Abstract: A wavelet transform is applied to successive images of a video sequence to obtain wavelet coefficients for each image, and directions of regularity are estimated in association with the wavelet coefficients. Recursive weights are also determined to be associated with the wavelet coefficients. An average multiscale bandlet coefficient associated with a wavelet coefficient for a current image is computed from this wavelet coefficient, the recursive weight associated therewith and a corresponding average multiscale bandlet coefficient computed for a previous image and associated with a wavelet coefficient offset according to the direction of regularity associated with the wavelet coefficient. The average multiscale bandlet coefficients can then be processed to produce an enhanced bandlet image to which an inverse wavelet transform is applied.

Abstract: A method for the recognition of objects in at least one digital image includes: a) simulating from the digital image a plurality of digital rotations and at least two digital tilts different from 1 in order to develop a simulated image for each rotation-tilt pair; and b) applying an algorithm generating values that are invariant in translation, rotation and zoom onto the simulated images in order to determine so-called SIF (scale invariant features) local characteristics used for recognizing objects. The SIFT method can be used in step b.

Abstract: Techniques for determining a feature in an image or soundtrack of one or more dimensions include receiving a subject image. A sparse transformed subject image is determined, which represents the subject image with a few significant coefficients compared to a number of values in the subject image. Multiple patch functions are received, which are based on a portion of a sparse transformed image for each of a training set of images and which represent learned features in the training set. A feature is determined to be in the subject image based on the transformed subject image and the plurality of patch functions. In various embodiments, a wavelet transformation or audio spectrogram is performed to produce the sparse transformed images. In some embodiments, the feature in the subject is determined regardless of feature location or size or orientation in the subject image.

Abstract: A method for the recognition of objects in at least one digital image includes: a) simulating from the digital image a plurality of digital rotations and at least two digital tilts different from 1 in order to develop a simulated image for each rotation-tilt pair; and b) applying an algorithm generating values that are invariant in translation, rotation and zoom onto the simulated images in order to determine so-called SIF (scale invariant features) local characteristics used for recognising objects. The SIFT method can be used in step b.

Abstract: A wavelet transform is applied to successive images of a video sequence to obtain wavelet coefficients for each image, and directions of regularity are estimated in association with the wavelet coefficients. Recursive weights are also determined to be associated with the wavelet coefficients. An average multiscale bandlet coefficient associated with a wavelet coefficient for a current image is computed from this wavelet coefficient, the recursive weight associated therewith and a corresponding average multiscale bandlet coefficient computed for a previous image and associated with a wavelet coefficient offset according to the direction of regularity associated with the wavelet coefficient. The average multiscale bandlet coefficients can then be processed to produce an enhanced bandlet image to which an inverse wavelet transform is applied.

Abstract: Techniques for determining a feature in an image or soundtrack of one or more dimensions include receiving a subject image. A sparse transformed subject image is determined, which represents the subject image with a few significant coefficients compared to a number of values in the subject image. Multiple patch functions are received, which are based on a portion of a sparse transformed image for each of a training set of images and which represent learned features in the training set. A feature is determined to be in the subject image based on the transformed subject image and the plurality of patch functions. In various embodiments, a wavelet transformation or audio spectrogram is performed to produce the sparse transformed images. In some embodiments, the feature in the subject is determined regardless of feature location or size or orientation in the subject image.