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深度學習
MATLAB計算機視覺與深度學習實戰-贈在線交流卡和本書源碼 版權信息
- ISBN：9787121315503
- 條形碼：9787121315503 ; 978-7-121-31550-3
- 裝幀：暫無
- 冊數：暫無
- 重量：暫無
- 所屬分類：>
MATLAB計算機視覺與深度學習實戰-贈在線交流卡和本書源碼 本書特色
《MATLAB 計算機視覺與深度學習實戰》詳細講解了 30 個 MATLAB 計算機視覺與深度學習案例（含可運行程序），涉及霧霾去噪、答題卡自動閱卷、肺部圖像分割、小波數字水印、圖像檢索、人臉二維碼識別、車牌定位及識別、霍夫曼圖像壓縮、手寫數字識別、英文字符文本識別、眼前節組織提取、全景圖像拼接、小波圖像融合、基于語音識別的音頻信號模擬燈控、路面裂縫檢測識別、視頻運動估計追蹤、Simulink 圖像處理、胸片及肝臟分割、基于深度學習的汽車目標檢測、基于計算機視覺的自動駕駛應用、基于深度學習的視覺場景識別等多項重要技術，涵蓋了數字圖像處理中幾乎所有的基本模塊，并延伸到了深度學習的理論及其應用方面。工欲善其事，必先利其器，《MATLAB 計算機視覺與深度學習實戰》對每個數字圖像處理的知識點都提供了豐富生動的案例素材，并詳細講解了其 MATLAB 實驗的核心程序，通過對這些示例程序的閱讀理解和仿真運行，讀者可以更加深刻地理解圖像處理的內容，并且更加熟練地掌握 MATLAB 中各種函數在圖像處理領域中的用法。本書以案例為基礎，結構布局緊湊，內容深入淺出，實驗簡捷高效，適合計算機、信號通信和自動化等相關專業的教師、本科生、研究生，以及廣大從事數字圖像處理的工程研發人員閱讀參考。
MATLAB計算機視覺與深度學習實戰-贈在線交流卡和本書源碼 內容簡介
《MATLAB計算機視覺與深度學習實戰》詳細講解了30個MATLAB計算機視覺與深度學習實戰案例，提供源碼及在線支持。 幾乎涵蓋了數字圖像處理中幾乎所有的基本模塊，并延伸到了深度學習的理論及其應用方面。 適合計算機、信號通信和自動化等相關專業的教師、本科生、研究生，以及廣大從事數字圖像處理的工程研發人員閱讀參考。
MATLAB計算機視覺與深度學習實戰-贈在線交流卡和本書源碼 目錄
第 1 章 基于直方圖優化的圖像去霧技術 1
1.1 案例背景 ·········································································································· 1
1.2 理論基礎 ·········································································································· 1
1.2.1 空域圖像增強 ····························································································· 1
1.2.2 直方圖均衡化 ····························································································· 2
1.3 程序實現 ·········································································································· 3
1.3.1 設計 GUI 界面 ···························································································· 4
1.3.2 全局直方圖處理 ·························································································· 4
1.3.3 局部直方圖處理 ·························································································· 7
1.3.4 Retinex 增強處理 ························································································· 9
1.4 延伸閱讀 ········································································································ 13
1.5 參考文獻 ········································································································ 13
第 2 章 基于 形態學的權重自適應圖像去噪 14
2.1 案例背景 ········································································································ 14
2.2 理論基礎 ········································································································ 15
2.2.1 圖像去噪方法 ··························································································· 15
2.2.2 數學形態學原理 ························································································ 16
2.2.3 權重自適應的多結構形態學去噪 ··································································· 16
2.3 程序實現 ········································································································ 17
2.4 延伸閱讀 ········································································································ 22
2.5 參考文獻 ········································································································ 23
第 3 章 基于多尺度形態學提取眼前節組織 24
3.1 案例背景 ········································································································ 24
3.2 理論基礎 ········································································································ 25
3.3 程序實現 ········································································································ 28
3.3.1 多尺度邊緣 ······························································································ 28
3.3.2 主處理函數 ······························································································ 29
3.3.3 形態學處理 ······························································································ 31
3.4 延伸閱讀 ········································································································ 33
3.5 參考文獻 ········································································································ 33
第 4 章 基于 Hough 變化的答題卡識別 34
4.1 案例背景 ········································································································ 34
4.2 理論基礎 ········································································································ 34
4.2.1 圖像二值化 ······························································································ 35
4.2.2 傾斜校正 ································································································· 35
4.2.3 圖像分割 ································································································· 38
4.3 程序實現 ········································································································ 40
4.4 延伸閱讀 ········································································································ 51
4.5 參考文獻 ········································································································ 51
第 5 章 基于閾值分割的車牌定位識別 52
5.1 案例背景 ·············
1.1 案例背景 ·········································································································· 1
1.2 理論基礎 ·········································································································· 1
1.2.1 空域圖像增強 ····························································································· 1
1.2.2 直方圖均衡化 ····························································································· 2
1.3 程序實現 ·········································································································· 3
1.3.1 設計 GUI 界面 ···························································································· 4
1.3.2 全局直方圖處理 ·························································································· 4
1.3.3 局部直方圖處理 ·························································································· 7
1.3.4 Retinex 增強處理 ························································································· 9
1.4 延伸閱讀 ········································································································ 13
1.5 參考文獻 ········································································································ 13
第 2 章 基于 形態學的權重自適應圖像去噪 14
2.1 案例背景 ········································································································ 14
2.2 理論基礎 ········································································································ 15
2.2.1 圖像去噪方法 ··························································································· 15
2.2.2 數學形態學原理 ························································································ 16
2.2.3 權重自適應的多結構形態學去噪 ··································································· 16
2.3 程序實現 ········································································································ 17
2.4 延伸閱讀 ········································································································ 22
2.5 參考文獻 ········································································································ 23
第 3 章 基于多尺度形態學提取眼前節組織 24
3.1 案例背景 ········································································································ 24
3.2 理論基礎 ········································································································ 25
3.3 程序實現 ········································································································ 28
3.3.1 多尺度邊緣 ······························································································ 28
3.3.2 主處理函數 ······························································································ 29
3.3.3 形態學處理 ······························································································ 31
3.4 延伸閱讀 ········································································································ 33
3.5 參考文獻 ········································································································ 33
第 4 章 基于 Hough 變化的答題卡識別 34
4.1 案例背景 ········································································································ 34
4.2 理論基礎 ········································································································ 34
4.2.1 圖像二值化 ······························································································ 35
4.2.2 傾斜校正 ································································································· 35
4.2.3 圖像分割 ································································································· 38
4.3 程序實現 ········································································································ 40
4.4 延伸閱讀 ········································································································ 51
4.5 參考文獻 ········································································································ 51
第 5 章 基于閾值分割的車牌定位識別 52
5.1 案例背景 ·············
展開全部
MATLAB計算機視覺與深度學習實戰-贈在線交流卡和本書源碼 作者簡介
劉衍琦，碩士，計算機視覺算法工程師，畢業于大連理工大學數學科學學院計算幾何與圖形圖像實驗室。長期從事圖像、聲紋、視頻檢索及其大數據應用，對以圖搜圖、圖文識別等進行過深入研究并成功應用于工程項目中，曾主編和參與編寫多套書籍。詹福宇，博士，飛行控制算法工程師，畢業于西北工業大學航空學院飛行器設計專業。擁有近10 年 M ATLAB/Sim ulink 開發使用經驗，熟悉 Sim ulink 基于模型設計的流程。于 2008 年創建m atlabsky.com ，編寫教程數百篇，解決問題數萬個，積累了豐富的相關經驗。蔣獻文，碩士，資深專業醫事放射師，畢業于中國醫藥大學醫學院臨床醫學研究所。從事醫學圖像處理、放射線射影技術、手術房計算機斷層與血管攝影技術，針對臨床放射技術學與圖像處理進行過深入研究并發表相關醫學論文。周華英，碩士，新能源汽車技術專業講師，畢業于北京交通大學交通運輸規劃與管理專業。長期進行純電動及混合動力汽車系統建模與控制、汽車動力系統與控制、電動汽車能量管理和控制優化等研究，曾主編和參與編寫多套書籍。
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