1 / 26

A Study of Approaches for Object Recognition

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

Télécharger la présentation

A Study of Approaches for Object Recognition

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.


Presentation Transcript

  1. A Study of Approaches for Object Recognition Presented by Wyman Wong 12/9/2005

  2. 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 • Conclusion and Future Work

  3. Introduction • 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

  4. Introduction • 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

  5. Introduction • 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. Nelson’s Approach • Recognition based on 2D Boundary Fragments • Prepare 53 clean images for each object and build 3D recognition database: Object Camera

  12. Nelson’s Approach • Test images used in Nelson’s experiment and their features

  13. 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

  14. 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

  15. 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

  16. 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

  17. Orientation assignment • Pre-compute the gradient magnitude and orientation • Use them to construct keypoint descriptor

  18. 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)

  19. 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

  20. 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

  21. 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

  22. 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

  23. Extension of SIFT • Color descriptors • Local texture measures incorporated into feature descriptors • Scale-invariant edge groupings • *Generic object class recognition

  24. 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

  25. Q & A Thank you very much!

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

More Related