ldp local directional pattern & ldn local directional number pattern
DESCRIPTION
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. Part One. 作者简介. 文章结构. 方法概述. - PowerPoint PPT PresentationTRANSCRIPT
LDP Local Directional Pattern &LDNLocal 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
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 RecognitionTaskeed 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
文章结构
• Introduction• LDP image descriptor
a. Local Binary Pattern (LBP)b. Local Directional Pattern (LDP)c. Robustness of LDPd. Rotation invariant LDPe. 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.
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
85 32 26
53 50 10
60 38 45
1 0 0
1 0
1 0 0
Threshold 50
( 0 0 1 1 1 0 0 0 ) 2 = 5 6
26 < 50 0
Local Directional Pattern (LDP)
Kirsch masks
North- East
North
North-West M
2M1
M4
M0
M5
M6
M7
M3
East
South
West
South-West
South-East
-3 -3 5
-3 0 5
-3 -3 5
5 5 5
-3 0 -3
-3 -3 -3
-3 5 5
-3 0 5
-3 -3 -3
5 -3 -3
5 0 -3
5 -3 -3
M3 M2 M1
M4 M0
M5 M6 M7
5 5 -3
5 0 -3
-3 -3 -3
-3 -3 -3
5 0 -3
5 5 -3
-3 -3 -3
-3 0 -3
5 5 5
-3 -3 -3
-3 0 5
-3 5 5
85
32
26
53
50
10
60
38
45
399
Computing…
85 32 26
53 50 10
60 38 45
313 97 503
537 399
161 97 161
Kirsch masks
0 0 1
1 1
0 0 0
LDP Binary Code =00010011LDP Decimal Code=19
LDPk
k=3
19
Robustness of LDP
noise & non-monotonic illumination changes
85 32 26
53 50 10
60 38 45
85 32 26
53 50 10
60 38 45
-4 -3 -6
-15 +8 +5
+5 +5 +2
81 29 32
38 58 15
65 43 47
LBP = 00111000LDP = 00010011
LBP = 00101000LDP = 00010011
Rotation invariant LDP
85 32 26
53 50 10
60 38 45
0 0 1
1 1
0 0 0
26 10 45
32 50 38
85 53 60
1 1 0
0 0
0 1 0
313 97 503
537 399
161 97 161
503 393 161
97 97
313 537 161
Rotation Invariant LDP Code = 00110001
LDP Descriptor
Accumulating the occurrence of LDP feature
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
(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.
Database FERET
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 RecognitionAdin 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
文章结构
• Introduction• Local Directional Number Pattern• Difference With Previous Work• Coding Scheme• Compass Masks
• Face description• Face recognition• Conclusions
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
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 methodIt makes the method very sensitive to noiseMoreover, these drawbacks are more
evident for bigger neighborhoods
Directional (LDiP) & Derivative (LDeP)
Miss some directional information (the responses’ sign) by treating alldirections equally
Sensitive to illumination changes and noise, as the bits in the code will flip and the code will represent a totally different characteristic
Key points of LDN
LBP
Direction
number
Signinformatio
n
gradientinformati
on
6-bit
LDN
Key points of LDN
Direction
number
Signinformatio
n
gradientinformati
on
6-bit
LDN
Coding Scheme
Direction
number
Signinformatio
n
+ -+ -
Coding Scheme
Compass Masks
Two kinds of masks
𝐿𝐷𝑁𝐾
𝐿𝐷𝑁𝜎𝐺
derivative-Gaussian mask
Kirsch masks
Compass Masks
Kirsch masks
North- East
North
North-West M
2M1
M4
M0
M5
M6
M7
M3
East
South
West
South-West
South-East
-3 -3 5
-3 0 5
-3 -3 5
5 5 5
-3 0 -3
-3 -3 -3
-3 5 5
-3 0 5
-3 -3 -3
5 -3 -3
5 0 -3
5 -3 -3
M3 M2 M1
M4 M0
M5 M6 M7
5 5 -3
5 0 -3
-3 -3 -3
-3 -3 -3
5 0 -3
5 5 -3
-3 -3 -3
-3 0 -3
5 5 5
-3 -3 -3
-3 0 5
-3 5 5
Compass Masks
derivative-Gaussian mask
• Compute code in gradient space • Therefore, use Gaussian smoothing to
stabilize the code in presence of noise
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
(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.
Database FERET
Experiment Results
small neighborhoods (3×3, 5×5, 7×7)medium neighborhoods (5×5, 7×7, 9×9) large neighborhoods (7×7, 9×9, 11×11)
Face recognition accuracy
Experiment Results
Noise EvaluationWith white Gaussian noise
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.
总结及未来工作
•如何选择一个描述子• 长度• 描述精度• 抗噪能力• 计算强度
•如何设计一个描述子• 舍弃冗余的信息• 整合多种信息来源• 信息压缩
Thank you!