LDP Local Directional Pattern & LDN Local Directional Number Pattern 报告人：黄倩颖

内容 两种局部编码模式构造描述子 LDP Local Directional Pattern LDN Local Directional Number Pattern 对Local Binary Pattern (LBP)的改良

Descriptor geometric-feature-based appearance-based geometric-feature-based –sparse 稀疏 appearance-based methods -dense 密集 geometric-feature-based appearance-based

Part One 作者简介 文章结构 方法概述 讲解提纲 LBP方法回顾 LDP的创新点 LDP的鲁棒性 LDP的旋转不变性 实验 结论

作者简介 Local Directional Pattern (LDP) – A Robust Image Descriptor for Object Recognition Taskeed Jabid, Md. Hasanul Kabir, Oksam Chae Department of Computer Engineering Kyung Hee University, Republic of Korea 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance Taskeed Jabid Human Computer Interaction, Computer Vision, Object Recognition Local Directional Pattern (LDP) for face recognition International Conference Consumer Electronics (ICCE), 2010 Cited by 44 同一个内容的论文投了很多会议

文章结构 Introduction LDP image descriptor Local Binary Pattern (LBP) Local Directional Pattern (LDP) Robustness of LDP Rotation invariant LDP LDP Descriptor Texture classification using LDP descriptor Face recognition using LDP descriptor Conclusions

Abstract LDP( Local Directional Pattern) is a local feature descriptor for describing local image feature. Though LBP is robust to monotonic illumination change but it is sensitive to non-monotonic illumination variation and also shows poor performance in the presence of random noise A LDP feature is obtained by computing the edge response values in all eight directions at each pixel position and generating a code from the relative strength magnitude. Each bit of code sequence is determined by considering a local neighborhood hence becomes robust in noisy situation. 非线性光 随机噪点 八个方向的边缘响应值 相对强度大小 考虑了周边的值，因此更具有鲁棒性

Part One 作者简介 文章结构 方法概述 讲解提纲 LBP方法回顾 LDP的创新点 LDP的鲁棒性 LDP的旋转不变性 实验 结论

讲解提纲 LBP方法回顾 LDP的创新点 LDP的鲁棒性 LDP的旋转不变性 实验 结论

Local Binary Pattern (LBP) Original LBP 26 < 50 0 85 32 26 53 50 10 60 38 45 1 Threshold 50 前情提要 选定一个位置，一个方向开始编码 （0 0 1 1 1 0 0 0）2 = 56

Local Directional Pattern (LDP) Kirsch masks North 5 -3 5 -3 -3 5 M3 M2 M1 North-West North- East 5 -3 M3 M2 M1 M4 M0 M5 M6 M7 399 M0 -3 5 85 32 26 53 50 10 60 38 45 M4 West East Keshk 加权求和 求各个方向趋势 -3 5 -3 5 -3 5 M5 M6 M7 South- West South- East South

19 Computing… LDPk Kirsch masks 85 32 26 53 50 10 60 38 45 k=3 313 97 503 537 399 161 Kirsch masks 19 LDPk k=3 1 K默认取3 从东边开始绕中心一周 LDP Binary Code = 00010011 LDP Decimal Code= 19

Robustness of LDP noise & non-monotonic illumination changes -4 -3 -6 85 32 26 53 50 10 60 38 45 81 29 32 38 58 15 65 43 47 -4 -3 -6 -15 +8 +5 +2 85 32 26 53 50 10 60 38 45 重点：A Robust Image Descriptor Since edge responses are more stable than intensity values, LDP pattern provides the same pattern value even presence of noise and non-monotonic illumination changes. 优于LBP LBP = 00111000 LDP = 00010011 LBP = 00101000 LDP = 00010011

Rotation invariant LDP 85 32 26 53 50 10 60 38 45 1 313 97 503 537 399 161 26 10 45 32 50 38 85 53 60 1 503 393 161 97 313 537 旋转不变性 总是从1开始 减少了描述子的个数 Rotation Invariant LDP Code = 00110001

LDP Descriptor Accumulating the occurrence of LDP feature 统计直方图

Experiments Texture Classification using LDP histogram Primary pictures from Brodatz texture album: (a) Bark, (b) Brick, (c) Bubbles, (d) Grass, (e) Leather, (f) Pigskin, (g) Raffia, (h) Sand, (i) Straw, (j) Water, (k) Weave, (l) Wood and (m) Wool

Experiments Texture Classification using LDP histogram

Experiments Extracted rotation invariant LDP features of each pixel of the image then combined to generate rotation invariant image descriptor using LDP histogram following equation.

Experiment Results The accuracy of the method Results

Face recognition using LDP descriptor Database FERET (a) fa set, used as a gallery set, contains frontal images of 1,196 people. (b) fb set (1,195 images) with an alternative facial expression than in the fa photograph. (c) fc set (194 images) taken under different lighting conditions. (d) dup I set (722 images) taken later in time. (e) dup II set (234 images) subset of the dup I set containing images that were taken at least a year after the corresponding gallery image.

Face recognition using LDP descriptor Classification using LDP histogram Template matching

Experiment Results

Part Two 作者简介 文章结构 方法概述 讲解提纲 LBP LDP缺点 LDN 三个关键点 人脸描述 实验 结论及未来工作

作者简介 Local Directional Number Pattern for Face Analysis: Face and Expression Recognition Adin Ramirez Rivera,Student Member, IEEE, Jorge Rojas Castillo,Student Member, IEEE, and Oksam Chae,Member, IEEE IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 22, NO. 5, MAY 2013 Cited by 2 | Year 2012 | Adin Ramirez Rivera Image Processing, Computer Vision Content-Aware Dark Image Enhancement through Channel Division IEEE Transactions on Image Processing 21 (9), 3967-3980 Cited by 9 | Year 2012 较新的论文

文章结构 Introduction Local Directional Number Pattern Face description Difference With Previous Work Coding Scheme Compass Masks Face description Face recognition Conclusions

A novel local feature descriptor Abstract A novel local feature descriptor LDN encodes the directional information of the face’s textures in a compact way, producing a more discriminative code than current methods 具有识别性

Part Two 作者简介 文章结构 方法概述 讲解提纲 LBP LDP缺点 LDN 三个关键点 人脸描述 实验 结论及未来工作

讲解提纲 LBP LDP缺点 LDN 三个关键点 人脸描述 实验 结论及未来工作

LBP The method discards most of the information in the neighborhood. It limits the accuracy of the method It makes the method very sensitive to noise Moreover, these drawbacks are more evident for bigger neighborhoods

Directional (LDiP) & Derivative (LDeP) Miss some directional information (the responses’ sign) by treating all directions equally Sensitive to illumination changes and noise, as the bits in the code will flip and the code will represent a totally different characteristic 边缘响应 方向没有指明 计算的时候忽略了响应的符号问题

LDN LBP Key points of LDN Direction number Sign information gradient The coding scheme is based on directional numbers, instead of bit strings, which encodes the information of the neighborhood in a more efficient way 基于方向码 名字来源 The implicit use of sign information, in comparison with previous directional and derivative methods we encode more information in less space, and, at the same time, discriminate more textures 隐含方向 The use of gradient information makes the method robust against illumination changes and noise 6-bit

LDN Key points of LDN Direction number Sign information gradient 逐个来看下他是怎么达到目的的 6-bit

Coding Scheme - + - + Direction number Sign information 同一张图像上 方向不同的区分 we pick the prominent information of each pixel’s neighborhood. Therefore, our method filters and gives more importance to the local information before coding it, while other methods weight the grouped (coded) information

Coding Scheme

Compass Masks 𝐿𝐷𝑁 𝐾 𝐿𝐷𝑁 𝜎 𝐺 Kirsch masks derivative-Gaussian mask gradient information Compass Masks Two kinds of masks 𝐿𝐷𝑁 𝐾 Kirsch masks 𝐿𝐷𝑁 𝜎 𝐺 derivative-Gaussian mask

M3 M2 M1 M4 M0 M5 M6 M7 Compass Masks Kirsch masks North 5 -3 5 -3 -3 5 -3 -3 5 M3 M2 M1 North-West North- East 5 -3 M3 M2 M1 M4 M0 M5 M6 M7 M4 M0 -3 5 West East -3 5 -3 5 -3 5 M5 M6 M7 South- West South- East South

Compass Masks derivative-Gaussian mask Compute code in gradient space Therefore, use Gaussian smoothing to stabilize the code in presence of noise 受 Kirsch Mask的启发 Generate a compass mask,{M0σ,...,M7σ}, by rotating Mσ, 45°apart, in eight different directions

Compass Masks derivative-Gaussian mask

Face Descriptor Histogram LH & MLH

Face Descriptor Two kinds of descriptor Code in LH Code in MLH must be

Face Recognition Chi-Square dissimilarity measure

Face recognition using LDP descriptor Database FERET (a) fa set, used as a gallery set, contains frontal images of 1,196 people. (b) fb set (1,195 images) with an alternative facial expression than in the fa photograph. (c) fc set (194 images) taken under different lighting conditions. (d) dup I set (722 images) taken later in time. (e) dup II set (234 images) subset of the dup I set containing images that were taken at least a year after the corresponding gallery image.

Experiment Results Face recognition accuracy small neighborhoods (3×3, 5×5, 7×7) medium neighborhoods (5×5, 7×7, 9×9) large neighborhoods (7×7, 9×9, 11×11) 没有预处理 LPQ GGPP 相位信息 phase information

Experiment Results Noise Evaluation With white Gaussian noise GGPP Global Gabor Phase Pattern

Conclusion Combination of different sizes (small, medium and large) gives better recognition rates for certain conditions. Evaluated LDN under expression, time lapse and illumination variations, and found that it is reliable and robust throughout all these conditions. 在特定情况下，使用不同大小的组合达到更好效果 经过作者的测试，LDN能经受表情、时间变化、光照变化等考验，在各种方法中表现较好

总结及未来工作 如何选择一个描述子 如何设计一个描述子 长度 描述精度 抗噪能力 计算强度 舍弃冗余的信息 整合多种信息来源 信息压缩 作为一个预科研究生，困惑这个总结应该怎么讲。 那我只要就从这个描述子说起 一个算法拿来做什么达到什么样的效果。 写论文就是写小说。武侠风格尤佳。首先营造一个紧张的氛围，烘托出一个必须解决的严重问题。接着绿叶登场，挨个败下阵来。然后主人公出现，在精巧的情节布局之下，他的特长刚好得到最大限度地发挥，他的缺陷刚好都不重要，甚至还能变废为宝。于是主人公拯救了学术界，并在剧终谦虚地表示，由于时间有限自己的独门武功还有6层没有修炼完。（李尧）

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