In the fields of computer vision and , the Harris affine region detector belongs to the category of feature detection. Feature detection is a preprocessing step of several algorithms that rely on identifying characteristic points or interest points so to make correspondences between images, recognize textures, categorize objects or build panoramas.
The Harris affine detector can identify similar regions between images that are related through affine transformations and have different illuminations. These affine-invariant detectors should be capable of identifying similar regions in images taken from different viewpoints that are related by a simple geometric transformation: scaling, rotation and shearing. These detected regions have been called both invariant and covariant. On one hand, the regions are detected invariant of the image transformation but the regions covariantly change with image transformation. Do not dwell too much on these two naming conventions; the important thing to understand is that the design of these interest points will make them compatible across images taken from several viewpoints. Other detectors that are affine-invariant include Hessian affine region detector, Maximally stable extremal regions, Kadir–Brady saliency detector, edge-based regions (EBR) and intensity-extrema-based regions (IBR).
Mikolajczyk and Schmid (2002) first described the Harris affine detector as it is used today in An Affine Invariant Interest Point Detector. Earlier works in this direction include use of affine shape adaptation by Lindeberg and Garding for computing affine invariant image descriptors and in this way reducing the influence of perspective image deformations, the use affine adapted feature points for wide baseline matching by Baumberg and the first use of scale invariant feature points by Lindeberg; for an overview of the theoretical background. The Harris affine detector relies on the combination of corner points detected through Harris corner detection, multi-scale analysis through Gaussian scale space and affine normalization using an iterative affine shape adaptation algorithm.
Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.
The course presents the detection of ionizing radiation in the keV and MeV energy ranges. Physical processes of radiation/matter interaction are introduced. All steps of detection are covered, as well
[[Fichier:Matching of two images using the SIFT method.jpg|thumb|right|alt=Exemple de mise en correspondance de deux images par la méthode SIFT : des lignes vertes relient entre eux les descripteurs communs à un tableau et une photo de ce même tableau, de moindre qualité, ayant subi des transformations. |Exemple de résultat de la comparaison de deux images par la méthode SIFT (Fantasia ou Jeu de la poudre, devant la porte d’entrée de la ville de Méquinez, par Eugène Delacroix, 1832).
In computer vision and , a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in the image such as points, edges or objects. Features may also be the result of a general neighborhood operation or feature detection applied to the image. Other examples of features are related to motion in image sequences, or to shapes defined in terms of curves or boundaries between different image regions.
Couvre les bases de la vision par ordinateur, en se concentrant sur les techniques de traitement d'image et la détection de caractéristiques.
Explore la symétrie dans la géométrie moderne, en mettant l'accent sur les isométries, les réflexions, les rotations et les traductions, soulignant l'importance des rotations de 180 degrés.
Explore les principes de détection dans les détecteurs d'ionisation gazeuse et le comportement des régions de temps mort, de perte d'énergie, de dérive de charge et de détection.
Effective Prognostics and Health Management (PHM) relies on accurate prediction of the Remaining Useful Life (RUL). Data-driven RUL prediction techniques rely heavily on the representativeness of the available time-to-failure trajectories. Therefore, these ...
Endoscopy is the gold standard procedure for early detection and treatment of numerous diseases. Obtaining 3D reconstructions from real endoscopic videos would facilitate the development of assistive tools for practitioners, but it is a challenging problem ...
In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...