Multisensory Imagery Cues for Object Separation, Specularity Detection and Deep Learning based Inpainting

Multisensory imagery cues have been actively investigated in diverse applications in the computer vision community to provide additional geometric information that is either absent or difficult to capture from mainstream two-dimensional imaging. The inherent features of multispectral polarimetric light field imagery (MSPLFI) include object distribution over spectra, surface properties, shape, shading and pixel flow in light space. The aim of this dissertation is to explore these inherent properties to exploit new structures and methodologies for the tasks of object separation, specularity detection and deep learning-based inpainting in MSPLFI. In the first part of this research, an application to separate foreground objects from the background in both outdoor and indoor scenes using multispectral polarimetric imagery (MSPI) cues is examined. Based on the pixel neighbourhood relationship, an on-demand clustering technique is proposed and implemented to separate artificial objects from natural background in a complex outdoor scene. However, due to indoor scenes only containing artificial objects, with vast variations in energy levels among spectra, a multiband fusion technique followed by a background segmentation algorithm is proposed to separate the foreground from the background. In this regard, first, each spectrum is decomposed into low and high frequencies using the fast Fourier transform (FFT) method. Second, principal component analysis (PCA) is applied on both frequency images of the individual spectrum and then combined with the first principal components as a fused image. Finally, a polarimetric background segmentation (BS) algorithm based on the Stokes vector is proposed and implemented on the fused image. The performance of the proposed approaches are evaluated and compared using publicly available MSPI datasets and the dice similarity coefficient (DSC). The proposed multiband fusion and BS methods demonstrate better fusion quality and higher segmentation accuracy compared with other studies for several metrics, including mean absolute percentage error (MAPE), peak signal-to-noise ratio (PSNR), Pearson correlation coefficient (PCOR) mutual information (MI), accuracy, Geometric Mean (G-mean), precision, recall and F1-score. In the second part of this work, a twofold framework for specular reflection detection (SRD) and specular reflection inpainting (SRI) in transparent objects is proposed. The SRD algorithm is based on the mean, the covariance and the Mahalanobis distance for predicting anomalous pixels in MSPLFI. The SRI algorithm first selects four-connected neighbouring pixels from sub-aperture images and then replaces the SRD pixel with the closest matched pixel. For both algorithms, a 6D MSPLFI transparent object dataset is captured from multisensory imagery cues due to the unavailability of this kind of dataset. The experimental results demonstrate that the proposed algorithms predict higher SRD accuracy and better SRI quality than the existing approaches reported in this part in terms of F1-score, G-mean, accuracy, the structural similarity index (SSIM), the PSNR, the mean squared error (IMMSE) and the mean absolute deviation (MAD). However, due to synthesising SRD pixels based on the pixel neighbourhood relationship, the proposed inpainting method in this research produces artefacts and errors when inpainting large specularity areas with irregular holes. Therefore, in the last part of this research, the emphasis is on inpainting large specularity areas with irregular holes based on the deep feature extraction from multisensory imagery cues. The proposed six-stage deep learning inpainting (DLI) framework is based on the generative adversarial network (GAN) architecture and consists of a generator network and a discriminator network. First, pixels’ global flow in the sub-aperture images is calculated by applying the large displacement optical flow (LDOF) method. The proposed training algorithm combines global flow with local flow and coarse inpainting results predicted from the baseline method. The generator attempts to generate best-matched features, while the discriminator seeks to predict the maximum difference between the predicted results and the actual results. The experimental results demonstrate that in terms of the PSNR, MSSIM, IMMSE and MAD, the proposed DLI framework predicts superior inpainting quality to the baseline method and the previous part of this research

Application of Multi-Sensor Fusion Technology in Target Detection and Recognition

Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems