170 research outputs found
Monte Carlo Linear Clustering with Single-Point Supervision is Enough for Infrared Small Target Detection
Single-frame infrared small target (SIRST) detection aims at separating small
targets from clutter backgrounds on infrared images. Recently, deep learning
based methods have achieved promising performance on SIRST detection, but at
the cost of a large amount of training data with expensive pixel-level
annotations. To reduce the annotation burden, we propose the first method to
achieve SIRST detection with single-point supervision. The core idea of this
work is to recover the per-pixel mask of each target from the given single
point label by using clustering approaches, which looks simple but is indeed
challenging since targets are always insalient and accompanied with background
clutters. To handle this issue, we introduce randomness to the clustering
process by adding noise to the input images, and then obtain much more reliable
pseudo masks by averaging the clustered results. Thanks to this "Monte Carlo"
clustering approach, our method can accurately recover pseudo masks and thus
turn arbitrary fully supervised SIRST detection networks into weakly supervised
ones with only single point annotation. Experiments on four datasets
demonstrate that our method can be applied to existing SIRST detection networks
to achieve comparable performance with their fully supervised counterparts,
which reveals that single-point supervision is strong enough for SIRST
detection. Our code will be available at:
https://github.com/YeRen123455/SIRST-Single-Point-Supervision
One-Stage Cascade Refinement Networks for Infrared Small Target Detection
Single-frame InfraRed Small Target (SIRST) detection has been a challenging
task due to a lack of inherent characteristics, imprecise bounding box
regression, a scarcity of real-world datasets, and sensitive localization
evaluation. In this paper, we propose a comprehensive solution to these
challenges. First, we find that the existing anchor-free label assignment
method is prone to mislabeling small targets as background, leading to their
omission by detectors. To overcome this issue, we propose an all-scale
pseudo-box-based label assignment scheme that relaxes the constraints on scale
and decouples the spatial assignment from the size of the ground-truth target.
Second, motivated by the structured prior of feature pyramids, we introduce the
one-stage cascade refinement network (OSCAR), which uses the high-level head as
soft proposals for the low-level refinement head. This allows OSCAR to process
the same target in a cascade coarse-to-fine manner. Finally, we present a new
research benchmark for infrared small target detection, consisting of the
SIRST-V2 dataset of real-world, high-resolution single-frame targets, the
normalized contrast evaluation metric, and the DeepInfrared toolkit for
detection. We conduct extensive ablation studies to evaluate the components of
OSCAR and compare its performance to state-of-the-art model-driven and
data-driven methods on the SIRST-V2 benchmark. Our results demonstrate that a
top-down cascade refinement framework can improve the accuracy of infrared
small target detection without sacrificing efficiency. The DeepInfrared
toolkit, dataset, and trained models are available at
https://github.com/YimianDai/open-deepinfrared to advance further research in
this field.Comment: Submitted to TGR
Sketch-based subspace clustering of hyperspectral images
Sparse subspace clustering (SSC) techniques provide the state-of-the-art in clustering of hyperspectral images (HSIs). However, their computational complexity hinders their applicability to large-scale HSIs. In this paper, we propose a large-scale SSC-based method, which can effectively process large HSIs while also achieving improved clustering accuracy compared to the current SSC methods. We build our approach based on an emerging concept of sketched subspace clustering, which was to our knowledge not explored at all in hyperspectral imaging yet. Moreover, there are only scarce results on any large-scale SSC approaches for HSI. We show that a direct application of sketched SSC does not provide a satisfactory performance on HSIs but it does provide an excellent basis for an effective and elegant method that we build by extending this approach with a spatial prior and deriving the corresponding solver. In particular, a random matrix constructed by the Johnson-Lindenstrauss transform is first used to sketch the self-representation dictionary as a compact dictionary, which significantly reduces the number of sparse coefficients to be solved, thereby reducing the overall complexity. In order to alleviate the effect of noise and within-class spectral variations of HSIs, we employ a total variation constraint on the coefficient matrix, which accounts for the spatial dependencies among the neighbouring pixels. We derive an efficient solver for the resulting optimization problem, and we theoretically prove its convergence property under mild conditions. The experimental results on real HSIs show a notable improvement in comparison with the traditional SSC-based methods and the state-of-the-art methods for clustering of large-scale images
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
Lessons learned from the 1st Ariel Machine Learning Challenge: Correcting transiting exoplanet light curves for stellar spots
The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation. However, several big challenges remain, many of which could be addressed using machine learning methodology. For instance, the most prolific method for detecting exoplanets and inferring several of their characteristics, transit photometry, is very sensitive to the presence of stellar spots. The current practice in the literature is to identify the effects of spots visually and correct for them manually or discard the affected data. This paper explores a first step towards fully automating the efficient and precise derivation of transit depths from transit light curves in the presence of stellar spots. The primary focus of the paper is to present in detail a diverse arsenal of methods for doing so. The methods and results we present were obtained in the context of the 1st Machine Learning Challenge organized for the European Space Agency’s upcoming Ariel mission. We first present the problem, the simulated Ariel-like data and outline the Challenge while identifying best practices for organizing similar challenges in the future. Finally, we present the solutions obtained by the top-5 winning teams, provide their code and discuss their implications. Successful solutions either construct highly non-linear (w.r.t. the raw data) models with minimal preprocessing –deep neural networks and ensemble methods– or amount to obtaining meaningful statistics from the light curves, constructing linear models on which yields comparably good predictive performance
Recent Advances in Image Restoration with Applications to Real World Problems
In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included
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