INTERMEDIATE VIEW RECONSTRUCTION FOR MULTISCOPIC 3D DISPLAY

This thesis focuses on Intermediate View Reconstruction (IVR) which generates additional images from the available stereo images. The main application of IVR is to generate the content of multiscopic 3D displays, and it can be applied to generate different viewpoints to Free-viewpoint TV (FTV). Although IVR is considered a good approach to generate additional images, there are some problems with the reconstruction process, such as detecting and handling the occlusion areas, preserving the discontinuity at edges, and reducing image artifices through formation of the texture of the intermediate image. The occlusion area is defined as the visibility of such an area in one image and its disappearance in the other one. Solving IVR problems is considered a significant challenge for researchers. In this thesis, several novel algorithms have been specifically designed to solve IVR challenges by employing them in a highly robust intermediate view reconstruction algorithm. Computer simulation and experimental results confirm the importance of occluded areas in IVR. Therefore, we propose a novel occlusion detection algorithm and another novel algorithm to Inpaint those areas. Then, these proposed algorithms are employed in a novel occlusion-aware intermediate view reconstruction that finds an intermediate image with a given disparity between two input images. This novelty is addressed by adding occlusion awareness to the reconstruction algorithm and proposing three quality improvement techniques to reduce image artifices: filling the re-sampling holes, removing ghost contours, and handling the disocclusion area. We compared the proposed algorithms to the previously well-known algorithms on each field qualitatively and quantitatively. The obtained results show that our algorithms are superior to the previous well-known algorithms. The performance of the proposed reconstruction algorithm is tested under 13 real images and 13 synthetic images. Moreover, analysis of a human-trial experiment conducted with 21 participants confirmed that the reconstructed images from our proposed algorithm have very high quality compared with the reconstructed images from the other existing algorithms

Modelling and analysis of plant image data for crop growth monitoring in horticulture

Plants can be characterised by a range of attributes, and measuring these attributes accurately and reliably is a major challenge for the horticulture industry. The measurement of those plant characteristics that are most relevant to a grower has previously been tackled almost exclusively by a combination of manual measurement and visual inspection. The purpose of this work is to propose an automated image analysis approach in order to provide an objective measure of plant attributes to remove subjective factors from assessment and to reduce labour requirements in the glasshouse. This thesis describes a stereopsis approach for estimating plant height, since height information cannot be easily determined from a single image. The stereopsis algorithm proposed in this thesis is efficient in terms of the running time, and is more accurate when compared with other algorithms. The estimated geometry, together with colour information from the image, are then used to build a statistical plant surface model, which represents all the information from the visible spectrum. A self-organising map approach can be adopted to model plant surface attributes, but the model can be improved by using a probabilistic model such as a mixture model formulated in a Bayesian framework. Details of both methods are discussed in this thesis. A Kalman filter is developed to track the plant model over time, extending the model to the time dimension, which enables smoothing of the noisy measurements to produce a development trend for a crop. The outcome of this work could lead to a number of potentially important applications in horticulture

Modelling and analysis of plant image data for crop growth monitoring in horticulture

Plants can be characterised by a range of attributes, and measuring these attributes accurately and reliably is a major challenge for the horticulture industry. The measurement of those plant characteristics that are most relevant to a grower has previously been tackled almost exclusively by a combination of manual measurement and visual inspection. The purpose of this work is to propose an automated image analysis approach in order to provide an objective measure of plant attributes to remove subjective factors from assessment and to reduce labour requirements in the glasshouse. This thesis describes a stereopsis approach for estimating plant height, since height information cannot be easily determined from a single image. The stereopsis algorithm proposed in this thesis is efficient in terms of the running time, and is more accurate when compared with other algorithms. The estimated geometry, together with colour information from the image, are then used to build a statistical plant surface model, which represents all the information from the visible spectrum. A self-organising map approach can be adopted to model plant surface attributes, but the model can be improved by using a probabilistic model such as a mixture model formulated in a Bayesian framework. Details of both methods are discussed in this thesis. A Kalman filter is developed to track the plant model over time, extending the model to the time dimension, which enables smoothing of the noisy measurements to produce a development trend for a crop. The outcome of this work could lead to a number of potentially important applications in horticulture.EThOS - Electronic Theses Online ServiceHorticultural Development Council (Great Britain) (HDC) (CP 37)GBUnited Kingdo

Modelling and analysis of plant image data for crop growth monitoring in horticulture

Plants can be characterised by a range of attributes, and measuring these attributes accurately and reliably is a major challenge for the horticulture industry. The measurement of those plant characteristics that are most relevant to a grower has previously been tackled almost exclusively by a combination of manual measurement and visual inspection. The purpose of this work is to propose an automated image analysis approach in order to provide an objective measure of plant attributes to remove subjective factors from assessment and to reduce labour requirements in the glasshouse. This thesis describes a stereopsis approach for estimating plant height, since height information cannot be easily determined from a single image. The stereopsis algorithm proposed in this thesis is efficient in terms of the running time, and is more accurate when compared with other algorithms. The estimated geometry, together with colour information from the image, are then used to build a statistical plant surface model, which represents all the information from the visible spectrum. A self-organising map approach can be adopted to model plant surface attributes, but the model can be improved by using a probabilistic model such as a mixture model formulated in a Bayesian framework. Details of both methods are discussed in this thesis. A Kalman filter is developed to track the plant model over time, extending the model to the time dimension, which enables smoothing of the noisy measurements to produce a development trend for a crop. The outcome of this work could lead to a number of potentially important applications in horticulture.EThOS - Electronic Theses Online ServiceHorticultural Development Council (Great Britain) (HDC) (CP 37)GBUnited Kingdo

Dense Vision in Image-guided Surgery

Image-guided surgery needs an efficient and effective camera tracking system in order to perform augmented reality for overlaying preoperative models or label cancerous tissues on the 2D video images of the surgical scene. Tracking in endoscopic/laparoscopic scenes however is an extremely difficult task primarily due to tissue deformation, instrument invasion into the surgical scene and the presence of specular highlights. State of the art feature-based SLAM systems such as PTAM fail in tracking such scenes since the number of good features to track is very limited. When the scene is smoky and when there are instrument motions, it will cause feature-based tracking to fail immediately. The work of this thesis provides a systematic approach to this problem using dense vision. We initially attempted to register a 3D preoperative model with multiple 2D endoscopic/laparoscopic images using a dense method but this approach did not perform well. We subsequently proposed stereo reconstruction to directly obtain the 3D structure of the scene. By using the dense reconstructed model together with robust estimation, we demonstrate that dense stereo tracking can be incredibly robust even within extremely challenging endoscopic/laparoscopic scenes. Several validation experiments have been conducted in this thesis. The proposed stereo reconstruction algorithm has turned out to be the state of the art method for several publicly available ground truth datasets. Furthermore, the proposed robust dense stereo tracking algorithm has been proved highly accurate in synthetic environment (< 0.1 mm RMSE) and qualitatively extremely robust when being applied to real scenes in RALP prostatectomy surgery. This is an important step toward achieving accurate image-guided laparoscopic surgery.Open Acces

Single View Modeling and View Synthesis

This thesis develops new algorithms to produce 3D content from a single camera. Today, amateurs can use hand-held camcorders to capture and display the 3D world in 2D, using mature technologies. However, there is always a strong desire to record and re-explore the 3D world in 3D. To achieve this goal, current approaches usually make use of a camera array, which suffers from tedious setup and calibration processes, as well as lack of portability, limiting its application to lab experiments. In this thesis, I try to produce the 3D contents using a single camera, making it as simple as shooting pictures. It requires a new front end capturing device rather than a regular camcorder, as well as more sophisticated algorithms. First, in order to capture the highly detailed object surfaces, I designed and developed a depth camera based on a novel technique called light fall-off stereo (LFS). The LFS depth camera outputs color+depth image sequences and achieves 30 fps, which is necessary for capturing dynamic scenes. Based on the output color+depth images, I developed a new approach that builds 3D models of dynamic and deformable objects. While the camera can only capture part of a whole object at any instance, partial surfaces are assembled together to form a complete 3D model by a novel warping algorithm. Inspired by the success of single view 3D modeling, I extended my exploration into 2D-3D video conversion that does not utilize a depth camera. I developed a semi-automatic system that converts monocular videos into stereoscopic videos, via view synthesis. It combines motion analysis with user interaction, aiming to transfer as much depth inferring work from the user to the computer. I developed two new methods that analyze the optical flow in order to provide additional qualitative depth constraints. The automatically extracted depth information is presented in the user interface to assist with user labeling work. In this thesis, I developed new algorithms to produce 3D contents from a single camera. Depending on the input data, my algorithm can build high fidelity 3D models for dynamic and deformable objects if depth maps are provided. Otherwise, it can turn the video clips into stereoscopic video

Omnidirectional Stereo Vision for Autonomous Vehicles

Environment perception with cameras is an important requirement for many applications for autonomous vehicles and robots. This work presents a stereoscopic omnidirectional camera system for autonomous vehicles which resolves the problem of a limited field of view and provides a 360° panoramic view of the environment. We present a new projection model for these cameras and show that the camera setup overcomes major drawbacks of traditional perspective cameras in many applications

Sensor fusion in distributed cortical circuits

The substantial motion of the nature is to balance, to survive, and to reach perfection. The evolution in biological systems is a key signature of this quintessence. Survival cannot be achieved without understanding the surrounding world. How can a fruit fly live without searching for food, and thereby with no form of perception that guides the behavior? The nervous system of fruit fly with hundred thousand of neurons can perform very complicated tasks that are beyond the power of an advanced supercomputer. Recently developed computing machines are made by billions of transistors and they are remarkably fast in precise calculations. But these machines are unable to perform a single task that an insect is able to do by means of thousands of neurons. The complexity of information processing and data compression in a single biological neuron and neural circuits are not comparable with that of developed today in transistors and integrated circuits. On the other hand, the style of information processing in neural systems is also very different from that of employed by microprocessors which is mostly centralized. Almost all cognitive functions are generated by a combined effort of multiple brain areas. In mammals, Cortical regions are organized hierarchically, and they are reciprocally interconnected, exchanging the information from multiple senses. This hierarchy in circuit level, also preserves the sensory world within different levels of complexity and within the scope of multiple modalities. The main behavioral advantage of that is to understand the real-world through multiple sensory systems, and thereby to provide a robust and coherent form of perception. When the quality of a sensory signal drops, the brain can alternatively employ other information pathways to handle cognitive tasks, or even to calibrate the error-prone sensory node. Mammalian brain also takes a good advantage of multimodal processing in learning and development; where one sensory system helps another sensory modality to develop. Multisensory integration is considered as one of the main factors that generates consciousness in human. Although, we still do not know where exactly the information is consolidated into a single percept, and what is the underpinning neural mechanism of this process? One straightforward hypothesis suggests that the uni-sensory signals are pooled in a ploy-sensory convergence zone, which creates a unified form of perception. But it is hard to believe that there is just one single dedicated region that realizes this functionality. Using a set of realistic neuro-computational principles, I have explored theoretically how multisensory integration can be performed within a distributed hierarchical circuit. I argued that the interaction of cortical populations can be interpreted as a specific form of relation satisfaction in which the information preserved in one neural ensemble must agree with incoming signals from connected populations according to a relation function. This relation function can be seen as a coherency function which is implicitly learnt through synaptic strength. Apart from the fact that the real world is composed of multisensory attributes, the sensory signals are subject to uncertainty. This requires a cortical mechanism to incorporate the statistical parameters of the sensory world in neural circuits and to deal with the issue of inaccuracy in perception. I argued in this thesis how the intrinsic stochasticity of neural activity enables a systematic mechanism to encode probabilistic quantities within neural circuits, e.g. reliability, prior probability. The systematic benefit of neural stochasticity is well paraphrased by the problem of Duns Scotus paradox: imagine a donkey with a deterministic brain that is exposed to two identical food rewards. This may make the animal suffer and die starving because of indecision. In this thesis, I have introduced an optimal encoding framework that can describe the probability function of a Gaussian-like random variable in a pool of Poisson neurons. Thereafter a distributed neural model is proposed that can optimally combine conditional probabilities over sensory signals, in order to compute Bayesian Multisensory Causal Inference. This process is known as a complex multisensory function in the cortex. Recently it is found that this process is performed within a distributed hierarchy in sensory cortex. Our work is amongst the first successful attempts that put a mechanistic spotlight on understanding the underlying neural mechanism of Multisensory Causal Perception in the brain, and in general the theory of decentralized multisensory integration in sensory cortex. Engineering information processing concepts in the brain and developing new computing technologies have been recently growing. Neuromorphic Engineering is a new branch that undertakes this mission. In a dedicated part of this thesis, I have proposed a Neuromorphic algorithm for event-based stereoscopic fusion. This algorithm is anchored in the idea of cooperative computing that dictates the defined epipolar and temporal constraints of the stereoscopic setup, to the neural dynamics. The performance of this algorithm is tested using a pair of silicon retinas

Sensor fusion in distributed cortical circuits

The substantial motion of the nature is to balance, to survive, and to reach perfection. The evolution in biological systems is a key signature of this quintessence. Survival cannot be achieved without understanding the surrounding world. How can a fruit fly live without searching for food, and thereby with no form of perception that guides the behavior? The nervous system of fruit fly with hundred thousand of neurons can perform very complicated tasks that are beyond the power of an advanced supercomputer. Recently developed computing machines are made by billions of transistors and they are remarkably fast in precise calculations. But these machines are unable to perform a single task that an insect is able to do by means of thousands of neurons. The complexity of information processing and data compression in a single biological neuron and neural circuits are not comparable with that of developed today in transistors and integrated circuits. On the other hand, the style of information processing in neural systems is also very different from that of employed by microprocessors which is mostly centralized. Almost all cognitive functions are generated by a combined effort of multiple brain areas. In mammals, Cortical regions are organized hierarchically, and they are reciprocally interconnected, exchanging the information from multiple senses. This hierarchy in circuit level, also preserves the sensory world within different levels of complexity and within the scope of multiple modalities. The main behavioral advantage of that is to understand the real-world through multiple sensory systems, and thereby to provide a robust and coherent form of perception. When the quality of a sensory signal drops, the brain can alternatively employ other information pathways to handle cognitive tasks, or even to calibrate the error-prone sensory node. Mammalian brain also takes a good advantage of multimodal processing in learning and development; where one sensory system helps another sensory modality to develop. Multisensory integration is considered as one of the main factors that generates consciousness in human. Although, we still do not know where exactly the information is consolidated into a single percept, and what is the underpinning neural mechanism of this process? One straightforward hypothesis suggests that the uni-sensory signals are pooled in a ploy-sensory convergence zone, which creates a unified form of perception. But it is hard to believe that there is just one single dedicated region that realizes this functionality. Using a set of realistic neuro-computational principles, I have explored theoretically how multisensory integration can be performed within a distributed hierarchical circuit. I argued that the interaction of cortical populations can be interpreted as a specific form of relation satisfaction in which the information preserved in one neural ensemble must agree with incoming signals from connected populations according to a relation function. This relation function can be seen as a coherency function which is implicitly learnt through synaptic strength. Apart from the fact that the real world is composed of multisensory attributes, the sensory signals are subject to uncertainty. This requires a cortical mechanism to incorporate the statistical parameters of the sensory world in neural circuits and to deal with the issue of inaccuracy in perception. I argued in this thesis how the intrinsic stochasticity of neural activity enables a systematic mechanism to encode probabilistic quantities within neural circuits, e.g. reliability, prior probability. The systematic benefit of neural stochasticity is well paraphrased by the problem of Duns Scotus paradox: imagine a donkey with a deterministic brain that is exposed to two identical food rewards. This may make the animal suffer and die starving because of indecision. In this thesis, I have introduced an optimal encoding framework that can describe the probability function of a Gaussian-like random variable in a pool of Poisson neurons. Thereafter a distributed neural model is proposed that can optimally combine conditional probabilities over sensory signals, in order to compute Bayesian Multisensory Causal Inference. This process is known as a complex multisensory function in the cortex. Recently it is found that this process is performed within a distributed hierarchy in sensory cortex. Our work is amongst the first successful attempts that put a mechanistic spotlight on understanding the underlying neural mechanism of Multisensory Causal Perception in the brain, and in general the theory of decentralized multisensory integration in sensory cortex. Engineering information processing concepts in the brain and developing new computing technologies have been recently growing. Neuromorphic Engineering is a new branch that undertakes this mission. In a dedicated part of this thesis, I have proposed a Neuromorphic algorithm for event-based stereoscopic fusion. This algorithm is anchored in the idea of cooperative computing that dictates the defined epipolar and temporal constraints of the stereoscopic setup, to the neural dynamics. The performance of this algorithm is tested using a pair of silicon retinas