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MATLAB计算机视觉与深度学习实战-赠在线交流卡和本书源码 版权信息
- ISBN：9787121315503
- 条形码：9787121315503 ; 978-7-121-31550-3
- 装帧：暂无
- 册数：暂无
- 重量：暂无
- 所属分类：>
MATLAB计算机视觉与深度学习实战-赠在线交流卡和本书源码 本书特色
《MATLAB 计算机视觉与深度学习实战》详细讲解了 30 个 MATLAB 计算机视觉与深度学习案例（含可运行程序），涉及雾霾去噪、答题卡自动阅卷、肺部图像分割、小波数字水印、图像检索、人脸二维码识别、车牌定位及识别、霍夫曼图像压缩、手写数字识别、英文字符文本识别、眼前节组织提取、全景图像拼接、小波图像融合、基于语音识别的音频信号模拟灯控、路面裂缝检测识别、视频运动估计追踪、Simulink 图像处理、胸片及肝脏分割、基于深度学习的汽车目标检测、基于计算机视觉的自动驾驶应用、基于深度学习的视觉场景识别等多项重要技术，涵盖了数字图像处理中几乎所有的基本模块，并延伸到了深度学习的理论及其应用方面。工欲善其事，必先利其器，《MATLAB 计算机视觉与深度学习实战》对每个数字图像处理的知识点都提供了丰富生动的案例素材，并详细讲解了其 MATLAB 实验的核心程序，通过对这些示例程序的阅读理解和仿真运行，读者可以更加深刻地理解图像处理的内容，并且更加熟练地掌握 MATLAB 中各种函数在图像处理领域中的用法。本书以案例为基础，结构布局紧凑，内容深入浅出，实验简捷高效，适合计算机、信号通信和自动化等相关专业的教师、本科生、研究生，以及广大从事数字图像处理的工程研发人员阅读参考。
MATLAB计算机视觉与深度学习实战-赠在线交流卡和本书源码 内容简介
《MATLAB计算机视觉与深度学习实战》详细讲解了30个MATLAB计算机视觉与深度学习实战案例，提供源码及在线支持。 几乎涵盖了数字图像处理中几乎所有的基本模块，并延伸到了深度学习的理论及其应用方面。 适合计算机、信号通信和自动化等相关专业的教师、本科生、研究生，以及广大从事数字图像处理的工程研发人员阅读参考。
MATLAB计算机视觉与深度学习实战-赠在线交流卡和本书源码 目录
第 1 章 基于直方图优化的图像去雾技术 1
1.1 案例背景 ·········································································································· 1
1.2 理论基础 ·········································································································· 1
1.2.1 空域图像增强 ····························································································· 1
1.2.2 直方图均衡化 ····························································································· 2
1.3 程序实现 ·········································································································· 3
1.3.1 设计 GUI 界面 ···························································································· 4
1.3.2 全局直方图处理 ·························································································· 4
1.3.3 局部直方图处理 ·························································································· 7
1.3.4 Retinex 增强处理 ························································································· 9
1.4 延伸阅读 ········································································································ 13
1.5 参考文献 ········································································································ 13
第 2 章 基于 形态学的权重自适应图像去噪 14
2.1 案例背景 ········································································································ 14
2.2 理论基础 ········································································································ 15
2.2.1 图像去噪方法 ··························································································· 15
2.2.2 数学形态学原理 ························································································ 16
2.2.3 权重自适应的多结构形态学去噪 ··································································· 16
2.3 程序实现 ········································································································ 17
2.4 延伸阅读 ········································································································ 22
2.5 参考文献 ········································································································ 23
第 3 章 基于多尺度形态学提取眼前节组织 24
3.1 案例背景 ········································································································ 24
3.2 理论基础 ········································································································ 25
3.3 程序实现 ········································································································ 28
3.3.1 多尺度边缘 ······························································································ 28
3.3.2 主处理函数 ······························································································ 29
3.3.3 形态学处理 ······························································································ 31
3.4 延伸阅读 ········································································································ 33
3.5 参考文献 ········································································································ 33
第 4 章 基于 Hough 变化的答题卡识别 34
4.1 案例背景 ········································································································ 34
4.2 理论基础 ········································································································ 34
4.2.1 图像二值化 ······························································································ 35
4.2.2 倾斜校正 ································································································· 35
4.2.3 图像分割 ································································································· 38
4.3 程序实现 ········································································································ 40
4.4 延伸阅读 ········································································································ 51
4.5 参考文献 ········································································································ 51
第 5 章 基于阈值分割的车牌定位识别 52
5.1 案例背景 ·············
1.1 案例背景 ·········································································································· 1
1.2 理论基础 ·········································································································· 1
1.2.1 空域图像增强 ····························································································· 1
1.2.2 直方图均衡化 ····························································································· 2
1.3 程序实现 ·········································································································· 3
1.3.1 设计 GUI 界面 ···························································································· 4
1.3.2 全局直方图处理 ·························································································· 4
1.3.3 局部直方图处理 ·························································································· 7
1.3.4 Retinex 增强处理 ························································································· 9
1.4 延伸阅读 ········································································································ 13
1.5 参考文献 ········································································································ 13
第 2 章 基于 形态学的权重自适应图像去噪 14
2.1 案例背景 ········································································································ 14
2.2 理论基础 ········································································································ 15
2.2.1 图像去噪方法 ··························································································· 15
2.2.2 数学形态学原理 ························································································ 16
2.2.3 权重自适应的多结构形态学去噪 ··································································· 16
2.3 程序实现 ········································································································ 17
2.4 延伸阅读 ········································································································ 22
2.5 参考文献 ········································································································ 23
第 3 章 基于多尺度形态学提取眼前节组织 24
3.1 案例背景 ········································································································ 24
3.2 理论基础 ········································································································ 25
3.3 程序实现 ········································································································ 28
3.3.1 多尺度边缘 ······························································································ 28
3.3.2 主处理函数 ······························································································ 29
3.3.3 形态学处理 ······························································································ 31
3.4 延伸阅读 ········································································································ 33
3.5 参考文献 ········································································································ 33
第 4 章 基于 Hough 变化的答题卡识别 34
4.1 案例背景 ········································································································ 34
4.2 理论基础 ········································································································ 34
4.2.1 图像二值化 ······························································································ 35
4.2.2 倾斜校正 ································································································· 35
4.2.3 图像分割 ································································································· 38
4.3 程序实现 ········································································································ 40
4.4 延伸阅读 ········································································································ 51
4.5 参考文献 ········································································································ 51
第 5 章 基于阈值分割的车牌定位识别 52
5.1 案例背景 ·············
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MATLAB计算机视觉与深度学习实战-赠在线交流卡和本书源码 作者简介
刘衍琦，硕士，计算机视觉算法工程师，毕业于大连理工大学数学科学学院计算几何与图形图像实验室。长期从事图像、声纹、视频检索及其大数据应用，对以图搜图、图文识别等进行过深入研究并成功应用于工程项目中，曾主编和参与编写多套书籍。詹福宇，博士，飞行控制算法工程师，毕业于西北工业大学航空学院飞行器设计专业。拥有近10 年 M ATLAB/Sim ulink 开发使用经验，熟悉 Sim ulink 基于模型设计的流程。于 2008 年创建m atlabsky.com ，编写教程数百篇，解决问题数万个，积累了丰富的相关经验。蒋献文，硕士，资深专业医事放射师，毕业于中国医药大学医学院临床医学研究所。从事医学图像处理、放射线射影技术、手术房计算机断层与血管摄影技术，针对临床放射技术学与图像处理进行过深入研究并发表相关医学论文。周华英，硕士，新能源汽车技术专业讲师，毕业于北京交通大学交通运输规划与管理专业。长期进行纯电动及混合动力汽车系统建模与控制、汽车动力系统与控制、电动汽车能量管理和控制优化等研究，曾主编和参与编写多套书籍。
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