Spatio-temporal Video Parsing for Abnormality Detection

Abnormality detection in video poses particular challenges due to the infinite size of the class of all irregular objects and behaviors. Thus no (or by far not enough) abnormal training samples are available and we need to find abnormalities in test data without actually knowing what they are. Nevertheless, the prevailing concept of the field is to directly search for individual abnormal local patches or image regions independent of another. To address this problem, we propose a method for joint detection of abnormalities in videos by spatio-temporal video parsing. The goal of video parsing is to find a set of indispensable normal spatio-temporal object hypotheses that jointly explain all the foreground of a video, while, at the same time, being supported by normal training samples. Consequently, we avoid a direct detection of abnormalities and discover them indirectly as those hypotheses which are needed for covering the foreground without finding an explanation for themselves by normal samples. Abnormalities are localized by MAP inference in a graphical model and we solve it efficiently by formulating it as a convex optimization problem. We experimentally evaluate our approach on several challenging benchmark sets, improving over the state-of-the-art on all standard benchmarks both in terms of abnormality classification and localization.Comment: 15 pages, 12 figures, 3 table

Concordance of copy number abnormality detection using SNP arrays and Multiplex Ligation-dependent Probe Amplification (MLPA) in acute lymphoblastic leukaemia

In acute lymphoblastic leukaemia, MLPA has been used in research studies to identify clinically relevant copy number abnormality (CNA) profiles. However, in diagnostic settings other techniques are often employed. We assess whether equivalent CNA profiles are called using SNP arrays, ensuring platform independence. We demonstrate concordance between SNP6.0 and MLPA CNA calling on 143 leukaemia samples from two UK trials; comparing 1,287 calls within eight genes and a region. The techniques are 99% concordant using manually augmented calling, and 98% concordant using an automated pipeline. We classify these discordant calls and examine reasons for discordance. In nine cases the circular binary segmentation (CBS) algorithm failed to detect focal abnormalities or those flanking gaps in IKZF1 probe coverage. Eight cases were discordant due to probe design differences, with focal abnormalities detectable using one technique not observable by the other. Risk classification using manually augmented array calling resulted in four out of 143 patients being assigned to a different CNA risk group and eight patients using the automated pipeline. We conclude that MLPA defined CNA profiles can be accurately mirrored by SNP6.0 or similar array platforms. Automated calling using the CBS algorithm proved successful, except for IKZF1 which should be manually inspected

Errors in chromosome segregation during oogenesis and early embryogenesis

Errors in chromosome segregation occurring during human oogenesis and early embryogenesis are very common. Meiotic chromosome development during oogenesis is subdivided into three distinct phases. The crucial events, including meiotic chromosome pairing and recombination, take place from around 11 weeks until birth. Oogenesis is then arrested until ovulation, when the first meiotic division takes place, with the second meiotic division not completed until after fertilization. It is generally accepted that most aneuploid fetal conditions, such as trisomy 21 Down syndrome, are due to maternal chromosome segregation errors. The underlying reasons are not yet fully understood. It is also clear that superimposed on the maternal meiotic chromosome segregation errors, there are a large number of mitotic errors taking place post-zygotically during the first few cell divisions in the embryo. In this chapter, we summarise current knowledge of errors in chromosome segregation during oogenesis and early embryogenesis, with special reference to the clinical implications for successful assisted reproduction

Abnormal Event Detection in Videos using Spatiotemporal Autoencoder

We present an efficient method for detecting anomalies in videos. Recent applications of convolutional neural networks have shown promises of convolutional layers for object detection and recognition, especially in images. However, convolutional neural networks are supervised and require labels as learning signals. We propose a spatiotemporal architecture for anomaly detection in videos including crowded scenes. Our architecture includes two main components, one for spatial feature representation, and one for learning the temporal evolution of the spatial features. Experimental results on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of our method is comparable to state-of-the-art methods at a considerable speed of up to 140 fps

Localized anomaly detection via hierarchical integrated activity discovery

2014 Spring.Includes bibliographical references.With the increasing number and variety of camera installations, unsupervised methods that learn typical activities have become popular for anomaly detection. In this thesis, we consider recent methods based on temporal probabilistic models and improve them in multiple ways. Our contributions are the following: (i) we integrate the low level processing and the temporal activity modeling, showing how this feedback improves the overall quality of the captured information, (ii) we show how the same approach can be taken to do hierarchical multi-camera processing, (iii) we use spatial analysis of the anomalies both to perform local anomaly detection and to frame automatically the detected anomalies. We illustrate the approach on both traffic data and videos coming from a metro station. We also investigate the application of topic models in Brain Computing Interfaces for Mental Task classification. We observe a classification accuracy of up to 68% for four Mental Tasks on individual subjects

Visual Anomaly Detection in Event Sequence Data

Anomaly detection is a common analytical task that aims to identify rare cases that differ from the typical cases that make up the majority of a dataset. When applied to the analysis of event sequence data, the task of anomaly detection can be complex because the sequential and temporal nature of such data results in diverse definitions and flexible forms of anomalies. This, in turn, increases the difficulty in interpreting detected anomalies. In this paper, we propose an unsupervised anomaly detection algorithm based on Variational AutoEncoders (VAE) to estimate underlying normal progressions for each given sequence represented as occurrence probabilities of events along the sequence progression. Events in violation of their occurrence probability are identified as abnormal. We also introduce a visualization system, EventThread3, to support interactive exploration and interpretations of anomalies within the context of normal sequence progressions in the dataset through comprehensive one-to-many sequence comparison. Finally, we quantitatively evaluate the performance of our anomaly detection algorithm and demonstrate the effectiveness of our system through a case study