128,156 research outputs found
Log-Spiral Keypoint: A Robust Approach toward Image Patch Matching
Matching of keypoints across image patches forms the basis of computer vision applications, such as object detection, recognition, and tracking in real-world images. Most of keypoint methods are mainly used to match the high-resolution images, which always utilize an image pyramid for multiscale keypoint detection. In this paper, we propose a novel keypoint method to improve the matching performance of image patches with the low-resolution and small size. The location, scale, and orientation of keypoints are directly estimated from an original image patch using a Log-Spiral sampling pattern for keypoint detection without consideration of image pyramid. A Log-Spiral sampling pattern for keypoint description and two bit-generated functions are designed for generating a binary descriptor. Extensive experiments show that the proposed method is more effective and robust than existing binary-based methods for image patch matching
Anytime Stereo Image Depth Estimation on Mobile Devices
Many applications of stereo depth estimation in robotics require the
generation of accurate disparity maps in real time under significant
computational constraints. Current state-of-the-art algorithms force a choice
between either generating accurate mappings at a slow pace, or quickly
generating inaccurate ones, and additionally these methods typically require
far too many parameters to be usable on power- or memory-constrained devices.
Motivated by these shortcomings, we propose a novel approach for disparity
prediction in the anytime setting. In contrast to prior work, our end-to-end
learned approach can trade off computation and accuracy at inference time.
Depth estimation is performed in stages, during which the model can be queried
at any time to output its current best estimate. Our final model can process
1242375 resolution images within a range of 10-35 FPS on an NVIDIA
Jetson TX2 module with only marginal increases in error -- using two orders of
magnitude fewer parameters than the most competitive baseline. The source code
is available at https://github.com/mileyan/AnyNet .Comment: Accepted by ICRA201
Single Frame Image super Resolution using Learned Directionlets
In this paper, a new directionally adaptive, learning based, single image
super resolution method using multiple direction wavelet transform, called
Directionlets is presented. This method uses directionlets to effectively
capture directional features and to extract edge information along different
directions of a set of available high resolution images .This information is
used as the training set for super resolving a low resolution input image and
the Directionlet coefficients at finer scales of its high-resolution image are
learned locally from this training set and the inverse Directionlet transform
recovers the super-resolved high resolution image. The simulation results
showed that the proposed approach outperforms standard interpolation techniques
like Cubic spline interpolation as well as standard Wavelet-based learning,
both visually and in terms of the mean squared error (mse) values. This method
gives good result with aliased images also.Comment: 14 pages,6 figure
High-quality Image Restoration from Partial Mixed Adaptive-Random Measurements
A novel framework to construct an efficient sensing (measurement) matrix,
called mixed adaptive-random (MAR) matrix, is introduced for directly acquiring
a compressed image representation. The mixed sampling (sensing) procedure
hybridizes adaptive edge measurements extracted from a low-resolution image
with uniform random measurements predefined for the high-resolution image to be
recovered. The mixed sensing matrix seamlessly captures important information
of an image, and meanwhile approximately satisfies the restricted isometry
property. To recover the high-resolution image from MAR measurements, the total
variation algorithm based on the compressive sensing theory is employed for
solving the Lagrangian regularization problem. Both peak signal-to-noise ratio
and structural similarity results demonstrate the MAR sensing framework shows
much better recovery performance than the completely random sensing one. The
work is particularly helpful for high-performance and lost-cost data
acquisition.Comment: 16 pages, 8 figure
Real-time self-adaptive deep stereo
Deep convolutional neural networks trained end-to-end are the
state-of-the-art methods to regress dense disparity maps from stereo pairs.
These models, however, suffer from a notable decrease in accuracy when exposed
to scenarios significantly different from the training set, e.g., real vs
synthetic images, etc.). We argue that it is extremely unlikely to gather
enough samples to achieve effective training/tuning in any target domain, thus
making this setup impractical for many applications. Instead, we propose to
perform unsupervised and continuous online adaptation of a deep stereo network,
which allows for preserving its accuracy in any environment. However, this
strategy is extremely computationally demanding and thus prevents real-time
inference. We address this issue introducing a new lightweight, yet effective,
deep stereo architecture, Modularly ADaptive Network (MADNet) and developing a
Modular ADaptation (MAD) algorithm, which independently trains sub-portions of
the network. By deploying MADNet together with MAD we introduce the first
real-time self-adaptive deep stereo system enabling competitive performance on
heterogeneous datasets.Comment: Accepted at CVPR2019 as oral presentation. Code Available
https://github.com/CVLAB-Unibo/Real-time-self-adaptive-deep-stere
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