6,898 research outputs found
Spike to Spike Model and Applications: A biological plausible approach for the motion processing
We propose V1 and MT functional models for biological motion recognition. Our V1 model transforms a video stream into spike trains through local motion detectors. The spike trains are the inputs of a spiking MT network. Each entity in the MT network corresponds to a simplified model of an MT cell. From the spike trains of MT cells a motion map of velocity distribution is built representing a sequence. Biological plausibility of both models is discused in detail in the paper. In order to show the efficiency of these models, the motion maps here obtained are used in the biological motion recognition task. We ran the experiments using two databases Giese and Weizmann, containing two (march, walk) and ten (e.g., march, jump, run) different classes, respectively. The results revealed that the motion map here proposed could be used as a reliable motion representation
Motion clouds: model-based stimulus synthesis of natural-like random textures for the study of motion perception
Choosing an appropriate set of stimuli is essential to characterize the
response of a sensory system to a particular functional dimension, such as the
eye movement following the motion of a visual scene. Here, we describe a
framework to generate random texture movies with controlled information
content, i.e., Motion Clouds. These stimuli are defined using a generative
model that is based on controlled experimental parametrization. We show that
Motion Clouds correspond to dense mixing of localized moving gratings with
random positions. Their global envelope is similar to natural-like stimulation
with an approximate full-field translation corresponding to a retinal slip. We
describe the construction of these stimuli mathematically and propose an
open-source Python-based implementation. Examples of the use of this framework
are shown. We also propose extensions to other modalities such as color vision,
touch, and audition
Learning to Extract Motion from Videos in Convolutional Neural Networks
This paper shows how to extract dense optical flow from videos with a
convolutional neural network (CNN). The proposed model constitutes a potential
building block for deeper architectures to allow using motion without resorting
to an external algorithm, \eg for recognition in videos. We derive our network
architecture from signal processing principles to provide desired invariances
to image contrast, phase and texture. We constrain weights within the network
to enforce strict rotation invariance and substantially reduce the number of
parameters to learn. We demonstrate end-to-end training on only 8 sequences of
the Middlebury dataset, orders of magnitude less than competing CNN-based
motion estimation methods, and obtain comparable performance to classical
methods on the Middlebury benchmark. Importantly, our method outputs a
distributed representation of motion that allows representing multiple,
transparent motions, and dynamic textures. Our contributions on network design
and rotation invariance offer insights nonspecific to motion estimation
Research on integration of visual and motion cues for flight simulation and ride quality investigation
Vestibular perception and integration of several sensory inputs in simulation were studied. The relationship between tilt sensation induced by moving fields and those produced by actual body tilt is discussed. Linearvection studies were included and the application of the vestibular model for perception of orientation based on motion cues is presented. Other areas of examination includes visual cues in approach to landing, and a comparison of linear and nonlinear wash out filters using a model of the human vestibular system is given
Theoretical Engineering and Satellite Comlink of a PTVD-SHAM System
This paper focuses on super helical memory system's design, 'Engineering,
Architectural and Satellite Communications' as a theoretical approach of an
invention-model to 'store time-data'. The current release entails three
concepts: 1- an in-depth theoretical physics engineering of the chip including
its, 2- architectural concept based on VLSI methods, and 3- the time-data
versus data-time algorithm. The 'Parallel Time Varying & Data Super-helical
Access Memory' (PTVD-SHAM), possesses a waterfall effect in its architecture
dealing with the process of voltage output-switch into diverse logic and
quantum states described as 'Boolean logic & image-logic', respectively.
Quantum dot computational methods are explained by utilizing coiled carbon
nanotubes (CCNTs) and CNT field effect transistors (CNFETs) in the chip's
architecture. Quantum confinement, categorized quantum well substrate, and
B-field flux involvements are discussed in theory. Multi-access of coherent
sequences of 'qubit addressing' in any magnitude, gained as pre-defined, here
e.g., the 'big O notation' asymptotically confined into singularity while
possessing a magnitude of 'infinity' for the orientation of array displacement.
Gaussian curvature of k(k<0) is debated in aim of specifying the
2D electron gas characteristics, data storage system for defining short and
long time cycles for different CCNT diameters where space-time continuum is
folded by chance for the particle. Precise pre/post data timing for, e.g.,
seismic waves before earthquake mantle-reach event occurrence, including time
varying self-clocking devices in diverse geographic locations for radar systems
is illustrated in the Subsections of the paper. The theoretical fabrication
process, electromigration between chip's components is discussed as well.Comment: 50 pages, 10 figures (3 multi-figures), 2 tables. v.1: 1 postulate
entailing hypothetical ideas, design and model on future technological
advances of PTVD-SHAM. The results of the previous paper [arXiv:0707.1151v6],
are extended in order to prove some introductory conjectures in theoretical
engineering advanced to architectural analysi
A survey of visual preprocessing and shape representation techniques
Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)
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