Higher-order regularization and morphological techniques for image segmentation

La segmentación de imágenes es un campo importante de la visión computacional y una de las áreas de investigación más activas, con aplicaciones en comprensión de imágenes, detección de objetos, reconocimiento facial, vigilancia de vídeo o procesamiento de imagen médica. La segmentación de imágenes es un problema difícil en general, pero especialmente en entornos científicos y biomédicos, donde las técnicas de adquisición imagen proporcionan imágenes ruidosas. Además, en muchos de estos casos se necesita una precisión casi perfecta. En esta tesis, revisamos y comparamos primero algunas de las técnicas ampliamente usadas para la segmentación de imágenes médicas. Estas técnicas usan clasificadores a nivel de pixel e introducen regularización sobre pares de píxeles que es normalmente insuficiente. Estudiamos las dificultades que presentan para capturar la información de alto nivel sobre los objetos a segmentar. Esta deficiencia da lugar a detecciones erróneas, bordes irregulares, configuraciones con topología errónea y formas inválidas. Para solucionar estos problemas, proponemos un nuevo método de regularización de alto nivel que aprende información topológica y de forma a partir de los datos de entrenamiento de una forma no paramétrica usando potenciales de orden superior. Los potenciales de orden superior se están popularizando en visión por computador, pero la representación exacta de un potencial de orden superior definido sobre muchas variables es computacionalmente inviable. Usamos una representación compacta de los potenciales basada en un conjunto finito de patrones aprendidos de los datos de entrenamiento que, a su vez, depende de las observaciones. Gracias a esta representación, los potenciales de orden superior pueden ser convertidos a potenciales de orden 2 con algunas variables auxiliares añadidas. Experimentos con imágenes reales y sintéticas confirman que nuestro modelo soluciona los errores de aproximaciones más débiles. Incluso con una regularización de alto nivel, una precisión exacta es inalcanzable, y se requeire de edición manual de los resultados de la segmentación automática. La edición manual es tediosa y pesada, y cualquier herramienta de ayuda es muy apreciada. Estas herramientas necesitan ser precisas, pero también lo suficientemente rápidas para ser usadas de forma interactiva. Los contornos activos son una buena solución: son buenos para detecciones precisas de fronteras y, en lugar de buscar una solución global, proporcionan un ajuste fino a resultados que ya existían previamente. Sin embargo, requieren una representación implícita que les permita trabajar con cambios topológicos del contorno, y esto da lugar a ecuaciones en derivadas parciales (EDP) que son costosas de resolver computacionalmente y pueden presentar problemas de estabilidad numérica. Presentamos una aproximación morfológica a la evolución de contornos basada en un nuevo operador morfológico de curvatura que es válido para superficies de cualquier dimensión. Aproximamos la solución numérica de la EDP de la evolución de contorno mediante la aplicación sucesiva de un conjunto de operadores morfológicos aplicados sobre una función de conjuntos de nivel. Estos operadores son muy rápidos, no sufren de problemas de estabilidad numérica y no degradan la función de los conjuntos de nivel, de modo que no hay necesidad de reinicializarlo. Además, su implementación es mucho más sencilla que la de las EDP, ya que no requieren usar sofisticados algoritmos numéricos. Desde un punto de vista teórico, profundizamos en las conexiones entre operadores morfológicos y diferenciales, e introducimos nuevos resultados en este área. Validamos nuestra aproximación proporcionando una implementación morfológica de los contornos geodésicos activos, los contornos activos sin bordes, y los turbopíxeles. En los experimentos realizados, las implementaciones morfológicas convergen a soluciones equivalentes a aquéllas logradas mediante soluciones numéricas tradicionales, pero con ganancias significativas en simplicidad, velocidad y estabilidad. ABSTRACT Image segmentation is an important field in computer vision and one of its most active research areas, with applications in image understanding, object detection, face recognition, video surveillance or medical image processing. Image segmentation is a challenging problem in general, but especially in the biological and medical image fields, where the imaging techniques usually produce cluttered and noisy images and near-perfect accuracy is required in many cases. In this thesis we first review and compare some standard techniques widely used for medical image segmentation. These techniques use pixel-wise classifiers and introduce weak pairwise regularization which is insufficient in many cases. We study their difficulties to capture high-level structural information about the objects to segment. This deficiency leads to many erroneous detections, ragged boundaries, incorrect topological configurations and wrong shapes. To deal with these problems, we propose a new regularization method that learns shape and topological information from training data in a nonparametric way using high-order potentials. High-order potentials are becoming increasingly popular in computer vision. However, the exact representation of a general higher order potential defined over many variables is computationally infeasible. We use a compact representation of the potentials based on a finite set of patterns learned fromtraining data that, in turn, depends on the observations. Thanks to this representation, high-order potentials can be converted into pairwise potentials with some added auxiliary variables and minimized with tree-reweighted message passing (TRW) and belief propagation (BP) techniques. Both synthetic and real experiments confirm that our model fixes the errors of weaker approaches. Even with high-level regularization, perfect accuracy is still unattainable, and human editing of the segmentation results is necessary. The manual edition is tedious and cumbersome, and tools that assist the user are greatly appreciated. These tools need to be precise, but also fast enough to be used in real-time. Active contours are a good solution: they are good for precise boundary detection and, instead of finding a global solution, they provide a fine tuning to previously existing results. However, they require an implicit representation to deal with topological changes of the contour, and this leads to PDEs that are computationally costly to solve and may present numerical stability issues. We present a morphological approach to contour evolution based on a new curvature morphological operator valid for surfaces of any dimension. We approximate the numerical solution of the contour evolution PDE by the successive application of a set of morphological operators defined on a binary level-set. These operators are very fast, do not suffer numerical stability issues, and do not degrade the level set function, so there is no need to reinitialize it. Moreover, their implementation is much easier than their PDE counterpart, since they do not require the use of sophisticated numerical algorithms. From a theoretical point of view, we delve into the connections between differential andmorphological operators, and introduce novel results in this area. We validate the approach providing amorphological implementation of the geodesic active contours, the active contours without borders, and turbopixels. In the experiments conducted, the morphological implementations converge to solutions equivalent to those achieved by traditional numerical solutions, but with significant gains in simplicity, speed, and stability.

Human MutSalpha recognizes damaged DNA base pairs containing O6-methylguanine, O4-methylthymine, or the cisplatin-d(GpG) adduct.

Bacterial and mammalian mismatch repair systems have been implicated in the cellular response to certain types of DNA damage, and genetic defects in this pathway are known to confer resistance to the cytotoxic effects of DNA-methylating agents. Such observations suggest that in addition to their ability to recognize DNA base-pairing errors, members of the MutS family may also respond to genetic lesions produced by DNA damage. We show that the human mismatch recognition activity MutSalpha recognizes several types of DNA lesion including the 1,2-intrastrand d(GpG) crosslink produced by cis-diamminedichloroplatinum(II), as well as base pairs between O6-methylguanine and thymine or cytosine, or between O4-methylthymine and adenine. However, the protein fails to recognize 1,3-intrastrand adduct produced by trans-diamminedichloroplatinum(II) at a d(GpTpG) sequence. These observations imply direct involvement of the mismatch repair system in the cytotoxic effects of DNA-methylating agents and suggest that recognition of 1,2-intrastrand cis-diamminedichloroplatinum(II) adducts by MutSalpha may be involved in the cytotoxic action of this chemotherapeutic agent.

Parallel Computational Intelligence-Based Multi-Camera Surveillance System

In this work, we present a multi-camera surveillance system based on the use of self-organizing neural networks to represent events on video. The system processes several tasks in parallel using GPUs (graphic processor units). It addresses multiple vision tasks at various levels, such as segmentation, representation or characterization, analysis and monitoring of the movement. These features allow the construction of a robust representation of the environment and interpret the behavior of mobile agents in the scene. It is also necessary to integrate the vision module into a global system that operates in a complex environment by receiving images from multiple acquisition devices at video frequency. Offering relevant information to higher level systems, monitoring and making decisions in real time, it must accomplish a set of requirements, such as: time constraints, high availability, robustness, high processing speed and re-configurability. We have built a system able to represent and analyze the motion in video acquired by a multi-camera network and to process multi-source data in parallel on a multi-GPU architecture.

Gabor-based region covariance matrices for face recognition

This paper presents a new method for human face recognition by utilizing Gabor-based region covariance matrices as face descriptors. Both pixel locations and Gabor coefficients are employed to form the covariance matrices. Experimental results demonstrate the advantages of this proposed method.

An activity theory approach to strategy as practice

This chapter introduces activity theory as an approach for studying strategy as practice. Activity theory conceptualizes the ongoing construction of activity as a product of activity systems, comprising the actor, the community with which that actor interacts and those symbolic and material tools that mediate between actors, their community and their pursuit of activity. The focus on the mediating role of tools and cultural artefacts in human activity seems especially promising for advancing the strategy-as-practice agenda, for example as a theoretical resource for the growing interest in sociomateriality and the role of tools and artefacts in (strategy) practice (for example, Balogun et al. 2014; Lanzara 2009; Nicolini 2009; Spee and Jarzabkowski 2009; Stetsenko 2005). Despite its potential, in a recent review Vaara and Whittington (2012) identified only three strategy-as-practice articles explicitly applying an activity theory lens. In the wider area of practice-based studies in organizations, activity theory has been slightly more popular (for example, Blackler 1993; 1995; Blackler, Crump and McDonald 2000; Engeström, Kerosuo and Kajamaa 2007; Groleau 2006; Holt 2008; Miettinen and Virkkunen 2005). It still lags behind its potential, however, primarily because of its origins as a social psychology theory developed in Russia with little initial recognition outside the Russian context, particularly in the area of strategy and organization theory, until recently (Miettinen, Samra-Fredericks and Yanow 2009). This chapter explores activity theory as a resource for studying strategy as practice as it is socially accomplished by individuals in interaction with their wider social group and the artefacts of interaction. In particular, activity theory’s focus on actors as social individuals provides a conceptual basis for studying the core question in strategy-as-practice research: what strategy practitioners do. The chapter is structured in three parts. First, an overview of activity theory is provided. Second, activity theory as a practice-based approach to studying organizational action is introduced and an activity system conceptual framework is developed. Third, the elements of the activity system are explained in more detail and explicitly linked to each of the core SAP concepts: practitioners, practices and praxis. In doing so, links are made to existing strategy-as-practice research, with brief empirical examples of topics that might be addressed using activity theory. Throughout the chapter, we introduce key authors in the development of activity theory and its use in management and adjacent disciplinary fields, as further resources for those wishing to make greater use of activity theory.

Contextual anomaly detection in crowded surveillance scenes

This work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour.

Panoramic Sea Happening (After Kantor): Video Installation

Panoramic Sea Happening (After Kantor) is a 7 minute durational film that reimagines part of Tadeusz Kantor's original sea happenings from 1967 in a landscape in which the sea has retreated. The conductor of Kantor’s original performance is replaced with a sound object cast adrift on a beach in Dungeness (UK). The object plays back the sound of the sea into the landscape, which was performed live and then filmed from three distinct angles. The first angle mimics the position of the conductor in Kantor’s original happening, facing outwards into the horizon of the beach and recalls the image in Kantor’s work of a human figure undertaking the absurd task of orchestrating the sound of a gigantic expanse of water. The second angle exposes the machine itself and the large cone that amplifies the sound, reinforcing the isolation of the object. The third angle reveals a decommissioned nuclear power station and sound objects used as a warning system for the power plant. Dungeness is a location where the sea has been retreating from the land, leaving traces of human activity through the disused boat winches, abandoned cabins and the decommissioned nuclear buildings. It is a place in which the footprint of the anthropocene is keenly felt. The sound object is intended to act as an anthropomorphic figure, ghosting the original conductor and offering the sound of the sea back into the landscape through a wide mouthpiece, echoing Kantor’s own load hailer in the original sequence of sea happenings. It speculates on Kantor's theory of the bio-object, which proposed a symbiotic relationship between the human and the nonhuman object in performance, as a possible instrument to access a form of geologic imagination. In this configuration, the human itself is absent, but is evoked through the objects left behind. The sound object, helpless in a red dingy, might be thought of as a co-conspirator with the viewer, enabling a looking back to the past in a landscape of an inevitable future. The work was originally commissioned by the University of Kent in collaboration with the Polish Cultural Institute for the Symposium Kantorbury Kantorbury in Canterbury (UK) to mark the 100 years since Tadeusz Kantor’s birth (15 - 19 September 2015). It should be projected and requires stereo speakers.

Lost in translation? The potential psychobiotic Lactobacillus rhamnosus (JB-1) fails to modulate stress or cognitive performance in healthy male subjects

Background: Preclinical studies have identified certain probiotics as psychobiotics a live microorganisms with a potential mental health benefit. Lactobacillus rhamnosus (JB-1) has been shown to reduce stress-related behaviour, corticosterone release and alter central expression of GABA receptors in an anxious mouse strain. However, it is unclear if this single putative psychobiotic strain has psychotropic activity in humans. Consequently, we aimed to examine if these promising preclinical findings could be translated to healthy human volunteers. Objectives: To determine the impact of L. rhamnosus on stress-related behaviours, physiology, inflammatory response, cognitive performance and brain activity patterns in healthy male participants. An 8 week, randomized, placebo-controlled, cross-over design was employed. Twenty-nine healthy male volunteers participated. Participants completed self-report stress measures, cognitive assessments and resting electroencephalography (EEG). Plasma IL10, IL1β, IL6, IL8 and TNFα levels and whole blood Toll-like 4 (TLR-4) agonist-induced cytokine release were determined by multiplex ELISA. Salivary cortisol was determined by ELISA and subjective stress measures were assessed before, during and after a socially evaluated cold pressor test (SECPT). Results: There was no overall effect of probiotic treatment on measures of mood, anxiety, stress or sleep quality and no significant effect of probiotic over placebo on subjective stress measures, or the HPA response to the SECPT. Visuospatial memory performance, attention switching, rapid visual information processing, emotion recognition and associated EEG measures did not show improvement over placebo. No significant anti-inflammatory effects were seen as assessed by basal and stimulated cytokine levels. Conclusions: L. rhamnosus was not superior to placebo in modifying stress-related measures, HPA response, inflammation or cognitive performance in healthy male participants. These findings highlight the challenges associated with moving promising preclinical studies, conducted in an anxious mouse strain, to healthy human participants. Future interventional studies investigating the effect of this psychobiotic in populations with stress-related disorders are required.

An activity theory framework for industrial design

Ethnography has gained wide acceptance in the industrial design profession and curriculum as a means of understanding the user. However, there is considerable confusion about the particularities of its practice accompanied by the absence of an interoperable vocabulary. The consequent interdisciplinary effort is a power play between disciplines whereby the methodological view of ethnography marginalises its theoretical and analytical components. In doing so, it restricts the potential of ethnography suggesting the need for alternative methods of informing the design process. This article suggests that activity theory, with an emphasis on human activity as the fundamental unit of study, is an appropriate methodology for the generation of user requirements. The process is illustrated through the adaptation of an ethnographic case study, for the design of classroom furniture in India.

Human brain regions required for the dividing and switching of attention between two features of a single object

This combined PET and ERP study was designed to identify the brain regions activated in switching and divided attention between different features of a single object using matched sensory stimuli and motor response. The ERP data have previously been reported in this journal [64]. We now present the corresponding PET data. We identified partially overlapping neural networks with paradigms requiring the switching or dividing of attention between the elements of complex visual stimuli. Regions of activation were found in the prefrontal and temporal cortices and cerebellum. Each task resulted in different prefrontal cortical regions of activation lending support to the functional subspecialisation of the prefrontal and temporal cortices being based on the cognitive operations required rather than the stimuli themselves.

Inertial motion capture and live performance (with a focus on dance)

3D Motion capture is a fast evolving field and recent inertial technology may expand the artistic possibilities for its use in live performance. Inertial motion capture has three attributes that make it suitable for use with live performance; it is portable, easy to use and can operate in real-time. Using four projects, this paper discusses the suitability of inertial motion capture to live performance with a particular emphasis on dance. Dance is an artistic application of human movement and motion capture is the means to record human movement as digital data. As such, dance is clearly a field in which the use of real-time motion capture is likely to become more common, particularly as projected visual effects including real-time video are already often used in dance performances. Understandably, animation generated in real-time using motion capture is not as extensive or as clean as the highly mediated animation used in movies and games, but the quality is still impressive and the ‘liveness’ of the animation has compensating features that offer new ways of communicating with an audience.

Textures of optical flow for real-time anomaly detection in crowds

Automated visual surveillance of crowds is a rapidly growing area of research. In this paper we focus on motion representation for the purpose of abnormality detection in crowded scenes. We propose a novel visual representation called textures of optical flow. The proposed representation measures the uniformity of a flow field in order to detect anomalous objects such as bicycles, vehicles and skateboarders; and can be combined with spatial information to detect other forms of abnormality. We demonstrate that the proposed approach outperforms state-of-the-art anomaly detection algorithms on a large, publicly-available dataset.

Physical activity and nutrition program for seniors (PANS) : protocol of a randomized controlled trial

Background Along with reduced levels of physical activity, older Australian's mean energy consumption has increased. Now over 60% of older Australians are considered overweight or obese. This study aims to confirm if a low-cost, accessible physical activity and nutrition program can improve levels of physical activity and diet of insufficiently active 60-70 year-olds. Methods/Design This 12-month home-based randomised controlled trial (RCT) will consist of a nutrition and physical activity intervention for insufficiently active people aged 60 to 70 years from low to medium socio-economic areas. Six-hundred participants will be recruited from the Australian Federal Electoral Role and randomly assigned to the intervention (n = 300) and control (n = 300) groups. The study is based on the Social Cognitive Theory and Precede-Proceed Model, incorporating voluntary cooperation and self-efficacy. The intervention includes a specially designed booklet that provides participants with information and encourages dietary and physical activity goal setting. The booklet will be supported by an exercise chart, calendar, bi-monthly newsletters, resistance bands and pedometers, along with phone and email contact. Data will be collected over three time points: pre-intervention, immediately post-intervention and 6-months post-study. Discussion This trial will provide valuable information for community-based strategies to improve older adults' physical activity and dietary intake. The project will provide guidelines for appropriate sample recruitment, and the development, implementation and evaluation of a minimal intervention program, as well as information on minimising barriers to participation in similar programs.

Simple classification of walking activities using commodity smart phones

People interact with mobile computing devices everywhere, while sitting, walking, running or even driving. Adapting the interface to suit these contexts is important, thus this paper proposes a simple human activity classification system. Our approach uses a vector magnitude recognition technique to detect and classify when a person is stationary (or not walking), casually walking, or jogging, without any prior training. The user study has confirmed the accuracy.