SlideServe
Télécharger Obtenir Pro
Menu
Accueil Pro
Préférences
ApparenceLight Aide et commentaires
© SlideServe · v2026

Object Recognition

sheng avatar
sheng
11 diapositives · 308 Views · Oct 12, 2014

Object Recognition. Article: Distinctive Image Features from Scale -Invariant Keypoints. C. Pantofaru and M. Hebert. CMU Technical Report, September 2005. Xiang Li

Object Recognition - slide 1
01 / 11Object Recognition
1 / 17

Télécharger la présentation

Object Recognition
Une image / un lien ci-dessous est fourni (tel quel) pour télécharger la présentation

Politique de téléchargement : le contenu du site vous est fourni TEL QUEL pour votre information et votre usage personnel ; il ne peut être vendu, concédé sous licence ni partagé sur d’autres sites sans l’accord de son auteur.Le contenu vous est fourni TEL QUEL, pour votre information et votre usage personnel uniquement. Téléchargez la présentation en cliquant sur ce lien. Lors du téléchargement, si vous ne parvenez pas à télécharger une présentation, l’éditeur a peut-être supprimé le fichier de son serveur.Pendant le téléchargement, si vous ne parvenez pas à obtenir une présentation, le fichier a peut-être été supprimé par l’éditeur.

F I N

Plus de contenu associé

Transcription de la présentation

  1. Object Recognition Article: Distinctive Image Features from Scale -Invariant Keypoints. C. Pantofaru and M. Hebert. CMU Technical Report, September 2005. Xiang Li Jan, 21, 2009

  2. Introduction • Scale-space peak selection • Peak detection • Frequency of sampling in scale • Frequency of sampling in spatial domain • Keypoint localization • Orientation assignment • Keypoint descriptor • Application

  3. Scale-space peak selection • difference-of-Gaussian function D(x, y, σ) = (G(x, y, k) - G(x, y, σ)) * I(x; y) = L(x, y, k) - L(x, y, σ) Q: Why there is a need to difference Gaussian function instead of pure Gaussian function? (Mahdi)

  4. Peak detection • To detect the locations of all local maxima and minima (peaks) of D(x, y, σ) • Using the sampling frequency which provide the best results under a realistic simulation.

  5. Frequency of sampling in scale • The highest repeatability is obtained when sampling 3 scales per octave.

  6. Frequency of sampling in spatial domain • Repeatability and Cost • we have chosen to use σ = 1.6, which provides close to optimal repeatability. Q : There is a cost to using a large σ in terms of efficiency and a reduced number of keypoints (shown by the lower line). How did he get the conclusion?

  7. Keypoint localization • Taylor expansion • For the experiments in this paper, all peaks with a value of D(^x) less than 0.03 were discarded.

  8. Orientation assignment The experimental stability of orientation assignment under differing amounts of image noise.

  9. Keypoint descriptor • Based on a model of biological vision • Different computational mechanism • Descriptor representation • Descriptor testing • Sensitivity to affine change • Matching to large databases

  10. Application • Keypoint matching • Efficient nearest neighbor indexing Q: What are bins which are to be searched in Best-Bin-First (BBF) algorithm in feature space? (Dustin) • A modification of the k-d tree algorithm called the best-bin-first search method can identify the nearest neighbors with high probability using only a limited amount of computation. Q: In Page 21, it said if the ratio is greater than 0.8, it should be rejected. How did he get the number 0.8? (Xiang LI)

  11. Question Q: What is Hough transform? (Mahdi) • It is used to search for keys that agree upon a particular model pose. • A feature extraction technique used in image analysis, computer vision, and digital image processing. • It is to find imperfect instances of objects within a certain class of shapes by a voting procedure. • This voting procedure is carried out in a parameter space, from which object candidates are obtained as local maxima in a so-called accumulator space that is explicitly constructed by the algorithm for computing the Hough transform. • Ballard, D.H., “Generalizing the Hough transform to detectarbitrary patterns,” Pattern Recognition, 13, 2 (1981), pp.111-122.