Infrared thermography (IRT, or thermal video) uses thermographic cameras to detect and record radiation in the longwavelength infrared range of the electromagnetic spectrum. It allows sensing environments beyond the visual perception limitations, and thus has been widely used in many civilian and military applications. Even though current thermal cameras are able to provide high resolution and bit-depth images, there are significant challenges to be addressed in specific applications such as poor contrast, low target signature resolution, etc. This paper addresses quality improvement in IRT images for object recognition. A systematic approach based on image bias correction and deep learning is proposed to increase target signature resolution and optimise the baseline quality of inputs for object recognition. Our main objective is to maximise the useful information on the object to be detected even when the number of pixels on target is adversely small. The experimental results show that our approach can significantly improve target resolution and thus helps making object recognition more efficient in automatic target detection/recognition systems (ATD/R).
A modular vehicle detection system, using a two-stage hypothesis generation (HG) and hypothesis combination
(HC) approach is presented. The HG stage consists of a set of simple algorithms which parse multi-modal data and provide a set of possible vehicle locations. These hypotheses are subsequently fused in a combination stage. This modular design allows the system to utilise additional modalities where available, and the combination of multiple information sources is shown to reduce false positive detections. The system uses Thales' high-resolution long wave infrared polarimeter and a four-band visible/near infrared multispectral system. Vehicle cues are taken from motion
ow vectors, thermal intensity hot spots, and regions with a locally high degree of linear polarisation. Results using image sequences gathered from a moving vehicle are shown, and the performance of the system is assessed with Receiver Operator Characteristics.
We report the theory and implementation of new approaches for the processing of 3D range data in pursuit of library-based object recognition and registration. The image data is obtained from an active LaDAR system (scanned Time-Correlated Single Photon Count or time-gated Burst Illumination Laser) and describes the range and 3D surface characteristics of remote objects at specific views. The reflected laser signal returns are generally embedded in noise and clutter of uncertain origin. We have applied the Markov Chain Monte Carlo (MCMC) methodology, using random sampling of the search space, to evaluate the number, positions and amplitudes of returns in such scenarios. We describe the use of methods for removing outliers and smoothing these time-of-flight generated depth images, based on least median of squares and anisotropic diffusion, respectively. Further, we outline and demonstrate procedures for registration and pose determination of objects from range data. This consists of three phases, namely point feature extraction, pose clustering and registration. The first computes a surface metric facilitating candidate correspondence determination, using the technique of pair-wise geometric histograms. The second is carried out by a leader-based algorithm, which does not require the number of clusters to be pre-specified. The third is an extension of the iterative closest points (ICP) method, being specifically designed for mesh representations. Collectively, these processes allow an object within a scene - described by a 3D range image - to be matched with a preformed model from a database.
Our objective has been to find a preferred method for the identification of static targets in single IR images, concentrating on appearance-based methods. This has included thermal modelling of IR signatures and the identification of images of different objects with variation in pose and thermal state. Using principal component analysis, the variances among the images are extracted and represented in a low-dimensional feature eigenspace. Any new image can be projected into the eigenspace by taking an inner product with the basis. The object of interest can be recognized by a nearest-neighbour classification rule, made more accurate by application of over-sampling to the surface manifold by B-spline surface fitting, and made more efficient by a k-d tree search algorithm. To address the problems of recognizing targets in noisy and cluttered images, we have employed a random sampling approach that is based on the principle of high-breakdown point estimation. We have generated a database of images using visible and thermal cameras, in addition to scene simulation software, for use in the learning and recognition/evaluation phases. Our experiments indicate that application of the robust algorithm can reduce the recovery error of the true model image data, for example by a factor of five when the images contain 40% randomly changed image pixels.
We present a microscopic model of emission in a series of strain-compensated GaInAs/GaAsSb type-II superlattice structures with infrared applications. The need for an improved understanding of the optoelectronic characteristics of these systems, both in terms of basic physics and technological applications, is identified. The band lineup in heterostructures containing alloys is frequently determined using the Model Solid theory with linear interpolation of input parameters between those of the constituent compounds. However, for the present superlattices, this approach did not provide a description of the band lineups which was consistent with experimental data. Band lineups were subsequently fitted to achieve spectral cutoff measurements, and we found that these offsets were in better agreement with experimental data than those predicted using the above method. On using these lineups as input to our empirical pseudopotential model, lineshapes exhibiting good agreement with experiment were computed. We analyze the role played by wave-function confinement in determining spectral features and investigate the potentially degrading effects of Auger recombination on device performance. The results of this study advance the characterization of these systems, indicating links between their microscopic properties and optical spectra.