829 research outputs found
The Inhuman Overhang: On Differential Heterogenesis and Multi-Scalar Modeling
As a philosophical paradigm, differential heterogenesis offers us a novel descriptive vantage with which to inscribe Deleuze’s virtuality within the terrain of “differential becoming,” conjugating “pure saliences” so as to parse economies, microhistories, insurgencies, and epistemological evolutionary processes that can be conceived of independently from their representational form. Unlike Gestalt theory’s oppositional constructions, the advantage of this aperture is that it posits a dynamic context to both media and its analysis, rendering them functionally tractable and set in relation to other objects, rather than as sedentary identities. Surveying the genealogy of differential heterogenesis with particular interest in the legacy of Lautman’s dialectic, I make the case for a reading of the Deleuzean virtual that departs from an event-oriented approach, galvanizing Sarti and Citti’s dynamic a priori vis-à-vis Deleuze’s philosophy of difference. Specifically, I posit differential heterogenesis as frame with which to examine our contemporaneous epistemic shift as it relates to multi-scalar computational modeling while paying particular attention to neuro-inferential modes of inductive learning and homologous cognitive architecture. Carving a bricolage between Mark Wilson’s work on the “greediness of scales” and Deleuze’s “scales of reality”, this project threads between static ecologies and active externalism vis-à-vis endocentric frames of reference and syntactical scaffolding
Recognizing complex faces and gaits via novel probabilistic models
In the field of computer vision, developing automated systems to recognize people
under unconstrained scenarios is a partially solved problem. In unconstrained sce-
narios a number of common variations and complexities such as occlusion, illumi-
nation, cluttered background and so on impose vast uncertainty to the recognition
process. Among the various biometrics that have been emerging recently, this
dissertation focus on two of them namely face and gait recognition.
Firstly we address the problem of recognizing faces with major occlusions amidst
other variations such as pose, scale, expression and illumination using a novel
PRObabilistic Component based Interpretation Model (PROCIM) inspired by key
psychophysical principles that are closely related to reasoning under uncertainty.
The model basically employs Bayesian Networks to establish, learn, interpret and
exploit intrinsic similarity mappings from the face domain. Then, by incorporating
e cient inference strategies, robust decisions are made for successfully recognizing
faces under uncertainty. PROCIM reports improved recognition rates over recent
approaches.
Secondly we address the newly upcoming gait recognition problem and show that
PROCIM can be easily adapted to the gait domain as well. We scienti cally
de ne and formulate sub-gaits and propose a novel modular training scheme to
e ciently learn subtle sub-gait characteristics from the gait domain. Our results
show that the proposed model is robust to several uncertainties and yields sig-
ni cant recognition performance. Apart from PROCIM, nally we show how a
simple component based gait reasoning can be coherently modeled using the re-
cently prominent Markov Logic Networks (MLNs) by intuitively fusing imaging,
logic and graphs.
We have discovered that face and gait domains exhibit interesting similarity map-
pings between object entities and their components. We have proposed intuitive
probabilistic methods to model these mappings to perform recognition under vari-
ous uncertainty elements. Extensive experimental validations justi es the robust-
ness of the proposed methods over the state-of-the-art techniques.
Activity profiling for minimally invasive surgery
Imperial Users onl
A cortical model of object perception based on Bayesian networks and belief propagation.
Evidence suggests that high-level feedback plays an important role in visual perception by shaping
the response in lower cortical levels (Sillito et al. 2006, Angelucci and Bullier 2003, Bullier
2001, Harrison et al. 2007). A notable example of this is reflected by the retinotopic activation
of V1 and V2 neurons in response to illusory contours, such as Kanizsa figures, which has been
reported in numerous studies (Maertens et al. 2008, Seghier and Vuilleumier 2006, Halgren et al.
2003, Lee 2003, Lee and Nguyen 2001). The illusory contour activity emerges first in lateral
occipital cortex (LOC), then in V2 and finally in V1, strongly suggesting that the response is
driven by feedback connections. Generative models and Bayesian belief propagation have been
suggested to provide a theoretical framework that can account for feedback connectivity, explain
psychophysical and physiological results, and map well onto the hierarchical distributed
cortical connectivity (Friston and Kiebel 2009, Dayan et al. 1995, Knill and Richards 1996,
Geisler and Kersten 2002, Yuille and Kersten 2006, Deneve 2008a, George and Hawkins 2009,
Lee and Mumford 2003, Rao 2006, Litvak and Ullman 2009, Steimer et al. 2009).
The present study explores the role of feedback in object perception, taking as a starting point
the HMAX model, a biologically inspired hierarchical model of object recognition (Riesenhuber
and Poggio 1999, Serre et al. 2007b), and extending it to include feedback connectivity.
A Bayesian network that captures the structure and properties of the HMAX model is
developed, replacing the classical deterministic view with a probabilistic interpretation. The
proposed model approximates the selectivity and invariance operations of the HMAX model
using the belief propagation algorithm. Hence, the model not only achieves successful feedforward
recognition invariant to position and size, but is also able to reproduce modulatory effects
of higher-level feedback, such as illusory contour completion, attention and mental imagery.
Overall, the model provides a biophysiologically plausible interpretation, based on state-of-theart
probabilistic approaches and supported by current experimental evidence, of the interaction
between top-down global feedback and bottom-up local evidence in the context of hierarchical
object perception
Simple low cost causal discovery using mutual information and domain knowledge
PhDThis thesis examines causal discovery within datasets, in particular observational datasets where
normal experimental manipulation is not possible. A number of machine learning techniques
are examined in relation to their use of knowledge and the insights they can provide regarding
the situation under study. Their use of prior knowledge and the causal knowledge produced by
the learners are examined. Current causal learning algorithms are discussed in terms of their
strengths and limitations. The main contribution of the thesis is a new causal learner LUMIN
that operates with a polynomial time complexity in both the number of variables and records
examined. It makes no prior assumptions about the form of the relationships and is capable of
making extensive use of available domain information. This learner is compared to a number of
current learning algorithms and it is shown to be competitive with them
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