Motion magnification in coronal seismology

We introduce a new method for the investigation of low-amplitude transverse oscillations of solar plasma non-uniformities, such as coronal loops, individual strands in coronal arcades, jets, prominence fibrils, polar plumes, and other contrast features, observed with imaging instruments. The method is based on the two-dimensional dual tree complex wavelet transform (DT$\mathbb{C}$WT). It allows us to magnify transverse, in the plane-of-the-sky, quasi-periodic motions of contrast features in image sequences. The tests performed on the artificial data cubes imitating exponentially decaying, multi-periodic and frequency-modulated kink oscillations of coronal loops showed the effectiveness, reliability and robustness of this technique. The algorithm was found to give linear scaling of the magnified amplitudes with the original amplitudes provided they are sufficiently small. Also, the magnification is independent of the oscillation period in a broad range of the periods. The application of this technique to SDO/AIA EUV data cubes of a non-flaring active region allowed for the improved detection of low-amplitude decay-less oscillations in the majority of loops.Comment: Accepted for publication in Solar Physic

A machine vision extension for the Ruby programming language

Dynamically typed scripting languages have become popular in recent years. Although interpreted languages allow for substantial reduction of software development time, they are often rejected due to performance concerns. In this paper we present an extension for the programming language Ruby, called HornetsEye, which facilitates the development of real-time machine vision algorithms within Ruby. Apart from providing integration of crucial libraries for input and output, HornetsEye provides fast native implementations (compiled code) for a generic set of array operators. Different array operators were compared with equivalent implementations in C++. Not only was it possible to achieve comparable real-time performance, but also to exceed the efficiency of the C++ implementation in several cases. Implementations of several algorithms were given to demonstrate how the array operators can be used to create concise implementations.</p

An Approach for Object Tracking in Video Sequences

In recent past there has been a significant increase in number of applications effectively utilizing digital videos because of less costly but superior devices. This upsurge in video acquisition has led to huge augmentation of data, which are quite impossible to handle manually. Therefore, an automated means of processing these videos is indispensable. In this thesis one such attempt has been made to track objects in videos. Object tracking comprises two closely related processes; object detection followed by tracking of the detected objects. Algorithms on these two processes are proposed in this thesis. Simple object detection algorithms compare a static background frame at pixel level with the current frame in a video. Existing methods in this domain first try to detect objects and then remove shadows associated with them, which is a two-stage process. The proposed approach combines both the stages into a single stage. Two different algorithms are proposed on object detection. First one to model the background and the next to extract the objects and remove shadows from them. Initially, from first few frames the nature of each pixel is determined as stationary or non-stationary and considering only the stationary pixels a background model is developed. Subsequently, a local thresholding technique is used to extract objects and discard shadows. After successfully detecting all the foreground objects, two different algorithms are proposed for tracking the objects and updating the background model. The first algorithm suggests a centroid searching technique, where a centroid in current frame is estimated from the previous frame. Its accuracy is verified by comparing the entropy of dual-tree complex wavelet coefficients in the bounding boxes of both the frames. If estimation becomes inaccurate, a dynamic window is utilized to search for accurate centroid. The second algorithm updates the background using a randomized updating scheme. Both stages of the proposed tracking model is simulated with various recorded videos. Simulation results are compared with the recent schemes to show the superiority of the model