1,197 research outputs found

    Motion Segmentation from Clustering of Sparse Point Features Using Spatially Constrained Mixture Models

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    Motion is one of the strongest cues available for segmentation. While motion segmentation finds wide ranging applications in object detection, tracking, surveillance, robotics, image and video compression, scene reconstruction, video editing, and so on, it faces various challenges such as accurate motion recovery from noisy data, varying complexity of the models required to describe the computed image motion, the dynamic nature of the scene that may include a large number of independently moving objects undergoing occlusions, and the need to make high-level decisions while dealing with long image sequences. Keeping the sparse point features as the pivotal point, this thesis presents three distinct approaches that address some of the above mentioned motion segmentation challenges. The first part deals with the detection and tracking of sparse point features in image sequences. A framework is proposed where point features can be tracked jointly. Traditionally, sparse features have been tracked independently of one another. Combining the ideas from Lucas-Kanade and Horn-Schunck, this thesis presents a technique in which the estimated motion of a feature is influenced by the motion of the neighboring features. The joint feature tracking algorithm leads to an improved tracking performance over the standard Lucas-Kanade based tracking approach, especially while tracking features in untextured regions. The second part is related to motion segmentation using sparse point feature trajectories. The approach utilizes a spatially constrained mixture model framework and a greedy EM algorithm to group point features. In contrast to previous work, the algorithm is incremental in nature and allows for an arbitrary number of objects traveling at different relative speeds to be segmented, thus eliminating the need for an explicit initialization of the number of groups. The primary parameter used by the algorithm is the amount of evidence that must be accumulated before the features are grouped. A statistical goodness-of-fit test monitors the change in the motion parameters of a group over time in order to automatically update the reference frame. The approach works in real time and is able to segment various challenging sequences captured from still and moving cameras that contain multiple independently moving objects and motion blur. The third part of this thesis deals with the use of specialized models for motion segmentation. The articulated human motion is chosen as a representative example that requires a complex model to be accurately described. A motion-based approach for segmentation, tracking, and pose estimation of articulated bodies is presented. The human body is represented using the trajectories of a number of sparse points. A novel motion descriptor encodes the spatial relationships of the motion vectors representing various parts of the person and can discriminate between articulated and non-articulated motions, as well as between various pose and view angles. Furthermore, a nearest neighbor search for the closest motion descriptor from the labeled training data consisting of the human gait cycle in multiple views is performed, and this distance is fed to a Hidden Markov Model defined over multiple poses and viewpoints to obtain temporally consistent pose estimates. Experimental results on various sequences of walking subjects with multiple viewpoints and scale demonstrate the effectiveness of the approach. In particular, the purely motion based approach is able to track people in night-time sequences, even when the appearance based cues are not available. Finally, an application of image segmentation is presented in the context of iris segmentation. Iris is a widely used biometric for recognition and is known to be highly accurate if the segmentation of the iris region is near perfect. Non-ideal situations arise when the iris undergoes occlusion by eyelashes or eyelids, or the overall quality of the segmented iris is affected by illumination changes, or due to out-of-plane rotation of the eye. The proposed iris segmentation approach combines the appearance and the geometry of the eye to segment iris regions from non-ideal images. The image is modeled as a Markov random field, and a graph cuts based energy minimization algorithm is applied to label the pixels either as eyelashes, pupil, iris, or background using texture and image intensity information. The iris shape is modeled as an ellipse and is used to refine the pixel based segmentation. The results indicate the effectiveness of the segmentation algorithm in handling non-ideal iris images

    A Geometric Approach for Deciphering Protein Structure from Cryo-EM Volumes

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    Electron Cryo-Microscopy or cryo-EM is an area that has received much attention in the recent past. Compared to the traditional methods of X-Ray Crystallography and NMR Spectroscopy, cryo-EM can be used to image much larger complexes, in many different conformations, and under a wide range of biochemical conditions. This is because it does not require the complex to be crystallisable. However, cryo-EM reconstructions are limited to intermediate resolutions, with the state-of-the-art being 3.6A, where secondary structure elements can be visually identified but not individual amino acid residues. This lack of atomic level resolution creates new computational challenges for protein structure identification. In this dissertation, we present a suite of geometric algorithms to address several aspects of protein modeling using cryo-EM density maps. Specifically, we develop novel methods to capture the shape of density volumes as geometric skeletons. We then use these skeletons to find secondary structure elements: SSEs) of a given protein, to identify the correspondence between these SSEs and those predicted from the primary sequence, and to register high-resolution protein structures onto the density volume. In addition, we designed and developed Gorgon, an interactive molecular modeling system, that integrates the above methods with other interactive routines to generate reliable and accurate protein backbone models

    Shells and Spheres: A Novel Framework for Variable Scale Statistical Image Analysis

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    A framework for analyzing images, called emph{Shells and Spheres},has been developed based on a set of spheres with adjustable radii,with exactly one sphere centered at each image pixel. This set ofspheres, known as a emph{sphere map}, is considered optimized wheneach sphere reaches, but does not cross, the nearest boundary.Calculations denoted as emph{Variable-Scale Statistics} (VSS) areperformed on populations of pixels within spheres, as well aspopulations of adjacent and overlapping spheres, in order to deducethe proper radius of each sphere. Spheres grow or shrink by addingor deleting an outer shell one pixel thick . Unlike conventionalfixed-scale kernels, our spherical operators consider as many pixelsas possible to differentiate between objects and accuratelydelineate boundaries. The term ``sphere" is used for brevity, thoughthe approach is not limited to 3D and is valid in nn-dimensions.The approach is illustrated using both real images and noiselesssynthetic images containing objects with uniform intensity, and moreclosely examined and validated using various synthetic images withadded white noise and multiple contrast enhanced CT scans of theaortic arch. A particular algorithm using Shells and Spheres isdescribed and demonstrated on segmentation of the aortic arch in acontrast-enhanced CT scan, both in 2D and 3D

    Automation Process for Morphometric Analysis of Volumetric CT Data from Pulmonary Vasculature in Rats

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    With advances in medical imaging scanners, it has become commonplace to generate large multidimensional datasets. These datasets require tools for a rapid, thorough analysis. To address this need, we have developed an automated algorithm for morphometric analysis incorporating A Visualization Workshop computational and image processing libraries for three-dimensional segmentation, vascular tree generation and structural hierarchical ordering with a two-stage numeric optimization procedure for estimating vessel diameters. We combine this new technique with our mathematical models of pulmonary vascular morphology to quantify structural and functional attributes of lung arterial trees. Our physiological studies require repeated measurements of vascular structure to determine differences in vessel biomechanical properties between animal models of pulmonary disease. Automation provides many advantages including significantly improved speed and minimized operator interaction and biasing. The results are validated by comparison with previously published rat pulmonary arterial micro-CT data analysis techniques, in which vessels were manually mapped and measured using intense operator intervention

    Color Image Segmentation Using the Bee Algorithm in the Markovian Framework

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    This thesis presents color image segmentation as a vital step of image analysis in computer vision. A survey of the Markov Random Field (MRF) with four different implementation methods for its parameter estimation is provided. In addition, a survey of swarm intelligence and a number of swarm based algorithms are presented. The MRF model is used for color image segmentation in the framework. This thesis introduces a new image segmentation implementation that uses the bee algorithm as an optimization tool in the Markovian framework. The experiments show that the new proposed method performs faster than the existing implementation methods with about the same segmentation accuracy

    Automatic segmentation of kidney and liver tumors in CT images

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    Automatic segmentation of hepatic lesions in computed tomography (CT) images is a challenging task to perform due to heterogeneous, diffusive shape of tumors and complex background. To address the problem more and more researchers rely on assistance of deep convolutional neural networks (CNN) with 2D or 3D type architecture that have proven to be effective in a wide range of computer vision tasks, including medical image processing. In this technical report, we carry out research focused on more careful approach to the process of learning rather than on complex architecture of the CNN. We have chosen MICCAI 2017 LiTS dataset for training process and the public 3DIRCADb dataset for validation of our method. The proposed algorithm reached DICE score 78.8% on the 3DIRCADb dataset. The described method was then applied to the 2019 Kidney Tumor Segmentation (KiTS-2019) challenge, where our single submission achieved 96.38% for kidney and 67.38% for tumor Dice scores
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