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

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

Markov-Gibbs Random Field Approach for Modeling of Skin Surface Textures

Medical imaging has been contributing to dermatology by providing computer-based assistance by 2D digital imaging of skin and processing of images. Skin imaging can be more effective by inclusion of 3D skin features. Furthermore, clinical examination of skin consists of both visual and tactile inspection. The tactile sensation is related to 3D surface profiles and mechanical parameters. The 3D imaging of skin can also be integrated with haptic technology for computer-based tactile inspection. The research objective of this work is to model 3D surface textures of skin. A 3D image acquisition set up capturing skin surface textures at high resolution (~0.1 mm) has been used. An algorithm to extract 2D grayscale texture (height map) from 3D texture has been presented. The extracted 2D textures are then modeled using Markov-Gibbs random field (MGRF) modeling technique. The modeling results for MGRF model depend on input texture characteristics. The homogeneous, spatially invariant texture patterns are modeled successfully. From the observation of skin samples, we classify three key features of3D skin profiles i.e. curvature of underlying limb, wrinkles/line like features and fine textures. The skin samples are distributed in three input sets to see the MGRF model's response to each of these 3D features. First set consists of all three features. Second set is obtained after elimination of curvature and contains both wrinkle/line like features and fine textures. Third set is also obtained after elimination of curvature but consists of fine textures only. MGRF modeling for set I did not result in any visual similarity. Hence the curvature of underlying limbs cannot be modeled successfully and makes an inhomogeneous feature. For set 2 the wrinkle/line like features can be modeled with low/medium visual similarity depending on the spatial invariance. The results for set 3 show that fine textures of skin are almost always modeled successfully with medium/high visual similarity and make a homogeneous feature. We conclude that the MGRF model is able to model fine textures of skin successfully which are on scale of~ 0.1 mm. The surface profiles on this resolution can provide haptic sensation of roughness and friction. Therefore fine textures can be an important clue to different skin conditions perceived through tactile inspection via a haptic device

Detection of Ulcerative Colitis Severity and Enhancement of Informative Frame Filtering Using Texture Analysis in Colonoscopy Videos

There are several types of disorders that affect our colon’s ability to function properly such as colorectal cancer, ulcerative colitis, diverticulitis, irritable bowel syndrome and colonic polyps. Automatic detection of these diseases would inform the endoscopist of possible sub-optimal inspection during the colonoscopy procedure as well as save time during post-procedure evaluation. But existing systems only detects few of those disorders like colonic polyps. In this dissertation, we address the automatic detection of another important disorder called ulcerative colitis. We propose a novel texture feature extraction technique to detect the severity of ulcerative colitis in block, image, and video levels. We also enhance the current informative frame filtering methods by detecting water and bubble frames using our proposed technique. Our feature extraction algorithm based on accumulation of pixel value difference provides better accuracy at faster speed than the existing methods making it highly suitable for real-time systems. We also propose a hybrid approach in which our feature method is combined with existing feature method(s) to provide even better accuracy. We extend the block and image level detection method to video level severity score calculation and shot segmentation. Also, the proposed novel feature extraction method can detect water and bubble frames in colonoscopy videos with very high accuracy in significantly less processing time even when clustering is used to reduce the training size by 10 times

Illumination Variance In Video Processing

Doctor of Philosophy and was awarded by Brunel University LondonInthisthesiswefocusontheimpactofilluminationchangesinvideoand we discuss how we can minimize the impact of illumination variance in video processing systems. Identifyingandremovingshadowsautomaticallyisaverywellestablished and an important topic in image and video processing. Having shadowless image data would benefit many other systems such as video surveillance, tracking and object recognition algorithms. Anovelapproachtoautomaticallydetectandremoveshadowsispresented in this paper. This new method is based on the observation that, owing to the relative movement of the sun, the length and position of a shadow changes linearly over a relatively long period of time in outdoor environments,wecanconvenientlydistinguishashadowfromotherdark regions in an input video. Then we can identify the Reference Shadow as the one with the highest confidence of the mentioned linear changes. Once one shadow is detected, the rest of the shadow can also be identifiedandremoved. Wehaveprovidedmanyexperimentsandourmethod is fully capable of detecting and removing the shadows of stationary and moving objects. Additionally we have explained how reference shadows can be used to detect textures that reflect the light and shiny materials such as metal, glass and water. ..

Personal Identification Based on Live Iris Image Analysis

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Reflection Detection and Removal From Image Sequences

Cílem mé diplomové práce bylo studium existujících metod pro detekci a odstranění odlesků ze sekvence snímků, nalezení jejich omezení a návrh možných vylepšení. Konkrétně jsme se zaměřili na rovinné spekulární povrchy, jejichž vzhled může být modelován superpozicí odrážené a přenášené vrstvy. Prozkoumali jsme především metody využívající vzájemný pohyb vrstev jako hlavní klíč k jejich oddělení. Popsali jsme jejich společný případ selhání, který spočívá v neschopnosti správného oddělení oblastí s nevýraznou texturou ve směru pohybu kamery. Výsledkem našeho úsilí je metoda řešící tento problém. Jejím hlavním přínosem je nový způsob odhadu hran obou vrstev, kdy důraz je kladen na správné oddělení hran zmíněných problematických oblastí. Věnovali jsme se také následnému odhadu barev jednotlivých vrstev, kdy se vedle hran vrstev využívá i odhad jejich hloubkových map, a popsali jsme alternativní přístup ke klasické kvadratické optimalizaci.The aim of the Master's thesis was to study existing methods for detection and removal of specular reflection from image sequences, to find their limitations and to suggest possibilities of their improvements. Particularly, an attention was paid to planar specular (i.e. mirror-like) surfaces whose appearance can be modeled by linear superposition of reflection and transmission layer. We reviewed the existing motion-based methods and described their common degenerate case in terms of disability to correctly recover regions with low frequency in the direction of camera motion. A new method designed to eliminate this degenerate case was suggested. Its main contribution is a new approach to layer gradients estimation where a special treatment of the gradients forming edges of the problematic regions is taken. We focused also on the following color recovery process and described an alternative approach to the traditional quadratic programming.