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An attempt has been made to discuss some of the developments, in a personalized historical perspective, regarding a viable answer to the ‘Flavor Riddle’ through a minor addition to the Fritzsch’s original proposal about texture zero mass matrices.

We propose a parallel algorithm for the generation of colour textures based upon the non-linear equations of the "multiple class random neural network model". A neuron is used to obtain the texture value of each pixel in the bit-map plane. Each neuron interacts with its immediate planar neighbours in order to obtain the texture for the whole plane. A model which uses at most 4(C² + C) parameters for the whole network, where C is the number of colours, is proposed. Numerical iterations of the non-linear field equations of the neural network model, starting with a randomly generated image, are shown to produce textures having different desirable features such as granularity, inclination and randomness. The experimental evaluation shows that the random network provides good results, at a computational cost which is considerably less than that of other approaches such as Markov random fields.

A number of methods for classifying textures and for segmentation of textured images make use of multichannel filtering techniques because of their computational simplicity. On the other hand, biological experiments have shown the existence and the properties of visual channels and make it possible for us to select the "best" filter bank. In a performing artificial vision system, three important problems are to be solved: the rigorous sampling of the spatial frequencies, the scale related problem and parallel processing. This paper presents a mathematical solution to the logarithmic 1-D sampling of the spatial frequencies of a signal characterized by its energy spectrum. This solution obeys the signal theory. It is then extended to the 2-D sampling of the output energy image generated by what we call a "homothetic filter bank". While verifying the Shannon theorem, our system is compatible with the visual cells' sensitivities to frequencies and orientations. Scale problems are often encountered in computer vision, especially in methods using multichannel filtering. Our approach gives us a representation of an input textured image with a continuous multiresolution. With this representation, an interpretation of the information content of the image, invariant to scale and to orientation, is made possible. Any scale change in the image is represented by a simple translation along a logarithmic frequency-axis. In the same manner, a rotation corresponds to a translation along a linear orientation-axis. In our "homothetic visual-filter bank" (HFB) theory, it is shown that the frequency-filter function can be changed according to the application under consideration. In cases where texture discrimination is difficult, e.g. if two different textures have the same power spectrum, the solution may be to extract phase information using a classical complex Gabor function. But, we have shown that in order to design a parallel vision system, it is more pertinent to use the differences of Gaussian functions (DOG) which are real functions.

An attempt has been made to discuss some of the developments, in a personalized historical perspective, regarding a viable answer to the ‘Flavor Riddle’ through a minor addition to the Fritzsch’s original proposal about texture zero mass matrices.

We propose a parallel algorithm for the generation of colour textures based upon the non-linear equations of the "multiple class random neural network model". A neuron is used to obtain the texture value of each pixel in the bit-map plane. Each neuron interacts with its immediate planar neighbours in order to obtain the texture for the whole plane. A model which uses at most 4(C² + C) parameters for the whole network, where C is the number of colours, is proposed. Numerical iterations of the non-linear field equations of the neural network model, starting with a randomly generated image, are shown to produce textures having different desirable features such as granularity, inclination and randomness. The experimental evaluation shows that the random network provides good results, at a computational cost which is considerably less than that of other approaches such as Markov random fields.

The microstructure and texture of cold rolled low carbon steel produced by compact strip production after batch and continuous annealing have been investigated. Optical microscopy, bulk X-ray texture analysis and micro orientation analysis with Electron Back-Scattered Diffraction (EBSD) were conducted to investigate the general microstructure and texture in correlation with the mechanical properties as well as formability under the two annealing procedures. The results indicate that the microstructure of the cold-rolled steel after batch annealing exhibit special orientation relationship with the matrix in comparison to continuous annealing. As an explanation of the oval-shaped grains character, the recrystallization microstructure and texture evolution was discussed. It is concluded that the annealing plays a significant effect on the special orientation and texture formation of the CSP strip, and thus to the formability and mechanical behavior of the final products.