相关概念视频
01:29Aggregates Classification
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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02:16Light Acquisition
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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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01:31Classification of Systems-II
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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01:26Classification of Systems-I
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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使用多视图图像增强果种植品种分类.
Silvia Krug1,2, Tino Hutschenreuther2
1Department of Computer and Electrical Engineering, Mid Sweden University, Holmgatan 10, 851 70 Sundsvall, Sweden.
Journal of imaging
|April 26, 2024
概括
多视图机器学习通过分析多个观点来改善果品种的分类. 结合视图的简单方法为移动应用程序提供了对内存高效的权衡.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 农业科学 农业科学
背景情况:
- 果品种的分类存在挑战,原因是类之间的高度相似性和单一类内的变异.
- 人类专家利用多视角分析,考虑各种果视角来准确识别.
- 之前的研究探索了单视图机器学习用于果分类,表明复杂任务的局限性.
研究的目的:
- 为果品种分类建立和评估一个多视图机器学习方法.
- 将最先进的多视图方法的性能与更简单,预处理的单视图技术进行比较.
- 评估在资源有限的移动设备上部署果分类模型的可行性.
主要方法:
- 组合模型与两个单一网络方法的比较:一个在没有专业化的情况下对所有视图进行训练,另一个使用组合视图.
- 使用定制果品种数据集进行培训和评估.
- 基于准确性,内存足迹和计算要求的模型性能分析.
主要成果:
- 最先进的组合模型实现了最高的分类准确性.
- 将视图组合成一个图像将精度降低了3%,但将内存需求降低了40%.
- 更简单的方法与增强的预处理证明了准确性和资源效率之间的可行的权衡.
结论:
- 多视图分类,特别是组合方法,为果品种识别提供了卓越的性能.
- 预处理的综合视图方法为资源有限的移动应用提供了实用解决方案.
- 进一步的研究可以优化这些简单的方法,以便在设备上高效地分类果.