A fast-Fourier-transform method of topography and interferometry is proposed. By computer processing of a noncontour type of fringe pattern, automatic discrimination is achieved between elevation and depression of the object or wave-front form, which has not been possible by the fringe-contour-generation techniques. The method has advantages over moire topography and conventional fringe-contour interferometry in both accuracy and sensitivity. Unlike fringe-scanning techniques, the method is easy to apply because it uses no moving components.

Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry

Can we improve detection in the thermal domain by borrowing features from rich domains like visual RGB? In this paper, we propose a pseudo-multimodal object detector trained on natural image domain data to help improve the performance of object detection in thermal images. We assume access to a large-scale dataset in the visual RGB domain and relatively smaller dataset (in terms of instances) in the thermal domain, as is common today. We propose the use of well-known image-to-image translation frameworks to generate pseudo-RGB equivalents of a given thermal image and then use a multi-modal architecture for object detection in the thermal image. We show that our framework outperforms existing benchmarks without the explicit need for paired training examples from the two domains. We also show that our framework has the ability to learn with less data from thermal domain when using our approach.

/pdf/borrow-from-anywhere-pseudo-multi-modal-object-detection-in-5dsmofcmzj.pdf

Borrow From Anywhere: Pseudo Multi-Modal Object Detection in Thermal Imagery

A survey of scheduling with parallel batch (p-batch) processing

The coordination between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) is a proactive research topic whose great value of application has attracted vast attention. This paper outlines the motivations for studying the cooperative control of UAVs and UGVs, and attempts to make a comprehensive investigation and analysis on recent research in this field. First, a taxonomy for classification of existing unmanned aerial and ground vehicles systems (UAGVSs) is proposed, and a generalized optimization framework is developed to allow the decision-making problems for different types of UAGVSs to be described in a unified way. By following the proposed taxonomy, we show how different types of UAGVSs can be built to realize the goal of a common task, that is target tracking, and how optimization problems can be formulated for a UAGVS to perform specific tasks. This paper presents an optimization perspective to model and analyze different types of UAGVSs, and serves as a guidance and reference for developing UAGVSs.

Coordination Between Unmanned Aerial and Ground Vehicles: A Taxonomy and Optimization Perspective

Pedestrian-vehicle accidents that occur at night are a major social problem worldwide. Advanced driver assistance systems that are equipped with cameras have been designed to automatically prevent such accidents. Among the various types of cameras used in such systems, far-infrared (FIR) cameras are favorable because they are invariant to illumination changes. Therefore, this paper focuses on a pedestrian nighttime tracking system with an FIR camera that is able to discern thermal energy and is mounted on the forward roof part of a vehicle. Since the temperature difference between the pedestrian and background depends on the season and the weather, we therefore propose two models to detect pedestrians according to the season and the weather, which are determined using Weber–Fechner's law. For tracking pedestrians, we perform real-time online learning to track pedestrians using boosted random ferns and update the trackers at each frame. In particular, we link detection responses to trajectories based on similarities in position, size, and appearance. There is no standard data set for evaluating the tracking performance using an FIR camera; thus, we created the Keimyung University tracking data set (KMUTD) by combining the KMU sudden pedestrian crossing (SPC) data set [21] for summer nights with additional tracking data for winter nights. The KMUTD contains video sequences involving a moving camera, moving pedestrians, sudden shape deformations, unexpected motion changes, and partial or full occlusions between pedestrians at night. The proposed algorithm is successfully applied to various pedestrian video sequences of the KMUTD; specifically, the proposed algorithm yields more accurate tracking performance than other existing methods.

Pedestrian Tracking Using Online Boosted Random Ferns Learning in Far-Infrared Imagery for Safe Driving at Night

Tabu Search with two approaches to parallel flowshop evaluation on CUDA platform

Generalised wide are search and surveillance is a common-place tasking for multi-sensory equipped autonomous systems. Here we present on a key supporting topic to this task - the automatic interpretation, fusion and detected target reporting from multi-modal sensor information received from multiple autonomous platforms deployed for wide-area environment search. We detail the realization of a real-time methodology for the automated detection of people and vehicles using combined visible-band (EO), thermal-band (IR) and radar sensing from a deployed network of multiple autonomous platforms (ground and aerial). This facilities real-time target detection, reported with varying levels of confidence, using information from both multiple sensors and multiple sensor platforms to provide environment-wide situational awareness. A range of automatic classification approaches are proposed, driven by underlying machine learning techniques, that facilitate the automatic detection of either target type with cross-modal target confirmation. Extended results are presented that show both the detection of people and vehicles under varying conditions in both isolated rural and cluttered urban environments with minimal false positive detection. Performance evaluation is presented at an episodic level with individual classifiers optimized for maximal each object of interest (vehicle/person) detection over a given search path/pattern of the environment, across all sensors and modalities, rather than on a per sensor sample basis. Episodic target detection, evaluated over a number of wide-area environment search and reporting tasks, generally exceeds 90%+ for the targets considered here.

/pdf/multi-modal-target-detection-for-autonomous-wide-area-search-r1udrt8hr4.pdf

Multi-modal target detection for autonomous wide area search and surveillance

We address the challenge of human behaviour analysis within automated image understanding. Whilst prior work concentrates on this task within visible-band (EO) imagery, by contrast we target basic human pose classification in thermal-band (infrared, IR) imagery. By leveraging the key advantages of limb localization this imagery offers we target two distinct human pose classification problems of varying complexity: 1) identifying passive or active individuals within the scene and 2) the identification of individuals potentially carrying weapons. Both approaches use a discrete set of features capturing body pose characteristics from which a range of machine learning techniques are then employed for final classification. Significant success is shown on these challenging tasks over a wide range of environmental conditions within the wider context of automated human target tracking in thermal-band (IR) imagery.

/pdf/human-pose-classification-within-the-context-of-near-ir-2h4lzb387n.pdf

Human pose classification within the context of near-IR imagery tracking

Shearography is a full-field interferometric optical technique that is usually used for the qualitative investigation of defects in non-destructive testing applications. The optical configuration is sensitive directly to displacement gradient, a parameter closely related to the surface strain. The component of the displacement gradient that is measured is determined by the illumination and viewing directions and by the direction of the applied shear. The sensitivity is governed by the magnitude of applied shear and by the optical wavelength. Full characterisation of the surface strain requires a measurement of six-components of displacement gradient; this is achieved in shearography by forming a number of distinct measurement channels using multiple illumination, or viewing, directions. In this paper the authors discuss the quantitative measurement of the strain field around a fatigue crack, using a time-division-multiplexed diode laser shearography instrument. To investigate moving objects, a pulsed laser provides a method of freezing the object position at two points in the loading cycle. A shearography instrument incorporating two frequency doubled pulsed Nd:YAG lasers, with a common injection seeder is described. The measurement channels are spatially-multiplexed by viewing from four directions using an optical fibre imaging bundle, with optical processing at a remotely located interferometer head. Preliminary experimental measurements are presented.

Multicomponent laser shearography for the investigation of defects in rotating machinery

The possibility of porting algorithms to graphics processing units (GPUs) raises significant interest among researchers. The natural next step is to employ multiple GPUs, but communication overhead may limit further performance improvement. In this paper, we investigate techniques reducing overhead on hybrid CPU---GPU platforms, including careful data layout and usage of GPU memory spaces, and use of non-blocking communication. In addition, we propose an accurate automatic load balancing technique for heterogeneous environments. We validate our approach on a hybrid Jacobi solver for 2D Laplace's Equation. Experiments carried out using various graphics hardware and types of connectivity have confirmed that the proposed data layout allows our fastest CUDA kernels to reach the analytical limit for memory bandwidth (up to 106 GB/s on NVidia GTX 480), and that the non-blocking communication significantly reduces overhead, allowing for almost linear speed-up, even when communication is carried out over relatively slow networks.

Reducing Communication Overhead in Multi-GPU Hybrid Solver for 2D Laplace's Equation

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