# Motivation. Lecture 31: Object Recognition: SIFT Keys. Simple Example. Simple Example. Simple Example

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1 Lecture 31: Object Recognition: SIFT Keys Motivation Want to recognize a known objects from unknown viewpoints. find them in an image database of models Local Feature based Approaches Represent appearance of object by little intensity/feature patches. Try to match patches from object to image Geometrically consistent matches tell you the location and pose of the object Represent object by set of 11x11 intensity templates extracted around Harris corners. harris corners our object model Match patches to new image using NCC. Find matches consistent with affine transformation using RANSAC 1

2 Inlier matches let you solve for location and pose of object in the image. Problem with Using NCC to match intensity patches puts restrictions on the amount of overall rotation and scaling allowed between the model and the image appearance. model template ncc ncc ncc matches well no match no match More General : SIFT Keys SIFT Keys: General Idea Reliably extract same image points regardless of new magnification and rotation of the image. Normalize image patches, extract feature vector Match feature vectors using correlation David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp SIFT Keys: General Idea Want to detect/match same features regardless of Recall: Scale Space Basic idea: different scales are appropriate for describing different objects in the image, and we may not know the correct scale/size ahead of time. Translation : easy, almost every feature extraction and correlation matching algorithm in vision is translation invariant Rotation : harder. Guess a canonical orientation for each patch from local s Scaling : hardest of all. Create a multi-scale representation of the image and appeal to scale space theory to determine correct scale at each point. 2

3 Scale Selection Local Scale Space Maxima Lindeberg proposes that the natural scale for describing a feature is the scale at which a normalized derivative for detecting that feature achieves a local maximum both spatially and in scale. DnormL is the DoG operator, in this case. Laplacian operator. Example for blob detection Scale Recall: LoG Blob Finding LoG filter extrema locates blobs maxima = dark blobs on light background minima = light blobs on dark background Scale of blob (size ; radius in pixels) is determined by the sigma parameter of the LoG filter. Scale (sigma) Extrema in Space and Scale DOG level L-1 DOG level L DOG level L+1 Space LoG sigma = 2 LoG sigma = 10 Hint: when finding maxima or minima at level L, use DownSample or UpSample as necessary to make DOG images at level L-1 and L+1 the same size as L. SIFT Keys: General Idea Sift Key Steps Want to detect/match same features regardless of Translation : easy, almost every feature extraction and correlation matching algorithm in vision is translation invariant Rotation : harder. Guess a canonical orientation for each patch from local s Scaling : hardest of all. Create a multi-scale representation of the image and appeal to scale space theory to determine correct scale at each point. 3

4 Example Keypoint location = extrema location Keypoint scale is scale of the DOG image gaussian image (at closest scale, from pyramid) orientation weighted weighted orientation histogram. Each bucket contains sum of weighted s corresponding to angles that fall within that bucket..* = weighted by 2D gaussian kernel weighted orientation 36 buckets 10 degree range of angles in each bucket, i.e. 0 <=ang<10 : bucket 1 10<=ang<20 : bucket 2 20<=ang<30 : bucket 3 weighted weighted orientation histogram. There may be multiple orientations. peak 80% of peak value peak Second peak 80% of peak value orientation degrees Orientation of keypoint is approximately 25 degrees In this case, generate duplicate keypoints, one with orientation at 25 degrees, one at 155 degrees. Design decision: you may want to limit number of possible multiple peaks to two. 4

5 Example of KeyPoint Detection SIFT Vector to make things more insensitive to changes in lighting or small changes in geometry, Lowe constructs feature vector from image s. Each keypoint has a center point (location), an orientation (rotation) and a radius (scale). At this point, we could try to correlate patches (after first normalizing to a canonical orientation and scale). SIFT Vector Sift Key Matching Model Verification Application: Object Recognition Compute SIFT keys of models and store in a database 5

6 Application: Object Recognition Application: Object Recognition For sets of 3 SIFT key matches, compute affine transformation and perform model verification. database of models find them in an image Application: Object Recognition Application: Landmark Recognition Note: since these are local, parts-based descriptors, they perform well even when some parts are missing (i.e. under occlusion). Application: Generating Panoramas Application: Generating Panoramas Brown and Lowe, ICCV03 Brown and Lowe, ICCV03 6

7 State of the Art Fully affine invariant local feature descriptors. State of the Art Affine invariant descriptors can handle larger changes in viewpoint. State of the Art For More Information SIFT keys David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp Affine local-feature methods 7

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