4,421 research outputs found
Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation
The photo-response non-uniformity (PRNU) is a distinctive image sensor
characteristic, and an imaging device inadvertently introduces its sensor's
PRNU into all media it captures. Therefore, the PRNU can be regarded as a
camera fingerprint and used for source attribution. The imaging pipeline in a
camera, however, involves various processing steps that are detrimental to PRNU
estimation. In the context of photographic images, these challenges are
successfully addressed and the method for estimating a sensor's PRNU pattern is
well established. However, various additional challenges related to generation
of videos remain largely untackled. With this perspective, this work introduces
methods to mitigate disruptive effects of widely deployed H.264 and H.265 video
compression standards on PRNU estimation. Our approach involves an intervention
in the decoding process to eliminate a filtering procedure applied at the
decoder to reduce blockiness. It also utilizes decoding parameters to develop a
weighting scheme and adjust the contribution of video frames at the macroblock
level to PRNU estimation process. Results obtained on videos captured by 28
cameras show that our approach increases the PRNU matching metric up to more
than five times over the conventional estimation method tailored for photos
Adding Cues to Binary Feature Descriptors for Visual Place Recognition
In this paper we propose an approach to embed continuous and selector cues in
binary feature descriptors used for visual place recognition. The embedding is
achieved by extending each feature descriptor with a binary string that encodes
a cue and supports the Hamming distance metric. Augmenting the descriptors in
such a way has the advantage of being transparent to the procedure used to
compare them. We present two concrete applications of our methodology,
demonstrating the two considered types of cues. In addition to that, we
conducted on these applications a broad quantitative and comparative evaluation
covering five benchmark datasets and several state-of-the-art image retrieval
approaches in combination with various binary descriptor types.Comment: 8 pages, 8 figures, source: www.gitlab.com/srrg-software/srrg_bench,
submitted to ICRA 201
Fast Color Quantization Using Weighted Sort-Means Clustering
Color quantization is an important operation with numerous applications in
graphics and image processing. Most quantization methods are essentially based
on data clustering algorithms. However, despite its popularity as a general
purpose clustering algorithm, k-means has not received much respect in the
color quantization literature because of its high computational requirements
and sensitivity to initialization. In this paper, a fast color quantization
method based on k-means is presented. The method involves several modifications
to the conventional (batch) k-means algorithm including data reduction, sample
weighting, and the use of triangle inequality to speed up the nearest neighbor
search. Experiments on a diverse set of images demonstrate that, with the
proposed modifications, k-means becomes very competitive with state-of-the-art
color quantization methods in terms of both effectiveness and efficiency.Comment: 30 pages, 2 figures, 4 table
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