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Invariant Local Feature for Object Recognition. Presented by Wyman 2/05/2006. 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

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introduction
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
introduction3
Introduction
  • Two main streams of approaches:
    • Model-Based Object Recognition
    • 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 those in the feature database
matching with local features

Repeatedly Detected

Matching with Local Features
  • One of the possible solution
    • Matching with invariant local features
      • Robust to Occlusion, clutter background
      • cf. global features
  • Three phases:
    • Detection
    • Description
    • Matching

Accurate, Fast

Distinctive

Invariance

research direction
Research Direction
  • Study and improve the invariant local features
    • Detection, description and matching
  • Study and improve object recognition / matching using invariant local features
  • Area to improve
    • Distinctiveness
    • Invariance
    • Efficiency
outline
Outline
  • State-of-the-art techniques
    • Descriptor
    • Matching
  • Conclusion & Future Works
outline7
Outline
  • State-of-the-art techniques
    • Descriptor
      • Performance evaluation
      • Current extension using color
      • Possible way to improve – Color Orientation
    • Matching
  • Conclusion & Future Work
outline8
Outline
  • State-of-the-art techniques
    • Descriptor
      • Performance evaluation
      • Current extension using color
      • Possible way to improve – Color Orientation
    • Matching
      • Cross-bin distance
      • Performance evaluation
      • Possible way to improve – Aggregation of Content
  • Conclusion & Future Work
performance evaluation of descriptors
Performance Evaluation of Descriptors
  • We aim to compare the performance of three state-of-the-art local feature descriptors: SIFT, PCA-SIFT and GLOH
  • Same experimental setup as that used in “Performance Evaluation of Local Descriptors” TPAMI 2005
    • Different evaluation criterion
    • Different result
  • In each experiment, each descriptor describe features from
    • Harris corner detector
    • Harris-affine covariant detector
      • Output regions that are invariant to viewpoint change
sift scale invariant feature transform
SIFT – Scale Invariant Feature Transform
  • Descriptor overview:
    • Find local orientation as the dominant gradient direction  Rotation Invariant
    • Compute gradient orientation histograms of several small windows (128 values for each point) relative to the local orientation  Viewpoint Invariant
    • Normalize the descriptor to make it invariant to intensity change  Illumination

D.Lowe. “Distinctive Image Features from Scale-Invariant Keypoints”. IJCV 2004

pca sift
PCA-SIFT
  • Rotate feature region to dominant gradient direction same as SIFT
  • Pre-compute an eigenspace for local gradient patches of size 41x41
  • 2x39x39=3042 elements
  • Only keep 20 components
  • A more compact descriptor
  • Sensitive to viewpoint change

Y. K. Rahul. Pca-sift: A more distinctive representation for local image descriptors. CVPR 2004

gloh gradient location orientation histogram
GLOH (Gradient location-orientation histogram)
  • Different from SIFT in sampling method
    • 17 log-polar location bins
    • 16 orientation bins
  • Analyze the 17x16=272 Dimensions
  • Apply PCA analysis, keep 128 components

PCA on Orientation Histogram

VS

PCA on Gradient Patch

17 Log-polar location bins

C. S. Krystian Mikolajczyk. A performance evaluation of local descriptors. TPAMI 2005

performance evaluation
Performance Evaluation

Scale + Rotation (bark)

  • Data Set
    • From Visual Geometry Group

Viewpoint change (graf)

Illumination change (leuven)

Viewpoint change (wall)

Blur

Blurring (bikes)

performance evaluation14

Total # possible matches

Performance Evaluation
  • Evaluation Criteria
    • Match features from first image to the second one based on the nearest neighbor distance ratio
      • That is, two features are matched if first nearest neighbor is much closer than the second nearest neighbor
      • This is different from the threshold-based criterion used in “A Performance Evaluation of Local Descriptors” TPAMI 2005
    • Count the number of correct matches and the number of false matches obtained for an image pair
    • The results are plotted in form of recall versus 1-precision curves
performance evaluation15

Viewpoint change (graf)

Performance Evaluation

Viewpoint change (wall)

Scale + Rotation (bark)

Blurring (bikes)

Illumination change (leuven)

performance evaluation result
Performance Evaluation Result
  • For accuracy  SIFT
  • For speed  PCA-SIFT
  • In large database  ?
start from scratch
Start from Scratch
  • Comparison of my descriptor with SIFT
    • Simply designed vs carefully designed
  • Result
    • SIFT is a carefully designed descriptor, it remains robust when the degree of transformation increases

Increasing illumination change

Increasing affine change

Increasing affine change

Increasing blur

extension using color
Extension using Color
  • Weijier extends local feature descriptors with color information, by concatenating a color descriptor, K, to the shape descriptor, S, according to
  • where B is the combined color and shape descriptor and is a weighting parameter and ^ indicates that the vector is normalized.

J. van de Weijer and C. Schmid. Coloring local feature extraction. ECCV2006.

proposed extension using color
Proposed Extension using Color
  • Problem statement
    • Orientation of local feature patch are obtained from the monochrome intensity image
    • Color feature patches on the right has the same grayscale patches shown on the left. Thus, they are assigned the same orientation histogram
    • If we can generate significant orientation histogram for each of them, we can further improve the distinctiveness of the shape descriptor, SIFT

feature matching
Feature Matching
  • Original distance metric designed for SIFT, PCA-SIFT and GLOH is bin-to-bin Euclidean distance
  • Problems:
    • Sensitive to quantization effects
    • Sensitive to distortion problems due to deformation, illumination change and noise
feature matching diffusion distance
Feature Matching – Diffusion Distance
  • Haibin Ling proposed a new distance metric for histogram-based descriptor called diffusion distance
  • Summing value in all layers of the distance pyramid with exponentially decreasing size

Gaussian Blur

In 3 directions

3D case

Gaussian Blur

In 1 direction

1D case

H. Ling and K. Okada. Diffusion distance for histogram comparison. CVPR06.

feature matching performance evaluation
Feature Matching – Performance Evaluation
  • Same setup as the previous experiment
  • Recall vs 1-prevision curve for image pair with affine transformation
feature matching performance evaluation23
Feature Matching – Performance Evaluation

Data set. The synthetic deformation data set from Haibin Ling

Images in the data set and the evaluation method needs to be improved

proposed extension
Proposed Extension
  • Robust aggregation of the histogram, such as average orientation direction and center of mass of derivatives, can be also used in comparison
  • Diffusion distance can be viewed as a form of comparison using the aggregate information
    • Its aggregation of histogram bins is obtained by repeatedly convolving the histogram with Gaussian kernels
    • Summation of the distance between each aggregation pair of two histograms gives the diffusion distance

Histogram A

Histogram B

128 bins

128 bins

64 bins

64 bins

32 bins

32 bins

Aggregation:1. Average of gradient magnitude over location bins 2. Bin reduction in orientation bins

conclusion and future work
Conclusion and Future Work
  • Presented
    • Result of performance evaluation of some state-of-the-art descriptors and feature matching distance metric
    • Possible way to improve the description and matching step
  • TODO
    • Incorporate color information into local features
      • Improve feature’s distinctiveness
    • Design a distance metric for comparing SIFT feature’s histogram
      • Invariant to deformation (like diffusion distance)
      • Improve feature’s distinctiveness
slide26

Q & A

Thank you very much!

models of image change
Models of Image Change
  • Geometry
    • Rotation
    • Similarity (rotation + uniform scale)
    • Affine (scale dependent on direction)valid for: orthographic camera, locally planar object
  • Photometry
    • Affine intensity change (I  aI + b)