345 research outputs found
Visuospatial coding as ubiquitous scaffolding for human cognition
For more than 100 years we have known that the visual field is mapped onto the surface of visual cortex, imposing an inherently spatial reference frame on visual information processing. Recent studies highlight visuospatial coding not only throughout visual cortex, but also brain areas not typically considered visual. Such widespread access to visuospatial coding raises important questions about its role in wider cognitive functioning. Here, we synthesise these recent developments and propose that visuospatial coding scaffolds human cognition by providing a reference frame through which neural computations interface with environmental statistics and task demands via perception–action loops
Angular variation as a monocular cue for spatial percepcion
Monocular cues are spatial sensory inputs which are picked up exclusively from one eye. They are in majority static features that
provide depth information and are extensively used in graphic art to create realistic representations of a scene. Since the spatial
information contained in these cues is picked up from the retinal image, the existence of a link between it and the theory of direct
perception can be conveniently assumed. According to this theory, spatial information of an environment is directly contained in the
optic array. Thus, this assumption makes possible the modeling of visual perception processes through computational approaches.
In this thesis, angular variation is considered as a monocular cue, and the concept of direct perception is adopted by a computer
vision approach that considers it as a suitable principle from which innovative techniques to calculate spatial information can be
developed.
The expected spatial information to be obtained from this monocular cue is the position and orientation of an object with respect to
the observer, which in computer vision is a well known field of research called 2D-3D pose estimation. In this thesis, the attempt to
establish the angular variation as a monocular cue and thus the achievement of a computational approach to direct perception is
carried out by the development of a set of pose estimation methods. Parting from conventional strategies to solve the pose
estimation problem, a first approach imposes constraint equations to relate object and image features. In this sense, two algorithms
based on a simple line rotation motion analysis were developed. These algorithms successfully provide pose information; however,
they depend strongly on scene data conditions. To overcome this limitation, a second approach inspired in the biological processes
performed by the human visual system was developed. It is based in the proper content of the image and defines a computational
approach to direct perception.
The set of developed algorithms analyzes the visual properties provided by angular variations. The aim is to gather valuable data
from which spatial information can be obtained and used to emulate a visual perception process by establishing a 2D-3D metric
relation. Since it is considered fundamental in the visual-motor coordination and consequently essential to interact with the
environment, a significant cognitive effect is produced by the application of the developed computational approach in environments
mediated by technology. In this work, this cognitive effect is demonstrated by an experimental study where a number of participants
were asked to complete an action-perception task. The main purpose of the study was to analyze the visual guided behavior in
teleoperation and the cognitive effect caused by the addition of 3D information. The results presented a significant influence of the
3D aid in the skill improvement, which showed an enhancement of the sense of presence.Las señales monoculares son entradas sensoriales capturadas exclusivamente por un
solo ojo que ayudan a la percepción de distancia o espacio. Son en su mayoría
características estáticas que proveen información de profundidad y son muy
utilizadas en arte gráfico para crear apariencias reales de una escena. Dado que la
información espacial contenida en dichas señales son extraídas de la retina, la
existencia de una relación entre esta extracción de información y la teoría de
percepción directa puede ser convenientemente asumida. De acuerdo a esta teoría, la
información espacial de todo le que vemos está directamente contenido en el arreglo
óptico. Por lo tanto, esta suposición hace posible el modelado de procesos de
percepción visual a través de enfoques computacionales. En esta tesis doctoral, la
variación angular es considerada como una señal monocular, y el concepto de
percepción directa adoptado por un enfoque basado en algoritmos de visión por
computador que lo consideran un principio apropiado para el desarrollo de nuevas
técnicas de cálculo de información espacial.
La información espacial esperada a obtener de esta señal monocular es la posición y
orientación de un objeto con respecto al observador, lo cual en visión por computador
es un conocido campo de investigación llamado estimación de la pose 2D-3D. En esta
tesis doctoral, establecer la variación angular como señal monocular y conseguir un
modelo matemático que describa la percepción directa, se lleva a cabo mediante el
desarrollo de un grupo de métodos de estimación de la pose. Partiendo de estrategias
convencionales, un primer enfoque implanta restricciones geométricas en ecuaciones
para relacionar características del objeto y la imagen. En este caso, dos algoritmos
basados en el análisis de movimientos de rotación de una línea recta fueron
desarrollados. Estos algoritmos exitosamente proveen información de la pose. Sin
embargo, dependen fuertemente de condiciones de la escena. Para superar esta
limitación, un segundo enfoque inspirado en los procesos biológicos ejecutados por el
sistema visual humano fue desarrollado. Está basado en el propio contenido de la
imagen y define un enfoque computacional a la percepción directa.
El grupo de algoritmos desarrollados analiza las propiedades visuales suministradas
por variaciones angulares. El propósito principal es el de reunir datos de importancia
con los cuales la información espacial pueda ser obtenida y utilizada para emular
procesos de percepción visual mediante el establecimiento de relaciones métricas 2D-
3D. Debido a que dicha relación es considerada fundamental en la coordinación
visuomotora y consecuentemente esencial para interactuar con lo que nos rodea, un
efecto cognitivo significativo puede ser producido por la aplicación de métodos de
L
estimación de pose en entornos mediados tecnológicamente. En esta tesis doctoral, este
efecto cognitivo ha sido demostrado por un estudio experimental en el cual un número
de participantes fueron invitados a ejecutar una tarea de acción-percepción. El
propósito principal de este estudio fue el análisis de la conducta guiada visualmente en
teleoperación y el efecto cognitivo causado por la inclusión de información 3D. Los
resultados han presentado una influencia notable de la ayuda 3D en la mejora de la
habilidad, así como un aumento de la sensación de presencia
Biometric Systems
Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications
When I Look into Your Eyes: A Survey on Computer Vision Contributions for Human Gaze Estimation and Tracking
The automatic detection of eye positions, their temporal consistency, and their mapping into a line of sight in the real world (to find where a person is looking at) is reported in the scientific literature as gaze tracking. This has become a very hot topic in the field of computer vision during the last decades, with a surprising and continuously growing number of application fields. A very long journey has been made from the first pioneering works, and this continuous search for more accurate solutions process has been further boosted in the last decade when deep neural networks have revolutionized the whole machine learning area, and gaze tracking as well. In this arena, it is being increasingly useful to find guidance through survey/review articles collecting most relevant works and putting clear pros and cons of existing techniques, also by introducing a precise taxonomy. This kind of manuscripts allows researchers and technicians to choose the better way to move towards their application or scientific goals. In the literature, there exist holistic and specifically technological survey documents (even if not updated), but, unfortunately, there is not an overview discussing how the great advancements in computer vision have impacted gaze tracking. Thus, this work represents an attempt to fill this gap, also introducing a wider point of view that brings to a new taxonomy (extending the consolidated ones) by considering gaze tracking as a more exhaustive task that aims at estimating gaze target from different perspectives: from the eye of the beholder (first-person view), from an external camera framing the beholder’s, from a third-person view looking at the scene where the beholder is placed in, and from an external view independent from the beholder
Supervised and unsupervised weight and delay adaptation learning in temporal coding spiking neural networks
Artificial neural networks are learning paradigms which mimic the biological neural system. The temporal coding Spiking Neural Network, a relatively new artificial neural network paradigm, is considered to be computationally more powerful than the conventional neural network. Research on the network of spiking neurons is an emerging field and has potential for wider investigation. This research explores alternative learning models with temporal coding spiking neural networks for clustering and classification tasks. Neurons are known to be operating in two modes namely, as integrators and coincidence detectors. Previous temporal coding spiking neural networks, realising spiking neurons as integrators, were utilised for analytical studies. Temporal coding spiking neural networks applied successfully for clustering and classification tasks realised spiking neurons as coincidence detectors and encoded input in formation in the connection delays through a weight adaptation technique. These learning models select suitably delayed connections by enhancing the weights of those connections while weakening the others. This research investigates the learning in temporal coding spiking neural networks with spiking neurons as integrators and coincidence detectors. Focus is given to both supervised and unsupervised learning through weight as well as through delay adaptation. Three novel models for learning in temporal coding spiking neural networks are presented in this research. The first spiking neural network model, Self- Organising Weight Adaptation Spiking Neural Network (SOWA_SNN) realises the spiking neuron as integrator. This model adapts and encodes input information in its connection weights. The second learning model, Self-Organising Delay Adaptation Spiking Neural Network (SODA_SNN) and the third model, Super vised Delay Adaptation Spiking Neural Network (SDA_SNN) realise the spiking neuron as coincidence detector. These two models adapt the connection delays in order to detect temporal patterns through coincidence detection. The first two models were developed for clustering applications and the third for classification tasks. All three models employ Hebbian-based learning rules to update the network connection parameters by utilising the difference between the input and output spike times. The proposed temporal coding spiking neural network models were implemented as discrete models in software and their characteristics and capabilities were analysed through simulations on three bench mark data sets and a high dimensional data set. All three models were able to cluster or classify the analysed data sets efficiently with a high degree of accuracy. The performance of the proposed models, was found to be better than the existing spiking neural network models as well as conventional neural networks. The proposed learning paradigms could be applied to a wide range of applications including manufacturing, business and biomedical domains.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Neurofly 2008 abstracts : the 12th European Drosophila neurobiology conference 6-10 September 2008 Wuerzburg, Germany
This volume consists of a collection of conference abstracts
Supervised and unsupervised weight and delay adaptation learning in temporal coding spiking neural networks
Artificial neural networks are learning paradigms which mimic the biological neural system. The temporal coding Spiking Neural Network, a relatively new artificial neural network paradigm, is considered to be computationally more powerful than the conventional neural network. Research on the network of spiking neurons is an emerging field and has potential for wider investigation. This research explores alternative learning models with temporal coding spiking neural networks for clustering and classification tasks. Neurons are known to be operating in two modes namely, as integrators and coincidence detectors. Previous temporal coding spiking neural networks, realising spiking neurons as integrators, were utilised for analytical studies. Temporal coding spiking neural networks applied successfully for clustering and classification tasks realised spiking neurons as coincidence detectors and encoded input in formation in the connection delays through a weight adaptation technique. These learning models select suitably delayed connections by enhancing the weights of those connections while weakening the others. This research investigates the learning in temporal coding spiking neural networks with spiking neurons as integrators and coincidence detectors. Focus is given to both supervised and unsupervised learning through weight as well as through delay adaptation. Three novel models for learning in temporal coding spiking neural networks are presented in this research. The first spiking neural network model, Self- Organising Weight Adaptation Spiking Neural Network (SOWA_SNN) realises the spiking neuron as integrator. This model adapts and encodes input information in its connection weights. The second learning model, Self-Organising Delay Adaptation Spiking Neural Network (SODA_SNN) and the third model, Super vised Delay Adaptation Spiking Neural Network (SDA_SNN) realise the spiking neuron as coincidence detector. These two models adapt the connection delays in order to detect temporal patterns through coincidence detection. The first two models were developed for clustering applications and the third for classification tasks. All three models employ Hebbian-based learning rules to update the network connection parameters by utilising the difference between the input and output spike times. The proposed temporal coding spiking neural network models were implemented as discrete models in software and their characteristics and capabilities were analysed through simulations on three bench mark data sets and a high dimensional data set. All three models were able to cluster or classify the analysed data sets efficiently with a high degree of accuracy. The performance of the proposed models, was found to be better than the existing spiking neural network models as well as conventional neural networks. The proposed learning paradigms could be applied to a wide range of applications including manufacturing, business and biomedical domains
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Deep learning assisted MRI guided attenuation correction in PET
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPositron emission tomography (PET) is a unique imaging modality that provides physiological
and functional details of the tissue at the molecular level. However, the acquired PET images
have some limitations such as the attenuation. PET attenuation correction is an essential step to
obtain the full potential of PET quantification. With the wide use of hybrid PET/MR scanners,
magnetic resonance (MR) images are used to address the problem of PET attenuation correction.
The MR images segmentation is one simple and robust approach to create pseudo computed
tomography (CT) images, which are used to generate attenuation coefficient maps to correct the
PET attenuation. Recently, deep learning has been proposed and used as a promising technique
to efficiently perform MR and various medical images segmentation.
In this research work, deep learning guided segmentation approaches have been proposed
to enhance the bone class segmentation of MR brain images in order to generate accurate
pseudo-CT images. The first approach has introduced the combination of handcrafted features
with deep learning features to enrich the set of features. Multiresolution analysis techniques,
which generate multiscale and multidirectional coefficients of an image such as contourlet and
shearlet transforms, are applied and combined with deep convolutional neural network (CNN)
features. Different experiments have been conducted to investigate the number of selected
coefficients and the insertion location of the handcrafted features.
The second approach aims at reducing the segmentation algorithm’s complexity while
maintaining the segmentation performance. An attention based convolutional encode-decoder
network has been proposed to adaptively recalibrate the deep network features. This attention based
network consists of two different squeeze and excitation blocks that excite the features
spatially and channel wise. The two blocks are combined sequentially to decrease the number
of network’s parameters and reduces the model complexity. The third approach has been focuses on the application of transfer learning from different MR sequences such as T1 weighted (T1-w) and T2 weighted (T2-w) images. A
pretrained model with T1-w MR sequences is fine tuned to perform the segmentation of T2-w
images. Multiple fine tuning approaches and experiments have been conducted to study the best
fine tuning mechanism that is able to build an efficient segmentation model for both T1-w and
T2-w segmentation. Clinical datasets of fifty patients with different conditions and diagnosis have been
used to carry an objective evaluation to measure the segmentation performance of the results
obtained by the three proposed methods. The first and second approaches have been validated
with other studies in the literature that applied deep network based segmentation technique to
perform MR based attenuation correction for PET images. The proposed methods have shown
an enhancement in the bone segmentation with an increase of dice similarity coefficient (DSC)
from 0.6179 to 0.6567 using an ensemble of CNNs with an improvement percentage of 6.3%.
The proposed excitation-based CNN has decreased the model complexity by decreasing the
number of trainable parameters by more than 46% where less computing resources are required
to train the model. The proposed hybrid transfer learning method has shown its superiority to
build a multi-sequences (T1-w and T2-w) segmentation approach compared to other applied
transfer learning methods especially with the bone class where the DSC is increased from 0.3841
to 0.5393. Moreover, the hybrid transfer learning approach requires less computing time than
transfer learning using open and conservative fine tuning
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