Exhaustive linearization for robust camera pose and focal length estimation

We propose a novel approach for the estimation of the pose and focal length of a camera from a set of 3D-to-2D point correspondences. Our method compares favorably to competing approaches in that it is both more accurate than existing closed form solutions, as well as faster and also more accurate than iterative ones. Our approach is inspired on the EPnP algorithm, a recent O(n) solution for the calibrated case. Yet we show that considering the focal length as an additional unknown renders the linearization and relinearization techniques of the original approach no longer valid, especially with large amounts of noise. We present new methodologies to circumvent this limitation termed exhaustive linearization and exhaustive relinearization which perform a systematic exploration of the solution space in closed form. The method is evaluated on both real and synthetic data, and our results show that besides producing precise focal length estimation, the retrieved camera pose is almost as accurate as the one computed using the EPnP, which assumes a calibrated camera.Peer ReviewedPostprint (author’s final draft

Development of an Atlas-Based Segmentation of Cranial Nerves Using Shape-Aware Discrete Deformable Models for Neurosurgical Planning and Simulation

Twelve pairs of cranial nerves arise from the brain or brainstem and control our sensory functions such as vision, hearing, smell and taste as well as several motor functions to the head and neck including facial expressions and eye movement. Often, these cranial nerves are difficult to detect in MRI data, and thus represent problems in neurosurgery planning and simulation, due to their thin anatomical structure, in the face of low imaging resolution as well as image artifacts. As a result, they may be at risk in neurosurgical procedures around the skull base, which might have dire consequences such as the loss of eyesight or hearing and facial paralysis. Consequently, it is of great importance to clearly delineate cranial nerves in medical images for avoidance in the planning of neurosurgical procedures and for targeting in the treatment of cranial nerve disorders. In this research, we propose to develop a digital atlas methodology that will be used to segment the cranial nerves from patient image data. The atlas will be created from high-resolution MRI data based on a discrete deformable contour model called 1-Simplex mesh. Each of the cranial nerves will be modeled using its centerline and radius information where the centerline is estimated in a semi-automatic approach by finding a shortest path between two user-defined end points. The cranial nerve atlas is then made more robust by integrating a Statistical Shape Model so that the atlas can identify and segment nerves from images characterized by artifacts or low resolution. To the best of our knowledge, no such digital atlas methodology exists for segmenting nerves cranial nerves from MRI data. Therefore, our proposed system has important benefits to the neurosurgical community

Automatic video segmentation employing object/camera modeling techniques

Practically established video compression and storage techniques still process video sequences as rectangular images without further semantic structure. However, humans watching a video sequence immediately recognize acting objects as semantic units. This semantic object separation is currently not reflected in the technical system, making it difficult to manipulate the video at the object level. The realization of object-based manipulation will introduce many new possibilities for working with videos like composing new scenes from pre-existing video objects or enabling user-interaction with the scene. Moreover, object-based video compression, as defined in the MPEG-4 standard, can provide high compression ratios because the foreground objects can be sent independently from the background. In the case that the scene background is static, the background views can even be combined into a large panoramic sprite image, from which the current camera view is extracted. This results in a higher compression ratio since the sprite image for each scene only has to be sent once. A prerequisite for employing object-based video processing is automatic (or at least user-assisted semi-automatic) segmentation of the input video into semantic units, the video objects. This segmentation is a difficult problem because the computer does not have the vast amount of pre-knowledge that humans subconsciously use for object detection. Thus, even the simple definition of the desired output of a segmentation system is difficult. The subject of this thesis is to provide algorithms for segmentation that are applicable to common video material and that are computationally efficient. The thesis is conceptually separated into three parts. In Part I, an automatic segmentation system for general video content is described in detail. Part II introduces object models as a tool to incorporate userdefined knowledge about the objects to be extracted into the segmentation process. Part III concentrates on the modeling of camera motion in order to relate the observed camera motion to real-world camera parameters. The segmentation system that is described in Part I is based on a background-subtraction technique. The pure background image that is required for this technique is synthesized from the input video itself. Sequences that contain rotational camera motion can also be processed since the camera motion is estimated and the input images are aligned into a panoramic scene-background. This approach is fully compatible to the MPEG-4 video-encoding framework, such that the segmentation system can be easily combined with an object-based MPEG-4 video codec. After an introduction to the theory of projective geometry in Chapter 2, which is required for the derivation of camera-motion models, the estimation of camera motion is discussed in Chapters 3 and 4. It is important that the camera-motion estimation is not influenced by foreground object motion. At the same time, the estimation should provide accurate motion parameters such that all input frames can be combined seamlessly into a background image. The core motion estimation is based on a feature-based approach where the motion parameters are determined with a robust-estimation algorithm (RANSAC) in order to distinguish the camera motion from simultaneously visible object motion. Our experiments showed that the robustness of the original RANSAC algorithm in practice does not reach the theoretically predicted performance. An analysis of the problem has revealed that this is caused by numerical instabilities that can be significantly reduced by a modification that we describe in Chapter 4. The synthetization of static-background images is discussed in Chapter 5. In particular, we present a new algorithm for the removal of the foreground objects from the background image such that a pure scene background remains. The proposed algorithm is optimized to synthesize the background even for difficult scenes in which the background is only visible for short periods of time. The problem is solved by clustering the image content for each region over time, such that each cluster comprises static content. Furthermore, it is exploited that the times, in which foreground objects appear in an image region, are similar to the corresponding times of neighboring image areas. The reconstructed background could be used directly as the sprite image in an MPEG-4 video coder. However, we have discovered that the counterintuitive approach of splitting the background into several independent parts can reduce the overall amount of data. In the case of general camera motion, the construction of a single sprite image is even impossible. In Chapter 6, a multi-sprite partitioning algorithm is presented, which separates the video sequence into a number of segments, for which independent sprites are synthesized. The partitioning is computed in such a way that the total area of the resulting sprites is minimized, while simultaneously satisfying additional constraints. These include a limited sprite-buffer size at the decoder, and the restriction that the image resolution in the sprite should never fall below the input-image resolution. The described multisprite approach is fully compatible to the MPEG-4 standard, but provides three advantages. First, any arbitrary rotational camera motion can be processed. Second, the coding-cost for transmitting the sprite images is lower, and finally, the quality of the decoded sprite images is better than in previously proposed sprite-generation algorithms. Segmentation masks for the foreground objects are computed with a change-detection algorithm that compares the pure background image with the input images. A special effect that occurs in the change detection is the problem of image misregistration. Since the change detection compares co-located image pixels in the camera-motion compensated images, a small error in the motion estimation can introduce segmentation errors because non-corresponding pixels are compared. We approach this problem in Chapter 7 by integrating risk-maps into the segmentation algorithm that identify pixels for which misregistration would probably result in errors. For these image areas, the change-detection algorithm is modified to disregard the difference values for the pixels marked in the risk-map. This modification significantly reduces the number of false object detections in fine-textured image areas. The algorithmic building-blocks described above can be combined into a segmentation system in various ways, depending on whether camera motion has to be considered or whether real-time execution is required. These different systems and example applications are discussed in Chapter 8. Part II of the thesis extends the described segmentation system to consider object models in the analysis. Object models allow the user to specify which objects should be extracted from the video. In Chapters 9 and 10, a graph-based object model is presented in which the features of the main object regions are summarized in the graph nodes, and the spatial relations between these regions are expressed with the graph edges. The segmentation algorithm is extended by an object-detection algorithm that searches the input image for the user-defined object model. We provide two objectdetection algorithms. The first one is specific for cartoon sequences and uses an efficient sub-graph matching algorithm, whereas the second processes natural video sequences. With the object-model extension, the segmentation system can be controlled to extract individual objects, even if the input sequence comprises many objects. Chapter 11 proposes an alternative approach to incorporate object models into a segmentation algorithm. The chapter describes a semi-automatic segmentation algorithm, in which the user coarsely marks the object and the computer refines this to the exact object boundary. Afterwards, the object is tracked automatically through the sequence. In this algorithm, the object model is defined as the texture along the object contour. This texture is extracted in the first frame and then used during the object tracking to localize the original object. The core of the algorithm uses a graph representation of the image and a newly developed algorithm for computing shortest circular-paths in planar graphs. The proposed algorithm is faster than the currently known algorithms for this problem, and it can also be applied to many alternative problems like shape matching. Part III of the thesis elaborates on different techniques to derive information about the physical 3-D world from the camera motion. In the segmentation system, we employ camera-motion estimation, but the obtained parameters have no direct physical meaning. Chapter 12 discusses an extension to the camera-motion estimation to factorize the motion parameters into physically meaningful parameters (rotation angles, focal-length) using camera autocalibration techniques. The speciality of the algorithm is that it can process camera motion that spans several sprites by employing the above multi-sprite technique. Consequently, the algorithm can be applied to arbitrary rotational camera motion. For the analysis of video sequences, it is often required to determine and follow the position of the objects. Clearly, the object position in image coordinates provides little information if the viewing direction of the camera is not known. Chapter 13 provides a new algorithm to deduce the transformation between the image coordinates and the real-world coordinates for the special application of sport-video analysis. In sport videos, the camera view can be derived from markings on the playing field. For this reason, we employ a model of the playing field that describes the arrangement of lines. After detecting significant lines in the input image, a combinatorial search is carried out to establish correspondences between lines in the input image and lines in the model. The algorithm requires no information about the specific color of the playing field and it is very robust to occlusions or poor lighting conditions. Moreover, the algorithm is generic in the sense that it can be applied to any type of sport by simply exchanging the model of the playing field. In Chapter 14, we again consider panoramic background images and particularly focus ib their visualization. Apart from the planar backgroundsprites discussed previously, a frequently-used visualization technique for panoramic images are projections onto a cylinder surface which is unwrapped into a rectangular image. However, the disadvantage of this approach is that the viewer has no good orientation in the panoramic image because he looks into all directions at the same time. In order to provide a more intuitive presentation of wide-angle views, we have developed a visualization technique specialized for the case of indoor environments. We present an algorithm to determine the 3-D shape of the room in which the image was captured, or, more generally, to compute a complete floor plan if several panoramic images captured in each of the rooms are provided. Based on the obtained 3-D geometry, a graphical model of the rooms is constructed, where the walls are displayed with textures that are extracted from the panoramic images. This representation enables to conduct virtual walk-throughs in the reconstructed room and therefore, provides a better orientation for the user. Summarizing, we can conclude that all segmentation techniques employ some definition of foreground objects. These definitions are either explicit, using object models like in Part II of this thesis, or they are implicitly defined like in the background synthetization in Part I. The results of this thesis show that implicit descriptions, which extract their definition from video content, work well when the sequence is long enough to extract this information reliably. However, high-level semantics are difficult to integrate into the segmentation approaches that are based on implicit models. Intead, those semantics should be added as postprocessing steps. On the other hand, explicit object models apply semantic pre-knowledge at early stages of the segmentation. Moreover, they can be applied to short video sequences or even still pictures since no background model has to be extracted from the video. The definition of a general object-modeling technique that is widely applicable and that also enables an accurate segmentation remains an important yet challenging problem for further research

Video object segmentation and applications in temporal alignment and aspect learning

Modern computer vision has seen recently significant progress in learning visual concepts from examples. This progress has been fuelled by recent models of visual appearance as well as recently collected large-scale datasets of manually annotated still images. Video is a promising alternative, as it inherently contains much richer information compared to still images. For instance, in video we can observe an object move which allows us to differentiate it from its surroundings, or we can observe a smooth transition between different viewpoints of the same object instance. This richness in information allows us to effectively tackle tasks that would otherwise be very difficult if we only considered still images, or even adress tasks that are video-specific. Our first contribution is a computationally efficient technique for video object segmentation. Our method relies solely on motion in order to rapidly create a rough initial estimate of the foreground object. This rough initial estimate is then refined through an energy formulation to be spatio-temporally smooth. The method is able to handle rapidly moving backgrounds and objects, as well as non-rigid deformations and articulations without having prior knowledge about the objects appearance, size or location. In addition to this class-agnostic method, we present a class-specific method that incorporates additional class-specific appearance cues when the class of the foreground object is known in advance (e.g. a video of a car). For our second contribution, we propose a novel model for temporal video alignment with regard to the viewpoint of the foreground object (i.e., a pair of aligned frames shows the same object viewpoint) Our work relies on our video object segmentation technique to automatically localise the foreground objects and extract appearance measurements solely from them instead of the background. Our model is able to temporally align realistic videos, where events may occur in a different order, or occur only in one of the videos. This is in contrast to previous works that typically assume that the videos show a scripted sequence of events and can simply be aligned by stretching or compressing one of the videos. As a final contribution, we once again use our video object segmentation technique as a basis for automatic visual aspect discovery from videos of an object class. Compared to previous works, we use a broader definition of an aspect that considers four factors of variation: viewpoint, articulated pose, occlusions and cropping by the image border. We pose the aspect discovery task as a clustering problem and provide an extensive experimental exploration on the benefits of object segmentation for this task

Morphological Analysis for Object Recognition, Matching, and Applications

This thesis deals with the detection and classifcation of objects in visual images and with the analysis of shape changes between object instances. Whereas the task of object recognition focuses on learning models which describe common properties between instances of a specific category, the analysis of the specific differences between instances is also relevant to understand the objects and the categories themselves. This research is governed by the idea that important properties for the automatic perception and understanding of objects are transmitted through their geometry or shape. Therefore, models for object recognition and shape matching are devised which exploit the geometry and properties of the objects, using as little user supervision as possible. In order to learn object models for detection in a reliable manner, suitable object representations are required. The key idea in this work is to use a richer representation of the object shape within the object model in order to increase the description power and thus the performance of the whole system. For this purpose, we first investigate the integration of curvature information of shapes in the object model which is learned. Since natural objects intrinsically exhibit curved boundaries, an object is better described if this shape cue is integrated. This subject extends the widely used object representation based on gradient orientation histograms by incorporating a robust histogram-based description of curvature. We show that integrating this information substantially improves detection results over descriptors that solely rely upon histograms of orientated gradients. The impact of using richer shape representations for object recognition is further investigated through a novel method which goes beyond traditional bounding-box representations for objects. Visual recognition requires learning object models from training data. Commonly, training samples are annotated by marking only the bounding-box of objects since this appears to be the best trade-off between labeling information and effectiveness. However, objects are typically not box-shaped. Thus, the usual parametrization of objects using a bounding box seems inappropriate since such a box contains a significant amount of background clutter. Therefore, the presented approach learns object models for detection while simultaneously learning to segregate objects from clutter and extracting their overall shape, without however, requiring manual segmentation of the training samples. Shape equivalence is another interesting property related to shape. It refers to the ability of perceiving two distinct objects as having the same or similar shape. This thesis also explores the usage of this ability to detect objects in unsupervised scenarios, that is where no annotation of training data is available for learning a statistical model. For this purpose, a dataset of historical Chinese cartoons drawn during the Cultural Revolution and immediately thereafter is analyzed. Relevant objects in this dataset are emphasized through annuli of light rays. The idea of our method is to consider the different annuli as shape equivalent objects, that is, as objects sharing the same shape and devise a method to detect them. Thereafter, it is possible to indirectly infer the position, size and scale of the emphasized objects using the annuli detections. Not only commonalities among objects, but also the specific differences between them are perceived by a visual system. These differences can be understood through the analysis of how objects and their shape change. For this reason, this thesis also develops a novel methodology for analyzing the shape deformation between a single pair of images under missing correspondences. The key observation is that objects cannot deform arbitrarily, but rather the deformation itself follows the geometry and constraints imposed by the object itself. We describe the overall complex object deformation using a piecewise linear model. Thereby, we are able to identify each of the parts in the shape which share the same deformation. Thus, we are able to understand how an object and its parts were transformed. A remarkable property of the algorithm is the ability to automatically estimate the model complexity according to the overall complexity of the shape deformation. Specifically, the introduced methodology is used to analyze the deformation between original instances and reproductions of artworks. The nature of the analyzed alterations ranges from deliberate modifications by the artist to geometrical errors accumulated during the reproduction process of the image. The usage of this method within this application shows how productive the interaction between computer vision and the field of the humanities is. The goal is not to supplant human expertise, but to enhance and deepen connoisseurship about a given problem

Geometric and photometric affine invariant image registration

This thesis aims to present a solution to the correspondence problem for the registration of wide-baseline images taken from uncalibrated cameras. We propose an affine invariant descriptor that combines the geometry and photometry of the scene to find correspondences between both views. The geometric affine invariant component of the descriptor is based on the affine arc-length metric, whereas the photometry is analysed by invariant colour moments. A graph structure represents the spatial distribution of the primitive features; i.e. nodes correspond to detected high-curvature points, whereas arcs represent connectivities by extracted contours. After matching, we refine the search for correspondences by using a maximum likelihood robust algorithm. We have evaluated the system over synthetic and real data. The method is endemic to propagation of errors introduced by approximations in the system.BAE SystemsSelex Sensors and Airborne System

PERCEPTION FOR SURVEILLANCE: LEARNING SELF-LOCALISATION AND INTRUDERS DETECTION FROM MONOCULAR IMAGES OF AN AERIAL ROBOT IN OUTDOOR URBAN ENVIRONMENTS

Unmanned aerial vehicles (UAVs), more commonly named drones, are one of the most versatile robotic platforms for their high mobility and low-cost design. Therefore, they have been applied to numerous civil applications. These robots generally can complete autonomous or semi-autonomous missions by undertaking complex calculations on their autopilot system based on the sensors' observations to control their attitude and speed and to plan and track a trajectory for navigating in a possibly unknown environment without human intervention. However, to enable higher degrees of autonomy, the perception system is paramount for extracting valuable knowledge that allows interaction with the external world. Therefore, this thesis aims to solve the core perception challenges of an autonomous surveillance application carried out by an aerial robot in an outdoor urban environment. We address a simplified use case of patrolling missions to monitor a confined area around buildings that is supposedly under access restriction. Hence, we identify the main research questions involved in this application context. On the one hand, the drone has to locate itself in a controlled navigation environment, keep track of its pose while flying, and understand the geometrical structure of the 3D scene around it. On the other hand, the surveillance mission entails detecting and localising people in the monitored area. Consequently, we develop numerous methodologies to address these challenging questions. Furthermore, constraining the UAV's sensor array to a monocular RGB camera, we approach the raised problems with algorithms in the computer vision field. First, we train a neural network with an unsupervised learning paradigm to predict the drone ego-motion and the geometrical scene structure. Hence, we introduce a novel algorithm that integrates a model-free epipolar method to adjust online the rotational drift of the trajectory estimated by the trained pose network. Second, we employ an efficient Convolutional Neural Network (CNN) architecture to regress the UAV global metric pose directly from a single colour image. Moreover, we investigate how dynamic objects in the camera field of view affect the localisation performance of such an approach. Following, we discuss the implementation of an object detection network and derive the equations to find the 3D position of the detected people in a reconstructed environment. Next, we describe the theory behind structure-from-motion and use it to recreate a 3D model of a dataset recorded with a drone at the University of Luxembourg's Belval campus. Ultimately, we perform multiple experiments to validate and evaluate our proposed algorithms with other state-of-the-art methodologies. Results show the superiority of our methods in different metrics. Also, in our analysis, we determine the limitations and highlight the benefits of the adopted strategies compared to other approaches. Finally, the introduced dataset provides an additional tool for benchmarking perception algorithms and future application developments