• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 3053
  • 998
  • 553
  • 353
  • 280
  • 146
  • 105
  • 80
  • 79
  • 56
  • 51
  • 47
  • 42
  • 31
  • 26
  • Tagged with
  • 7570
  • 2210
  • 1456
  • 1352
  • 1260
  • 918
  • 908
  • 841
  • 721
  • 720
  • 685
  • 584
  • 582
  • 539
  • 529
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

Pen-Chant : Acoustic Emissions of Handwriting and Drawing

Seniuk, Andrew G. 27 September 2009 (has links)
The sounds generated by a writing instrument ("pen-chant") provide a rich and under-utilized source of information for pattern recognition. We examine the feasibility of recognition of handwritten cursive text, exclusively through an analysis of acoustic emissions. We design and implement a family of recognizers using a template matching approach, with templates and similarity measures derived variously from: smoothed amplitude signal with fixed resolution, discrete sequence of magnitudes obtained from peaks in the smoothed amplitude signal, and ordered tree obtained from a scale space signal representation. Test results are presented for recognition of isolated lowercase cursive characters and for whole words. We also present qualitative results for recognizing gestures such as circling, scratch-out, check-marks, and hatching. Our first set of results, using samples provided by the author, yield recognition rates of over 70% (alphabet) and 90% (26 words), with a confidence of 8%, based solely on acoustic emissions. Our second set of results uses data gathered from nine writers. These results demonstrate that acoustic emissions are a rich source of information, usable - on their own or in conjunction with image-based features - to solve pattern recognition problems. In future work, this approach can be applied to writer identification, handwriting and gesture-based computer input technology, emotion recognition, and temporal analysis of sketches. / Thesis (Master, Computing) -- Queen's University, 2009-09-27 08:56:53.895

Implementación de una herramienta de integración de varios tipos de interacción humano-computadora para el desarrollo de nuevos sistemas multimodales / Implementation of an integration tool of several types of human-computer interaction for the development of new multimodal systems

Alzamora M., Alzamora, Manuel I., Huamán, Andrés E., Barrientos, Alfredo, Villalta Riega, Rosario del Pilar January 2018 (has links)
Las personas interactúan con su entorno de forma multimodal. Esto es, con el uso simultaneo de sus sentidos. En los últimos años, se ha buscado una interacción multimodal humano-computador desarrollando nuevos dispositivos y usando diferentes canales de comunicación con el fin de brindar una experiencia de usuario interactiva más natural. Este trabajo presenta una herramienta que permite la integración de diferentes tipos de interacción humano computador y probarlo sobre una solución multimodal. / Revisión por pares

Learning Semantic Features for Visual Recognition

Liu, Jingen 05 February 2010 (has links)
Visual recognition (e.g., object, scene and action recognition) is an active area of research in computer vision due to its increasing number of real-world applications such as video (image) indexing and search, intelligent surveillance, human-machine interaction, robot navigation, etc. Effective modeling of the objects, scenes and actions is critical for visual recognition. Recently, bag of visual words (BoVW) representation, in which the image patches or video cuboids are quantized into visual words (i.e., mid-level features) based on their appearance similarity using clustering, has been widely and successfully explored. The advantages of this representation are: no explicit detection of objects or object parts and their tracking are required; the representation is somewhat tolerant to within-class deformations, and it is efficient for matching. However, the performance of the BoVW is sensitive to the size of the visual vocabulary. Therefore, computationally expensive cross-validation is needed to find the appropriate quantization granularity. This limitation is partially due to the fact that the visual words are not semantically meaningful. This limits the effectiveness and compactness of the representation. To overcome these shortcomings, in this thesis we present principled approach to learn a semantic vocabulary (i.e. high-level features) from a large amount of visual words (mid-level features). In this context, the thesis makes two major contributions. First, we have developed an algorithm to discover a compact yet discriminative semantic vocabulary. This vocabulary is obtained by grouping the visual-words based on their distribution in videos (images) into visual-word clusters. The mutual information (MI) be- tween the clusters and the videos (images) depicts the discriminative power of the semantic vocabulary, while the MI between visual-words and visual-word clusters measures the compactness of the vocabulary. We apply the information bottleneck (IB) algorithm to find the optimal number of visual-word clusters by finding the good tradeoff between compactness and discriminative power. We tested our proposed approach on the state-of-the-art KTH dataset, and obtained average accuracy of 94.2%. However, this approach performs one-side clustering, because only visual words are clustered regardless of which video they appear in. In order to leverage the co-occurrence of visual words and images, we have developed the co-clustering algorithm to simultaneously group the visual words and images. We tested our approach on the publicly available fifteen scene dataset and have obtained about 4% increase in the average accuracy compared to the one side clustering approaches. Second, instead of grouping the mid-level features, we first embed the features into a low-dimensional semantic space by manifold learning, and then perform the clustering. We apply Diffusion Maps (DM) to capture the local geometric structure of the mid-level feature space. The DM embedding is able to preserve the explicitly defined diffusion distance, which reflects the semantic similarity between any two features. Furthermore, the DM provides multi-scale analysis capability by adjusting the time steps in the Markov transition matrix. The experiments on KTH dataset show that DM can perform much better (about 3% to 6% improvement in average accuracy) than other manifold learning approaches and IB method. Above methods use only single type of features. In order to combine multiple heterogeneous features for visual recognition, we further propose the Fielder Embedding to capture the complicated semantic relationships between all entities (i.e., videos, images,heterogeneous features). The discovered relationships are then employed to further increase the recognition rate. We tested our approach on Weizmann dataset, and achieved about 17% 21% improvements in the average accuracy. / Ph.D. / School of Electrical Engineering and Computer Science / Engineering and Computer Science / Computer Science PhD

Developing test strips for naked-eye detection of alpha-hydroxy acids using indicator-displacement assays: an application of molecular recognition

Nguyen, Binh Thanh 28 August 2008 (has links)
Not available / text

Molecular recognition: structural and energetic aspects of preorganization, substrate specificity, and oligomerization

Benfield, Aaron Pillans 28 August 2008 (has links)
Not available / text

Speaker Recognition in a handheld computer

Domínguez Sánchez, Carlos January 2010 (has links)
Handheld computers are widely used, be it a mobile phone, personal digital assistant (PDA), or a media player. Although these devices are personal, often a small set of persons can use a given device, for example a group of friends or a family. The most natural way to communicate for most humans is through speech. Therefore a natural way for these devices to know who is using them is for the device to listen to the user’s speech, i.e., to recognize the speaker based upon their speech. This project exploits the microphone built into most of these devices and asks whether it is possible to develop an effective speaker recognition system which can operate within the limited resources of these devices (as compared to a desktop PC). The goal of this speaker recognition is to distinguish between the small set of people that could share a handheld device and those outside of this small set. Therefore the criteria is that the device should work for any of the members of this small set and not work for anyone outside of this small set. Furthermore, within this small set the device should recognize which specific person within this small group is using it. An application for a Windows Mobile PDA has been developed using C++. This application and its underlying theoretical concepts, as well as parts of the code and the results obtained (in terms of accuracy rate and performance) are presented in this thesis. The experiments conducted within this research indicate that it is feasible to recognize the user based upon their speech is within a small group and further more to identify which member of the group is the user. This has great potential for automatically configuring devices within a home or office environment for the specific user. Potentially all a user needs to do is speak within hearing range of the device to identify themselves to the device. The device in turn can configure itself for this user. / Handdatorer används mycket, det kan vara en mobiltelefon, handdator (PDA) eller en media spelare. Även om dessa enheter är personliga, kan en liten uppsättning med personer ofta använda en viss enhet, t.ex. en grupp av vänner eller en familj. Det mest naturliga sättet att kommunicera för de flesta människor är att tala. Därför ett naturligt sätt för dessa enheten att veta vem som använder dem är för enheten att lyssna på användarens röst, till exempel att erkänna talaren baserat på deras röst. Detta projekt utnyttjar mikrofonen inbyggd i de flesta av dessa enheter och frågar om det är möjligt att utveckla ett effektivt system högtalare erkännande som kan verka inom de begränsade resurserna av dessa enheter (jämfört med en stationär dator). Målet med denna högtalare erkännande är att skilja mellan den lilla set av människor som skulle kunna dela en handdator och de utanför detta lilla set. Därför kriterierna är att enheten bör arbeta för någon av medlemmarna i detta lilla set och inte fungerar för någon utanför detta lilla set. Övrigt inom denna lilla set, bör enheten erkänna som specifik person inom denna lilla grupp. En ansökan om emph Windows Mobile PDA har utvecklats med C++. Denna ansökan och det underliggande teoretiska begreppet, liksom delar av koden och uppnådda resultat (i form av noggrannhet hastighet och prestanda) presenteras i denna avhandling. Experimenten som utförs inom denna forskning visar att det är möjligt att känna användaren baserat på deras röst inom en liten grupp och ytterligare mer att identifiera vilken medlem i gruppen är användaren. Detta har stor potential för att automatiskt konfigurera enheter inom en hemifrån eller från kontoret till den specifika användaren. Potentiellt behöver en användare tala inom hörhåll för att identifiera sig till enheten. Enheten kan konfigurera själv för denna användare.

Names and faces: the role of name labels in the formation of face representations

Gordon, Iris 31 May 2011 (has links)
Although previous research in event-related potentials (ERPs) has focused on the conditions under which faces are recognized, less research has focused on the process by which face representations are acquired and maintained. In Experiment I, participants were required to monitor for a target "Joe" face that was shown amongst a series of distractor "Other" faces. At the half-way point, participants were instructed to switch targets from the Joe face to a previous distractor face that is now labeled "Bob". The ERP analysis focused on the posterior N250 component known to index face familiarity and the P300 component associated with context updating and response decision. Results showed that the N250 increased in negativity to target Joe face compared to the Bob face and a designated Other face. In the second half of the experiment, a more negative N250 was produced to the now target Bob face compared to the Other face. Critical1y, the more negative N250 to the Joe face was maintained even though Joe was no longer the target. The P300 component followed a similar pattern of brain response where the Joe face elicited a significantly larger P300 amplitude than the Other and Bob face. In the Bob half of the experiment, the Bob face elicited a reliably larger P300 than the Other faces and the heightened P300 to the Joe face was sustained. In Experiment 2, we examined whether the increased N2S0 negativity and enhanced P300 to Joe was due to simple naming effects. Participants were introduced to both Joe and Bob faces and names at the beginning of the experiment. During the first half of the experiment, participants were to monitor for the Joe face and at the half-way point, they were instructed to switch targets to the Bob face. Findings show that N250 negativity significantly increased to the Joe face relative to the Bob and Other faces in the first half of the experiment and an increased N250 negativity was found for target Bob face and the non-target Joe face in the second half. An increased P300 amplitude was demonstrated to the target Joe and Bob faces in the first and second halves of the experiment, respectively. Importantly, the P300 amplitude elicited by the Joe face equaled the P300 amplitude to the Bob face even though it was no longer the target face.The findings from Experiment 1 and 2 suggest that the N250 component is not solely determined by name labeling, exposure or task-relevancy, but it is the combination of these factors that contribute to the acquisition of enduring face representations. / Graduate

Face perception : sensitivity to feature displacement in normal, negative and inverted images

Kemp, Richard Ian January 1995 (has links)
No description available.

Does the 'special' nature of face processing extend to the working memory system?

Turk, David J. January 2001 (has links)
No description available.

Improving the acoustic modelling of speech using modular/ensemble combinations of heterogeneous neural networks

Antoniou, Christos Andrea January 2000 (has links)
No description available.

Page generated in 0.0911 seconds