A new tendency in the design of modern signal processing methods is the creation of hybrid algorithms. This paper gives an overview of different signal processing algorithms situated halfway between Markovian and neural paradigms. A new systematic way to classify these algorithms is proposed. Four specific classes of models are described. The first one is made up of algorithms based upon either one of the two paradigms, but including some parts of the other one. The second class includes algorithms proposing a parallel or sequential cooperation of two independent Markovian and neural parts. The third class tends to show Markov models (MMs) as a special case of neural networks (NNs), or conversely NNs as a special case of MMs. These algorithms concentrate mainly on bringing together respective learning methods. The fourth class of algorithms are hybrids, neither purely Markovian nor neural. They can be seen as belonging to a more general class of models, presenting features from both paradigms. The first two classes essentially include models with structural modifications, while two later classes propose algorithmic modifications. For the sake of clarity, only main mathematical formulas are given. Specific applications are intentionally avoided to give a wider view of the subject. The references provide more details for interested readers.

A new textured image model is proposed. This model is described by means of a neuro-Markovian hybrid approach using a Kohonen map and a hidden Markov model (HMM). Each state of the HMM describes one resolution level in the image. The change of the state corresponds to the change of image analysis resolution level. The HMM observation space is composed of clusters which are estimated using a Kohonen map. The second role of the Kohonen algorithm is to achieve a segmentation which is done in parallel with the one proceeded by a Viterbi algorithm. The results given by the both algorithms present some complementarity.