Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images.

We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects

Spatial and temporal background modelling of non-stationary visual scenes

PhDThe prevalence of electronic imaging systems in everyday life has become increasingly apparent in recent years. Applications are to be found in medical scanning, automated manufacture, and perhaps most significantly, surveillance. Metropolitan areas, shopping malls, and road traffic management all employ and benefit from an unprecedented quantity of video cameras for monitoring purposes. But the high cost and limited effectiveness of employing humans as the final link in the monitoring chain has driven scientists to seek solutions based on machine vision techniques. Whilst the field of machine vision has enjoyed consistent rapid development in the last 20 years, some of the most fundamental issues still remain to be solved in a satisfactory manner. Central to a great many vision applications is the concept of segmentation, and in particular, most practical systems perform background subtraction as one of the first stages of video processing. This involves separation of ‘interesting foreground’ from the less informative but persistent background. But the definition of what is ‘interesting’ is somewhat subjective, and liable to be application specific. Furthermore, the background may be interpreted as including the visual appearance of normal activity of any agents present in the scene, human or otherwise. Thus a background model might be called upon to absorb lighting changes, moving trees and foliage, or normal traffic flow and pedestrian activity, in order to effect what might be termed in ‘biologically-inspired’ vision as pre-attentive selection. This challenge is one of the Holy Grails of the computer vision field, and consequently the subject has received considerable attention. This thesis sets out to address some of the limitations of contemporary methods of background segmentation by investigating methods of inducing local mutual support amongst pixels in three starkly contrasting paradigms: (1) locality in the spatial domain, (2) locality in the shortterm time domain, and (3) locality in the domain of cyclic repetition frequency. Conventional per pixel models, such as those based on Gaussian Mixture Models, offer no spatial support between adjacent pixels at all. At the other extreme, eigenspace models impose a structure in which every image pixel bears the same relation to every other pixel. But Markov Random Fields permit definition of arbitrary local cliques by construction of a suitable graph, and 3 are used here to facilitate a novel structure capable of exploiting probabilistic local cooccurrence of adjacent Local Binary Patterns. The result is a method exhibiting strong sensitivity to multiple learned local pattern hypotheses, whilst relying solely on monochrome image data. Many background models enforce temporal consistency constraints on a pixel in attempt to confirm background membership before being accepted as part of the model, and typically some control over this process is exercised by a learning rate parameter. But in busy scenes, a true background pixel may be visible for a relatively small fraction of the time and in a temporally fragmented fashion, thus hindering such background acquisition. However, support in terms of temporal locality may still be achieved by using Combinatorial Optimization to derive shortterm background estimates which induce a similar consistency, but are considerably more robust to disturbance. A novel technique is presented here in which the short-term estimates act as ‘pre-filtered’ data from which a far more compact eigen-background may be constructed. Many scenes entail elements exhibiting repetitive periodic behaviour. Some road junctions employing traffic signals are among these, yet little is to be found amongst the literature regarding the explicit modelling of such periodic processes in a scene. Previous work focussing on gait recognition has demonstrated approaches based on recurrence of self-similarity by which local periodicity may be identified. The present work harnesses and extends this method in order to characterize scenes displaying multiple distinct periodicities by building a spatio-temporal model. The model may then be used to highlight abnormality in scene activity. Furthermore, a Phase Locked Loop technique with a novel phase detector is detailed, enabling such a model to maintain correct synchronization with scene activity in spite of noise and drift of periodicity. This thesis contends that these three approaches are all manifestations of the same broad underlying concept: local support in each of the space, time and frequency domains, and furthermore, that the support can be harnessed practically, as will be demonstrated experimentally

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

Reconstruction Bottlenecks in Object-Centric Generative Models

A range of methods with suitable inductive biases exist to learn interpretable object-centric representations of images without supervision. However, these are largely restricted to visually simple images; robust object discovery in real-world sensory datasets remains elusive. To increase the understanding of such inductive biases, we empirically investigate the role of "reconstruction bottlenecks" for scene decomposition in GENESIS, a recent VAE-based model. We show such bottlenecks determine reconstruction and segmentation quality and critically influence model behaviour.Comment: 10 pages, 7 Figures, Workshop on Object-Oriented Learning at ICML 202