What is recorded here?
A local intensity-change matrix and determinant/trace response distinguish corners from edges without relying on a few sampled shift directions, supporting combined feature extraction. A camera needs recognizable points to track a scene as it moves. A plain patch changes little when shifted; an edge changes strongly in one direction; a corner changes in several directions. Harris and Stephens summarized these differences in a small matrix and used its determinant and trace to produce a corner response. This corrected direction-sampling and window problems in the earlier Moravec detector and combined corner detection with edge extraction. The matrix description is rotation invariant in its local mathematical model. It is not a guarantee of reliable matching at every scale, viewpoint or lighting condition. The original paper showed natural-scene examples and explicitly left better junction completion and adaptive thresholds for future work. The original image and tracking results have not been independently reproduced here.
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Scientific scope
The detector uses local image-gradient statistics, a specified window and response/threshold choices. Its geometric interpretation concerns the local model; noise, scale, aperture ambiguity and textured regions can limit detection. It is not a universal object-recognition algorithm or exact projective invariance. Reported image examples have not been independently replayed.
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People and systems
Chris Harris; Mike Stephens
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English catalogue narrative
Harris and Stephens: tell corners from edges for visual tracking
A local intensity-change matrix and determinant/trace response distinguish corners from edges without relying on a few sampled shift directions, supporting combined feature extraction.
A camera needs recognizable points to track a scene as it moves. A plain patch changes little when shifted; an edge changes strongly in one direction; a corner changes in several directions. Harris and Stephens summarized these differences in a small matrix and used its determinant and trace to produce a corner response. This corrected direction-sampling and window problems in the earlier Moravec detector and combined corner detection with edge extraction. The matrix description is rotation invariant in its local mathematical model. It is not a guarantee of reliable matching at every scale, viewpoint or lighting condition. The original paper showed natural-scene examples and explicitly left better junction completion and adaptive thresholds for future work. The original image and tracking results have not been independently reproduced here.
Scientific scope
The detector uses local image-gradient statistics, a specified window and response/threshold choices. Its geometric interpretation concerns the local model; noise, scale, aperture ambiguity and textured regions can limit detection. It is not a universal object-recognition algorithm or exact projective invariance. Reported image examples have not been independently replayed.
People and systems
Chris Harris; Mike Stephens