fig. 4 may therefore be used to evaluate the performance of particular feature tracking algorithm 61. However, additional application of the process can be used whereby the user intervenes in automatic scene modeling and camera path algorithms in order to produce higher quality results.
  • the user when viewing a three-dimensional flow display such as that of Figs. 7, 8 or 9, the user can identify anomalies and other problem areas in the flow, such as unsmooth tracks, tracks that appear to flow in physically impossible directions, crossing tracks, and interrupted tracks as before. Once such tracks have been identified, the user can alter or remove them entirely from subsequent processing in order to reduce the noise in the input to automatic algorithms and thereby improve their output.
  • anomalies and other problem areas in the flow such as unsmooth tracks, tracks that appear to flow in physically impossible directions, crossing tracks, and interrupted tracks as before.
  • process may begin from an idle state 100, performing the states 102 through 112 as in Fig. 4. However, at the end of state 112, a state 130 may be entered in which the user identifies a bad track from three-dimensional displays such as the track that was shown in Fig. 9.
  • this track can be identified as a track which should be removed from further analysis.
  • this state for example, an entry is made in the feature array 58 to indicate the status is "bad.”
  • the user may enter a state 140 in which an anomaly in a track is identified.
  • the system 10 may permit the user to specify a correction to this particular track. This correction is reflected in a modification to the entries in the feature point array such as by modifying the location of an x,y point in the array or visually changing its corresponding path vector with the input device 23.
  • the corrected tracks are then applied in state 150 to the subsequent feature track 61, camera modeling 63, or scene modeling 62.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Multimedia (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Quality & Reliability (AREA)
  • Stereoscopic And Panoramic Photography (AREA)
  • Testing, Inspecting, Measuring Of Stereoscopic Televisions And Televisions (AREA)
  • Image Analysis (AREA)

Abstract

L'invention concerne une technique de visualisation tridimensionnelle permettant à un opérateur de visualiser le résultat d'une analyse de flux optique par une suite d'images. Cette technique consiste tout d'abord à construire une piste représentant le mouvement de chaque point caractéristique d'une suite d'images, puis à construire une piste pour chacun desdits point caractéristiques, selon la précision sous-pixel souhaitée. Cette technique consiste à afficher les pistes dans un système de coordonnées tridimensionnel, dans lequel les coordonnées en x et en y correspondent aux coordonnées du point caractéristique dans le système de coordonnées de l'image, les coordonnées en z étant un nombre associé à l'ordonnancement temporel de chaque image de la suite d'images. On obtient ainsi un affichage de pistes représentant l'évolution dudit flux optique dans le temps.
PCT/US1999/028063 1998-11-24 1999-11-23 Visualiseur pour flux optique par une sequence temporelle tridimensionnelle WO2000033253A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU16340/00A AU1634000A (en) 1998-11-24 1999-11-23 Viewer for optical flow through a 3d time sequence

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US10962798P 1998-11-24 1998-11-24
US60/109,627 1998-11-24
US44702199A 1999-11-22 1999-11-22
US09/447,021 1999-11-22

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WO2000033253A1 true WO2000033253A1 (fr) 2000-06-08
WO2000033253A8 WO2000033253A8 (fr) 2001-06-14

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1202578A2 (fr) * 2000-10-30 2002-05-02 Monolith Co., Ltd. Appareil et méthode de mise en correspondace d'images
WO2003003309A1 (fr) * 2001-06-29 2003-01-09 Honeywell International, Inc. Procede de monitorage d'un objet en mouvement et systeme correspondant
US8406506B2 (en) 2010-05-18 2013-03-26 Honda Motor Co., Ltd. Fast sub-pixel optical flow estimation
WO2015096509A1 (fr) * 2013-12-26 2015-07-02 华中科技大学 Procédé d'estimation robuste des axes de rotation et du barycentre d'un objet spatial, d'après des flux de lumière binoculaire
EP3179443A4 (fr) * 2014-08-05 2017-08-02 Panasonic Corporation Procédé et dispositif de correction et de vérification

Citations (1)

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EP0551595A2 (fr) * 1991-12-17 1993-07-21 Eastman Kodak Company Techniques de visualisation pour séquences d'images acquises dans le temps

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EP0551595A2 (fr) * 1991-12-17 1993-07-21 Eastman Kodak Company Techniques de visualisation pour séquences d'images acquises dans le temps

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Title
CHAUDHURY K ET AL: "DETECTING 3D FLOW", PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION,US,LOS ALAMITOS, IEEE COMP. SOC. PRESS, vol. CONF. 11, 1994, pages 1073 - 1078, XP000478450, ISBN: 0-8186-5332-9 *
SHAH M ET AL: "MOTION TRAJECTORIES", IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS,US,IEEE INC. NEW YORK, vol. 23, no. 4, 1 July 1993 (1993-07-01), pages 1138 - 1150, XP000418415, ISSN: 0018-9472 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1202578A2 (fr) * 2000-10-30 2002-05-02 Monolith Co., Ltd. Appareil et méthode de mise en correspondace d'images
EP1202578A3 (fr) * 2000-10-30 2003-10-01 Monolith Co., Ltd. Appareil et méthode de mise en correspondace d'images
EP1830581A1 (fr) * 2000-10-30 2007-09-05 Monolith Co., Ltd. Appareil et méthode de mise en correspondace d'images
WO2003003309A1 (fr) * 2001-06-29 2003-01-09 Honeywell International, Inc. Procede de monitorage d'un objet en mouvement et systeme correspondant
US8406506B2 (en) 2010-05-18 2013-03-26 Honda Motor Co., Ltd. Fast sub-pixel optical flow estimation
WO2015096509A1 (fr) * 2013-12-26 2015-07-02 华中科技大学 Procédé d'estimation robuste des axes de rotation et du barycentre d'un objet spatial, d'après des flux de lumière binoculaire
EP3179443A4 (fr) * 2014-08-05 2017-08-02 Panasonic Corporation Procédé et dispositif de correction et de vérification

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WO2000033253A8 (fr) 2001-06-14

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