Two types of stochastic grammars are introduced for investigation: the (classical) unrestricted stochastic grammars and the (newly introduced) weakly restricted stochastic grammars. Especially consistency and conditions for it will be treated. The theory of multitype Galton-Watson branching

This exploratory paper quests for a stochastic and context sensitive grammar of images. The grammar should achieve the following four objectives and thus serves as a unified framework of representation, learning, and recognition for a large number of object categories. (i) The grammar represents

A new type of stochastic grammars is introduced for investigation: weakly restricted stochastic grammars. In this paper we will concentrate on the consistency problem. To find conditions for stochastic grammars to be consistent, the theory of multitype Galton-Watson branching processes

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Abstract. The computation of the probability of string generation according to stochastic grammars given only some of the symbols that compose it underlies pattern recognition problems concerning the prediction and/or recognition based on partial observations. This paper presents algorithms

this paper we present a more adequate model, the weakly restricted stochastic gram mar model. We give necessary and sufficient condieions to test in an efficient way whether such a grammar defines a stochastic language. Moreover, we will show that these grammars can be transformed

1. The problem: Getting from a lexicon to a grammar Phonological processes that are restricted to certain lexical items typically apply stochastically to novel items. The behavior of novel items reflects lexical trends (Hayes & Londe 2006,

This thesis addresses the problem of using probabilistic formal languages to describe and understand actions with explicit structure. The thesis explores several mechanisms of parsing the uncertain input string aided by a stochastic context-free grammar. This method, originating in speech

We consider object detection using a generic model for natural shapes. A common approach for object recognition involves matching object models directly to images. Another approach involves building intermediate representations via a generic grouping processes. We argue that these two processes (model-based recognition and grouping) may use similar computational mechanisms. By defining a generic model for shapes we can use model-based techniques to implement a mid-level vision grouping process. 1

Abstract. Stochastic grammar has been used in many video analysis and event recognition applications as an efficient model to represent large-scale video ac-tivity. However, in previous works, due to the limitation on representing paral-lel temporal relations, traditional stochastic grammar cannot