Methods and algorithms for video-based multi-point frequency measuring and mapping

Software for video-based multi-point frequency measuring and mapping: http://hdl.handle.net/10045/53429

Quantification of the magnification and distortion effects of a pediatric flexible video-bronchoscope

Background: Flexible video bronchoscopes, in particular the Olympus BF Type 3C160, are commonly used in pediatric respiratory medicine. There is no data on the magnification and distortion effects of these bronchoscopes yet important clinical decisions are made from the images. The aim of this study was to systematically describe the magnification and distortion of flexible bronchoscope images taken at various distances from the object. Methods: Using images of known objects and processing these by digital video and computer programs both magnification and distortion scales were derived. Results: Magnification changes as a linear function between 100 mm ( x 1) and 10 mm ( x 9.55) and then as an exponential function between 10 mm and 3 mm ( x 40) from the object. Magnification depends on the axis of orientation of the object to the optic axis or geometrical axis of the bronchoscope. Magnification also varies across the field of view with the central magnification being 39% greater than at the periphery of the field of view at 15 mm from the object. However, in the paediatric situation the diameter of the orifices is usually less than 10 mm and thus this limits the exposure to these peripheral limits of magnification reduction. Intraclass correlations for measurements and repeatability studies between instruments are very high, r = 0.96. Distortion occurs as both barrel and geometric types but both types are heterogeneous across the field of view. Distortion of geometric type ranges up to 30% at 3 mm from the object but may be as low as 5% depending on the position of the object in relation to the optic axis. Conclusion: We conclude that the optimal working distance range is between 40 and 10 mm from the object. However the clinician should be cognisant of both variations in magnification and distortion in clinical judgements.

Automatic initialisation of 3D deformable models for cartilage segmentation

Deformable models are a highly accurate and flexible approach to segmenting structures in medical images. The primary drawback of deformable models is that they are sensitive to initialisation, with accurate and robust results often requiring initialisation close to the true object in the image. Automatically obtaining a good initialisation is problematic for many structures in the body. The cartilages of the knee are a thin elastic material that cover the ends of the bone, absorbing shock and allowing smooth movement. The degeneration of these cartilages characterize the progression of osteoarthritis. The state of the art in the segmentation of the cartilage are 2D semi-automated algorithms. These algorithms require significant time and supervison by a clinical expert, so the development of an automatic segmentation algorithm for the cartilages is an important clinical goal. In this paper we present an approach towards this goal that allows us to automatically providing a good initialisation for deformable models of the patella cartilage, by utilising the strong spatial relationship of the cartilage to the underlying bone.

Methods of Color Images Procession for Further Identification of the Object

The offered paper deals with the problems of color images preliminary procession. Among these are: interference control (local ones and noise) and extraction of the object from the background on the stage preceding the process of contours extraction. It was considered for a long time that execution of smoothing in segmentation through the boundary extraction is inadmissible, but the described methods and the obtained results evidence about expedience of using the noise control methods.

A multimedia indexing and retrieval framework for multimedia database systems

The main challenges of multimedia data retrieval lie in the effective mapping between low-level features and high-level concepts, and in the individual users' subjective perceptions of multimedia content. ^ The objectives of this dissertation are to develop an integrated multimedia indexing and retrieval framework with the aim to bridge the gap between semantic concepts and low-level features. To achieve this goal, a set of core techniques have been developed, including image segmentation, content-based image retrieval, object tracking, video indexing, and video event detection. These core techniques are integrated in a systematic way to enable the semantic search for images/videos, and can be tailored to solve the problems in other multimedia related domains. In image retrieval, two new methods of bridging the semantic gap are proposed: (1) for general content-based image retrieval, a stochastic mechanism is utilized to enable the long-term learning of high-level concepts from a set of training data, such as user access frequencies and access patterns of images. (2) In addition to whole-image retrieval, a novel multiple instance learning framework is proposed for object-based image retrieval, by which a user is allowed to more effectively search for images that contain multiple objects of interest. An enhanced image segmentation algorithm is developed to extract the object information from images. This segmentation algorithm is further used in video indexing and retrieval, by which a robust video shot/scene segmentation method is developed based on low-level visual feature comparison, object tracking, and audio analysis. Based on shot boundaries, a novel data mining framework is further proposed to detect events in soccer videos, while fully utilizing the multi-modality features and object information obtained through video shot/scene detection. ^ Another contribution of this dissertation is the potential of the above techniques to be tailored and applied to other multimedia applications. This is demonstrated by their utilization in traffic video surveillance applications. The enhanced image segmentation algorithm, coupled with an adaptive background learning algorithm, improves the performance of vehicle identification. A sophisticated object tracking algorithm is proposed to track individual vehicles, while the spatial and temporal relationships of vehicle objects are modeled by an abstract semantic model. ^

A high efficient biometrics approach for unconstrained iris segmentation and recognition

This dissertation develops an innovative approach towards less-constrained iris biometrics. Two major contributions are made in this research endeavor: (1) Designed an award-winning segmentation algorithm in the less-constrained environment where image acquisition is made of subjects on the move and taken under visible lighting conditions, and (2) Developed a pioneering iris biometrics method coupling segmentation and recognition of the iris based on video of moving persons under different acquisitions scenarios. The first part of the dissertation introduces a robust and fast segmentation approach using still images contained in the UBIRIS (version 2) noisy iris database. The results show accuracy estimated at 98% when using 500 randomly selected images from the UBIRIS.v2 partial database, and estimated at 97% in a Noisy Iris Challenge Evaluation (NICE.I) in an international competition that involved 97 participants worldwide involving 35 countries, ranking this research group in sixth position. This accuracy is achieved with a processing speed nearing real time. The second part of this dissertation presents an innovative segmentation and recognition approach using video-based iris images. Following the segmentation stage which delineates the iris region through a novel segmentation strategy, some pioneering experiments on the recognition stage of the less-constrained video iris biometrics have been accomplished. In the video-based and less-constrained iris recognition, the test or subject iris videos/images and the enrolled iris images are acquired with different acquisition systems. In the matching step, the verification/identification result was accomplished by comparing the similarity distance of encoded signature from test images with each of the signature dataset from the enrolled iris images. With the improvements gained, the results proved to be highly accurate under the unconstrained environment which is more challenging. This has led to a false acceptance rate (FAR) of 0% and a false rejection rate (FRR) of 17.64% for 85 tested users with 305 test images from the video, which shows great promise and high practical implications for iris biometrics research and system design.

Digital surveillance based on video CODEC System-on-a-Chip (SoC) platforms

Today, most conventional surveillance networks are based on analog system, which has a lot of constraints like manpower and high-bandwidth requirements. It becomes the barrier for today's surveillance network development. This dissertation describes a digital surveillance network architecture based on the H.264 coding/decoding (CODEC) System-on-a-Chip (SoC) platform. The proposed digital surveillance network architecture includes three major layers: software layer, hardware layer, and the network layer. The following outlines the contributions to the proposed digital surveillance network architecture. (1) We implement an object recognition system and an object categorization system on the software layer by applying several Digital Image Processing (DIP) algorithms. (2) For better compression ratio and higher video quality transfer, we implement two new modules on the hardware layer of the H.264 CODEC core, i.e., the background elimination module and the Directional Discrete Cosine Transform (DDCT) module. (3) Furthermore, we introduce a Digital Signal Processor (DSP) sub-system on the main bus of H.264 SoC platforms as the major hardware support system for our software architecture. Thus we combine the software and hardware platforms to be an intelligent surveillance node. Lab results show that the proposed surveillance node can dramatically save the network resources like bandwidth and storage capacity.

Fast 2D/3D object representation with growing neural gas

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction.

Video anomaly detection in real-time on a Power-Aware Heterogeneous Platform

FPGAs and GPUs are often used when real-time performance in video processing is required. An accelerated processor is chosen based on task-specific priorities (power consumption, processing time and detection accuracy), and this decision is normally made once at design time. All three characteristics are important, particularly in battery-powered systems. Here we propose a method for moving selection of processing platform from a single design-time choice to a continuous run time one.We implement Histogram of Oriented Gradients (HOG) detectors for cars and people and Mixture of Gaussians (MoG) motion detectors running across FPGA, GPU and CPU in a heterogeneous system. We use this to detect illegally parked vehicles in urban scenes. Power, time and accuracy information for each detector is characterised. An anomaly measure is assigned to each detected object based on its trajectory and location, when compared to learned contextual movement patterns. This drives processor and implementation selection, so that scenes with high behavioural anomalies are processed with faster but more power hungry implementations, but routine or static time periods are processed with power-optimised, less accurate, slower versions. Real-time performance is evaluated on video datasets including i-LIDS. Compared to power-optimised static selection, automatic dynamic implementation mapping is 10% more accurate but draws 12W extra power in our testbed desktop system.

Ticking Boxes: (re)constructing the wristwatch as a luxury object

Today the high-price mechanical wristwatch is recognized as a luxury object redolent with notions of adventure, sporting achievement, elevated social status, and technical precision. Through an examination of the segmentation of the current luxury wristwatch market and key moments in the historical development of the wristwatch, this article will explain why these connotations exist. In particular, the article will explain the role that the early development of the wristwatch as a piece of military technical equipment and the mechanical wristwatch’s revitalization as a luxury good in response to the development of commercial quartz timekeeping technology have played in reconstructing the wristwatch as an object type. By utilizing network theory and the analytical tool of complexity, and drawing on fieldwork undertaken in London and Switzerland amongst the manufacturers, distributors, retailers, and consumers of high-value wristwatches, the article will explain how the wristwatch can simultaneously be seen as functional tool, fashion statement, status symbol, and anachronism. This insight into the true nature of the wristwatch as a multivalent and semiotically charged object will also be used to inform reflections on the likely impact of generally perceived current threats to the luxury watch industry: the rise in ethical material sourcing campaigns, the stubborn gender imbalance in watch sales, and the recent appearance of smart watches and similar digital devices.

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.

Football Video Annotation Based on Player Motion Recognition Using Enhanced Entropy

This paper presents a semi-parametric Algorithm for parsing football video structures. The approach works on a two interleaved based process that closely collaborate towards a common goal. The core part of the proposed method focus perform a fast automatic football video annotation by looking at the enhance entropy variance within a series of shot frames. The entropy is extracted on the Hue parameter from the HSV color system, not as a global feature but in spatial domain to identify regions within a shot that will characterize a certain activity within the shot period. The second part of the algorithm works towards the identification of dominant color regions that could represent players and playfield for further activity recognition. Experimental Results shows that the proposed football video segmentation algorithm performs with high accuracy.

The incorporation of violence by women video artists

This paper examines the way in which women video artists embodied violence in their video pieces as a strategy of critique of the patriarchal regime. Since the 1960s several generations of women artists used different strategies of self-harm or explored the physical and mental limits of their bodies to express the anguish of those who are excluded from the patriarchal society on sexist and/or racist grounds. Considering the guiding line that covers three fields – art, gender, and feminist social movements – as well as their key thinkers and scholars in Sociology, Fine Arts and the Humanities, we have built the object of study of this essay, namely, the relationship between women's video art focused on the body, violence and gender along with feminist social movements in the period ranging from 1967 to 2007, in a Western context. The methodology used had as its primary goal to create a link between the micro-sociological level of expressions, body gestures and behaviours in the videos and the macro-sociological level of broader, institutionalized social forces that are at the origin of inequalities, such as dimensions of gender and «race». This study concluded that at least since the 1960s there is the denunciation by women video artists of the general circumstances women live under, while enduring violence of various kinds, such as socio-cultural, psychological and sexual violence against women.

Sviluppo di un algoritmo di Object Detection per la privacy in ambito industriale

In questa tesi è descritto il lavoro svolto presso un'azienda informatica locale, allo scopo di ricerca ed implementazione di un algoritmo per individuare ed offuscare i volti presenti all'interno di video di e-learning in ambito industriale, al fine di garantire la privacy degli operai presenti. Tale algoritmo sarebbe stato poi da includere in un modulo software da inserire all'interno di un applicazione web già esistente per la gestione di questi video. Si è ricercata una soluzione ad hoc considerando le caratteristiche particolare del problema in questione, studiando le principali tecniche della Computer Vision per comprendere meglio quale strada percorrere. Si è deciso quindi di implementare un algoritmo di Blob Tracking basato sul colore.

Texting and Driving: rilevazione di utilizzo dello smartphone alla guida tramite Object Detection e Machine Learning

Questa tesi si ispira a lavori precedentemente portati avanti da altri studenti e si pone il problema della possibilit\`a di riconoscere se uno smartphone \`e utilizzato da un utente mentre esso si trova alla guida di un'autovettura. In essa verranno presentati vari metodi per risolvere questo problema di Machine Learning, ovvero realizzazione di dataset per l'allenamento di modelli e creazione e allenamento di modelli stessi, dediti al riconoscimento di un problema di classificazione binaria e riconoscimento di oggetti tramite Object Detection. Il cercare di riconoscere se l'utente \`e alla guida o meno, avverr\`a tramite l'output della fotocamera frontale dello smartphone, quindi lavoreremo su immagini, video e frame. Arriveremo a riconoscere la posizione della persona rappresentata da questi fotogrammi tramite un modello di Object Detection, che riconosce cintura e finestrino e determina se sono appartenenti al sedile e alla posizione del conducente o del passeggero. Vedremo alla fine, attraverso un'attenta analisi dei risultati ottenuti su ben 8 video diversi che saranno divisi in molti frame, che si ottengono risultati molto interessanti, dai quali si pu\`o prendere spunto per la creazione di un importante sistema di sicurezza alla guida.