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Titlebook: Evolutionary Computer Vision; The First Footprints Gustavo Olague Textbook 2016 Springer-Verlag Berlin Heidelberg 2016 Artificial Vision.Co

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發(fā)表于 2025-3-25 07:01:25 | 只看該作者
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發(fā)表于 2025-3-25 08:42:42 | 只看該作者
Introductions, the chapter focuses on the challenge of recreating such abilities within a seeing machine. The introduction serves to formulate the process of image formation through the concept of a graph of a function. Later, the main motivation for writing this book is provided, emphasizing the approach that
23#
發(fā)表于 2025-3-25 12:54:29 | 只看該作者
Vision and Evolution: State of the Arthe exposition starts with brief summaries on the history of vision in art, mathematics and technology. Later, the history of computer vision is described with an emphasis on several paradigms that have been applied for solving the visual problem. In particular, the goal-driven strategy is introduced
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發(fā)表于 2025-3-25 18:54:20 | 只看該作者
Evolutionary Computing in the book to solve difficult optimization problems. The idea is to introduce basic concepts and principles of optimization in order to develop the mathematical tools useful in the design and analysis of the main evolutionary algorithms treated in the book. In particular, the concepts of function
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發(fā)表于 2025-3-26 08:53:26 | 只看該作者
Multiobjective Sensor Planning for Accurate Reconstruction design is offered in order to prepare for the analysis of sensor planning from a multiobjective standpoint. Thus, three main criteria – accurate 3D reconstruction, efficient robot motion, and computational cost – relevant to the task are introduced towards the achievement of Pareto optimal sensing
29#
發(fā)表于 2025-3-26 13:11:39 | 只看該作者
Evolutionary Visual Learning with Linear Genetic Programmingplicable to common image recognition problems. The method searches for optimal regions of interest, using texture information as its feature space and classification accuracy as the fitness function. Texture is analyzed based on the gray level cooccurrence matrix and classification is carried out by
30#
發(fā)表于 2025-3-26 17:01:29 | 只看該作者
Evolutionary Synthesis of Feature Descriptor Operators with Genetic Programmingsis of mathematical expressions that extract information derived from local image patches. These local features have been previously designed by human experts using traditional representations that have a clear and, preferably, mathematically wellfounded definition. We propose in this chapter that t
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