Dimensionality reduction and sparse representations in computer vision

The proliferation of camera equipped devices, such as netbooks, smartphones and game stations, has led to a significant increase in the production of visual content. This visual information could be used for understanding the environment and offering a natural interface between the users and their surroundings. However, the massive amounts of data and the high computational cost associated with them, encumbers the transfer of sophisticated vision algorithms to real life systems, especially ones that exhibit resource limitations such as restrictions in available memory, processing power and bandwidth. One approach for tackling these issues is to generate compact and descriptive representations of image data by exploiting inherent redundancies. We propose the investigation of dimensionality reduction and sparse representations in order to accomplish this task. In dimensionality reduction, the aim is to reduce the dimensions of the space where image data reside in order to allow resource constrained systems to handle them and, ideally, provide a more insightful description. This goal is achieved by exploiting the inherent redundancies that many classes of images, such as faces under different illumination conditions and objects from different viewpoints, exhibit. We explore the description of natural images by low dimensional non-linear models called image manifolds and investigate the performance of computer vision tasks such as recognition and classification using these low dimensional models. In addition to dimensionality reduction, we study a novel approach in representing images as a sparse linear combination of dictionary examples. We investigate how sparse image representations can be used for a variety of tasks including low level image modeling and higher level semantic information extraction. Using tools from dimensionality reduction and sparse representation, we propose the application of these methods in three hierarchical image layers, namely low-level features, mid-level structures and high-level attributes. Low level features are image descriptors that can be extracted directly from the raw image pixels and include pixel intensities, histograms, and gradients. In the first part of this work, we explore how various techniques in dimensionality reduction, ranging from traditional image compression to the recently proposed Random Projections method, affect the performance of computer vision algorithms such as face detection and face recognition. In addition, we discuss a method that is able to increase the spatial resolution of a single image, without using any training examples, according to the sparse representations framework. In the second part, we explore mid-level structures, including image manifolds and sparse models, produced by abstracting information from low-level features and offer compact modeling of high dimensional data. We propose novel techniques for generating more descriptive image representations and investigate their application in face recognition and object tracking. In the third part of this work, we propose the investigation of a novel framework for representing the semantic contents of images. This framework employs high level semantic attributes that aim to bridge the gap between the visual information of an image and its textual description by utilizing low level features and mid level structures. This innovative paradigm offers revolutionary possibilities including recognizing the category of an object from purely textual information without providing any explicit visual example

The impact of Social Responsibility on productivity and efficiency of US listed companies

We investigate whether inclusion and permanence in the Domini social index affects corporate performance on a sample of around 1000 firms in a 13-year interval by controlling for size, industry, business cycle and time invariant firm idiosyncratic characteristics. Our results find partial support to the hypothesis that corporate social responsibility (CSR) generates a transfer of wealth from shareholders to stakeholders. On the one side, the combination of entry and permanence into the Domini is shown to increase (reduce) significantly total sales per employee (returns on equity). On the other side, lower returns on equity seem nonetheless to be accompanied by relatively lower conditional volatility and lower reaction to extreme shocks of Domini stocks with respect to the control sample. The first two econometric findings match intrinsic characteristics since they are paralleled by significant differences in fixed effects between the control sample and firms which will become Domini affiliated in the sample period. Our conclusion is that Domini affiliation significantly reinforces traits of corporate identity which were already in place before entry. We also show that exit from Domini has strong negative effects on total sales per employee, returns on equity, investment and capital employed. An explanation for the “transfer of wealth” effect, suggested by the inspection of Domini criteria, is that social responsibility implies, on the one side, decisions leading to higher cost of labour and of intermediate output, but may, on the other side, enhance involvement, motivation and identification of the workforce with company goals with positive effects on productivity

Freud, Modularity, and the Principle of Charity

Within the philosophy of mind, a `hermeneutical' tradition sees psychology as discontinuous with natural-scientific domains. A characteristic ingredient of this tendency is `normativism', which makes obedience to rational norms an a priori condition on agency. In this thesis, I advance an argument against normativism which trades on the notion of a psychological module. Specifically, I show how modules can be envisioned which, because of their high degree of irrationality, challenge the normativist's principle of charity. As an illustration, I describe such a module that incorporates key features of the Freudian `id', and I suggest that Freudian theory generally puts pressure on charity constraints. In sum, I seek to substantially undermine the hermeneutical view of the mind by attacking one of its central pillars. In Chapter 1, after setting out the essential features of hermeneuticism, I sketch the historical background of recent normativism by considering Quine's employment of charity in his theory of meaning and mind. Most centrally, I reject pragmatic and heuristic readings of Quinean charity in favor of one that sees it as a constitutive constraint on attribution. In Chapter 2, I begin to clarify the content of Davidsonian charity, against which--in the first instance--my argument levels. I identify Maximization and Threshold Principles in Davidson's early papers, contrast Davidsonian charity with Richard Grandy's Principle of Humanity, and rebut typical arguments for charity principles. In Chapter 3, after identifying two additional Davidsonian charity principles (a Competence and a Compartment Principle) and describing the conception of a module figuring in my argument, I present my argument in schematic form. Then I critique attempts to rebut my argument through excluding modular processes from the scope of normativism (notably, via a personal-subpersonal distinction). In Chapter 4, I develop my argument in detail by describing a module that embodies basic forms of Freudian wish-fulfilment and demonstrating how it violates charity principles. Further, I rebut possible objections to my use of Freudian theory. In Chapter 5, I canvass various models of Freudian phenomena more generally and suggest that a version of my argument can be run with respect to such phenomena too

Probabilistic characterization and synthesis of complex driven systems

Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2000.Includes bibliographical references (leaves 194-204).Real-world systems that have characteristic input-output patterns but don't provide access to their internal states are as numerous as they are difficult to model. This dissertation introduces a modeling language for estimating and emulating the behavior of such systems given time series data. As a benchmark test, a digital violin is designed from observing the performance of an instrument. Cluster-weighted modeling (CWM), a mixture density estimator around local models, is presented as a framework for function approximation and for the prediction and characterization of nonlinear time series. The general model architecture and estimation algorithm are presented and extended to system characterization tools such as estimator uncertainty, predictor uncertainty and the correlation dimension of the data set. Furthermore a real-time implementation, a Hidden-Markov architecture, and function approximation under constraints are derived within the framework. CWM is then applied in the context of different problems and data sets, leading to architectures such as cluster-weighted classification, cluster-weighted estimation, and cluster-weighted sampling. Each application relies on a specific data representation, specific pre and post-processing algorithms, and a specific hybrid of CWM. The third part of this thesis introduces data-driven modeling of acoustic instruments, a novel technique for audio synthesis. CWM is applied along with new sensor technology and various audio representations to estimate models of violin-family instruments. The approach is demonstrated by synthesizing highly accurate violin sounds given off-line input data as well as cello sounds given real-time input data from a cello player.by Bernd Schoner.Ph.D

Deep Networks and Knowledge: from Rule Learning to Neural-Symbolic Argument Mining

Deep Learning has revolutionized the whole discipline of machine learning, heavily impacting fields such as Computer Vision, Natural Language Processing, and other domains concerned with the processing of raw inputs. Nonetheless, Deep Networks are still difficult to interpret, and their inference process is all but transparent. Moreover, there are still challenging tasks for Deep Networks: contexts where the success depends on structured knowledge that can not be easily provided to the networks in a standardized way. We aim to investigate the behavior of Deep Networks, assessing whether they are capable of learning complex concepts such as rules and constraints without explicit information, and then how to improve them by providing such symbolic knowledge in a general and modular way. We start by addressing two tasks: learning the rule of a game and learning to construct the solution to Constraint Satisfaction Problems. We provide the networks only with examples, without encoding any information regarding the task. We observe that the networks are capable of learning to play by the rules and to make feasible assignments in the CSPs. Then, we move to Argument Mining, a complex NLP task which consists of finding the argumentative elements in a document and identifying their relationships. We analyze Neural Attention, a mechanism widely used in NLP to improve networks' performance and interpretability, providing a taxonomy of its implementations. We exploit such a method to train an ensemble of deep residual networks and test them on four different corpora for Argument Mining, reaching or advancing the state of the art in most of the datasets we considered for this study. Finally, we realize the first implementation of neural-symbolic argument mining. We use the Logic Tensor Networks framework to introduce logic rules during the training process and establish that they give a positive contribution under multiple dimensions

Image partition and video segmentation using the Mumford-Shah functional

2010 - 2011The aim of this Thesis is to present an image partition and video segmentation procedure, based on the minimization of a modified version of Mumford-Shah functional. The Mumford-Shah functional used for image partition has been then extended to develop a video segmentation procedure. Differently by the image processing, in video analysis besides the usual spatial connectivity of pixels (or regions) on each single frame, we have a natural notion of “temporal” connectivity between pixels (or regions) on consecutive frames given by the optical flow. In this case, it makes sense to extend the tree data structure used to model a single image with a graph data structure that allows to handle a video sequence. The video segmentation procedure is based on minimization of a modified version of a Mumford-Shah functional. In particular the functional used for image partition allows to merge neighboring regions with similar color without considering their movement. Our idea has been to merge neighboring regions with similar color and similar optical flow vector. Also in this case the minimization of Mumford-Shah functional can be very complex if we consider each possible combination of the graph nodes. This computation becomes easy to do if we take into account a hierarchy of partitions constructed starting by the nodes of the graph.[edited by author]X n.s

A Generic Prognostic Framework for Remaining Useful Life Prediction of Complex Engineering Systems

Prognostics and Health Management (PHM) is a general term that encompasses methods used to evaluate system health, predict the onset of failure, and mitigate the risks associated with the degraded behavior. Multitudes of health monitoring techniques facilitating the detection and classification of the onset of failure have been developed for commercial and military applications. PHM system designers are currently focused on developing prognostic techniques and integrating diagnostic/prognostic approaches at the system level. This dissertation introduces a prognostic framework, which integrates several methodologies that are necessary for the general application of PHM to a variety of systems. A method is developed to represent the multidimensional system health status in the form of a scalar quantity called a health indicator. This method is able to indicate the effectiveness of the health indicator in terms of how well or how poorly the health indicator can distinguish healthy and faulty system exemplars. A usefulness criterion was developed which allows the practitioner to evaluate the practicability of using a particular prognostic model along with observed degradation evidence data. The criterion of usefulness is based on comparing the model uncertainty imposed primarily by imperfectness of degradation evidence data against the uncertainty associated with the time-to-failure prediction based on average reliability characteristics of the system. This dissertation identifies the major contributors to prognostic uncertainty and analyzes their effects. Further study of two important contributions resulted in the development of uncertainty management techniques to improve PHM performance. An analysis of uncertainty effects attributed to the random nature of the critical degradation threshold, , was performed. An analysis of uncertainty effects attributed to the presence of unobservable failure mechanisms affecting the system degradation process along with observable failure mechanisms was performed. A method was developed to reduce the effects of uncertainty on a prognostic model. This dissertation provides a method to incorporate prognostic information into optimization techniques aimed at finding an optimal control policy for equipment performing in an uncertain environment

Detecting Well-being in Digital Communities: An Interdisciplinary Engineering Approach for its Indicators

In this thesis, the challenges of defining, refining, and applying well-being as a progressive management indicator are addressed. This work\u27s implications and contributions are highly relevant for service research as it advances the integration of consumer well-being and the service value chain. It also provides a substantial contribution to policy and strategic management by integrating constituents\u27 values and experiences with recommendations for progressive community management

Use of multispectral data to identify farm intensification levels by applying emergent computing techniques

Concern about feeding an ever increasing population has long been one of humankind’s most pressing problems. This has been addressed throughout history by introducing into farming systems changes allowing them to produce more per unit of land area. However, these changes have also been linked to negative effects on the socio economic and environmental sphere, that have created the need for an integral understanding of this phenomenon. This thesis describes the application of learning machine methods to induct a relationship between the spectral response of farms’ land cover and their intensification levels from a sample of farming of Urdaneta municipality, Aragua state of Venezuela. Data collection like this is a necessary first steep to implement cost-effective methods that can help policymakers to conduct succesful planing tasks, especially in countries such as Venezuela where, in spite of there being areas capable of agricultural production, nearly 50% of the internal food requirements of recent years have been satisfied by importations. In this work, farm intensification levels are investigated through a sample of farms of Urdaneta Municipality, Aragua state of Venezuela. This area is characterised by a wide diversity of farming systems ranging from crop to crop-livestock systems and an increasing population density in regions capable of livestock and arable farming, making it a representative case of the main tropical rural zones. The methodology applied can be divided into two main phases. First an unsupervised classification was performed by applying principal component analysis and agglomerative cluster methods to a set of land use and land management indicators, with the aim to segregate farms into homogeneous groups from the intensification point of view. This procedure resulted in three clusters which were named extensive, semi-intensive and intensive. The land use indicators included the percentage area within each farm devoted to annual crops, orchard and pasture, while the land management indicators were percentage of cultivated land under irrigation, stocking rate, machinery and equipment index and permanent and temporary staff ratio, all of them built from data held on the 1996- 1997 venezuelan agricultural census. The previous clusters reached were compared to the ones obtained by applying the learning machine method known as self-organizing map, which is also an unsupervised classification technique, as a way to confirm the groups’ existence. In the second stage, the learning machine known as kernel adatron algorithm was implemented seeking to identify the intensification level of Urdaneta farms from a landsat image, which consisted of two sequential steps: namely training and validation. In the training step, a predetermined number of instances randomly selected from the data set were analysed looking for a pattern to establish a relationship between the label and the spectral response in an iterative process which was concluded when the machine found a linear function capable of separating the two classes with a maximum margin. The supervised classification finishes with the validation in which the kernel adatron classifies the unseen samples by using a generalisation of the relationships learned while training. Results suggest that farm intensification levels can be effectively derived from multi-spectral data by adopting a machine learning approach like the one described