3,995 research outputs found
Deep Bilateral Learning for Real-Time Image Enhancement
Performance is a critical challenge in mobile image processing. Given a
reference imaging pipeline, or even human-adjusted pairs of images, we seek to
reproduce the enhancements and enable real-time evaluation. For this, we
introduce a new neural network architecture inspired by bilateral grid
processing and local affine color transforms. Using pairs of input/output
images, we train a convolutional neural network to predict the coefficients of
a locally-affine model in bilateral space. Our architecture learns to make
local, global, and content-dependent decisions to approximate the desired image
transformation. At runtime, the neural network consumes a low-resolution
version of the input image, produces a set of affine transformations in
bilateral space, upsamples those transformations in an edge-preserving fashion
using a new slicing node, and then applies those upsampled transformations to
the full-resolution image. Our algorithm processes high-resolution images on a
smartphone in milliseconds, provides a real-time viewfinder at 1080p
resolution, and matches the quality of state-of-the-art approximation
techniques on a large class of image operators. Unlike previous work, our model
is trained off-line from data and therefore does not require access to the
original operator at runtime. This allows our model to learn complex,
scene-dependent transformations for which no reference implementation is
available, such as the photographic edits of a human retoucher.Comment: 12 pages, 14 figures, Siggraph 201
Going Further with Point Pair Features
Point Pair Features is a widely used method to detect 3D objects in point
clouds, however they are prone to fail in presence of sensor noise and
background clutter. We introduce novel sampling and voting schemes that
significantly reduces the influence of clutter and sensor noise. Our
experiments show that with our improvements, PPFs become competitive against
state-of-the-art methods as it outperforms them on several objects from
challenging benchmarks, at a low computational cost.Comment: Corrected post-print of manuscript accepted to the European
Conference on Computer Vision (ECCV) 2016;
https://link.springer.com/chapter/10.1007/978-3-319-46487-9_5
Profile Guided Dataflow Transformation for FPGAs and CPUs
This paper proposes a new high-level approach for optimising field programmable gate array (FPGA) designs. FPGA designs are commonly implemented in low-level hardware description languages (HDLs), which lack the abstractions necessary for identifying opportunities for significant performance improvements. Using a computer vision case study, we show that modelling computation with dataflow abstractions enables substantial restructuring of FPGA designs before lowering to the HDL level, and also improve CPU performance. Using the CPU transformations, runtime is reduced by 43 %. Using the FPGA transformations, clock frequency is increased from 67MHz to 110MHz. Our results outperform commercial low-level HDL optimisations, showcasing dataflow program abstraction as an amenable computation model for highly effective FPGA optimisation
Fair comparison of skin detection approaches on publicly available datasets
Skin detection is the process of discriminating skin and non-skin regions in
a digital image and it is widely used in several applications ranging from hand
gesture analysis to track body parts and face detection. Skin detection is a
challenging problem which has drawn extensive attention from the research
community, nevertheless a fair comparison among approaches is very difficult
due to the lack of a common benchmark and a unified testing protocol. In this
work, we investigate the most recent researches in this field and we propose a
fair comparison among approaches using several different datasets. The major
contributions of this work are an exhaustive literature review of skin color
detection approaches, a framework to evaluate and combine different skin
detector approaches, whose source code is made freely available for future
research, and an extensive experimental comparison among several recent methods
which have also been used to define an ensemble that works well in many
different problems. Experiments are carried out in 10 different datasets
including more than 10000 labelled images: experimental results confirm that
the best method here proposed obtains a very good performance with respect to
other stand-alone approaches, without requiring ad hoc parameter tuning. A
MATLAB version of the framework for testing and of the methods proposed in this
paper will be freely available from https://github.com/LorisNann
Navigating the roadblocks to spectral color reproduction: data-efficient multi-channel imaging and spectral color management
Commercialization of spectral imaging for color reproduction will require the identification and traversal of roadblocks to its success. Among the drawbacks associated with spectral reproduction is a tremendous increase in data capture bandwidth and processing throughput. Methods are proposed for attenuating these increases with data-efficient methods based on adaptive multi-channel visible-spectrum capture and with low-dimensional approaches to spectral color management. First, concepts of adaptive spectral capture are explored. Current spectral imaging approaches require tens of camera channels although previous research has shown that five to nine channels can be sufficient for scenes limited to pre-characterized spectra. New camera systems are proposed and evaluated that incorporate adaptive features reducing capture demands to a similar few channels with the advantage that a priori information about expected scenes is not needed at the time of system design. Second, proposals are made to address problems arising from the significant increase in dimensionality within the image processing stage of a spectral image workflow. An Interim Connection Space (ICS) is proposed as a reduced dimensionality bottleneck in the processing workflow allowing support of spectral color management. In combination these investigations into data-efficient approaches improve two critical points in the spectral reproduction workflow: capture and processing. The progress reported here should help the color reproduction community appreciate that the route to data-efficient multi-channel visible spectrum imaging is passable and can be considered for many imaging modalities
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