Abstract:
A method extracts a low-rank descriptor of a video acquired of a scene by first extracting a set of descriptors for each image in the video. The sets of descriptors for the video are aggregated to form a descriptor matrix. Iteratively, a low-rank descriptor matrix is determined from the descriptor matrix, as well as a selection matrix that associates each column in the descriptor matrix to a corresponding column in the low-rank descriptor matrix. The low-rank descriptor matrix is output upon convergence.
Abstract:
A pairwise distance computation transforms first and second signals using an absolute distance preserving mapping, such that a k-norm distance between the first mapped signal and the second mapped signal represents an absolute distance between the first signal and the second signal. The absolute distance preserving mapping maps an element of a first or a second signal to a vector having a size equal to a cardinality of the finite alphabet of the signals. The absolute distance preserving mapping determines a position N of the element in an ordered sequence of symbols of the finite alphabet and determines values for each of N elements of the vector as a fractional power 1/k of positive increments in the finite alphabet. The values for subsequent elements of the vector are determined as zero.
Abstract:
A method for authenticating biometric data determines a first set of descriptors of a fingerprint. Each descriptor in the first set represents a region of the fingerprint that includes multiple minutiae. The method compares each descriptor in the first set of descriptors with each descriptor in a second set of descriptors to determine a number of matching descriptors and compares the number of matching descriptors with a threshold for authenticating the biometric data.
Abstract:
In a decoder, a desired image is estimated by first retrieving coding modes from an encoded side information image. For each bitplane in the encoded side information image, syndrome bits or parity bits are decoded to obtain an estimated bitplane of quantized transform coefficients of the desired image. A quantization and a transform are applied to a prediction residual obtained using the coding modes, wherein the decoding uses the quantized transform coefficients of the encoded side information image, and is based on previously decoded bitplanes in a causal neighborhood. The estimated bitplanes of quantized transform coefficients of the desired image are combined to produce combined bitplanes. Then, an inverse quantization, an inverse transform and a prediction based on the coding modes are applied to the combined bitplanes to recover the estimate of the desired image.
Abstract:
A method classifies data to determine hidden states of a system, by first randomly permuting the data and inserting client to generate private data. A server classifies the private data according to a hidden Markov model (HMM) to obtain permuted noisy estimates of the states and the chaff, which are returned to the client. The client then removes the chaff, inverts the permuted noisy estimates to obtain unpermuted noisy estimates of the states.
Abstract:
A pairwise distance computation transforms first and second signals using an absolute distance preserving mapping, such that a k-norm distance between the first mapped signal and the second mapped signal represents an absolute distance between the first signal and the second signal. The absolute distance preserving mapping maps an element of a first or a second signal to a vector having a size equal to a cardinality of the finite alphabet of the signals. The absolute distance preserving mapping determines a position N of the element in an ordered sequence of symbols of the finite alphabet and determines values for each of N elements of the vector as a fractional power 1/k of positive increments in the finite alphabet. The values for subsequent elements of the vector are determined as zero.
Abstract:
A method classifies data to determine hidden states of a system, by first randomly permuting the data and inserting client to generate private data. A server classifies the private data according to a hidden Markov model (HMM) to obtain permuted noisy estimates of the states and the chaff, which are returned to the client. The client then removes the chaff, inverts the permuted noisy estimates to obtain unpermuted noisy estimates of the states.
Abstract:
A method authenticates an encryption of a probe vector of biometric data based on an encryption of an enrolment vector of the biometric data using consistency of discriminative elements of the biometric data. The method determines an encryption of a first distance between discriminative elements of an enrolment vector stored at a server and a probe vector presented for an authentication. The method also determines an encryption of a second distance between discriminative elements of a first consistency vector stored at the server and a second consistency vector presented for the authentication. Next, the biometric data is authenticated based on encryptions of the first and the second distances.
Abstract:
Distances between data are encoded by performing a random projection, followed by dithering and scaling, with a fixed scaling for all values. The resulting dithered and scaled projection is quantized using a non-monotonic 1-bit quantizer to form a vector of bits representing the signal. The distance between signals can be approximately calculated from the corresponding vectors of bits by computing the hamming distance of the two vectors of bits. The computation is approximately correct up to a specific distance, determined by the scaling, and not beyond that.
Abstract:
A method constructs a descriptor for an image of a scene, wherein the descriptor is associated with a vanishing point in the image by first quantizing an angular region around the vanishing point into a preset number of angular quantization bins, and a centroid of each angular quantization bin indicates a direction of the angular quantization bin. For each angular quantization bin, a sum of magnitudes of pixel gradients for pixels in the image at which a direction of the pixel gradient is aligned with the direction of the angular quantization bin is determined, wherein the steps are performed in a processor.