Joint infrared target recognition and segmentation using a shape manifold-aware level set.

We propose new techniques for joint recognition, segmentation and pose estimation of infrared (IR) targets. The problem is formulated in a probabilistic level set framework where a shape constrained generative model is used to provide a multi-class and multi-view shape prior and where the shape model involves a couplet of view and identity manifolds (CVIM). A level set energy function is then iteratively optimized under the shape constraints provided by the CVIM. Since both the view and identity variables are expressed explicitly in the objective function, this approach naturally accomplishes recognition, segmentation and pose estimation as joint products of the optimization process. For realistic target chips, we solve the resulting multi-modal optimization problem by adopting a particle swarm optimization (PSO) algorithm and then improve the computational efficiency by implementing a gradient-boosted PSO (GB-PSO). Evaluation was performed using the Military Sensing Information Analysis Center (SENSIAC) ATR database, and experimental results show that both of the PSO algorithms reduce the cost of shape matching during CVIM-based shape inference. Particularly, GB-PSO outperforms other recent ATR algorithms, which require intensive shape matching, either explicitly (with pre-segmentation) or implicitly (without pre-segmentation).

Joint target tracking, recognition and segmentation for infrared imagery using a shape manifold-based level set.

We propose a new integrated target tracking, recognition and segmentation algorithm, called ATR-Seg, for infrared imagery. ATR-Seg is formulated in a probabilistic shape-aware level set framework that incorporates a joint view-identity manifold (JVIM) for target shape modeling. As a shape generative model, JVIM features a unified manifold structure in the latent space that is embedded with one view-independent identity manifold and infinite identity-dependent view manifolds. In the ATR-Seg algorithm, the ATR problem formulated as a sequential level-set optimization process over the latent space of JVIM, so that tracking and recognition can be jointly optimized via implicit shape matching where target segmentation is achieved as a by-product without any pre-processing or feature extraction. Experimental results on the recently released SENSIAC ATR database demonstrate the advantages and effectiveness of ATR-Seg over two recent ATR algorithms that involve explicit shape matching.

Imaging of subclinical haemopoiesis after stem-cell transplantation in patients with haematological malignancies: a prospective pilot study.

BACKGROUND: Haemopoietic stem-cell transplantation (HSCT) eradicates host haemopoiesis before venous infusion of haemopoietic stem cells (HSCs). The pathway to cellular recovery has been difficult to study in human beings because of risks associated with interventions during aplasia. We investigated whether 18F-fluorothymidine (18F-FLT) imaging was safe during allogenic HSCT and allowed visualisation of early cellular proliferation and detection of patterns of cellular engraftment after HSCT. METHODS: Eligible patients were aged 18-55 years, had high-risk haematological malignancies. All patients underwent myeloablation followed by HSCT. The imaging primary endpoint was detection of early subclinical engraftment after HSCT with 18F-FLT PET or CT. Imaging was done 1 day before and 5 or 9, and 28 days, and 1 year after HSCT. This study is registered with ClinicalTrials.gov, number NCT01338987. FINDINGS: Between April 1, 2014, and Dec 31, 2015, 23 patients were enrolled and assessable for toxic effects after completing accrual. 18F-FLT was not associated with any adverse events or delayed engraftment. 18F-FLT imaging objectively identified subclinical bone-marrow recovery within 5 days of HSC infusion, which was up to 20 days before engraftment became clinically evident. Quantitatively, 18F-FLT intensity differed significantly between myeloablative infusion before HSCT and subclinical HSC recovery (p=0·00031). 18F-FLT biodistribution over time revealed a previously unknown path of cellular recovery of haemopoiesis in vivo that mirrored fetal ontogeny. INTERPRETATION: 18F-FLT allowed quantification and tracking of subclinical bone-marrow repopulation in human beings and revealed new insights into the biology of HSC recovery after HSCT. FUNDING: National Institutes of Health, Ben's Run/Ben's Gift, Albert and Elizabeth Tucker Foundation, Mex Frates Leukemia Fund, Jones Family fund, and Oklahoma Center for Adult Stem Cell Research.

Adaptive Kalman filtering for histogram-based appearance learning in infrared imagery.

Targets of interest in video acquired from imaging infrared sensors often exhibit profound appearance variations due to a variety of factors, including complex target maneuvers, ego-motion of the sensor platform, background clutter, etc., making it difficult to maintain a reliable detection process and track lock over extended time periods. Two key issues in overcoming this problem are how to represent the target and how to learn its appearance online. In this paper, we adopt a recent appearance model that estimates the pixel intensity histograms as well as the distribution of local standard deviations in both the foreground and background regions for robust target representation. Appearance learning is then cast as an adaptive Kalman filtering problem where the process and measurement noise variances are both unknown. We formulate this problem using both covariance matching and, for the first time in a visual tracking application, the recent autocovariance least-squares (ALS) method. Although convergence of the ALS algorithm is guaranteed only for the case of globally wide sense stationary process and measurement noises, we demonstrate for the first time that the technique can often be applied with great effectiveness under the much weaker assumption of piecewise stationarity. The performance advantages of the ALS method relative to the classical covariance matching are illustrated by means of simulated stationary and nonstationary systems. Against real data, our results show that the ALS-based algorithm outperforms the covariance matching as well as the traditional histogram similarity-based methods, achieving sub-pixel tracking accuracy against the well-known AMCOM closure sequences and the recent SENSIAC automatic target recognition dataset.

Properties of the Magnitude Terms of Orthogonal Scaling Functions.

The spectrum of the convolution of two continuous functions can be determined as the continuous Fourier transform of the cross-correlation function. The same can be said about the spectrum of the convolution of two infinite discrete sequences, which can be determined as the discrete time Fourier transform of the cross-correlation function of the two sequences. In current digital signal processing, the spectrum of the contiuous Fourier transform and the discrete time Fourier transform are approximately determined by numerical integration or by densely taking the discrete Fourier transform. It has been shown that all three transforms share many analogous properties. In this paper we will show another useful property of determining the spectrum terms of the convolution of two finite length sequences by determining the discrete Fourier transform of the modified cross-correlation function. In addition, two properties of the magnitude terms of orthogonal wavelet scaling functions are developed. These properties are used as constraints for an exhaustive search to determine an robust lower bound on conjoint localization of orthogonal scaling functions.