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

Illuminant Estimation by Voting

Obtaining an estimate of the illuminant color is an important component in many image analysis applications. Due to the complexity of the problem many restrictive assumptions are commonly applied, making the existing illuminant estimation methodologies not widely applicable on natural images. We propose a methodology which analyzes a large number of regions in an image. An illuminant estimate is obtained independently from each region and a global illumination color is computed by consensus. Each region itself is mainly composed by pixels which simultaneously exhibit both diffuse and specular reflection. This allows for a larger inclusion of pixels than purely specularitybased methods, while avoiding, at the same time, some of the restrictive assumptions of purely diffuse-based approaches. As such, our technique is particularly well-suited for analyzing real-world images. Experiments with laboratory data show that our methodology outperforms 75 % of other illuminant estimation methods. On natural images, the algorithm is very stable and provides qualitatively correct estimates. 1

Polarisation photometric stereo

© 2017 This paper concerns a novel approach to fuse two-source photometric stereo (PS) data with polarisation information for complete surface normal recovery for smooth or slightly rough surfaces. PS is a well-established method but is limited in application by its need for three or more well-spaced and known illumination sources and Lambertian reflectance. Polarisation methods are less studied but have shown promise for smooth surfaces under highly controlled capture conditions. However, such methods suffer from inherent ambiguities and the depolarising effects of surface roughness. The method presented in this paper goes some way to overcome these limitations by fusing the most reliable information from PS and polarisation. PS is used with only two sources to deduce a constrained mapping of the surface normal at each point onto a 2D plane. Phase information from polarisation is used to deduce a mapping onto a different plane. The paper then shows how the full surface normal can be obtained from the two mappings. The method is tested on a range of real-world images to demonstrate the advantages over standalone applications of PS or polarisation methods

Extended Intensity Range Imaging

A single composite image with an extended intensive range is generated by combining disjoining regions from different images of the same scene. The set of images is obtained with a charge-couple device (CCD) set for different flux integration times. By limiting differences in the integration times so that the ranges of output pixel values overlap considerably, individual pixels are assigned the value measured at each spatial location that is in the most sensitive range where the values are both below saturation and are most precisely specified. Integration times are lengthened geometrically from a minimum where all pixel values are below saturation until all dark regions emerge from the lowest quantization level. the method is applied to an example scene and the effect the composite images have on traditional low-level imaging methods also is examined

Activity profiling for minimally invasive surgery

Imperial Users onl

Statistical/Geometric Techniques for Object Representation and Recognition

Object modeling and recognition are key areas of research in computer vision and graphics with wide range of applications. Though research in these areas is not new, traditionally most of it has focused on analyzing problems under controlled environments. The challenges posed by real life applications demand for more general and robust solutions. The wide variety of objects with large intra-class variability makes the task very challenging. The difficulty in modeling and matching objects also vary depending on the input modality. In addition, the easy availability of sensors and storage have resulted in tremendous increase in the amount of data that needs to be processed which requires efficient algorithms suitable for large-size databases. In this dissertation, we address some of the challenges involved in modeling and matching of objects in realistic scenarios. Object matching in images require accounting for large variability in the appearance due to changes in illumination and view point. Any real world object is characterized by its underlying shape and albedo, which unlike the image intensity are insensitive to changes in illumination conditions. We propose a stochastic filtering framework for estimating object albedo from a single intensity image by formulating the albedo estimation as an image estimation problem. We also show how this albedo estimate can be used for illumination insensitive object matching and for more accurate shape recovery from a single image using standard shape from shading formulation. We start with the simpler problem where the pose of the object is known and only the illumination varies. We then extend the proposed approach to handle unknown pose in addition to illumination variations. We also use the estimated albedo maps for another important application, which is recognizing faces across age progression. Many approaches which address the problem of modeling and recognizing objects from images assume that the underlying objects are of diffused texture. But most real world objects exhibit a combination of diffused and specular properties. We propose an approach for separating the diffused and specular reflectance from a given color image so that the algorithms proposed for objects of diffused texture become applicable to a much wider range of real world objects. Representing and matching the 2D and 3D geometry of objects is also an integral part of object matching with applications in gesture recognition, activity classification, trademark and logo recognition, etc. The challenge in matching 2D/3D shapes lies in accounting for the different rigid and non-rigid deformations, large intra-class variability, noise and outliers. In addition, since shapes are usually represented as a collection of landmark points, the shape matching algorithm also has to deal with the challenges of missing or unknown correspondence across these data points. We propose an efficient shape indexing approach where the different feature vectors representing the shape are mapped to a hash table. For a query shape, we show how the similar shapes in the database can be efficiently retrieved without the need for establishing correspondence making the algorithm extremely fast and scalable. We also propose an approach for matching and registration of 3D point cloud data across unknown or missing correspondence using an implicit surface representation. Finally, we discuss possible future directions of this research

De-smokeGCN: Generative Cooperative Networks for Joint Surgical Smoke Detection and Removal

Surgical smoke removal algorithms can improve the quality of intra-operative imaging and reduce hazards in image-guided surgery, a highly desirable post-process for many clinical applications. These algorithms also enable effective computer vision tasks for future robotic surgery. In this paper, we present a new unsupervised learning framework for high-quality pixel-wise smoke detection and removal. One of the well recognized grand challenges in using convolutional neural networks (CNNs) for medical image processing is to obtain intra-operative medical imaging datasets for network training and validation, but availability and quality of these datasets are scarce. Our novel training framework does not require ground-truth image pairs. Instead, it learns purely from computer-generated simulation images. This approach opens up new avenues and bridges a substantial gap between conventional non-learning based methods and which requiring prior knowledge gained from extensive training datasets. Inspired by the Generative Adversarial Network (GAN), we have developed a novel generative-collaborative learning scheme that decomposes the de-smoke process into two separate tasks: smoke detection and smoke removal. The detection network is used as prior knowledge, and also as a loss function to maximize its support for training of the smoke removal network. Quantitative and qualitative studies show that the proposed training framework outperforms the state-of-the-art de-smoking approaches including the latest GAN framework (such as PIX2PIX). Although trained on synthetic images, experimental results on clinical images have proved the effectiveness of the proposed network for detecting and removing surgical smoke on both simulated and real-world laparoscopic images