The IPAC Image Subtraction and Discovery Pipeline for the intermediate Palomar Transient Factory

We describe the near real-time transient-source discovery engine for the intermediate Palomar Transient Factory (iPTF), currently in operations at the Infrared Processing and Analysis Center (IPAC), Caltech. We coin this system the IPAC/iPTF Discovery Engine (or IDE). We review the algorithms used for PSF-matching, image subtraction, detection, photometry, and machine-learned (ML) vetting of extracted transient candidates. We also review the performance of our ML classifier. For a limiting signal-to-noise ratio of 4 in relatively unconfused regions, "bogus" candidates from processing artifacts and imperfect image subtractions outnumber real transients by ~ 10:1. This can be considerably higher for image data with inaccurate astrometric and/or PSF-matching solutions. Despite this occasionally high contamination rate, the ML classifier is able to identify real transients with an efficiency (or completeness) of ~ 97% for a maximum tolerable false-positive rate of 1% when classifying raw candidates. All subtraction-image metrics, source features, ML probability-based real-bogus scores, contextual metadata from other surveys, and possible associations with known Solar System objects are stored in a relational database for retrieval by the various science working groups. We review our efforts in mitigating false-positives and our experience in optimizing the overall system in response to the multitude of science projects underway with iPTF.Comment: 66 pages, 21 figures, 7 tables, accepted by PAS

Automated Complexity-Sensitive Image Fusion

To construct a complete representation of a scene with environmental obstacles such as fog, smoke, darkness, or textural homogeneity, multisensor video streams captured in diferent modalities are considered. A computational method for automatically fusing multimodal image streams into a highly informative and unified stream is proposed. The method consists of the following steps: 1. Image registration is performed to align video frames in the visible band over time, adapting to the nonplanarity of the scene by automatically subdividing the image domain into regions approximating planar patches 2. Wavelet coefficients are computed for each of the input frames in each modality 3. Corresponding regions and points are compared using spatial and temporal information across various scales 4. Decision rules based on the results of multimodal image analysis are used to combine thewavelet coefficients from different modalities 5. The combined wavelet coefficients are inverted to produce an output frame containing useful information gathered from the available modalities Experiments show that the proposed system is capable of producing fused output containing the characteristics of color visible-spectrum imagery while adding information exclusive to infrared imagery, with attractive visual and informational properties

Sea state and rain: a second take on dual-frequency altimetry

TOPEX and Jason were the first two dual-frequency altimeters in space, with both operating at Ku- and C-band. Each thus gives two measurements of the normalized backscatter, sigma0, (from which wind speed is calculated) and two estimates of wave height. Departures from a well-defined relationship between the Ku- and C-band sigma0 values give an indication of rain.This paper investigates differences between the two instruments using data from Jason's verification phase. Jason's Ku-band estimates of wave height are ~1.8% less than TOPEX's, whereas its sigma0 values are higher. When these effects have been removed the root mean square (r.m.s.) mismatch between TOPEX and Jason's Ku-band observations is close to that for TOPEX's observations at its two frequencies, and the changes in sigma0 with varying wave height conditions are the same for the two altimeters. Rain flagging and quantitative estimates of rain rate are both based on the atmospheric attenuation derived from the sigma0 measurements at the two frequencies. The attenuation estimates of TOPEX and Jason agree very well, and a threshold of -0.5 dB is effective at removing the majority of spurious data records from the Jason GDRs. In the high sigma0 regime, anomalous data can be cause by processes other than rain. Consequently, for these low wind conditions, neither can reliable rain detection be based on altimetry alone, nor can a generic rain flag be expected to remove all suspect data

A Multicamera System for Gesture Tracking With Three Dimensional Hand Pose Estimation

The goal of any visual tracking system is to successfully detect then follow an object of interest through a sequence of images. The difficulty of tracking an object depends on the dynamics, the motion and the characteristics of the object as well as on the environ ment. For example, tracking an articulated, self-occluding object such as a signing hand has proven to be a very difficult problem. The focus of this work is on tracking and pose estimation with applications to hand gesture interpretation. An approach that attempts to integrate the simplicity of a region tracker with single hand 3D pose estimation methods is presented. Additionally, this work delves into the pose estimation problem. This is ac complished by both analyzing hand templates composed of their morphological skeleton, and addressing the skeleton\u27s inherent instability. Ligature points along the skeleton are flagged in order to determine their effect on skeletal instabilities. Tested on real data, the analysis finds the flagging of ligature points to proportionally increase the match strength of high similarity image-template pairs by about 6%. The effectiveness of this approach is further demonstrated in a real-time multicamera hand tracking system that tracks hand gestures through three-dimensional space as well as estimate the three-dimensional pose of the hand

Modelling and analysis of plant image data for crop growth monitoring in horticulture

Plants can be characterised by a range of attributes, and measuring these attributes accurately and reliably is a major challenge for the horticulture industry. The measurement of those plant characteristics that are most relevant to a grower has previously been tackled almost exclusively by a combination of manual measurement and visual inspection. The purpose of this work is to propose an automated image analysis approach in order to provide an objective measure of plant attributes to remove subjective factors from assessment and to reduce labour requirements in the glasshouse. This thesis describes a stereopsis approach for estimating plant height, since height information cannot be easily determined from a single image. The stereopsis algorithm proposed in this thesis is efficient in terms of the running time, and is more accurate when compared with other algorithms. The estimated geometry, together with colour information from the image, are then used to build a statistical plant surface model, which represents all the information from the visible spectrum. A self-organising map approach can be adopted to model plant surface attributes, but the model can be improved by using a probabilistic model such as a mixture model formulated in a Bayesian framework. Details of both methods are discussed in this thesis. A Kalman filter is developed to track the plant model over time, extending the model to the time dimension, which enables smoothing of the noisy measurements to produce a development trend for a crop. The outcome of this work could lead to a number of potentially important applications in horticulture

Static Axisymmetric Vacuum Solutions and Non-Uniform Black Strings

We describe new numerical methods to solve the static axisymmetric vacuum Einstein equations in more than four dimensions. As an illustration, we study the compactified non-uniform black string phase connected to the uniform strings at the Gregory-Laflamme critical point. We compute solutions with a ratio of maximum to minimum horizon radius up to nine. For a fixed compactification radius, the mass of these solutions is larger than the mass of the classically unstable uniform strings. Thus they cannot be the end state of the instability.Comment: 48 pages, 13 colour figures; v2: references correcte

Low thrust orbit determination program

Logical flow and guidelines are provided for the construction of a low thrust orbit determination computer program. The program, tentatively called FRACAS (filter response analysis for continuously accelerating spacecraft), is capable of generating a reference low thrust trajectory, performing a linear covariance analysis of guidance and navigation processes, and analyzing trajectory nonlinearities in Monte Carlo fashion. The choice of trajectory, guidance and navigation models has been made after extensive literature surveys and investigation of previous software. A key part of program design relied upon experience gained in developing and using Martin Marietta Aerospace programs: TOPSEP (Targeting/Optimization for Solar Electric Propulsion), GODSEP (Guidance and Orbit Determination for SEP) and SIMSEP (Simulation of SEP)

Use of multi-scale phase-based methods to determine optical flow in dynamic scene analysis

Estimates of optical flow in images can be made by applying a complex periodic transform to the images and tracking the movement of points of constant phase in the complex output. This approach however suffers from the problem that filters of large width give information only about broad scale image features, whilst those of small spatial extent (high resolution) cannot track fast motion, which causes a feature to move a distance that is large compared to the filter-size. A method is presented in which the flow is measured at different scales, using a series of complex filters of decreasing width. The largest filter is used to give a large scale flow estimate at each image point. Estimates at smaller scales are then carried out by using the previous result as an a priori estimate. Rather than comparing the same region in different images in order to estimate flow, the regions to be compared are displaced from one another by an amount given by the most recent previous flow estimate. This results in an estimate of flow relative to the earlier estimate. The two estimates are then added together to give a new estimate of the absolute displacement. The process is repeated at successively smaller scales. The method can therefore detect small local velocity variations superimposed on the broad scale flow, even where the magnitude of the absolute displacement is larger than the scope of the smaller filters. Without the assistance of the earlier estimates in ‘tuning\u27 the smaller filters in this manner, a smaller filter could fail to capture these velocity variations, because the absolute displacement carry the feature out of range of-the filter during successive frames. The output of the method is a series of scale-dependent flow fields corresponding to different scales, reflecting the fact that motion in the real world is a scale-dependent quantity. Application of the method to some 1 dimensional test images gives good results, with realistic flow values that could be used as an aid to segmentation. Some synthetic 2-dimentional images containing only a small number of well defined features aIso yield good-results but the method performs poorly on a random-dot stereogram and on a real-world test image pair selected from the Hamburg Taxi sequence