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This article investigates the application of deep learning (DL) to the fusion of omnidirectional (O-D) infrared (IR) sensors and O-D visual sensors to improve the intelligent perception of autonomous robotic systems. Recent techniques primarily focus on O-D and conventional visual sensors for applications in localization, mapping, and tracking. The robotic vision systems have not sufficiently utilized the combination of O-D IR and O-D visual sensors, coupled with DL, for the extraction of vegetation material. We will be showing the contradiction between current approaches and our deep vegetation learning sensor fusion. This article introduces two architectures: 1) the application of two autoencoders feeding into a four-layer convolutional neural network (CNN) and 2) two deep CNN feature extractors feeding a deep CNN fusion network (DeepFuseNet) for the fusion of O-D IR and O-D visual sensors to better address the number of false detects inherent in indices-based spectral decomposition. We compare our DL results to our previous work with normalized difference vegetation index (NDVI) and IR region-based spectral fusion, and to traditional machine learning approaches. This work proves that the fusion of the O-D IR and O-D visual streams utilizing our DeepFuseNet DL approach outperforms both the previous NVDI fused with far-IR region segmentation and traditional machine learning approaches. Experimental results of our method validate a 92% reduction in false detects compared to traditional indices-based detection. This article contributes a novel method for the fusion of O-D visual and O-D IR sensors using two CNN feature extractors feeding into a deep CNN (DeepFuseNet). [ABSTRACT FROM AUTHOR]

In the context of vegetation detection, the fusion of omnidirectional (O-D) infrared (IR) and color vision sensors may increase the level of vegetation perception for unmanned robotic platforms. Current approaches are primarily focused on O-D color vision for localization, mapping, and tracking. A literature search found no significant research in our area of interest. The fusion of O-D IR and O-D color vision sensors for the extraction of feature material type has not been adequately addressed. We will look at augmenting indices-based spectral decomposition with IR region-based spectral decomposition to address the number of false detects inherent in indices-based spectral decomposition alone. Our work shows that the fusion of the normalized difference vegetation index (NDVI) from the O-D color camera fused with the IR thresholded signature region associated with the vegetation region minimizes the number of false detects seen with NDVI alone. The contribution of this paper is the demonstration of a new technique, thresholded region fusion technique for the fusion of O-D IR and O-D color. We also look at the Kinect vision sensor fused with the O-D IR camera. Our experimental validation demonstrates a 64% reduction in false detects in our method compared to classical indices-based detection. [ABSTRACT FROM AUTHOR]

Accurate sorting of beam projections is important in 4D cone beam computed tomography (4D CBCT) to improve the quality of the reconstructed 4D CBCT image by removing motion-induced artifacts. We propose image registration-based projection binning (IRPB), a novel marker-less binning method for 4D CBCT projections, which combines intensity-based feature point detection and trajectory tracking using random sample consensus. IRPB extracts breathing motion and phases by analyzing tissue feature point trajectories. We conducted experiments with two phantom and six patient datasets, including both regular and irregular respirations. In experiments, we compared the performance of the proposed IRPB, Amsterdam Shroud method (AS), Fourier transform-based method (FT), and local intensity feature tracking method (LIFT). The results showed that the average absolute phase shift of IRPB was 3.74 projections and 0.48 projections less than that of FT and LIFT, respectively. AS lost the most breathing cycles in the respiration extraction for the five patient datasets, so we could not compare the average absolute phase shift between IRPB and AS. Based on the peak signal-to-noise ratio (PSNR) of the reconstructed 4D CBCT images, IRPB had 5.08, 1.05, and 2.90 dB larger PSNR than AS, FT, and LIFT, respectively. The average Structure SIMilarity Index (SSIM) of the 4D CBCT image reconstructed by IRPB, AS, and LIFT were 0.87, 0.74, 0.84, and 0.70, respectively. These results demonstrated that IRPB has superior performance to the other standard methods. [ABSTRACT FROM PUBLISHER]

To enhance the quality of economically efficient healthcare, we propose a preventive planning service for next-generation screening based on a longitudinal prediction. This newly proposed framework may bring important advancements in prevention by identifying the early stages of cancer, which will help in further diagnoses and initial treatment planning. The preventive service may also solve the obstacles of cost and availability of scanners in screening. For nonstationary medical data, anomaly detection is the key problem in the prediction of cancer staging. To address anomaly detection in a huge stream of databases, we applied a composite kernel to the prediction of cancer staging for the first time. The proposed longitudinal analysis of composite kernels (LACK) is designed for the prediction of anomaly status and cancer stage for further diagnosis and the future likelihood of cancer stage progression. The prediction error of LACK is relatively small even if the prediction is made far ahead of time. The computation time for nonstationary learning is reduced by \text33\% compared with stationary learning. [ABSTRACT FROM PUBLISHER]

Principal composite kernel feature analysis (PC-KFA) is presented to show kernel adaptations for nonlinear features of medical image data sets (MIDS) in computer-aided diagnosis (CAD). The proposed algorithm PC-KFA has extended the existing studies on kernel feature analysis (KFA), which extracts salient features from a sample of unclassified patterns by use of a kernel method. The principal composite process for PC-KFA herein has been applied to kernel principal component analysis [34] and to our previously developed accelerated kernel feature analysis [20]. Unlike other kernel-based feature selection algorithms, PC-KFA iteratively constructs a linear subspace of a high-dimensional feature space by maximizing a variance condition for the nonlinearly transformed samples, which we call data-dependent kernel approach. The resulting kernel subspace can be first chosen by principal component analysis, and then be processed for composite kernel subspace through the efficient combination representations used for further reconstruction and classification. Numerical experiments based on several MID feature spaces of cancer CAD data have shown that PC-KFA generates efficient and an effective feature representation, and has yielded a better classification performance for the proposed composite kernel subspace using a simple pattern classifier. [ABSTRACT FROM AUTHOR]

Accounting for respiration motion during imaging can help improve targeting precision in radiation therapy. We propose local intensity feature tracking (LIFT), a novel markerless breath phase sorting method in cone beam computed tomography (CBCT) scan images. The contributions of this study are twofold. First, LIFT extracts the respiratory signal from the CBCT projections of the thorax depending only on tissue feature points that exhibit respiration. Second, the extracted respiratory signal is shown to correlate with standard respiration signals. LIFT extracts feature points in the first CBCT projection of a sequence and tracks those points in consecutive projections forming trajectories. Clustering is applied to select trajectories showing an oscillating behavior similar to the breath motion. Those “breathing” trajectories are used in a 3-D reconstruction approach to recover the 3-D motion of the lung which represents the respiratory signal. Experiments were conducted on datasets exhibiting regular and irregular breathing patterns. Results showed that LIFT-based respiratory signal correlates with the diaphragm position-based signal with an average phase shift of 1.68 projections as well as with the internal marker-based signal with an average phase shift of 1.78 projections. LIFT was able to detect the respiratory signal in all projections of all datasets. [ABSTRACT FROM PUBLISHER]

Abstract: A semi-supervised learning method, the on-line accelerated kernel feature analysis (On-line AKFA) is presented. In On-line AKFA, features are extracted while data are being fed to the algorithm in small batches as the algorithm proceeds. The paper compares and contrasts the use of On-line AKFA and Off-line AKFA in CT colonography. On-line AKFA provides the flexibility to allow the feature space to dynamically adjust to changes in the input data with time during the training phase. The computational time, reconstruction accuracy, projection variance, and classification performance of the proposed method are experimentally evaluated for kernel principal component analysis (KPCA), Off-line AKFA, and On-line AKFA. Experimental results demonstrate a significant reduction in computation time for On-line AKFA compared to the other feature extraction methods considered in this paper. [Copyright &y& Elsevier]