Related papers: Image patch analysis and clustering of sunspots: a…
Magnetic field modifies the properties of waves in a complex way. Significant advances has been made recently in our understanding of the physics of waves in solar active regions with the help of analytical theories, numerical simulations,…
The internal dynamics of the Sun generate magnetic and plasma structures in the photosphere and overlying atmosphere across a wide range of spatial scales. Identifying the critical spatial scale is essential for interpreting physical…
In this paper, we address the problem of searching for semantically similar images from a large database. We present a compact coding approach, supervised quantization. Our approach simultaneously learns feature selection that linearly…
Recent progress in theoretical modeling of a sunspot is reviewed. The observed properties of umbral dots are well reproduced by realistic simulations of magnetoconvection in a vertical, monolithic magnetic field. To understand the penumbra,…
The study of astronomical phenomena through ground-based observations is always challenged by the distorting effects of Earth's atmosphere. Traditional methods of post-facto image correction, essential for correcting these distortions,…
High resolution solar observations show the complex structure of the magnetohydrodynamic (MHD) wave motion. We apply the techniques of POD and DMD to identify the dominant MHD wave modes in a sunspot using the intensity time series. The POD…
Image clustering, which involves grouping images into different clusters without labels, is a key task in unsupervised learning. Although previous deep clustering methods have achieved remarkable results, they only explore the intrinsic…
Conventional image processing for particle shadow image is usually time-consuming and suffers degraded image segmentation when dealing with the images consisting of complex-shaped and clustered particles with varying backgrounds. In this…
The physics of Sunspots is a fascinating and demanding field of research in solar astronomy. Interaction of magnetic fields and plasma flows takes place in a tangled magnetic geometry and occurs on spatial scales that pose a continuous…
In the present paper a novel graph-based approach to the shape decomposition problem is addressed. The shape is appropriately transformed into a visibility graph enriched with local neighborhood information. A two-step diffusion process is…
Context. The Sun shows an activity cycle that is caused by its varying global magnetic field. During a solar cycle, sunspots, i.e. extended regions of strong magnetic fields, occur in activity belts that are slowly migrating from middle to…
This paper proposes a novel approach for crowd counting in low to high density scenarios in static images. Current approaches cannot handle huge crowd diversity well and thus perform poorly in extreme cases, where the crowd density in…
Detailed knowledge of surface dynamics is one of the key points in understanding magnetic solar activity. The motions of the solar surface, to which we have direct access via the observations, tell us about the interaction between the…
We construct models describing the velocity field in the infall regions of clusters. The velocity field is the superposition of a systematic component and a "noise" component accounting for the effects of small scale substructure and…
Recent high-angular-resolution (up to 0.7") dust polarization observations toward star forming regions are summarized. With the Sub-Millimeter Array, the emission from the dense structures is traced and resolved. The detected magnetic field…
A natural way to characterize the cluster structure of a dataset is by finding regions containing a high density of data. This can be done in a nonparametric way with a kernel density estimate, whose modes and hence clusters can be found…
Estimating the number of clusters and cluster structures in unlabeled, complex, and high-dimensional datasets (like images) is challenging for traditional clustering algorithms. In recent years, a matrix reordering-based algorithm called…
Subspace clustering is a growing field of unsupervised learning that has gained much popularity in the computer vision community. Applications can be found in areas such as motion segmentation and face clustering. It assumes that data…
In the recent years, we witness a great interest in imaging, in a wide sense, using contrast agents. One of the reasons is that many imaging modalities, as the ones related to medical sciences, suffer from several shortcomings. The most…
Clustering algorithms are one of the main analytical methods to detect patterns in unlabeled data. Existing clustering methods typically treat samples in a dataset as points in a metric space and compute distances to group together similar…