Detectando agrupamientos y contornos: un estudio doble sobre representación de formas

Las formas juegan un rol clave en nuestro sistema cognitivo: en la percepción de las formas yace el principio de la formación de conceptos. Siguiendo esta línea de pensamiento, la escuela de la Gestalt ha estudiado extensivamente la percep- ción de formas como el proceso de asir características estructurales encontradas o impuestas sobre el material de estímulo.En resumen, tenemos dos modelos de formas: pueden existir físicamente o ser un producto de nuestros procesos cogni- tivos. El primer grupo está compuesto por formas que pueden ser definidas extra- yendo los contornos de objetos sólidos. En este trabajo nos restringiremos al caso bidimensional. Decimos entonces que las formas del primer tipo son formas planares. Atacamos el problema de detectar y reconocer formas planares. Cier- tas restricciones teóricas y prácticas nos llevan a definir una forma planar como cualquier pedazo de línea de nivel de una imagen. Comenzamos por establecer que los métodos a contrario existentes para de- tectar líneas de nivel son usualmente muy restrictivos: una curva debe ser enter- amente saliente para ser detectada. Esto se encuentra en clara contradicción con la observación de que pedazos de líneas de nivel coinciden con los contornos de los objetos. Por lo tanto proponemos una modificación en la que el algoritmo de detección es relajado, permitiendo la detección de curvas parcialmente salientes. En un segundo acercamiento, estudiamos la interacción entre dos maneras diferentes de determinar la prominencia de una línea de nivel. Proponemos un esquema para competición de características donde el contraste y la regularidad compiten entre ellos, resultando en que solamente las líneas de nivel contrastadas y regulares son consderedas salientes. Una tercera contribución es un algoritmo de limpieza que analiza líneas de nivel salientes, descartando los pedazos no salientes y conservando los salientes. Está basado en un algoritmo para detección de multisegmentos que fue extendido para trabajar con entradas periódicas. Finalmente, proponemos un descriptor de formas para codificar las formas detectadas, basado en el Shape Context global. Cada línea de nivel es codificada usando shape contexts, generando así un nuevo descriptor semi-local. A contin- uación adaptamos un algoritmShape plays a key role in our cognitive system: in the perception of shape lies the beginning of concept formation. Following this lines of thought, the Gestalt school has extensively studied shape perception as the grasping of structural fea- tures found in or imposed upon the stimulus material. In summary, we have two models for shapes: they can exist physically or be a product of our cognitive pro- cesses. The first group is formed by shapes that can be defined by extracting contours from solid objects. In this work we will restrict ourselves to the two dimensional case. Therefore we say that these shapes of the first type are planar shapes. We ad- dress the problem of detecting and recognizing planar shapes. A few theoretical and practical restrictions lead us to define a planar shape as any piece of mean- ingful level line of an image. We begin by stating that previous a contrario methods to detect level lines are often too restrictive: a curve must be entirely salient to be detected. This is clearly in contradiction with the observation that pieces to level lines coincide with object boundaries. Therefore we propose a modification in which the detection criterion is relaxed by permitting the detection of partially salient level lines. As a second approach, we study the interaction between two different ways of determining level line saliency: contrast and regularity. We propose a scheme for feature competition where contrast and regularity contend with each other, resulting in that only contrasted and regular level lines are considered salient. A third contribution is a clean-up algorithm that analyses salient level lines, discarding the non-salient pieces and returning the salient ones. It is based on an algorithm for multisegment detection, which was extended to work with periodic inputs. Finally, we propose a shape descriptor to encode the detected shapes, based on the global Shape Context. Each level line is encoded using shape contexts, thus generating a new semi-local descriptor. We then adapt an existing a contrario shape matching algorithm to our particular case. The second group is composed by shapes that do not correspond to a solid object but are formed by integrating several solid objects. The simplest shapes in this group are arrangements of points in two dimensions. Clustering techniques might be helpful in these situations. In a seminal work from 1971, Zahn faced the problem of finding perceptual clusters according to the proximity gestalt and proposed three basic principles for clustering algorithms: (1) only inter-point distances matter, (2) stable results across executions and (3) independence from the exploration strategy. A last implicit requirement is crucial: clusters may have arbitrary shapes and detection algorithms must be capable of dealing with this. In this part we will focus on designing clustering methods that completely fulfils the aforementioned requirements and that impose minimal assumptions on the data to be clustered. We begin by assessing the problem of validating clusters in a hierarchical struc- ture. Based on nonparametric density estimation methods, we propose to com- pute the saliency of a given cluster. Then, it is possible to select the most salient clusters in the hierarchy. In practice, the method shows a preference toward com- pact clusters and we propose a simple heuristic to correct this issue. In general, graph-based hierarchical methods require to first compute the com- plete graph of interpoint distances. For this reason, hierarchical methods are often considered slow. The most usually used, and the fastest hierarchical clustering al- gorithm is based on the Minimum Spanning Tree (MST). We therefore propose an algorithm to compute the MST while avoiding the intermediate step of computing the complete set of interpoint distances. Moreover, the algorithm can be fully par- allelized with ease. The algorithm exhibits good performance for low-dimensional datasets and allows for an approximate but robust solution for higher dimensions. Finally we propose a method to select clustered subtrees from the MST, by computing simple edge statistics. The method allows naturally to retrieve clus- ters with arbitrary shapes. It also works well in noisy situations, where noise is regarded as unclustered data, allowing to separate it from clustered data. We also show that the iterative application of the algorithm allows to solve a phenomenon called masking, where highly populated clusters avoid the detection less popu- lated ones.Fil:Tepper, Mariano. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina

Hand gesture recognition using deep learning neural networks

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonHuman Computer Interaction (HCI) is a broad field involving different types of interactions including gestures. Gesture recognition concerns non-verbal motions used as a means of communication in HCI. A system may be utilised to identify human gestures to convey information for device control. This represents a significant field within HCI involving device interfaces and users. The aim of gesture recognition is to record gestures that are formed in a certain way and then detected by a device such as a camera. Hand gestures can be used as a form of communication for many different applications. It may be used by people who possess different disabilities, including those with hearing-impairments, speech impairments and stroke patients, to communicate and fulfil their basic needs. Various studies have previously been conducted relating to hand gestures. Some studies proposed different techniques to implement the hand gesture experiments. For image processing there are multiple tools to extract features of images, as well as Artificial Intelligence which has varied classifiers to classify different types of data. 2D and 3D hand gestures request an effective algorithm to extract images and classify various mini gestures and movements. This research discusses this issue using different algorithms. To detect 2D or 3D hand gestures, this research proposed image processing tools such as Wavelet Transforms and Empirical Mode Decomposition to extract image features. The Artificial Neural Network (ANN) classifier which used to train and classify data besides Convolutional Neural Networks (CNN). These methods were examined in terms of multiple parameters such as execution time, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood, negative likelihood, receiver operating characteristic, area under ROC curve and root mean square. This research discusses four original contributions in the field of hand gestures. The first contribution is an implementation of two experiments using 2D hand gesture video where ten different gestures are detected in short and long distances using an iPhone 6 Plus with 4K resolution. The experiments are performed using WT and EMD for feature extraction while ANN and CNN for classification. The second contribution comprises 3D hand gesture video experiments where twelve gestures are recorded using holoscopic imaging system camera. The third contribution pertains experimental work carried out to detect seven common hand gestures. Finally, disparity experiments were performed using the left and the right 3D hand gesture videos to discover disparities. The results of comparison show the accuracy results of CNN being 100% compared to other techniques. CNN is clearly the most appropriate method to be used in a hand gesture system.Imam Abdulrahman bin Faisal Universit

Digital Histories

Historical scholarship is currently undergoing a digital turn. All historians have experienced this change in one way or another, by writing on word processors, applying quantitative methods on digitalized source materials, or using internet resources and digital tools. Digital Histories showcases this emerging wave of digital history research. It presents work by historians who – on their own or through collaborations with e.g. information technology specialists – have uncovered new, empirical historical knowledge through digital and computational methods. The topics of the volume range from the medieval period to the present day, including various parts of Europe. The chapters apply an exemplary array of methods, such as digital metadata analysis, machine learning, network analysis, topic modelling, named entity recognition, collocation analysis, critical search, and text and data mining. The volume argues that digital history is entering a mature phase, digital history ‘in action’, where its focus is shifting from the building of resources towards the making of new historical knowledge. This also involves novel challenges that digital methods pose to historical research, including awareness of the pitfalls and limitations of the digital tools and the necessity of new forms of digital source criticisms. Through its combination of empirical, conceptual and contextual studies, Digital Histories is a timely and pioneering contribution taking stock of how digital research currently advances historical scholarship

Illuminierte Urkunden. Beiträge aus Diplomatik, Kunstgeschichte und Digital Humanities / Illuminated Charters. Essays from Diplomatic, Art History and Digital Humanities

Illuminated documents have long been treated as research stepchildren. Not least because of the use of digital aids, they have increasingly come into the public eye in the past decade. The newly awakened research interest focuses on the changed performativity of certificates through the addition of decorative elements. The richly illustrated volume presents essays by researchers from eleven countries who examine illuminated certificates from different perspectives of their disciplines

The Hologram and its Antecedents 1891–1965: The Illusory History of a Three–Dimensional Illusion

Since 1962, a photographic invention by Gabriel Lippmann (1845-1921), his Nobel Prize winning interference colour photograph of 1891, has been cited by physicists as the antecedent of the three-dimensional hologram. However, Dennis Gabor (1900-1979) in his original publications on the hologram of 1948 and 1949 did not cite Lippmann’s work. This thesis explores how the hologram that featured in Gabor’s original theory, as an imaging technique to improve the electron-microscope, was significantly different from the hologram for which Gabor was awarded the Nobel Prize for Physics in 1971. The citation of Lippmann’s work as the antecedent to the hologram confirmed that the hologram was to be seen as a three-dimensional photograph, and attempted to give the invention a progressive historical lineage that would conform to photography’s existing history. This popular narrative, as demonstrated in this text, could overlook the pursuit of the hologram for Cold War surveillance by researchers at the University of Michigan on behalf of the United States military. This technology was, from 1955, engaged with aerial radar image processing, a significant application that was classified and hidden from the public, and initially from Gabor himself. Two researchers at the University of Michigan, Emmett Leith (1927–) and Juris Upatnieks (1936–) attracted the attention of the popular press for their development of a three-dimensional laser hologram. This thesis reveals the fragmented nature of the new discipline at the peak of holography’s popularity. This analysis explores some of the historical traits between the two Nobel Prize winning inventions, the Lippmann photograph and the hologram, that were exploited to promote a new imaging medium to the public. In presenting these technologies as images the text also reviews devices and papers––some cited within the popular Lippmann-to Gabor historical narrative––by father and son Frederic (1856-1937) and Herbert Ives (1882-1953), that have competed to produce a three-dimensional full-colour image.Funded with a grant from Arts and Humanities Research Board U

Library buildings around the world

"Library Buildings around the World" is a survey based on researches of several years. The objective was to gather library buildings on an international level starting with 1990

Putting Chinese natural knowledge to work in an eighteenth-century Swiss canton: the case of Dr Laurent Garcin

Symposium: S048 - Putting Chinese natural knowledge to work in the long eighteenth centuryThis paper takes as a case study the experience of the eighteenth-century Swiss physician, Laurent Garcin (1683-1752), with Chinese medical and pharmacological knowledge. A Neuchâtel bourgeois of Huguenot origin, who studied in Leiden with Hermann Boerhaave, Garcin spent nine years (1720-1729) in South and Southeast Asia as a surgeon in the service of the Dutch East India Company. Upon his return to Neuchâtel in 1739 he became primus inter pares in the small local community of physician-botanists, introducing them to the artificial sexual system of classification. He practiced medicine, incorporating treatments acquired during his travels. taught botany, collected rare plants for major botanical gardens, and contributed to the Journal Helvetique on a range of topics; he was elected a Fellow of the Royal Society of London, where two of his papers were read in translation and published in the Philosophical Transactions; one of these concerned the mangosteen (Garcinia mangostana), leading Linnaeus to name the genus Garcinia after Garcin. He was likewise consulted as an expert on the East Indies, exotic flora, and medicines, and contributed to important publications on these topics. During his time with the Dutch East India Company Garcin encountered Chinese medical practitioners whose work he evaluated favourably as being on a par with that of the Brahmin physicians, whom he particularly esteemed. Yet Garcin never went to China, basing his entire experience of Chinese medical practice on what he witnessed in the Chinese diaspora in Southeast Asia (the ‘East Indies’). This case demonstrates that there were myriad routes to Europeans developing an understanding of Chinese natural knowledge; the Chinese diaspora also afforded a valuable opportunity for comparisons of its knowledge and practice with other non-European bodies of medical and natural (e.g. pharmacological) knowledge.postprin