What is Shi-Tomasi?

What is Shi-Tomasi?

Tomasi made a small modification to it in their paper Good Features to Track which shows better results compared to Harris Corner Detector. The scoring function in Harris Corner Detector was given by: Instead of this, Shi-Tomasi proposed: If it is a greater than a threshold value, it is considered as a corner.

What is Shi-Tomasi corner detection?

Shi-Tomasi Corner Detection was published by J. Shi and C. Tomasi in their paper ‘Good Features to Track’. Here the basic intuition is that corners can be detected by looking for significant change in all direction.

How does Harris corner Detection works?

The Harris Corner Detector is just a mathematical way of determining which windows produce large variations when moved in any direction. With each window, a score R is associated. Based on this score, you can figure out which ones are corners and which ones are not.

How do you detect a corner?

Corner Detection

  1. When |R| is small, which happens when λ1 and λ2 are small, the region is flat.
  2. When R<0, which happens when λ1>>λ2 or vice versa, the region is an edge.
  3. When R is large, which happens when λ1 and λ2 are large and λ1∼λ2, the region is a corner.

What is canny edge detection algorithm?

The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images. It was developed by John F. Canny in 1986. Canny also produced a computational theory of edge detection explaining why the technique works.

What is the main idea for Harris corner detector write in one sentence?

The Harris corner detector is a corner detection operator that is commonly used in computer vision algorithms to extract corners and infer features of an image.

What is the main difference between the Moravec and the Harris Plessey corner detectors?

Moravec only considered shifts in discrete 45 degree angles whereas Harris considered all directions. Harris detector has proved to be more accurate in distinguishing between edges and corners. He used a circular Gaussian window to reduce noise.

What is your intuition behind what makes the Harris corner detector effective?

Interest Point Detection The basic intuition behind the Harris Detector is that sliding a small window over the image causes graident change in different directions. This can be used to detect corners as shifting the window in any direction will result in a large change.

What is block size in Harris corner?

blockSize – It is the size of neighbourhood considered for corner detection. ksize – Aperture parameter of the Sobel derivative used.

How do I use cornerSubPix?


  1. Load the image and find the corners using Harris Corner Detector as we did in the previous blog.
  2. Now, there may be a bunch of pixels at the corner, so we take their centroids.
  3. Then, we define the stopping criteria and refine the corners to subpixel accuracy using the cv2.cornerSubPix()

Is Harris corner detector translation invariant?

Corners are the important features in the image, and they are generally termed as interest points which are invariant to translation, rotation and illumination.

What is edge detection in OpenCV?

Edge detection is an image-processing technique, which is used to identify the boundaries (edges) of objects, or regions within an image. Edges are among the most important features associated with images.

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