37 research outputs found
Double Refinement Network for Efficient Indoor Monocular Depth Estimation
Monocular depth estimation is the task of obtaining a measure of distance for
each pixel using a single image. It is an important problem in computer vision
and is usually solved using neural networks. Though recent works in this area
have shown significant improvement in accuracy, the state-of-the-art methods
tend to require massive amounts of memory and time to process an image. The
main purpose of this work is to improve the performance of the latest solutions
with no decrease in accuracy. To this end, we introduce the Double Refinement
Network architecture. The proposed method achieves state-of-the-art results on
the standard benchmark RGB-D dataset NYU Depth v2, while its frames per second
rate is significantly higher (up to 18 times speedup per image at batch size 1)
and the RAM usage per image is lower
Unsupervised Deep Feature Transfer for Low Resolution Image Classification
In this paper, we propose a simple while effective unsupervised deep feature
transfer algorithm for low resolution image classification. No fine-tuning on
convenet filters is required in our method. We use pre-trained convenet to
extract features for both high- and low-resolution images, and then feed them
into a two-layer feature transfer network for knowledge transfer. A SVM
classifier is learned directly using these transferred low resolution features.
Our network can be embedded into the state-of-the-art deep neural networks as a
plug-in feature enhancement module. It preserves data structures in feature
space for high resolution images, and transfers the distinguishing features
from a well-structured source domain (high resolution features space) to a not
well-organized target domain (low resolution features space). Extensive
experiments on VOC2007 test set show that the proposed method achieves
significant improvements over the baseline of using feature extraction.Comment: 4 pages, accepted to ICCV19 Workshop and Challenge on Real-World
Recognition from Low-Quality Images and Video
DeepSTORM3D: dense three dimensional localization microscopy and point spread function design by deep learning
Localization microscopy is an imaging technique in which the positions of
individual nanoscale point emitters (e.g. fluorescent molecules) are determined
at high precision from their images. This is the key ingredient in
single/multiple-particle-tracking and several super-resolution microscopy
approaches. Localization in three-dimensions (3D) can be performed by modifying
the image that a point-source creates on the camera, namely, the point-spread
function (PSF). The PSF is engineered using additional optical elements to vary
distinctively with the depth of the point-source. However, localizing multiple
adjacent emitters in 3D poses a significant algorithmic challenge, due to the
lateral overlap of their PSFs. Here, we train a neural network to receive an
image containing densely overlapping PSFs of multiple emitters over a large
axial range and output a list of their 3D positions. Furthermore, we then use
the network to design the optimal PSF for the multi-emitter case. We
demonstrate our approach numerically as well as experimentally by 3D STORM
imaging of mitochondria, and volumetric imaging of dozens of
fluorescently-labeled telomeres occupying a mammalian nucleus in a single
snapshot.Comment: main text: 9 pages, 5 figures, supplementary information: 29 pages,
20 figure