Procedural Generation of Aesthetic Patterns from Dynamics and Iteration Processes

Aesthetic patterns are widely used nowadays, e.g., in jewellery design, carpet design, as textures and patterns on wallpapers, etc. Most of the work during the design stage is carried out by a designer manually. Therefore, it is highly useful to develop methods for aesthetic pattern generation. In this paper, we present methods for generating aesthetic patterns using the dynamics of a discrete dynamical system. The presented methods are based on the use of various iteration processes from fixed point theory (Mann, S, Noor, etc.) and the application of an affine combination of these iterations. Moreover, we propose new convergence tests that enrich the obtained patterns. The proposed methods generate patterns in a procedural way and can be easily implemented on the GPU. The presented examples show that using the proposed methods we are able to obtain a variety of interesting patterns. Moreover, the numerical examples show that the use of the GPU implementation with shaders allows the generation of patterns in real time and the speed-up (compared with a CPU implementation) ranges from about 1000 to 2500 times

Face recognition using statistical adapted local binary patterns.

Biometrics is the study of methods of recognizing humans based on their behavioral and physical characteristics or traits. Face recognition is one of the biometric modalities that received a great amount of attention from many researchers during the past few decades because of its potential applications in a variety of security domains. Face recognition however is not only concerned with recognizing human faces, but also with recognizing faces of non-biological entities or avatars. Fortunately, the need for secure and affordable virtual worlds is attracting the attention of many researchers who seek to find fast, automatic and reliable ways to identify virtual worlds’ avatars. In this work, I propose new techniques for recognizing avatar faces, which also can be applied to recognize human faces. Proposed methods are based mainly on a well-known and efficient local texture descriptor, Local Binary Pattern (LBP). I am applying different versions of LBP such as: Hierarchical Multi-scale Local Binary Patterns and Adaptive Local Binary Pattern with Directional Statistical Features in the wavelet space and discuss the effect of this application on the performance of each LBP version. In addition, I use a new version of LBP called Local Difference Pattern (LDP) with other well-known descriptors and classifiers to differentiate between human and avatar face images. The original LBP achieves high recognition rate if the tested images are pure but its performance gets worse if these images are corrupted by noise. To deal with this problem I propose a new definition to the original LBP in which the LBP descriptor will not threshold all the neighborhood pixel based on the central pixel value. A weight for each pixel in the neighborhood will be computed, a new value for each pixel will be calculated and then using simple statistical operations will be used to compute the new threshold, which will change automatically, based on the pixel’s values. This threshold can be applied with the original LBP or any other version of LBP and can be extended to work with Local Ternary Pattern (LTP) or any version of LTP to produce different versions of LTP for recognizing noisy avatar and human faces images

Interim research assessment 2003-2005 - Computer Science

This report primarily serves as a source of information for the 2007 Interim Research Assessment Committee for Computer Science at the three technical universities in the Netherlands. The report also provides information for others interested in our research activities

Redes sociais online : extração de conhecimento e análise espaço-temporal de eventos de difusão de informação

Orientador: Fernando José Von ZubenDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Com o surgimento e a popularização de Redes Sociais Online e de Serviços de Redes Sociais, pesquisadores da área de computação têm encontrado um campo fértil para o desenvolvimento de trabalhos com grande volume de dados, modelos envolvendo múltiplos agentes e dinâmicas espaço-temporais. Entretanto, mesmo com significativo elenco de pesquisas já publicadas no assunto, ainda existem aspectos das redes sociais cuja explicação é incipiente. Visando o aprofundamento do conhecimento da área, este trabalho investiga fenômenos de compartilhamento coletivo na rede, que caracterizam eventos de difusão de informação. A partir da observação de dados reais oriundos do serviço online Twitter, tais eventos são modelados, caracterizados e analisados. Com o uso de técnicas de aprendizado de máquina, são encontrados padrões nos processos espaço-temporais da rede, tornando possível a construção de classificadores de mensagens baseados em comportamento e a caracterização de comportamentos individuais, a partir de conexões sociaisAbstract: With the advent and popularization of Online Social Networks and Social Networking Services, computer science researchers have found fertile field for the development of studies using large volumes of data, multiple agents models and spatio-temporal dynamics. However, even with a significant amount of published research on the subject, there are still aspects of social networks whose explanation is incipient. In order to deepen the knowledge of the area, this work investigates phenomena of collective sharing on the network, characterizing information diffusion events. From the observation of real data obtained from the online service Twitter, we collect, model and characterize such events. Finally, using machine learning and computational data analysis, patterns are found on the network's spatio-temporal processes, making it possible to classify a message's topic from users behaviour and the characterization of individual behaviour, from social connectionsMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric

Attention-based Multi-Source-Free Domain Adaptation for EEG Emotion Recognition

Electroencephalography (EEG) based emotion recognition in affective brain-computer interfaces has advanced significantly in recent years. Unsupervised domain adaptation (UDA) methods have been successfully used to mitigate the need for large amounts of training data, which is required due to the inter-subject variability of EEG signals. Typical UDA solutions require access to raw source data to leverage the knowledge learned from the labelled source domains (previous subjects) across the target domain (a new subject), raising privacy concerns. To tackle this issue, we propose Attention-based Multi-Source-Free Domain Adaptation (AMFDA) for EEG emotion recognition. AMFDA attempts to transfer knowledge of source models to the target domain by aggregating adapted source models based on a set of learnable weights without accessing the source data. While the classifiers of source models are frozen, the set of learnable weights and the feature extractors are learned based on information maximization and a novel self-supervised pseudo-labelling method. A channel-wise attention layer is also used in the proposed framework to enhance the performance of source models, which in turn improves the performance of target models. We conducted extensive experiments on SEED and SEED-IV. The experimental results demonstrate that the proposed AMFDA method performs comparably to UDA state-of-the-art methods

MULTIMEDIA ON GEOGRAPHIC NETWORK

In this thesis we investigate the topic of the multimedia contents distribution on a geo- graphic network which is a rarefied and huge field. First of all we have to classify the main parts necessary in the multimedia distribution on a geographic network. The main aspects of a geographic network that will be highlighted in this thesis are: the mechanism used to retrieve the sources of the multimedia content; in the case of the peer-to-peer network on geographic network one of the most important mechanism is the query flooding protocol. The kind of overlay network (peer-to-peer) used to distribute the multimedia content. The usage of this overlay network in a multicast network. The security of the overlay network over a geographic network. Therefore the first topic which is investigated in this thesis is the query flooding protocol that can be used in any kind of query operation on a peer-to-peer network. For this protocol we achieve an analytical model through a complex analysis of the proxies network. In this analysis we can see how the proxies permit an improvement in the performance with respect to the routing operations in a generic network of routers. Moreover we address a simple formulation and framework about the performance of the network with and without layer 7 (proxy) and we apply them in three different types of scenarios to show the advantages achieved with the usage of proxies instead of routers. Through the query flooding operation, each peer of the peer-to-peer network can achieve the list of the peers that hold the desired multimedia content. In a multimedia content dis- tribution system, after the previous step in which the list of the peers that hold the desired multimedia content is retrieved, it is necessary to establish the kind of peer-to-peer network used to distribute this multimedia content to the peers that require it. Therefore the second aspect analysed in this thesis, is how the peer-to-peer network is built so that it is possible to provide the multimedia content to the vast majority of peers (that require this content) with the minimum delay. The construction of the peer-to-peer networks used for the distribution of the multimedia contents is not a very investigated field. Thus in this thesis we produce new algorithms used to build peer-to-peer networks in an incremental way on asymmetric and radio channel and we establish which algorithm is better with respect to the maximum delay of the network, the maximization of the number of peers accepted in the network and the minimization of the bit error probability of each peer of the peer-to-peer network. In this thesis, we propose an usage of the overlay network (peer-to-peer network) in a multicast network. We introduce an innovative mechanism that exploits the peer-to-peer network to make reliable a standard unreliable multicast network. Moreover we present an analytical model for this innovative mechanism. Finally the last aspect of a geographic network is the security of the communications among a group of peers. Thus to ensure the maximum level of security with secure commu- nications among a group of three or more peers, in this thesis we propose a new protocol, based on the Massey Omura protocol, which can allow the communications among the peers of a peer-to-peer network in a secure way. Moreover we present the security prob- lems of this Massey Omura Multiple Users Protocol and how it is possible to avoid these issues through a specific encryption function and a specific decryption function by chang- ing the encryption and decryption keys of each peer when the source peer changes. Finally we present a new cryptography protocol which we use to share the decryption shared key that is used in the Massey Omura Multiple Users Protocol

The computational face for facial emotion analysis: Computer based emotion analysis from the face

Facial expressions are considered to be the most revealing way of understanding the human psychological state during face-to-face communication. It is believed that a more natural interaction between humans and machines can be undertaken through the detailed understanding of the different facial expressions which imitate the manner by which humans communicate with each other. In this research, we study the different aspects of facial emotion detection, analysis and investigate possible hidden identity clues within the facial expressions. We study a deeper aspect of facial expressions whereby we try to identify gender and human identity - which can be considered as a form of emotional biometric - using only the dynamic characteristics of the smile expressions. Further, we present a statistical model for analysing the relationship between facial features and Duchenne (real) and non-Duchenne (posed) smiles. Thus, we identify that the expressions in the eyes contain discriminating features between Duchenne and non-Duchenne smiles. Our results indicate that facial expressions can be identified through facial movement analysis models where we get an accuracy rate of 86% for classifying the six universal facial expressions and 94% for classifying the common 18 facial action units. Further, we successfully identify the gender using only the dynamic characteristics of the smile expression whereby we obtain an 86% classification rate. Likewise, we present a framework to study the possibility of using the smile as a biometric whereby we show that the human smile is unique and stable.Al-Zaytoonah Universit