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A Study of Approaches for Object Recognition. Presented by Wyman Wong 12/9/2005. Outlines. Introduction Model-Based Object Recognition AAM Inverse Composition AAM View-Based Object Recognition Recognition based on boundary fragments Recognition based on SIFT Proposed Research

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a study of approaches for object recognition

A Study of Approaches for Object Recognition

Presented by Wyman Wong


  • Introduction
  • Model-Based Object Recognition
    • AAM
    • Inverse Composition AAM
  • View-Based Object Recognition
    • Recognition based on boundary fragments
    • Recognition based on SIFT
  • Proposed Research
  • Conclusion and Future Work
  • Object Recognition
    • A task of finding 3D objects from 2D images (or even video) and classifying them into one of the many known object types
    • Closely related to the success of many computer vision applications
      • robotics, surveillance, registration … etc.
    • A difficult problem that a general and comprehensive solution to this problem has not been made
  • Two main streams of approaches:
    • Model-Based Object Recognition
      • 3D model of the object being recognized is available
      • Compare the 2D representation of the structure of an object with the 2D projection of the model
    • View-Based Object Recognition
      • 2D representations of the same object viewed at different angles and distances when available
      • Extract features (as the representations of object) and compare them to the features in the feature database
  • Pros and Cons of each main stream:
    • Model-Based Object Recognition
      • Model features can be predicted from just a few detected features based on the geometric constraints
      • Models sacrifice its generality
    • View-Based Object Recognition
      • Greater generality and more easily trainable from visual data
      • Matching is done by comparing the entire objects, some methods may be sensitive to clutter and occlusion
model based object recognition
Model-Based Object Recognition
  • Commonly used in face recognition
  • General Steps:
    • Locate the object,
    • locate and label its structure,
    • adjust the model's parameters until the model generates an image similar enough to the real object.
  • Active Appearance Models (AAM) have been proved to be highly useful models for face recognition
active appearance models
Active Appearance Models
  • They model shape and appearance of objects separately
  • Shape: the vertex locations of a mesh
  • Appearance: the pixels’ values of a mesh
  • Both of the parameters above used PCA to generalize the face recognition to generic face
  • Fitting an AAM: non-linear optimization solution is applied which iteratively solve for incremental additive updates to the shape and appearance coefficients
inverse compositional aams
Inverse Compositional AAMs
  • The major difference of these models with AAMs is the fitting algorithm
  • AAM: additive incremental update shape and appearance parameters
  • ICAAM: inverse compositional update – The algorithm updates the entire warp by composing the current warp with the computed incremental warp
view based object recognition
View-Based Object Recognition
  • Common approaches:
    • Correlation-based template matching (Li, W. et al. 95)
      • SEA, PDE, … etc
      • Not effective when the following happens:
        • illumination of environment changes
        • Posture and scale of object changes
        • Occlusion
    • Color Histogram (Swain, M.J. 90)
      • Construct histogram for an object and match it over image
      • It is robust to changing of viewpoint and occlusion
      • But it requires good isolation and segmentation of objects
view based object recognition10
View-Based Object Recognition
  • Common approaches:
    • Feature based
      • Extract features from the image that are salient and match only to those features when searching all location for matches
      • Feature types: groupings of edges, SIFT … etc
      • Feature’s property preferences:
        • View invariant
        • Detected frequently enough for reliable recognition
        • Distinctive
      • Image descriptor is created based on detected features to increase the matching performance
      • Image descriptor = Key / Index to database of features
      • Descriptor’s property preferences:
        • Invariant to scaling, rotation, illumination, affine transformation and noise
nelson s approach
Nelson’s Approach
  • Recognition based on 2D Boundary Fragments
  • Prepare 53 clean images for each object and build 3D recognition database:



nelson s approach12
Nelson’s Approach
  • Test images used in Nelson’s experiment and their features
nelson s approach13
Nelson’s Approach
  • Nelson’s experiment has shown his approach has high accuracy
    • 97.0% success rate for 24 objects database
  • under the following conditions:
    • Large number of images
    • Clean images
    • Very different objects
    • No occlusion and clutter
lowe s approach
Lowe’s Approach
  • Recognition based on Scale Invariant Feature Transform (SIFT)
    • SIFT generates distinctive invariant features
    • SIFT based image descriptors are generally most resistant to common image deformations (Mikolajczyk 2005)
    • SIFT – four steps:
      • Scale-space extrema detection
      • Keypoint localization
      • Orientation assignment
      • Keypoint descriptor computation
scale space extrema detection
Scale-space extrema detection
  • DOG ~ LOG
  • Search over all sample points in all scales and find extrema that are local maxima or minima in laplacian space

Small keypoints  Solve occlusion problem

Large keypoints  Robust to noise and image blur

keypoint localization
Keypoint localization
  • Reject keypoints with the following properties:
    • Low contrast (sensitive to noise)
    • Localized along edge (sliding effect)
  • Solution:
    • Filter points with value D below 0.03
    • Apply Hessian edge detector
orientation assignment
Orientation assignment
  • Pre-compute the gradient magnitude and orientation
  • Use them to construct keypoint descriptor
keypoint descriptor computation
Keypoint descriptor computation
  • Create orientation histogram over 4x4 sample regions around the keypoint locations
  • Each histogram contains 8 orientation bins
  • 4x4x8 = 128 elements vectors (distinctively representing a feature)
object recognition based on sift
Object Recognition based on SIFT
  • Nearest-neighbor algorithm
  • Matching: assign features to objects
  • There can be many wrong matches
    • Solution
      • Identify clusters of features
      • Generalized Hough transform
  • Determine pose of object and then discard outliers
proposed research
Proposed Research
  • Personally, I think model-based approach does have better performance
  • Success of model-based approach requires:
    • All models of objects to be detected
    • Automatically construct models
    • Automatically select the best model
  • How do the system know which 3D model to be used on a specific image of object?
    • By view-based approach
    • Human looks at an image of object for a moment and then realize which model to be used on that object
    • Then use the specific model to refine the identification of the specific object
hybrid of bottom up and top down
Hybrid of bottom-up and top-down
  • View-based approaches just presented are bottom-up approaches
    • Features: edges, extrema (Low Level)
    • Descriptors of features
    • Matching
    • Identification of object (High Level)
  • Can it be like that?
    • Features
    • Matching (Lower Level)
    • Guessing of object (Higher Level)
    • Matching (Lower Level)
    • Guessing of object (Higher Level)
    • Identification of object
hierarchy of features
Hierarchy of features
  • Lowe’s system
    • All features have equal weight in voting of object during identification of object (subject to be verified by examining the opened source code)
    • Special features do not have enough voting power to shift the result to the correct one
    • Consider the following scenario:
      • Two objects have many similar features, a1to a100 are similar to b1to b100, and have just one very different feature, a* for object A and b* for object B
      • Many a1to a100 may be poorly captured by imaging device and mismatched as b1to b100 , even we can still recognize the feature a*, the system may still think the object is B

Object A

Object B

extension of sift
Extension of SIFT
  • Color descriptors
  • Local texture measures incorporated into feature descriptors
  • Scale-invariant edge groupings
  • *Generic object class recognition
conclusion and future work
Conclusion and Future Work
  • Discussed the different approaches in object recognition
  • Discussed what is SIFT and how it works
  • Discussed the possible extensions to SIFT
  • Design hybrid approach
  • Design extensions

Q & A

Thank you very much!

things to be understood
Things to be understood
  • Find extrema over same scale space is good, why need to find over different scale?