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 both the hierarchical decompositions from scenes, to objects, parts, primitives and pixels by terminal and non-terminal nodes and the contexts for spatial and functional relations by horizontal links between the nodes. It formulates each object category as the set of all possible valid configurations produced by the grammar. (ii) The grammar is embodied in a simple And–Or graph representation where each Or-node points to alternative sub-configurations and an And-node is decomposed into a number of components. This representation supports recursive top-down/bottom-up procedures for image parsing under the Bayesian framework and make it convenient to scale up in complexity. Given an input image, the image parsing task constructs a most probable parse graph on-the-fly as the output interpretation and this parse graph is a subgraph of the And–Or graph after * Song-Chun Zhu is also affiliated with the Lotus Hill Research Institute, China. making choice on the Or-nodes. (iii) A probabilistic model is defined on this And–Or graph representation to account for the natural occurrence frequency of objects and parts as well as their relations. This model is learned from a relatively small training set per category and then sampled to synthesize a large number of configurations to cover novel object instances in the test set. This generalization capability is mostly missing in discriminative machine learning methods and can largely improve recognition performance in experiments. (iv) To fill the well-known semantic gap between symbols and raw signals, the grammar includes a series of visual dictionaries and organizes them through graph composition. At the bottom-level the dictionary is a set of image primitives each having a number of anchor points with open bonds to link with other primitives. These primitives can be combined to form larger and larger graph structures for parts and objects. The ambiguities in inferring local primitives shall be resolved through top-down computation using larger structures. Finally these primitives forms a primal sketch representation which will generate the input image with every pixels explained. The proposal grammar integrates three prominent representations in the literature: stochastic grammars for composition, Markov (or graphical) models for contexts, and sparse coding with primitives (wavelets). It also combines the structure-based and appearance based methods in the vision literature. Finally the paper presents three case studies to illustrate the proposed grammar.

编译原理语法分析实习，武汉大学计算机学院，课程报告。

solidity-antlr4：ANTLR4的固态语法

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2020版高中英语 Unit 2 The Olympic Games Section Ⅳ Grammar课时跟踪训练 2.doc

a grammar system for shape construction

语言:English (United States) 为您发送，共享，推文或发布的所有内容的最佳写入样式和语法检查器。 prowritingaid在Chrome Store中被评为4.8 / 5。没有其他写作助理或语法检查员如此高度评级。为什么prowritingaid特别？---------------------------有时你想要快速修复你的写作，但有时你需要更多。我们提供两者：语法检查，因为您编写错误，并深入报告，帮助您加强和波兰最重要的工作。您的技术和信心将通过我们的独特建议，文章，视频和测验组合来增长，使技能开发乐趣和互动。我们的价格是合理的，因为我们的用户爱我们并推荐我们。这意味着我们不需要在广告上度过我们所有的钱。你将成为一个更好的作家，他们的潜在Craft.io品。PROWRITINGAID提供什么？-------------------------------PROWRITINGAID超越语法检查，将您的良好写作变为伟大的写作。它可以： - 语法检查，拼写检查和风格改进; - 检查拼写，连字符和资本化的一致性; - 消除陈词滥调和冗余; - 检查术语问题; -