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Affine invariant features from the trace transform.

Maria Petrou1, Alexander Kadyrov

  • 1School of Electronics and Physical Sciences, University of Surrey, Guildford, GU2 7XH, UK. m.petrou@surrey.ac.uk

IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 24, 2004
PubMed
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This study introduces a new method using trace transforms to create image features invariant to affine transformations. These features improve image retrieval accuracy compared to existing object retrieval techniques.

Area of Science:

  • Computer Vision
  • Image Processing
  • Geometric Transformations

Background:

  • The Radon transform is a fundamental tool in image analysis.
  • Image feature invariance is crucial for robust object recognition and retrieval.
  • Existing methods may lack invariance to complex transformations like affine transforms.

Purpose of the Study:

  • To develop a methodology for computing image features invariant to affine transformations.
  • To generalize the concept of the trace transform for enhanced image analysis.
  • To evaluate the effectiveness of these invariant features in image database retrieval.

Main Methods:

  • Generalizing the trace transform to handle affine transformations.
  • Defining and computing appropriate functionals from image data.

Related Experiment Videos

  • Implementing image descriptors based on these functionals.
  • Utilizing these descriptors for image retrieval tasks.

    • Main Results:

    • Successfully computed image features invariant to affine transformations.
    • Demonstrated the utility of these features in an image retrieval system.
    • Achieved competitive or superior performance compared to state-of-the-art methods in object retrieval.

    Conclusions:

    • The proposed trace transform-based methodology provides effective affine-invariant image descriptors.
    • These descriptors enhance the performance of image retrieval systems.
    • The approach offers a valuable contribution to invariant feature representation in computer vision.