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Outline of object recognition
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<h2 id="approaches-based-on-cad-like-object-models">Approaches based on CAD-like object models</h2> <ul><li><a href="/facts/Edge_detection/9VplpIcS">Edge detection</a></li> <li><a href="/facts/Primal_sketch/mc7wyhYl">Primal sketch</a></li> <li>Marr, Mohan and Nevatia<a class="footnote-ref" id="fnref:1" href="#fn:1"><sup>1</sup></a></li> <li>Lowe</li> <li><a href="/facts/Olivier_Faugeras/EYoePVg9">Olivier Faugeras</a></li></ul> <h3>Recognition by parts</h3> <ul><li>Generalized cylinders (<a href="/facts/Thomas_Binford/kF5ceDav">Thomas Binford</a>)</li> <li><a href="/facts/Geon_(psychology)/C9jBAoq7">Geons</a> (<a href="/facts/Irving_Biederman/cJ4sJjPF">Irving Biederman</a>)</li> <li>Dickinson, Forsyth and Ponce</li></ul> <h2 id="appearance-based-methods">Appearance-based methods</h2> <ul><li>Use example images (called templates or exemplars) of the objects to perform recognition</li> <li>Objects look different under varying conditions: <ul><li>Changes in lighting or color</li> <li>Changes in viewing direction</li> <li>Changes in size/shape</li></ul></li> <li>A single exemplar is unlikely to succeed reliably. However, it is impossible to represent all appearances of an object.</li></ul> <h3>Edge matching</h3> <ul><li>Uses edge detection techniques, such as the <a href="/facts/Canny_edge_detector/z4EExfNu">Canny edge detection</a>, to find edges.</li> <li>Changes in lighting and color usually don't have much effect on image edges</li> <li>Strategy: <ol><li>Detect edges in template and image</li> <li>Compare edges images to find the template</li> <li>Must consider range of possible template positions</li></ol></li> <li>Measurements: <ul><li>Good – count the number of overlapping edges. Not robust to changes in shape</li> <li>Better – count the number of template edge pixels with some distance of an edge in the search image</li> <li>Best – determine probability distribution of distance to nearest edge in search image (if template at correct position). Estimate likelihood of each template position generating image</li></ul></li></ul> <h3>Divide-and-Conquer search</h3> <ul><li>Strategy: <ul><li>Consider all positions as a set (a cell in the space of positions)</li> <li>Determine lower bound on score at best position in cell</li> <li>If bound is too large, prune cell</li> <li>If bound is not too large, divide cell into subcells and try each subcell recursively</li> <li>Process stops when cell is “small enough”</li></ul></li> <li>Unlike multi-resolution search, this technique is guaranteed to find all matches that meet the criterion (assuming that the lower bound is accurate)</li> <li>Finding the Bound: <ul><li>To find the lower bound on the best score, look at score for the template position represented by the center of the cell</li> <li>Subtract maximum change from the “center” position for any other position in cell (occurs at cell corners)</li></ul></li> <li>Complexities arise from determining bounds on distance</li></ul> <h3>Greyscale matching</h3> <ul><li>Edges are (mostly) robust to illumination changes, however they throw away a lot of information</li> <li>Must compute pixel distance as a function of both pixel position and pixel intensity</li> <li>Can be applied to color also</li></ul> <h3>Gradient matching</h3> <ul><li>Another way to be robust to illumination changes without throwing away as much information is to compare image gradients</li> <li>Matching is performed like matching greyscale images</li> <li>Simple alternative: Use (normalized) correlation</li></ul> <h3>Histograms of receptive field responses</h3> <ul><li>Avoids explicit point correspondences</li> <li>Relations between different image points implicitly coded in the receptive field responses</li> <li>Swain and Ballard (1991),<a class="footnote-ref" id="fnref:2" href="#fn:2"><sup>2</sup></a> Schiele and Crowley (2000),<a class="footnote-ref" id="fnref:3" href="#fn:3"><sup>3</sup></a> Linde and Lindeberg (2004, 2012)<a class="footnote-ref" id="fnref:4" href="#fn:4"><sup>4</sup></a><a class="footnote-ref" id="fnref:5" href="#fn:5"><sup>5</sup></a></li></ul> <h3>Large modelbases</h3> <ul><li>One approach to efficiently searching the database for a specific image to use eigenvectors of the templates (called <a href="/facts/Eigenface/UWrNytKT">eigenfaces</a>)</li> <li>Modelbases are a collection of geometric models of the objects that should be recognized</li></ul> <h2 id="feature-based-methods">Feature-based methods</h2> <ul><li>a search is used to find feasible matches between object features and <a href="/facts/Image_feature/GdfWRp5U">image features</a>.</li> <li>the primary constraint is that a single position of the object must account for all of the feasible matches.</li> <li>methods that <a href="/facts/Feature_extraction/BSMX5e8I">extract features</a> from the objects to be recognized and the images to be searched. <ul><li>surface patches</li> <li>corners</li> <li>linear edges</li></ul></li></ul> <h3>Interpretation trees</h3> <ul><li>A method for searching for feasible matches, is to search through a tree.</li> <li>Each node in the tree represents a set of matches. <ul><li>Root node represents empty set</li> <li>Each other node is the union of the matches in the parent node and one additional match.</li> <li>Wildcard is used for features with no match</li></ul></li> <li>Nodes are “pruned” when the set of matches is infeasible. <ul><li>A pruned node has no children</li></ul></li> <li>Historically significant and still used, but less commonly</li></ul> <h3>Hypothesize and test</h3> <ul><li>General Idea: <ul><li>Hypothesize a <a href="/facts/Correspondence_problem/YBe0HbVX">correspondence</a> between a collection of image features and a collection of object features</li> <li>Then use this to generate a hypothesis about the projection from the object coordinate frame to the image frame</li> <li>Use this projection hypothesis to generate a rendering of the object. This step is usually known as backprojection</li> <li>Compare the rendering to the image, and, if the two are sufficiently similar, accept the hypothesis</li></ul></li> <li>Obtaining Hypothesis: <ul><li>There are a variety of different ways of generating hypotheses.</li> <li>When camera intrinsic parameters are known, the hypothesis is equivalent to a hypothetical position and orientation – <a href="/facts/Pose_(computer_vision)/APuxJEKM">pose</a> – for the object.</li> <li>Utilize geometric constraints</li> <li>Construct a correspondence for small sets of object features to every correctly sized subset of image points. (These are the hypotheses)</li></ul></li> <li>Three basic approaches: <ul><li>Obtaining Hypotheses by Pose Consistency</li> <li>Obtaining Hypotheses by Pose Clustering</li> <li>Obtaining Hypotheses by Using Invariants</li></ul></li> <li>Expense search that is also redundant, but can be improved using Randomization and/or Grouping <ul><li>Randomization <ul><li>Examining small sets of image features until likelihood of missing object becomes small</li> <li>For each set of image features, all possible matching sets of model features must be considered.</li> <li>Formula: (1 – Wc)k = Z <ul><li>W = the fraction of image points that are “good” (w ~ m/n)</li> <li>c = the number of correspondences necessary</li> <li>k = the number of trials</li> <li>Z = the probability of every trial using one (or more) incorrect correspondences</li></ul></li></ul></li> <li>Grouping <ul><li>If we can determine groups of points that are likely to come from the same object, we can reduce the number of hypotheses that need to be examined</li></ul></li></ul></li></ul> <h3>Pose consistency</h3> <ul><li>Also called Alignment, since the object is being aligned to the image</li> <li>Correspondences between image features and model features are not independent – Geometric constraints</li> <li>A small number of correspondences yields the object position – the others must be consistent with this</li> <li>General Idea: <ul><li>If we hypothesize a match between a sufficiently large group of image features and a sufficiently large group of object features, then we can recover the missing camera parameters from this hypothesis (and so render the rest of the object)</li></ul></li> <li>Strategy: <ul><li>Generate hypotheses using small number of correspondences (e.g. triples of points for 3D recognition)</li> <li>Project other model features into image (<a href="/facts/Backprojection/ujsgdlNf">backproject</a>) and verify additional correspondences</li></ul></li> <li>Use the smallest number of correspondences necessary to achieve discrete object poses</li></ul> <h3><a href="/facts/Pose_clustering/ARs3VIlD">Pose clustering</a></h3> <ul><li>General Idea: <ul><li>Each object leads to many correct sets of correspondences, each of which has (roughly) the same pose</li> <li>Vote on pose. Use an accumulator array that represents pose space for each object</li> <li>This is essentially a <a href="/facts/Hough_transform/cYmx4Duw">Hough transform</a></li></ul></li> <li>Strategy: <ul><li>For each object, set up an accumulator array that represents pose space – each element in the accumulator array corresponds to a “bucket” in pose space.</li> <li>Then take each image frame group, and hypothesize a correspondence between it and every frame group on every object</li> <li>For each of these correspondences, determine pose parameters and make an entry in the accumulator array for the current object at the pose value.</li> <li>If there are large numbers of votes in any object's accumulator array, this can be interpreted as evidence for the presence of that object at that pose.</li> <li>The evidence can be checked using a verification method</li></ul></li> <li>Note that this method uses sets of correspondences, rather than individual correspondences <ul><li>Implementation is easier, since each set yields a small number of possible object poses.</li></ul></li> <li>Improvement <ul><li>The noise resistance of this method can be improved by not counting votes for objects at poses where the vote is obviously unreliable</li></ul> § For example, in cases where, if the object was at that pose, the object frame group would be invisible. <ul><li>These improvements are sufficient to yield working systems</li></ul></li></ul> <h3><a href="/facts/Invariant_(physics)/UfHcehYs">Invariance</a></h3> <ul><li>There are geometric properties that are invariant to camera transformations</li> <li>Most easily developed for images of planar objects, but can be applied to other cases as well</li></ul> <h3><a href="/facts/Geometric_hashing/DPl2SNWx">Geometric hashing</a></h3> <ul><li>An algorithm that uses geometric invariants to vote for object hypotheses</li> <li>Similar to pose clustering, however instead of voting on pose, we are now voting on geometry</li> <li>A technique originally developed for matching geometric features (uncalibrated affine views of plane models) against a database of such features</li> <li>Widely used for pattern-matching, CAD/CAM, and medical imaging.</li> <li>It is difficult to choose the size of the buckets</li> <li>It is hard to be sure what “enough” means. Therefore, there may be some danger that the table will get clogged.</li></ul> <h3><a href="/facts/Scale-invariant_feature_transform/oX14fcpr">Scale-invariant feature transform</a> (SIFT)</h3> <ul><li>Keypoints of objects are first extracted from a set of reference images and stored in a database</li> <li>An object is recognized in a new image by individually comparing each feature from the new image to this database and finding candidate matching features based on Euclidean distance of their feature vectors.</li> <li>Lowe (2004)<a class="footnote-ref" id="fnref:6" href="#fn:6"><sup>6</sup></a><a class="footnote-ref" id="fnref:7" href="#fn:7"><sup>7</sup></a></li></ul> <h3><a href="/facts/Speeded_Up_Robust_Features/8yNYo6Kn">Speeded Up Robust Features</a> (SURF)</h3> <ul><li>A robust image detector & descriptor</li> <li>The standard version is several times faster than SIFT and claimed by its authors to be more robust against different image transformations than SIFT</li> <li>Based on sums of approximated <a href="/facts/Haar-like_feature/copGPcca">2D Haar wavelet responses</a> and made efficient use of integral images.</li> <li>Bay et al. (2008)<a class="footnote-ref" id="fnref:8" href="#fn:8"><sup>8</sup></a></li></ul> <h3>Bag of words representations</h3> <p class="note">See also: <a href="/facts/Bag_of_words_model_in_computer_vision/9wjmsWkq">Bag of words model in computer vision</a></p> <h2 id="genetic-algorithm">Genetic algorithm</h2> <p><a href="/facts/Genetic_algorithm/WP2AFWuW">Genetic algorithms</a> can operate without prior knowledge of a given dataset and can develop recognition procedures without human intervention. A recent project achieved 100 percent accuracy on the benchmark motorbike, face, airplane and car image datasets from Caltech and 99.4 percent accuracy on fish species image datasets.<a class="footnote-ref" id="fnref:9" href="#fn:9"><sup>9</sup></a><a class="footnote-ref" id="fnref:10" href="#fn:10"><sup>10</sup></a> </p> <h2 id="other-approaches">Other approaches</h2> <ul><li><a href="/facts/3D_object_recognition/6fKBbFj8">3D object recognition</a> and <a href="/facts/3D_reconstruction/MlnAUm3l">reconstruction</a><a class="footnote-ref" id="fnref:11" href="#fn:11"><sup>11</sup></a></li> <li><a href="/facts/Biologically_inspired/mJIRrPfL">Biologically inspired</a> object recognition</li> <li><a href="/facts/Artificial_neural_networks/6V1jMlkx">Artificial neural networks</a> and <a href="/facts/Deep_Learning/JLuwD3ea">Deep Learning</a> especially <a href="/facts/Convolutional_neural_network/kHEdnmGU">convolutional neural networks</a></li> <li><a href="/facts/Context_awareness/QvrnTjwq">Context</a><a class="footnote-ref" id="fnref:12" href="#fn:12"><sup>12</sup></a><a class="footnote-ref" id="fnref:13" href="#fn:13"><sup>13</sup></a></li> <li>Explicit and <a href="/facts/Implicit_surface/gKpWgFNH">implicit</a> 3D object models</li> <li>Fast indexing<a class="footnote-ref" id="fnref:14" href="#fn:14"><sup>14</sup></a></li> <li>Global scene representations<a class="footnote-ref" id="fnref:15" href="#fn:15"><sup>15</sup></a></li> <li>Gradient histograms</li> <li><a href="/facts/Stochastic_grammar/K0Gpnqp5">Stochastic grammars</a><a class="footnote-ref" id="fnref:16" href="#fn:16"><sup>16</sup></a></li> <li>Intraclass <a href="/facts/Transfer_learning/pNz4P2KP">transfer learning</a></li> <li><a href="/facts/Object_categorization_from_image_search/CDjxWxfY">Object categorization from image search</a></li> <li><a href="/facts/Reflectance/TT4sBwIw">Reflectance</a><a class="footnote-ref" id="fnref:17" href="#fn:17"><sup>17</sup></a></li> <li><a href="/facts/Shape_from_Shading/puLyprWu">Shape-from-shading</a><a class="footnote-ref" id="fnref:18" href="#fn:18"><sup>18</sup></a></li> <li><a href="/facts/Template_matching/sE13sU8W">Template matching</a></li> <li>Texture<a class="footnote-ref" id="fnref:19" href="#fn:19"><sup>19</sup></a></li> <li><a href="/facts/Topic_model/aCAuIjwe">Topic models</a><a class="footnote-ref" id="fnref:20" href="#fn:20"><sup>20</sup></a></li> <li><a href="/facts/Unsupervised_learning/8a6f61MC">Unsupervised learning</a></li> <li>Window-based detection</li> <li>Deformable Part Model</li> <li><a href="/facts/Bingham_distribution/aD7Amr2Q">Bingham distribution</a><a class="footnote-ref" id="fnref:21" href="#fn:21"><sup>21</sup></a></li></ul> <h2 id="applications">Applications</h2> <p>Object recognition methods has the following applications: </p> <ul><li><a href="/facts/Activity_recognition/DtxKG9gp">Activity recognition</a><a class="footnote-ref" id="fnref:22" href="#fn:22"><sup>22</sup></a></li> <li><a href="/facts/Automatic_image_annotation/8V2cAfwG">Automatic image annotation</a><a class="footnote-ref" id="fnref:23" href="#fn:23"><sup>23</sup></a><a class="footnote-ref" id="fnref:24" href="#fn:24"><sup>24</sup></a></li> <li><a href="/facts/Automatic_target_recognition/fH8ySeJd">Automatic target recognition</a></li> <li>Android Eyes - Object Recognition<a class="footnote-ref" id="fnref:25" href="#fn:25"><sup>25</sup></a></li> <li><a href="/facts/Computer-aided_diagnosis/M88tS5ax">Computer-aided diagnosis</a><a class="footnote-ref" id="fnref:26" href="#fn:26"><sup>26</sup></a></li> <li>Image <a href="/facts/Panorama/Klwoc8lk">panoramas</a><a class="footnote-ref" id="fnref:27" href="#fn:27"><sup>27</sup></a></li> <li><a href="/facts/Image_watermarking/73WXq5PK">Image watermarking</a><a class="footnote-ref" id="fnref:28" href="#fn:28"><sup>28</sup></a></li> <li>Global <a href="/facts/Robot_localization/bdGa1MTC">robot localization</a><a class="footnote-ref" id="fnref:29" href="#fn:29"><sup>29</sup></a></li> <li><a href="/facts/Face_detection/KVIvPRFA">Face detection</a><a class="footnote-ref" id="fnref:30" href="#fn:30"><sup>30</sup></a></li> <li><a href="/facts/Optical_Character_Recognition/kdTyxa1l">Optical Character Recognition</a><a class="footnote-ref" id="fnref:31" href="#fn:31"><sup>31</sup></a></li> <li>Manufacturing <a href="/facts/Quality_control/lnXbvqvp">quality control</a><a class="footnote-ref" id="fnref:32" href="#fn:32"><sup>32</sup></a></li> <li><a href="/facts/Content-based_image_retrieval/AQpmsoGE">Content-based image retrieval</a><a class="footnote-ref" id="fnref:33" href="#fn:33"><sup>33</sup></a></li> <li>Object Counting and Monitoring<a class="footnote-ref" id="fnref:34" href="#fn:34"><sup>34</sup></a></li> <li><a href="/facts/Automated_parking_system/xHB4bBJn">Automated parking systems</a><a class="footnote-ref" id="fnref:35" href="#fn:35"><sup>35</sup></a></li> <li>Visual Positioning and <a href="/facts/Video_tracking/nVh8FB3E">tracking</a><a class="footnote-ref" id="fnref:36" href="#fn:36"><sup>36</sup></a></li> <li><a href="/facts/Video_stabilization/DrBT5QVS">Video stabilization</a><a class="footnote-ref" id="fnref:37" href="#fn:37"><sup>37</sup></a></li> <li><a href="/facts/Pedestrian_detection/PA1NoBJk">Pedestrian detection</a></li> <li><a href="/facts/Intelligent_speed_assistance/FGDa2qWU">Intelligent speed assistance</a> (in car and other vehicles)</li></ul> <h2 id="surveys">Surveys</h2> <ul><li>Daniilides and Eklundh, Edelman.</li> <li>Roth, Peter M. & Winter, Martin (2008). <a href="https://web.archive.org/web/20150921140625/http://lrs.icg.tugraz.at/pubs/roth_tr-icg_08.pdf">"SURVEYOFAPPEARANCE-BASED METHODS FOR OBJECT RECOGNITION"</a> (PDF). <i>Technical Report</i>. ICG-TR-01/08. Archived from <a href="http://lrs.icg.tugraz.at/pubs/roth_tr-icg_08.pdf">the original</a> (PDF) on 2015-09-21. Retrieved 2016-02-26.</li></ul> <h2 id="see-also">See also</h2> <ul><li><a href="/facts/Histogram_of_oriented_gradients/mhSxR64K">Histogram of oriented gradients</a></li> <li><a href="/facts/Convolutional_neural_network/kHEdnmGU">Convolutional neural network</a></li> <li><a href="/facts/OpenCV/V1tXAxyz">OpenCV</a></li> <li><a href="/facts/Scale-invariant_feature_transform/oX14fcpr">Scale-invariant feature transform</a> (SIFT)</li> <li><a href="/facts/Object_detection/sCx3FK44">Object detection</a></li> <li><a href="http://www.scholarpedia.org/article/Scale_Invariant_Feature_Transform">Scholarpedia article on scale-invariant feature transform and related object recognition methods</a></li> <li><a href="/facts/Speeded_up_robust_features/8yNYo6Kn">SURF</a></li> <li><a href="/facts/Template_matching/sE13sU8W">Template matching</a></li> <li><a href="/facts/Integral_channel_feature/g2ay1Mle">Integral channel feature</a></li></ul> Lists <ul><li><a href="/facts/List_of_computer_vision_topics/XXXQKX8e">List of computer vision topics</a></li> <li><a href="/facts/List_of_emerging_technologies/00drktJ6">List of emerging technologies</a></li> <li><a href="/facts/Outline_of_artificial_intelligence/gpySbMRe">Outline of artificial intelligence</a></li></ul> <h2 id="notes">Notes</h2> <ul><li>Elgammal, Ahmed <a href="http://www.cs.rutgers.edu/~elgammal/classes/cs534/lectures/3D_modelbasedvision.pdf">"CS 534: Computer Vision 3D Model-based recognition"</a>, Dept of Computer Science, Rutgers University;</li> <li>Hartley, Richard and Zisserman, Andrew <a href="http://www8.cs.umu.se/kurser/TDBD09/VT02/cvbook/ch20csppose.pdf">"Multiple View Geometry in computer vision"</a>, Cambridge Press, 2000, <a href="/facts/ISBN_(identifier)/15AdSPa9">ISBN</a> 0-521-62304-9.</li> <li>Roth, Peter M. and Winter, Martin <a href="http://web.mit.edu/~wingated/www/introductions/appearance_based_methods.pdf">"Survey of Appearance-Based Methods for Object Recognition", Technical Report ICG-TR-01/08</a>, Inst. for Computer Graphics and Vision, Graz University of Technology, Austria; January 15, 2008.</li> <li>Collins, Robert <a href="http://www.cse.psu.edu/~rcollins/CSE486/lecture31_6pp.pdf">"Lecture 31: Object Recognition: SIFT Keys"</a>, CSE486, Penn State</li> <li><a href="https://web.archive.org/web/20201228051352/http://www.iprg.co.in/">IPRG</a>[<i>usurped</i>] Image Processing - Online Open Research Group</li> <li><a href="http://research.google.com/pubs/ChristianSzegedy.html">Christian Szegedy</a> <a href="https://web.archive.org/web/20150906143037/http://research.google.com/pubs/ChristianSzegedy.html">Archived</a> 2015-09-06 at the <a href="/facts/Wayback_Machine/nmQ3a6JC">Wayback Machine</a>, <a href="http://research.google.com/pubs/AlexanderToshev.html">Alexander Toshev</a> <a href="https://web.archive.org/web/20151004143144/http://research.google.com/pubs/AlexanderToshev.html">Archived</a> 2015-10-04 at the <a href="/facts/Wayback_Machine/nmQ3a6JC">Wayback Machine</a> and <a href="http://www.dumitru.ca/">Dumitru Erhan</a>. <a href="http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf">Deep Neural Networks for Object Detection</a>. <a href="http://papers.nips.cc/book/advances-in-neural-information-processing-systems-26-2013">Advances in Neural Information Processing Systems 26</a> <a href="https://web.archive.org/web/20200905031104/https://papers.nips.cc/book/advances-in-neural-information-processing-systems-26-2013">Archived</a> 2020-09-05 at the <a href="/facts/Wayback_Machine/nmQ3a6JC">Wayback Machine</a>, 2013. page 2553–2561.</li></ul> <h2 id="external-links">External links</h2> <h2 id="references">References</h2> <ol> <li id="fn:1"><p>Rahesh Mohan & Rakamant Nevatia (1992). "Perceptual organization for scene segmentation and description" (PDF). IEEE Trans Pattern Anal Mach Intell. <a href="http://iris.usc.edu/outlines/papers/1992/pami-mohan-92.pdf" target="_blank">http://iris.usc.edu/outlines/papers/1992/pami-mohan-92.pdf</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li> <li id="fn:2"><p>Swain, Michael J.; Ballard, Dana H. (1991-11-01). "Color indexing". International Journal of Computer Vision. 7 (1): 11–32. doi:10.1007/BF00130487. ISSN 1573-1405. S2CID 8167136. <a href="https://doi.org/10.1007/BF00130487" target="_blank">https://doi.org/10.1007/BF00130487</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li> <li id="fn:3"><p>Schiele, Bernt; Crowley, James L. (2000-01-01). "Recognition without Correspondence using Multidimensional Receptive Field Histograms". International Journal of Computer Vision. 36 (1): 31–50. doi:10.1023/A:1008120406972. ISSN 1573-1405. 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