Ce manuscrit est dédié à la détection et la caractérisation de points d'intérêt dans les maillages. Nous montrons tout d'abord les limitations de la mesure de courbure sur des contours francs, mesure habituellement utilisée dans le domaine de l'analyse de maillages. Nous présentons ensuite une généralisation de l'opérateur SUSAN pour les maillages, nommé SUSAN-3D. La mesure de saillance proposée quantifie les variations locales de la surface et classe directement les points analysés en cinq catégories : saillant, crête, plat, vallée et creux. Les maillages considérés sont à variété uniforme avec ou sans bords et peuvent être réguliers ou irréguliers, denses ou non et bruités ou non. Nous étudions ensuite les performances de SUSAN-3D en les comparant à celles de deux opérateurs de courbure : l'opérateur de Meyer et l'opérateur de Stokely. Deux méthodes de comparaison des mesures de saillance et courbure sont proposées et utilisées sur deux types d’objets : des sphères et des cubes. Les sphères permettent l'étude de la précision sur des surfaces différentiables et les cubes sur deux types de contours non-différentiables : les arêtes et les coins. Nous montrons au travers de ces études les avantages de notre méthode qui sont une forte répétabilité de la mesure, une faible sensibilité au bruit et la capacité d'analyser les surfaces peu denses. Enfin, nous présentons une extension multi-échelle et une automatisation de la détermination des échelles d'analyse qui font de SUSAN-3D un opérateur générique et autonome d’analyse et de caractérisation pour les maillages / This manuscript is dedicated to the detection and caracterization of interest points for 3D meshes. First of all, we show the limitations of the curvature measure on sharp edges, the measure usually used for the analysis of meshes. Then, we present a generalization of the SUSAN operator for meshes, named SUSAN-3D. The saliency measure proposed quantify the local variation of the surface and classify directly the analysed vertices in five classes: salient, crest, flat, valley and cavity. The meshes under consideration are manifolds and can be closed or non-closed, regulars or irregulars, dense or not and noised or not. The accuracy of the SUSAN-3D operator is compared to two curvature operators: the Meyer's operator and the Stokely's operator. Two comparison methods of saliency and curvature measures are described and used on two types of objects: spheres and cubes. The spheres allow the study of the accuracy for differentiable surfaces and the cubes for two types of sharp edges: crests and corners. Through these studies, we show the benefits of our method that are a strong repeatability of the measure, high robustness to noise and capacity to analyse non dense meshes. Finally, we present a multi-scale scheme and automation of the determination of the analysis scales that allow SUSAN-3D to be a general and autonomous operator for the analysis and caracterization of meshes

Cette thèse a pour objectif l’étude de trois problèmes : l’estimation de cartes de saillance de l’énergie du bruit apériodique de la marche humaine par la perception de profondeur pour la détection de pathologies, les modèles de détection d’objets saillants en général et les modèles légers en particulier par l’opposition de couleurs. Comme première contribution, nous proposons un système basé sur une caméra de profondeur et un tapis roulant, qui analyse les parties du corps du patient ayant un mouvement irrégulier, en termes de périodicité, pendant la marche. Nous supposons que la marche d'un sujet sain présente n'importe où dans son corps, pendant les cycles de marche, un signal de profondeur avec un motif périodique sans bruit. La présence de bruit et son importance peuvent être utilisées pour signaler la présence et l'étendue de pathologies chez le sujet. Notre système estime, à partir de chaque séquence vidéo, une carte couleur de saillance montrant les zones de fortes irrégularités de marche, en termes de périodicité, appelées énergie de bruit apériodique, de chaque sujet. Notre système permet aussi de détecter automatiquement les cartes des individus sains et ceux malades. Nous présentons ensuite deux approches pour la détection d’objets saillants. Bien qu’ayant fait l’objet de plusieurs travaux de recherche, la détection d'objets saillants reste un défi. La plupart des modèles traitent la couleur et la texture séparément et les considèrent donc implicitement comme des caractéristiques indépendantes, à tort. Comme deuxième contribution, nous proposons une nouvelle stratégie, à travers un modèle simple, presque sans paramètres internes, générant une carte de saillance robuste pour une image naturelle. Cette stratégie consiste à intégrer la couleur dans les motifs de texture pour caractériser une micro-texture colorée, ceci grâce au motif ternaire local (LTP) (descripteur de texture simple mais puissant) appliqué aux paires de couleurs. La dissemblance entre chaque paire de micro-textures colorées est calculée en tenant compte de la non-linéarité des micro-textures colorées et en préservant leurs distances, donnant une carte de saillance intermédiaire pour chaque espace de couleur. La carte de saillance finale est leur combinaison pour avoir des cartes robustes. Le développement des réseaux de neurones profonds a récemment permis des performances élevées. Cependant, il reste un défi de développer des modèles de même performance pour des appareils avec des ressources limitées. Comme troisième contribution, nous proposons une nouvelle approche pour un modèle léger de réseau neuronal profond de détection d'objets saillants, inspiré par les processus de double opposition du cortex visuel primaire, qui lient inextricablement la couleur et la forme dans la perception humaine des couleurs. Notre modèle proposé, CoSOV1net, est entraîné à partir de zéro, sans utiliser de ``backbones'' de classification d'images ou d'autres tâches. Les expériences sur les ensembles de données les plus utilisés et les plus complexes pour la détection d'objets saillants montrent que CoSOV1Net atteint des performances compétitives avec des modèles de l’état-de-l’art, tout en étant un modèle léger de détection d'objets saillants et pouvant être adapté aux environnements mobiles et aux appareils à ressources limitées. / The purpose of this thesis is to study three problems: the estimation of saliency maps of the aperiodic noise energy of human gait using depth perception for pathology detection, and to study models for salient objects detection in general and lightweight models in particular by color opposition. As our first contribution, we propose a system based on a depth camera and a treadmill, which analyzes the parts of the patient's body with irregular movement, in terms of periodicity, during walking. We assume that a healthy subject gait presents anywhere in his (her) body, during gait cycles, a depth signal with a periodic pattern without noise. The presence of noise and its importance can be used to point out presence and extent of the subject’s pathologies. Our system estimates, from each video sequence, a saliency map showing the areas of strong gait irregularities, in terms of periodicity, called aperiodic noise energy, of each subject. Our system also makes it possible to automatically detect the saliency map of healthy and sick subjects. We then present two approaches for salient objects detection. Although having been the subject of many research works, salient objects detection remains a challenge. Most models treat color and texture separately and therefore implicitly consider them as independent feature, erroneously. As a second contribution, we propose a new strategy through a simple model, almost without internal parameters, generating a robust saliency map for a natural image. This strategy consists in integrating color in texture patterns to characterize a colored micro-texture thanks to the local ternary pattern (LTP) (simple but powerful texture descriptor) applied to the color pairs. The dissimilarity between each colored micro-textures pair is computed considering non-linearity from colored micro-textures and preserving their distances. This gives an intermediate saliency map for each color space. The final saliency map is their combination to have robust saliency map. The development of deep neural networks has recently enabled high performance. However, it remains a challenge to develop models of the same performance for devices with limited resources. As a third contribution, we propose a new approach for a lightweight salient objects detection deep neural network model, inspired by the double opponent process in the primary visual cortex, which inextricably links color and shape in human color perception. Our proposed model, namely CoSOV1net, is trained from scratch, without using any image classification backbones or other tasks. Experiments on the most used and challenging datasets for salient objects detection show that CoSOV1Net achieves competitive performance with state-of-the-art models, yet it is a lightweight detection model and it is a salient objects detection that can be adapted to mobile environments and resource-constrained devices.

L’Optimisation Topologique (OT) est un sujet en plein essor qui suscite l’intérêt de nombreux chercheurs depuis ces deux dernières décennies dans le domaine de l’électromagnétisme. L’OT représente une méthode très attrayante et originale car elle permet de trouver des structures innovantes sans aucun a priori. Ce travail de thèse est orienté vers l’OT des dispositifs électromagnétiques en approfondissant plusieurs aspects du sujet. Tout d’abord, un outil d’OT est développé et testé, à partir des outils existant au L2EP. En effet, l’OT requiert un outil d’éléments finis et un outil d’optimisation devant être couplés. Une méthodologie originale d’OT fondée sur les principes de la Méthode de Densité est développée et testée. Un cas test académique est utilisé afin de tester et valider le couplage des outils et la méthodologie. Une approche visant à prendre en compte le comportement non-linéaire des matériaux ferromagnétiques avec nos outils OT est également mise en place. Ensuite, la méthode est appliquée afin d’optimiser un électroaimant en 3 dimensions, représentant un cas test proche de la réalité. Ce cas test permet de comparer les résultats avec un comportement linéaire et non-linéaire des matériaux. Diverses topologies sont présentées, par rapport aux différentes formulations du problème. Par la suite, la méthodologie est appliquée à un dispositif électromagnétique plus complexe : une Génératrice Synchrone à Pôles Saillants. Cet exemple nous permet de voir comment la définition du problème d’optimisation peut grandement affecter les résultats d’OT. Quelques topologies sont présentées, et leur faisabilité est analysée / Topology Optimisation (TO) is a fast growing topic that has been sparking the interest of many researchers for the past two decades in the electromagnetic community. Its attractiveness lies in the originality of finding innovative structures without any layout a priori. This thesis work is oriented towards the TO of electromagnetic devices by elaborating on various aspects of the subject. First of all, a tool for TO is developed and tested, based on the ‘home-made’ tools available at the L2EP. As TO requires a FE and an optimisation tool working together, a coupling is done using both. Furthermore, a TO methodology is developed and tested, based on the Density Method. An academic cubic test case is used to carry out all the tests, and validate the tools and methodology. An approach is also developed to consider the nonlinear behaviour of the ferromagnetic materials with our TO tools. Afterwards, the methodology is applied to a 3D electromagnet, which represents a more real test case. This test case also serves to compare the results with linear and nonlinear behaviour of the materials used. Various topologies are presented, for different problem formulations. Subsequently, the methodology is applied to a more complex electromagnetic device: a Salient Pole Synchronous Generator. This example allows us to see how the problem definition can largely affect TO results. Some topologies are presented and their viability is discussed

This research is focused on the extraction of visual set patterns in digital images, using relational properties like nearness and similarity measures, as well as descriptive properties such as texture, colour and image gradient directions. The problem considered in this thesis is application of topology in visual set pattern discovery, and consequently pattern generation. A visual set pattern is a collection of motif patterns generated from different unique points called seed motifs in the set. Each motif pattern is a descriptive neighbourhood of a seed motif. Such a neighbourhood is a set of points that are descriptively near a seed motif. A new similarity distance measure based on dot product between image feature vectors was introduced in this research, for image classification with the generated visual set patterns. An application of this approach to pattern generation can be useful in content based image retrieval and image classification.

Submitted by Luciana Sebin (lusebin@ufscar.br) on 2016-09-30T13:54:49Z No. of bitstreams: 1 DissCHVP.pdf: 4899707 bytes, checksum: e7de60b5431e48ddbc2b9016dae268c7 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-14T14:06:37Z (GMT) No. of bitstreams: 1 DissCHVP.pdf: 4899707 bytes, checksum: e7de60b5431e48ddbc2b9016dae268c7 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-14T14:06:48Z (GMT) No. of bitstreams: 1 DissCHVP.pdf: 4899707 bytes, checksum: e7de60b5431e48ddbc2b9016dae268c7 (MD5) / Made available in DSpace on 2016-10-14T14:06:58Z (GMT). No. of bitstreams: 1 DissCHVP.pdf: 4899707 bytes, checksum: e7de60b5431e48ddbc2b9016dae268c7 (MD5) Previous issue date: 2016-03-10 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / The magnetic resonance (MR) imaging has become an indispensable tool for the diagnosis and study of various diseases and syndromes of the central nervous system, such as Alzheimer’s disease (AD). In order to perform the precise diagnosis of a disease, as well as the evolutionary monitoring of a certain treatment, the neuroradiologist doctor often needs to measure and assess volume and shape changes in certain brain structures along a series of MR images. For that, the previous delineation of the structures of interest is necessary. In general, such task is manually done, with limited help from a computer, and therefore it has several problems. For this reason, many researchers have turned their efforts towards the development of automatic techniques for segmentation of brain structures in MR images. Among the various approaches proposed in the literature, techniques based on deformable models and anatomical atlases are among those which present the best results. However, one of the main difficulties in applying geometric deformable models is the initial positioning of the model. Thus, this research aimed to develop an atlas of 3D salient points (automatically detected from a set of MR images) and to investigate the applicability of such atlas in guiding the initial positioning of geometric deformable models representing brain structures, with the purpose of helping the automatic segmentation of such structures in MR images. The processing pipeline included the use of a 3D salient point detector based on the phase congruency measure, an adaptation of the shape contexts technique to create point descriptors and the estimation of a B-spline transform to map pairs of matching points. The results, evaluated using the Jaccard and Dice metrics before and after the model initializations, showed a significant gain in the tests involving synthetically deformed images of normal patients, but for images of clinical patients with AD the gain was marginal and can still be improved in future researches. Some ways to do such improvements are discussed in this work. / O imageamento por ressonância magnética (RM) tornou-se uma ferramenta indispensável no diagnóstico e estudo de diversas doenças e síndromes do sistema nervoso central, tais como a doença de Alzheimer (DA). Para que se possa realizar o diagnóstico preciso de uma doença, bem como o acompanhamento evolutivo de um determinado tratamento, o médico neurorradiologista frequentemente precisa medir e avaliar alterações de volume e forma em determinadas estruturas do cérebro ao longo de uma série de imagens de RM. Para isso, a delineação prévia das estruturas de interesse nas imagens é necessária. Em geral, essa tarefa é realizada manualmente, com ajuda limitada de um computador, e portanto possui diversos problemas. Por esse motivo, vários pesquisadores têm voltado seus esforços para o desenvolvimento de técnicas automáticas de segmentação de estruturas cerebrais em imagens de RM. Dentre as várias abordagens propostas na literatura, técnicas baseadas em modelos deformáveis e atlas anatômicos estão entre as que apresentam os melhores resultados. No entanto, uma das principais dificuldades na aplicação de modelos geométricos deformáveis é o posicionamento inicial do modelo. Assim, esta pesquisa teve por objetivo desenvolver um atlas de pontos salientes 3D (automaticamente detectados em um conjunto de imagens de RM) e investigar a aplicabilidade de tal atlas em guiar o posicionamento inicial de modelos geométricos deformáveis representando estruturas cerebrais, com o propósito de auxiliar a segmentação automática de tais estruturas em imagens de RM. O arcabouço de processamento incluiu o uso de um detector de pontos salientes 3D baseado na medida de congruência de fase, uma adaptação da técnica shape contexts para a criação de descritores de pontos e a estimação de uma transformação B-spline para mapear pares de pontos correspondentes. Os resultados, avaliados com as métricas Jaccard e Dice antes e após a inicialização dos modelos, mostraram um ganho significativo em testes envolvendo imagens sinteticamente deformadas de pacientes normais, mas em imagens de pacientes clínicos com DA o ganho foi marginal e ainda pode ser melhorado em pesquisas futuras. Algumas maneiras de se realizar tais melhorias são discutidas neste trabalho. / FAPESP: 2015/02232-1 / CAPES: 2014/11988-0

Made available in DSpace on 2014-12-17T14:56:12Z (GMT). No. of bitstreams: 1 SuelioFC_DISSERT.pdf: 1266723 bytes, checksum: 8001a23164a7a48663a60001950fc3e0 (MD5) Previous issue date: 2013-02-01 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / The stability of synchronous generators connected to power grid has been the object of study and research for years. The interest in this matter is justified by the fact that much of the electricity produced worldwide is obtained with the use of synchronous generators. In this respect, studies have been proposed using conventional and unconventional control techniques such as fuzzy logic, neural networks, and adaptive controllers to increase the stabilitymargin of the systemduring sudden failures and transient disturbances. Thismaster thesis presents a robust unconventional control strategy for maintaining the stability of power systems and regulation of output voltage of synchronous generators connected to the grid. The proposed control strategy comprises the integration of a sliding surface with a linear controller. This control structure is designed to prevent the power system losing synchronism after a sudden failure and regulation of the terminal voltage of the generator after the fault. The feasibility of the proposed control strategy was experimentally tested in a salient pole synchronous generator of 5 kVA in a laboratory structure / A estabilidade de geradores s?ncronos conectados a rede el?trica tem sido objeto de estudo e investiga??es durante anos. O interesse por este assunto ? justificado pelo fato de grande parte da energia el?trica produzida no mundo ser obtida com a utiliza??o de geradores s?ncronos. Nesse aspecto, muitos trabalhos t?m sido propostos utilizando t?cnicas de controle convencional e n?o convencional como l?gica fuzzy, redes neurais e controladores adaptativos visando aumentar a margem de estabilidade do sistema quando ele est? sujeito a falhas s?bitas e dist?rbios transit?rios. Este trabalho apresenta uma estrat?gia de controle robusta n?o-convencional para a manuten??o da estabilidade dos sistemas de pot?ncia e regula??o da tens?o de sa?da de geradores s?ncronos conectados ? rede el?trica. A estrat?gia de controle utilizada ? composta pela integra??o de uma superf?cie deslizante com um controlador linear. Esta estrutura de controle contribui para a preven??o dos sistemas de pot?ncia de perder o sincronismo ap?s uma falha s?bita e regula??o da tens?o terminal do gerador ap?s a falta. A viabilidade da estrat?gia de controle proposta foi testada experimentalmente em um gerador s?ncrono de p?los salientes de 5 kVA em uma estrutura de laborat?rio

Made available in DSpace on 2014-12-17T14:56:13Z (GMT). No. of bitstreams: 1 CecilioMSN_DISSERT.pdf: 1132732 bytes, checksum: 95ce32ef2cdf325116d25b76e2b1858a (MD5) Previous issue date: 2013-07-05 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / The power system stabilizers are used to suppress low-frequency electromechanical oscillations and improve the synchronous generator stability limits. This master thesis proposes a wavelet-based power system stabilizer, composed of a new methodology for extraction and compensation of electromechanical oscillations in electrical power systems based on the scaling coefficient energy of the maximal overlap discrete wavelet transform in order to reduce the effects of delay and attenuation of conventional power system stabilizers. Moreover, the wavelet coefficient energy is used for electric oscillation detection and triggering the power system stabilizer only in fault situations. The performance of the proposed power system stabilizer was assessed with experimental results and comparison with the conventional power system stabilizer. Furthermore, the effects of the mother wavelet were also evaluated in this work / Os estabilizadores de sistemas de pot?ncia s?o empregados para suprimir oscila??es eletromec?nicas, de baixa frequ?ncia, e estender os limites de estabilidade de geradores s?ncronos. Prop?e-se nesta disserta??o de mestrado um estabilizador de sistema de pot?ncia baseado nas wavelets, composta por uma novametodologia para extra??o e compensa??o de oscila??es eletromec?nicas em sistemas el?tricos de pot?ncia baseada nas energias dos coeficientes de aproxima??o da transformada wavelet discreta redundante, com o objetivo de reduzir os efeitos de atraso e atenua??es dos estabilizadores de sistemas de pot?ncia convencionais. Por outro lado, as energias dos coeficientes wavelet s?o utilizadas para detec??o das oscila??es el?tricas e habilita??o do estabilizador de sistema de pot?ncia proposto apenas nas situa??es de falta. A efic?cia do desempenho do estabilizador de sistema de pot?ncia proposto foi comprovada por meio de resultados experimentais, cujo desempenho foi comparado com o desempenho do estabilizador de sistema de pot?ncia convencional. Al?m disso, os efeitos das wavelets m?es tamb?m foram avaliados

This work develops a new mathematical model to the salient pole synchronous machines (SPSM), based on the abc reference system. The model considers the distribution and coil pitch factors of windings and develops a new function for the variable air gap, generated by the curvature of the polar mass. As a result, the development of the modeling takes into account the spatial harmonic components of: magneto motive force MMF(θ)h, electromagnetic ux density B(θ)h and variation function of the air gap g(θ)h. It is also proposed a new and simplied methodology using the locked rotor tests, volt-ampere method, to obtain the constants of the synchronous machine design, which allow the calculation of the modeling parameters and the terminals magnitudes determination. It presents also contributions to traditional methods of obtaining experimental inductances, using the locked rotor test. Yet it develops procedures and makes the simulation of the main temporal magnitudes at the generator terminals connected to the utility grid, electrical torque, speed, voltage and current. The model is validated through the theoretical and experimental confrontation of inductances, and also of the voltages and currents at the generator terminals connected to the utility grid. / Este trabalho desenvolve uma nova modelagem matemática para as máquinas síncronas de polos salientes (MSPS), baseada no sistema abc de referência. A modelagem considera os fatores de distribuição e de passo de bobina dos enrolamentos e desenvolve uma nova função para o entreferro variável, gerado pela curvatura da sapata polar. Como conseqüência o desenvolvimento da modelagem leva em consideração os componentes harmônicos espaciais de: força magneto motriz FMM(θ)h, densidade de uxo eletromagnético B(θ)h e da função de variação do entreferro g(θ)h. É também proposto uma nova e simplificada metodologia a partir dos testes de rotor bloqueado, método volt-ampere, para a obtenção das constantes de projeto da máquina síncrona que possibilitam os cálculos dos parâmetros da modelagem, bem como a determinação das grandezas terminais. Também apresenta contribuições aos métodos tradicionais de obtenção de indutâncias experimentais, a partir dos testes de rotor bloqueado. Ainda desenvolve os procedimentos e faz a simulação das principais grandezas temporais nos terminais do gerador conectado à rede da concessionária: torque elétrico, velocidade, corrente e tensão. A modelagem é convalidada através das confrontações teórico-experimental das indutâncias, e também dos resultados de correntes e tensões nos terminais do gerador conetado à rede da concessionária. / Doutor em Ciências

Submitted by Liliane Ferreira (ljuvencia30@gmail.com) on 2018-09-14T11:19:37Z No. of bitstreams: 2 Tese - José Mário Menescal de Oliveira - 2018.pdf: 3555364 bytes, checksum: 7da381c5690449c518f20f2a3fd49fea (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-09-17T10:48:33Z (GMT) No. of bitstreams: 2 Tese - José Mário Menescal de Oliveira - 2018.pdf: 3555364 bytes, checksum: 7da381c5690449c518f20f2a3fd49fea (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-09-17T10:48:33Z (GMT). No. of bitstreams: 2 Tese - José Mário Menescal de Oliveira - 2018.pdf: 3555364 bytes, checksum: 7da381c5690449c518f20f2a3fd49fea (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-07-13 / This thesis demonstrates the effects of harmonic pollution in a salient pole synchronous generator and an induction generator operating in parallel on an isolated system, supplying a non-linear load. The main contributions of this research-study consist of identifying and quantifying the oscillations that non-linear load cause on the electric variables of synchronous and induction generators, such as, the electromagnetic conjugate that presents oscillations of sixth harmonic due to the distorted currents. / Este trabalho mostra os efeitos da poluição harmônica em um gerador síncrono de polos salientes e um gerador de indução operando em paralelo em um sistema isolado suprindo carga não linear. As principais contribuições deste trabalho consistem em identificar e quantificar as oscilações que a carga não linear utilizada provoca nas variáveis elétricas dos geradores síncronos e dos geradores de indução, tal como, o conjugado eletromagnético que apresenta oscilações de sexto harmônico devido as correntes distorcidas.

Nowadays, we have millions of cameras in public places such as traffic junctions, railway stations etc., and capturing video data round the clock. This humongous data has resulted in an increased need for automation of visual surveillance. Analysis of crowd and traffic flows is an important step towards achieving this goal. In this work, we present our algorithms for identifying and segmenting dominant ows in surveillance scenarios. In the second part, we present our work aiming at predicting the visual saliency. The ability of humans to discriminate and selectively pay attention to few regions in the scene over the others is a key attentional mechanism. Here, we present our algorithms for predicting human eye fixations and segmenting salient objects in the scene. (i) Flow Analysis in Surveillance Videos: We propose algorithms for segmenting flows of static and dynamic nature in surveillance videos in an unsupervised manner. In static flows scenarios, we assume the motion patterns to be consistent over the entire duration of video and analyze them in the compressed domain using H.264 motion vectors. Our approach is based on modeling the motion vector field as a Conditional Random Field (CRF) and obtaining oriented motion segments which are merged to obtain the final flow segments. This approach in compressed domain is shown to be both accurate and computationally efficient. In the case of dynamic flow videos (e.g. flows at a traffic junction), we propose a method for segmenting the individual object flows over long durations. This long-term flow segmentation is achieved in the framework of CRF using local color and motion features. We propose a Dynamic Time Warping (DTW) based distance measure between flow segments for clustering them and generate representative dominant ow models. Using these dominant flow models, we perform path prediction for the vehicles entering the camera's field-of-view and detect anomalous motions. (ii) Visual Saliency Prediction using Deep Convolutional Neural Networks: We propose a deep fully convolutional neural network (CNN) - DeepFix, for accurately predicting eye fixations in the form of saliency maps. Unlike classical works which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts saliency map in an end-to-end manner. DeepFix is designed to capture visual semantics at multiple scales while taking global context into account. Generally, fully convolutional nets are spatially invariant which prevents them from modeling location dependent patterns (e.g. centre-bias). Our network overcomes this limitation by incorporating a novel Location Biased Convolutional layer. We experimentally show that our network outperforms other recent approaches by a significant margin. In general, human eye fixations correlate with locations of salient objects in the scene. However, only a handful of approaches have attempted to simultaneously address these related aspects of eye fixations and object saliency. In our work, we also propose a deep convolutional network capable of simultaneously predicting eye fixations and segmenting salient objects in a unified framework. We design the initial network layers, shared between both the tasks, such that they capture the global contextual aspects of saliency, while the deeper layers of the network address task specific aspects. Our network shows a significant improvement over the current state-of-the-art for both eye fixation prediction and salient object segmentation across a number of challenging datasets.