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Object Recognition

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Object Recognition

Object recognition - in computer vision is the task of finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes / scale or even when they are translated or rotated. Objects can even be recognized when they are partially obstructed from view. This task is still a challenge for computer vision systems. Many approaches to the task have been implemented over multiple decades.

Approaches based on CAD-like object models

Recognition by parts

Appearance-based methods

- Use example images (called templates or exemplars) of the objects to perform recognition

- Objects look different under varying conditions:

  • Changes in lighting or color
  • Changes in viewing direction
  • Changes in size / shape

- A single exemplar is unlikely to succeed reliably. However, it is impossible to represent all appearances of an object

1. Edge matching

  • Uses edge detection techniques, such as the Canny edge detection, to find edges.
  • Changes in lighting and color usually don’t have much effect on image edges
  • Strategy:
  1. Detect edges in template and image
  2. Compare edges images to find the template
  3. Must consider range of possible template positions
  • Measurements:
  • Good – count the number of overlapping edges. Not robust to changes in shape
  • Better – count the number of template edge pixels with some distance of an edge in the search image
  • 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

2. Divide-and-Conquer search

  • Strategy:
  • Consider all positions as a set (a cell in the space of positions)
  • Determine lower bound on score at best position in cell
  • If bound is too large, prune cell
  • If bound is not too large, divide cell into subcells and try each subcell recursively
  • Process stops when cell is “small enough”
  • Unlike multi-resolution search, this technique is guaranteed to find all matches that meet the criterion (assuming that the lower bound is accurate)
  • Finding the Bound:
  • To find the lower bound on the best score, look at score for the template position represented by the center of the cell
  • Subtract maximum change from the “center” position for any other position in cell (occurs at cell corners)
  • Complexities arise from determining bounds on distance

3. Greyscale matching

  • Edges are (mostly) robust to illumination changes, however they throw away a lot of information
  • Must compute pixel distance as a function of both pixel position and pixel intensity
  • Can be applied to color also

4. Gradient matching

  • Another way to be robust to illumination changes without throwing away as much information is to compare image gradients
  • Matching is performed like matching greyscale images
  • Simple alternative: Use (normalized) correlation

5. Histograms of receptive field responses

  • Avoids explicit point correspondences
  • Relations between different image points implicitly coded in the receptive field responses
  • Swain and Ballard (1991),[1] Schiele and Crowley (2000),[2] Linde and Lindeberg (2004, 2012)[3][4]

6. Large modelbases

  • One approach to efficiently searching the database for a specific image to use eigenvectors of the templates (called eigenfaces)
  • Modelbases are a collection of geometric models of the objects that should be recognised

Feature-based methods

- a search is used to find feasible matches between object features and image features.

- the primary constraint is that a single position of the object must account for all of the feasible matches.

- methods that extract features from the objects to be recognized and the images to be searched.

  • surface patches
  • corners
  • linear edges

1. Interpretation trees

  • A method for searching for feasible matches, is to search through a tree.
  • Each node in the tree represents a set of matches.
  • Root node represents empty set
  • Each other node is the union of the matches in the parent node and one additional match.
  • Wildcard is used for features with no match
  • Nodes are “pruned” when the set of matches is infeasible.
  • A pruned node has no children
  • Historically significant and still used, but less commonly

2. Hypothesize and test

  • General Idea:
  • Hypothesize a correspondence between a collection of image features and a collection of object features
  • Then use this to generate a hypothesis about the projection from the object coordinate frame to the image frame
  • Use this projection hypothesis to generate a rendering of the object. This step is usually known as backprojection
  • Compare the rendering to the image, and, if the two are sufficiently similar, accept the hypothesis
  • Obtaining Hypothesis:
  • There are a variety of different ways of generating hypotheses.
  • When camera intrinsic parameters are known, the hypothesis is equivalent to a hypothetical position and orientation – pose – for the object.
  • Utilize geometric constraints
  • Construct a correspondence for small sets of object features to every correctly sized subset of image points. (These are the hypotheses)
  • Three basic approaches:
  • Obtaining Hypotheses by Pose Consistency
  • Obtaining Hypotheses by Pose Clustering
  • Obtaining Hypotheses by Using Invariants
  • Expense search that is also redundant, but can be improved using Randomization and/or Grouping
  • Randomization
§ Examining small sets of image features until likelihood of missing object becomes small
§ For each set of image features, all possible matching sets of model features must be considered.
§ Formula:
( 1 – Wc)k = Z
W = the fraction of image points that are “good” (w ~ m/n)
c = the number of correspondences necessary
k = the number of trials
Z = the probability of every trial using one (or more) incorrect correspondences
  • Grouping
§ 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

3. Pose consistency

  • Also called Alignment, since the object is being aligned to the image
  • Correspondences between image features and model features are not independent – Geometric constraints
  • A small number of correspondences yields the object position – the others must be consistent with this
  • General Idea:
  • 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)
  • Strategy:
  • Generate hypotheses using small number of correspondences (e.g. triples of points for 3D recognition)
  • Project other model features into image (backproject) and verify additional correspondences
  • Use the smallest number of correspondences necessary to achieve discrete object poses

4. Pose clustering

  • General Idea:
  • Each object leads to many correct sets of correspondences, each of which has (roughly) the same pose
  • Vote on pose. Use an accumulator array that represents pose space for each object
  • This is essentially a Hough transform
  • Strategy:
  • 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.
  • Then take each image frame group, and hypothesize a correspondence between it and every frame group on every object
  • For each of these correspondences, determine pose parameters and make an entry in the accumulator array for the current object at the pose value.
  • 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.
  • The evidence can be checked using a verification method
  • Note that this method uses sets of correspondences, rather than individual correspondences
  • Implementation is easier, since each set yields a small number of possible object poses.
  • Improvement
  • The noise resistance of this method can be improved by not counting votes for objects at poses where the vote is obviously unreliable
§ For example, in cases where, if the object was at that pose, the object frame group would be invisible.
  • These improvements are sufficient to yield working systems

5. Invariance

  • There are geometric properties that are invariant to camera transformations
  • Most easily developed for images of planar objects, but can be applied to other cases as well

6. Geometric hashing

  • An algorithm that uses geometric invariants to vote for object hypotheses
  • Similar to pose clustering, however instead of voting on pose, we are now voting on geometry
  • A technique originally developed for matching geometric features (uncalibrated affine views of plane models) against a database of such features
  • Widely used for pattern-matching, CAD/CAM, and medical imaging.
  • It is difficult to choose the size of the buckets
  • It is hard to be sure what “enough” means. Therefore there my be some danger that the table will get clogged.

7. Scale-invariant feature transform (SIFT)

  • Keypoints of objects are first extracted from a set of reference images and stored in a database
  • 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.
  • Lowe (2004)[5][6]

8. Speeded Up Robust Features (SURF)

  • A robust image detector & descriptor
  • The standard version is several times faster than SIFT and claimed by its authors to be more robust against different image transformations than SIFT
  • Based on sums of approximated 2D Haar wavelet responses and made efficient use of integral images.
  • Bay et al (2008)[7]

Bag of words representations

Other approaches


Object recognition methods has the following applications:


Daniilides and Eklundh, Edelman.

Roth, Peter M. and Winter, Martin.

See also




  • Elgammal, Ahmed "CS 534: Computer Vision 3D Model-based recognition", Dept of Computer Science, Rutgers University;
  • Hartley, Richard and Zisserman, Andrew ISBN 0-521-62304-9.
  • Roth, Peter M. and Winter, Martin “Survey of Appearance-Based Methods for Object Recognition”, Technical Report ICG-TR-01/08, Inst. for Computer Graphics and Vision, Graz University of Technology, Austria; January 15, 2008.
  • Collins, Robert "Lecture 31: Object Recognition: SIFT Keys", CSE486, Penn State
  • IPRG Image Processing - Online Open Research Group
  • O. Ahmad, J. Debayle, and J. C. Pinoli. "A geometric-based method for recognizing overlapping polygonalshaped and semi-transparent particles in gray tone images", Pattern Recognition Letters 32(15), 2068–2079,2011.
  • O. Ahmad, J. Debayle, N. Gherras, B. Presles, G. Févotte, and J. C. Pinoli. "Recognizing overlapped particles during a crystallization process from in situ video images for measuring their size distributions.",In 10th SPIE International Conference on Quality Control by Artificial Vision (QCAV), Saint-Etienne, France,June 2011.
  • O. Ahmad, J. Debayle, N. Gherras, B. Presles, G. Févotte, and J. C. Pinoli. "Quantification of overlapping polygonal-shaped particles based on a new segmentation method of in situ images during crystallization.",Journal of Electronic Imaging, 21(2), 021115, 2012.
  • This outline displayed as a mindmap, at

External links

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