Wyniki wyszukiwania - BazTech

1

Distributed storage and recall of sentences

Ebner M.

Bio-Algorithms and Med-Systems

|

2017

|

Vol. 13, no. 2

89--101

EN

The human brain is able to learn language by processing written or spoken language. Recently, several deep neural networks have been successfully used for natural language generation. Although it is possible to train such networks, it remains unknown how these networks (or the brain) actually process language. A scalable method for distributed storage and recall of sentences within a neural network is presented. A corpus of 59 million words was used for training. A system using this method can efficiently identify sentences that can be considered reasonable replies to an input sentence. The system first selects a small number of seeds words which occur with low frequency in the corpus. These seed words are then used to generate answer sentences. Possible answers are scored using statistical data also obtained from the corpus. A number of sample answers generated by the system are shown to illustrate how the method works.

2

Is depth information and optical flow helpful for visual control?

Hansen J., Ebner M.

Bio-Algorithms and Med-Systems

|

2016

|

Vol. 12, no. 1

9--18

EN

The human visual system was shaped through natural evolution. We have used artificial evolution to investigate whether depth information and optical flow are helpful for visual control. Our experiments were carried out in simulation. The task was controlling a simulated racing car. We have used The Open Racing Car Simulator for our experiments. Genetic programming was used to evolve visual algorithms that transform input images (color, optical flow, or depth information) to control commands for a simulated racing car. We found that significantly better solutions were found when color, depth, and optical flow were available as input together compared with color, depth, or optical flow alone.

3

Depth map color constancy

Ebner M., Hansen J.

Bio-Algorithms and Med-Systems

|

2013

|

Vol. 9, no. 4

167--177

EN

A human observer is able to determine the color of objects independent of the light illuminating these objects. This ability is known as color constancy. In the first stages of visual information processing, data are analyzed with respect to wavelength composition, orientation, motion, and depth. With this contribution, we investigate whether depth information can help in estimating the color of the objects. We assume that local space average color is computed in V4 through resistively coupled neurons to estimate the color of the illuminant. We show how this computational model can be extended to incorporate depth information.

4

A computational model for color perception

Ebner M.

Bio-Algorithms and Med-Systems

|

2012

|

Vol. 8, no. 4

387--415

EN

Color is not a physical quantity of an object. It cannot be measured. We can only measure reflectance, i.e. the amount of light reflected for each wavelength. Nevertheless, we attach colors to the objects around us. A human observer perceives colors as being approximately constant irrespective of the illuminant which is used to illuminate the scene. Colors are a very important cue in everyday life. They can be used to recognize or distinguish different objects. Currently, we do not yet know how the brain arrives at a color constant or approximately color constant descriptor, i.e. what computational processing is actually performed by the brain. What we need is a computational description of color perception in particular and color vision in general. Only if we are able to write down a full computational theory of the visual system then we have understood how the visual system works. With this contribution, a computational model of color perception is presented. This model is much simpler compared to previous theories. It is able to compute a color constant descriptor even in the presence of spatially varying illuminants. According to this model, the cones respond approximately logarithmic to the irradiance entering the eye. Cells in V1 perform a change of the coordinate system such that colors are represented along a red-green, a blue-yellow and a black-white axis. Cells in V4 compute local space average color using a resistive grid. The resistive grid is formed by cells in V4. The left and right hemispheres are connected via the corpus callosum. A color constant descriptor which is presumably used for color based object recognition is computed by subtracting local space average color from the cone response within a rotated coordinate system.