Biomedical subjects

Heiko Neumann

Publications and source records attributed to Heiko Neumann.

8 recordsLinked to original sources

PubMed2007

A fast biologically inspired algorithm for recurrent motion estimation.

We have previously developed a neurodynamical model of motion segregation in cortical visual area V1 and MT of the dorsal stream. The model explains how motion ambiguities caused by the motion aperture problem can be solved for coherently moving objects of arbitrary size by means of cortical mechanisms. The major bottleneck in the development of a reliable biologically inspired technical system with real-time motion analysis capabilities based on this neural model is the amount of memory necessary for the representation of neural activation in velocity space. We propose a sparse coding framework for neural motion activity patterns and suggest a means by which initial activities are detected efficiently. We realize neural mechanisms such as shunting inhibition and feedback modulation in the sparse framework to implement an efficient algorithmic version of our neural model of cortical motion segregation. We demonstrate that the algorithm behaves similarly to the original neural model and is able to extract image motion from real world image sequences. Our investigation transfers a neuroscience model of cortical motion computation to achieve technologically demanding constraints such as real-time performance and hardware implementation. In addition, the proposed biologically inspired algorithm provides a tool for modeling investigations to achieve acceptable simulation time.

Algorithms↗

PubMed2006

Gradient representation and perception in the early visual system--a novel account of Mach band formation.

Recent evidence suggests that object surfaces and their properties are represented at early stages in the visual system of primates. Most likely invariant surface properties are extracted to endow primates with robust object recognition capabilities. In real-world scenes, luminance gradients are often superimposed on surfaces. We argue that gradients should also be represented in the visual system, since they encode highly variable information, such as shading, focal blur, and penumbral blur. We present a neuronal architecture which was designed and optimized for segregating and representing luminance gradients in real-world images. Our architecture in addition provides a novel theory for Mach bands, whereby corresponding psychophysical data are predicted consistently.

Contrast Sensitivity↗

PubMed2005

Recovering real-world images from single-scale boundaries with a novel filling-in architecture.

Filling-in models were successful in predicting psychophysical data for brightness perception. Nevertheless, their suitability for real-world image processing has never been examined. A unified architecture for both predicting psychophysical data and real-world image processing would constitute a powerful theory for early visual information processing. As a first contribution of the present paper, we identified three principal problems with current filling-in architectures, which hamper the goal of having such a unified architecture. To overcome these problems we propose an advance to filling-in theory, called BEATS filling-in, which is based on a novel nonlinear diffusion operator. BEATS filling-in furthermore introduces novel boundary structures. We compare, by means of simulation studies with real-world images, the performance of BEATS filling-in with the recently proposed confidence-based filling-in. As a second contribution we propose a novel mechanism for encoding luminance information in contrast responses ('multiplex contrasts'), which is based on recent neurophysiological findings. Again, by simulations, we show that 'multiplex contrasts' at a single, high-resolution filter scale are sufficient for recovering absolute luminance levels. Hence, 'multiplex contrasts' represent a novel theory addressing how the brain encodes and decodes luminance information.

Computer Simulation↗

PubMed2005

Neural mechanisms of human texture processing: texture boundary detection and visual search.

Texture of various appearances, geometric distortions, spatial frequency content and densities is utilized by the human visual system to segregate items from background and to enable recognition of complex geometric forms. For automatic, or pre-attentive, segmentation of a visual scene, sophisticated analysis and comparison of surface properties over wide areas of the visual field are required. We investigated the neural substrate underlying human texture processing, particularly the computational mechanisms of texture boundary detection. We present a neural network model which uses as building blocks model cortical areas that are bi-directionally linked to implement cycles of feedforward and feedback interaction for signal detection, hypothesis generation and testing within the infero-temporal pathway of form processing. In the spirit of Jake Beck's early investigations our model particularly builds upon two key hypotheses, namely that (i) texture segregation is based on boundary detection, rather than clustering homogeneous items, and (ii) texture boundaries are detected mainly on the basis of larger scenic contexts mediated by higher cortical areas, such as area V4. The latter constraint provides a basis for element grouping in accordance to the Gestalt laws of similarity and good continuation. It is shown through simulations that the model integrates a variety of psychophysical findings on texture processing and provides a link to the underlying physiology. The functional role of feedback processing is demonstrated by context dependent modulation of V1 cell activation, leading to sharply localized detection of texture boundaries. It furthermore explains why pre-attentive processing in visual search tasks can be directly linked to texture boundary processing as revealed by recent EEG studies on visual search.

Computer Simulation↗

PubMed2004

Disambiguating visual motion through contextual feedback modulation.

Motion of an extended boundary can be measured locally by neurons only orthogonal to its orientation (aperture problem) while this ambiguity is resolved for localized image features, such as corners or nonocclusion junctions. The integration of local motion signals sampled along the outline of a moving form reveals the object velocity. We propose a new model of V1-MT feedforward and feedback processing in which localized V1 motion signals are integrated along the feedforward path by model MT cells. Top-down feedback from MT cells in turn emphasizes model V1 motion activities of matching velocity by excitatory modulation and thus realizes an attentional gating mechanism. The model dynamics implement a guided filling-in process to disambiguate motion signals through biased on-center, off-surround competition. Our model makes predictions concerning the time course of cells in area MT and V1 and the disambiguation process of activity patterns in these areas and serves as a means to link physiological mechanisms with perceptual behavior. We further demonstrate that our model also successfully processes natural image sequences.

Computer Simulation↗

PubMed2004

Neural mechanisms for the robust representation of junctions.

Junctions provide important cues in various perceptual tasks, such as the determination of occlusion relationships for figure-ground separation, transparency perception, and object recognition, among others. In computer vision, junctions are used in a number of tasks, like point matching for image tracking or correspondence analysis. We propose a biologically motivated approach to junction representation in which junctions are implicitly characterized by high activity for multiple orientations within a cortical hypercolumn. A local measure of circular variance is suggested to extract junction points from this distributed representation. Initial orientation measurements are often fragmented and noisy. A coherent contour representation can be generated by a model of V1 utilizing mechanisms of collinear long-range integration and recurrent interaction. In the model, local oriented contrast estimates that are consistent within a more global context are enhanced while inconsistent activities are suppressed. In a series of computational experiments, we compare junction detection based on the new recurrent model with a feedforward model of complex cells. We show that localization accuracy and positive correctness in the detection of generic junction configurations such as L- and T-junctions is improved by the recurrent long-range interaction. Further, receiver operating characteristics analysis is used to evaluate the detection performance on both synthetic and camera images, showing the superior performance of the new approach. Overall, we propose that nonlocal interactions implemented by known mechanisms within V1 play an important role in detecting higher-order features such as corners and junctions.

Models, Neurological↗

PubMed1996

Mechanisms of Neural Architecture for Visual Contrast and Brightness Perception.

A neural architecture is proposed that serves as a framework for further empirical as well as theoretical investigations for a unified theory for contrast and brightness perception. The work further extends the brightness perception model developed by Grossberg and Todorovic. The proposed new computational architecture utilizes a (retinal) preprocessing stage with center-surround antagonisms of both polarities. The preprocessed data are shown to multiplex contrast as well as luminance information that can be de-multiplexed subsequently using a scheme of cross-channel interaction. Based on a hypothesized luminance-related channel, a three-stage process is suggested for brightness reconstruction. The separate channel for the representation of luminance-related information provides a key mechanism to assign the reconstructed brightness to an absolute reference level. The architecture provides a framework for the analysis of processes in brightness perception. Copyright 1996 Elsevier Science Ltd.

Journal Article↗

PubMedDate not supplied

A simple cell model with dominating opponent inhibition for robust image processing.

The extraction of oriented contrast information by cortical simple cells is a fundamental step in early visual processing. The orientation selectivity originates at least partly from the input of lateral geniculate nuclei neurons with properly aligned receptive fields. In the present article, we investigate the feedforward interactions between on- and off-pathways. Based on physiological evidence we propose a push-pull model with dominating opponent inhibition (DOI). We show that the model can account for empirical data on simple cells, such as contrast-invariant orientation tuning, sharpening of orientation tuning with increasing inhibition, and strong response decrements to stimuli with luminance gradient reversal. With identical parameter settings, we apply the model for the processing of synthetic and real world images. We show that the model with DOI can robustly extract oriented contrast information from noisy input. More important, noise is adaptively suppressed, i.e. the model simple cells do not respond to homogeneous regions of different noise levels, while remaining sensitive to small contrast changes. The image processing results reveal a possible functional role of the strong inhibition as observed empirically, namely to adaptively suppress responses to noisy input.

Animals↗