Adaptive Target Recognition: A Case Study Involving Airport Baggage Screening

This work addresses the question whether it is possible to design a computer-vision based automatic threat recognition (ATR) system so that it can adapt to changing specifications of a threat without having to create a new ATR each time. The changes in threat specifications, which may be warranted by intelligence reports and world events, are typically regarding the physical characteristics of what constitutes a threat: its material composition, its shape, its method of concealment, etc. Here we present our design of an AATR system (Adaptive ATR) that can adapt to changing specifications in materials characterization (meaning density, as measured by its x-ray attenuation coefficient), its mass, and its thickness. Our design uses a two-stage cascaded approach, in which the first stage is characterized by a high recall rate over the entire range of possibilities for the threat parameters that are allowed to change. The purpose of the second stage is to then fine-tune the performance of the overall system for the current threat specifications. The computational effort for this fine-tuning for achieving a desired PD/PFA rate is far less than what it would take to create a new classifier with the same overall performance for the new set of threat specifications

RMPD - A Recursive Mid-Point Displacement Algorithm for Path Planning

Motivated by what is required for real-time path planning, the paper starts out by presenting sRMPD, a new recursive "local" planner founded on the key notion that, unless made necessary by an obstacle, there must be no deviation from the shortest path between any two points, which would normally be a straight line path in the configuration space. Subsequently, we increase the power of sRMPD by using it as a "connect" subroutine call in a higher-level sampling-based algorithm mRMPD that is inspired by multi-RRT. As a consequence, mRMPD spawns a larger number of space exploring trees in regions of the configuration space that are characterized by a higher density of obstacles. The overall effect is a hybrid tree growing strategy with a trade-off between random exploration as made possible by multi-RRT based logic and immediate exploitation of opportunities to connect two states as made possible by sRMPD. The mRMPD planner can be biased with regard to this trade-off for solving different kinds of planning problems efficiently. Based on the test cases we have run, our experiments show that mRMPD can reduce planning time by up to 80% compared to basic RRT

ASASSN-14dq: A fast-declining type II-P Supernova in a low-luminosity host galaxy

Optical broadband (UBVRI) photometric and low-resolution spectroscopic observations of the type II-P supernova (SN) ASASSN-14dq are presented. ASASSN-14dq exploded in a low-luminosity/metallicity host galaxy UGC 11860, the signatures of which are present as weak iron lines in the photospheric phase spectra. The SN has a plateau duration of $\sim\,$90 d, with a plateau decline rate of 1.38 $\rm mag\ (100 d)^{-1}$ in V-band which is higher than most type II-P SNe. ASASSN-14dq is a luminous type II-P SN with a peak $V$-band absolute magnitude of -17.7$\,\pm\,$0.2 mag. The light curve of ASASSN-14dq indicates it to be a fast-declining type II-P SN, making it a transitional event between the type II-P and II-L SNe. The empirical relation between the steepness parameter and $\rm ^{56}Ni$ mass for type II SNe was rebuilt with the help of well-sampled light curves from the literature. A $\rm ^{56}Ni$ mass of $\sim\,$0.029 M$_{\odot}$ was estimated for ASASSN-14dq, which is slightly lower than the expected $\rm ^{56}Ni$ mass for a luminous type II-P SN. Using analytical light curve modelling, a progenitor radius of $\rm \sim3.6\times10^{13}$ cm, an ejecta mass of $\rm \sim10\ M_{\odot}$ and a total energy of $\rm \sim\,1.8\times 10^{51}$ ergs was estimated for this event. The photospheric velocity evolution of ASASSN-14dq resembles a type II-P SN, but the Balmer features (H$\alpha$ and H$\beta$) show relatively slow velocity evolution. The high-velocity H$\alpha$ feature in the plateau phase, the asymmetric H$\alpha$ emission line profile in the nebular phase and the inferred outburst parameters indicate an interaction of the SN ejecta with the circumstellar material (CSM).Comment: 28 pages, 29 figures, Accepted in MNRA

Skew Detection and Correction in Scanned Document Images.

During document scanning, skew is inevitably introduced into the incoming document image. Skew detection is one the first operations to be applied to scanned documents when converting data to a digital format. Its aim is to align an image before processing because text segmentation and recognition methods require properly aligned next lines. Different algorithms of skew detection are implemented. The first one is Scan line based skew detection. In this method the image is projected at several angles and the variance in the number of black pixels per projected scan line is determined. The angle at which the maximum variance occurs is the angle of skew.The second one is based on the Hough transform. Hough transform is performed on the scanned document image and the variance in ρ values is calculated for each value of θ. The angle that gives the maximum variance is the skew angle.The third approach is based on the base-point method. Here a concept of basepoint is introduced. After the successive base-points in every text line within a suitable sub-region were selected as samples for the straight-line fitting. The average of these baseline directions is computed, which corresponds to the degree of skew of the whole document image.All the above mentioned algorithm have been implemented and the results of each have been compared for accuracy

Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images

We propose a framework for the automatic one-shot segmentation of synthetic images generated by a StyleGAN. Our framework is based on the observation that the multi-scale hidden features in the GAN generator hold useful semantic information that can be utilized for automatic on-the-fly segmentation of the generated images. Using these features, our framework learns to segment synthetic images using a self-supervised contrastive clustering algorithm that projects the hidden features into a compact space for per-pixel classification. This contrastive learner is based on using a novel data augmentation strategy and a pixel-wise swapped prediction loss that leads to faster learning of the feature vectors for one-shot segmentation. We have tested our implementation on five standard benchmarks to yield a segmentation performance that not only outperforms the semi-supervised baselines by an average wIoU margin of 1.02 % but also improves the inference speeds by a factor of 4.5. Finally, we also show the results of using the proposed one-shot learner in implementing BagGAN, a framework for producing annotated synthetic baggage X-ray scans for threat detection. This framework was trained and tested on the PIDRay baggage benchmark to yield a performance comparable to its baseline segmenter based on manual annotations