24,565 research outputs found
Compressive video sampling
Compressive sampling is a novel framework that exploits sparsity of a signal in a transform domain to perform sampling below the Nyquist rate. In this paper, we apply compressive sampling to reduce the sampling rate of images/video. The key idea is to exploit the intra- and inter-frame correlation to improve signal recovery algorithms. The image is split into non-overlapping blocks of fixed size, which are independently compressively sampled exploiting sparsity of natural scenes in the Discrete Cosine Transform (DCT) domain. At the decoder, each block is recovered using useful information extracted from the recovery of a neighboring block. In the case of video, a previous frame is used to help recovery of consecutive frames. The iterative algorithm for signal recovery with side information that extends the standard orthogonal matching pursuit (OMP) algorithm is employed. Simulation results are given for Magnetic Resonance Imaging (MRI) and video sequences to illustrate advantages of the proposed solution compared to the case when side information is not used
Adaptive-Rate Compressive Sensing Using Side Information
We provide two novel adaptive-rate compressive sensing (CS) strategies for
sparse, time-varying signals using side information. Our first method utilizes
extra cross-validation measurements, and the second one exploits extra
low-resolution measurements. Unlike the majority of current CS techniques, we
do not assume that we know an upper bound on the number of significant
coefficients that comprise the images in the video sequence. Instead, we use
the side information to predict the number of significant coefficients in the
signal at the next time instant. For each image in the video sequence, our
techniques specify a fixed number of spatially-multiplexed CS measurements to
acquire, and adjust this quantity from image to image. Our strategies are
developed in the specific context of background subtraction for surveillance
video, and we experimentally validate the proposed methods on real video
sequences
Compressively Sensed Image Recognition
Compressive Sensing (CS) theory asserts that sparse signal reconstruction is
possible from a small number of linear measurements. Although CS enables
low-cost linear sampling, it requires non-linear and costly reconstruction.
Recent literature works show that compressive image classification is possible
in CS domain without reconstruction of the signal. In this work, we introduce a
DCT base method that extracts binary discriminative features directly from CS
measurements. These CS measurements can be obtained by using (i) a random or a
pseudo-random measurement matrix, or (ii) a measurement matrix whose elements
are learned from the training data to optimize the given classification task.
We further introduce feature fusion by concatenating Bag of Words (BoW)
representation of our binary features with one of the two state-of-the-art
CNN-based feature vectors. We show that our fused feature outperforms the
state-of-the-art in both cases.Comment: 6 pages, submitted/accepted, EUVIP 201
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