Abstract:
La présente invention concerne un procédé de détermination d'au moins un paramètre d'un code correcteur d'erreurs mis en œuvre en émission, dit paramètre de codage, par analyse d'un train binaire reçu. Selon l'invention, un tel procédé met en œuvre, une première étape permettant de définir grossièrement ledit au moins un paramètre de codage, et une deuxième étape permettant d'affiner ledit au moins un paramètre de codage.
Abstract:
The image compression method consists in processing a current frame of pixels displaced in the image to be compressed by using a network of N neurons, 1 through N, arranged in a same plane, each neuron having a number of inputs equal to the size of frames extracted from the image and such that, after an apprenticeship phase during which the network has organized itself, and in order that the neurons represent the most probable input values, each neuron receives, at the same time, to its inputs the pixels of the current frame and outputs a signal, or potential, dj, which is positive and is all the weacker as the status of the corresponding neuron j is close to the status of the inputs ei; coding the current frame of pixels by a code associated with the neuron j of which the potential dj is minimum. The invention applies particularly to the compression of television images.
Abstract:
The device for the automatic segmentation of images according to the invention is comprised of a data processor associated with a network of automatons organized in three levels; the inputs of each of the automatons are weighted by adjustable coefficients; the number of automatons of the first level is equal to the number of characteristic values calculated from observation windows taken first from an assembly of examples of image areas having different textures: their apprenticeship by the network allows to adjust the weighting coefficients of the network automatons till all examples of one same texture lead to a same configuration, characteristic of said texture, at the network output. The image segmentation comprises, from the assemblies of characteristic values calculated on image areas to be analyzed, the analysis of the network output in order to deduce the area texture. Application to the segmentation of aerial images or medical images, for example, and more generally to any type of images wherein the regions are discriminatable by their texture.