Digital Image Processing Simplified approach to Image Processing
- Slides: 132
Digital Image Processing 공주대학교 정보통신공학부 조우연 Simplified approach to Image Processing (classical & modern techniques in C)
DIP (Digital Image Processing) 영상처리시스템의 구조
DIP (Digital Image Processing) 영상의 디지털화
DIP (Digital Image Processing) 영상의 표본화(Sample)
DIP (Digital Image Processing) 표본화 간격에 따른 영상 의 비교
DIP (Digital Image Processing) 양자화(Quantization)
DIP (Digital Image Processing) - Color Space 칼라 이미지를 Gray 이미지로 변환
DIP (Digital Image Processing) - Color Space Color 색상(hue) Color 인지 채도(saturation) 명도(brightness) 여러가지 색상체계 RGB CMY YIQ HSI YCb. Cr
DIP (Digital Image Processing) - Color Space(RGB) RGB • Color Camera/Monitor 등에 사용 • 빨강(Red), 초록(Green) 파랑(Blue) 구성 Brighteness = 0. 2999 R + 0. 587 G + 0. 114 B
DIP (Digital Image Processing) - Color Space(RGB) 임의의 이미지에서 RGB 채널 분리
DIP (Digital Image Processing) - Color Space(CMY) RGB 공간을 CMY 공간으로 변환
DIP (Digital Image Processing) - Color Space(HIS) Color H : 색상(hue) S : 채도(saturation) 색상의 탁하고 맑음의 정도 I 또는 V : 명도(Value or Intensity) 밝기정도 RGB 공간을 HSI 공간으로 변환
DIP (Digital Image Processing) - Color Space(HIS) HSI공간
DIP (Digital Image Processing) - Color Space(YUV) YUV Color TV에 사용되는 컬러 공간 (PAL/SECAM) Y : Luminance U : Color Differences V : Color Differences 변환 공식 Y = 0. 299 R + 0. 587 G + 0. 114 B U = -0. 147 R -0. 289 G +0. 437 B V = 0. 615 R - 0. 515 G - 0. 100 B 역 변환 공식 R = 1. 000 Y + 0. 000 U + 1. 403 V G = 1. 000 Y - 0. 344 U - 0. 714 V B = 1. 000 Y + 1. 773 U + 0. 000 V
DIP (Digital Image Processing) - Color Space(YUV) RGB 공간을 YUV 공간으로 변환
DIP (Digital Image Processing) - Color Space(YIQ) YIQ Color TV에 사용되는 컬러 공간 (PAL/SECAM) Y : Luminance I : In-phase modulation Q : Quadradure – phase modulation 변환 공식 Y = 0. 299 R + 0. 587 G + 0. 114 B I = 0. 596 R - 0. 274 G - 0. 322 B Q = 0. 211 R - 0. 523 G - 0. 312 B 역 변환 공식 R = - 1. 129 Y + 3. 306 I - 3. 000 Q G = 1. 607 Y - 0. 934 I + 0. 386 Q B = 3. 458 Y - 3. 817 I + 5. 881 Q
DIP (Digital Image Processing) - Color Space(YIQ) RGB 공간을 YIQ 공간으로 변환
DIP (Digital Image Processing) - Color Space(YCb. Cr) YCb. Cr 영상 압축(MPEG)에서 사용하는 칼라 공간 Y : Luminance Cb : Color Differences Cr : Color Differences 변환 공식 Y = 77 R/256 + 150 G/256 + 29 B/256 Cb = [131 R/256 - 110 G/256 - 21 B/256] + 128 Cr = [131 R/256 - 44 R/256 - 87 G/256] + 128
DIP (Digital Image Processing) - Color Space(YCb. Cr) RGB 공간을 YCb. Cr 공간으로 변환
DIP (Digital Image Processing) 종류 Point Processing Area Processing Topological Processing Frame Processing
DIP (Digital Image Processing) - Point Processing (명암값조절 및 대비, 히스토그램) 단점 임계치 설정
DIP (Digital Image Processing) - Point Processing (명암값조절 및 대비, 히스토그램) 히스토그램 평활화(histogram equalization) 예
DIP (Digital Image Processing) - Area Processing 1(주파수 영역 처리) frequency 처리의 개념 f(x, y) FFT H(u, v) FFT-1 g(x, y)
DIP (Digital Image Processing) - Area Processing 1(Convolution) Convolution technique 사용한 예 ▶ Embossing - 양각효과을 만드는 것 ▶ Bluring - 영상을 부드럽게 ▶ Sharpning - 세세한 부분을 두드러지게 또는 강조 ▶ Edge Detection - 방향성 검출 ▶ Spatial Filtering (공간필터링)
DIP (Digital Image Processing) - Area Processing 1(Embossing) 엠보싱 효과 (Embossing Effect) 구리판을 양각한 결과를 생성 것 효과 마스크의 계수 -1 0 0 0 0 1 중앙값은 0, 합은 0
DIP (Digital Image Processing) - Area Processing 1(Sharpning) 샤프닝
DIP (Digital Image Processing) - Area Processing 2(Edge Extraction) 유사연산자 기법을 이용한 에지 검출
DIP (Digital Image Processing) - Area Processing 2(Edge Extraction) 차연산자 기법을 이용한 에지 검출
DIP (Digital Image Processing) - Area Processing 2(Edge Extraction) 경계값을 이용한 에지 강조/약화
DIP (Digital Image Processing) - Area Processing 2(First-Order Differential operator) 1차 미분자 종류
DIP (Digital Image Processing) - Area Processing 2(First-Order Differential operator) Prewitt 장점 돌출된 값을 비교적 평균화 함 단점 수평과 수직에 놓여진 에지에 민감하게 반응.
DIP (Digital Image Processing) - Area Processing 2(First-Order Differential operator) Sobel 장점 돌출된 값을 비교적 평균화 함 단점 수평과 수직에 놓여진 에지에 민감하게 반응.
DIP (Digital Image Processing) - Area Processing 2(Second-Order Differential operator) 2차 미분 (Second-Order Differential operator) 1차 미분 연산자가 에지가 존재하는 영역을 지나면 민감하게 반응 2차 미분 이를 보완할 목적으로 쓰여지는 방법 1) Laplacian 2차 미분자 종류 2) Lo. G(Laplacian of Gaussina) 3) Do. G(Difference of Gauss-ian) 장점 추출된 에지 윤곽선이 폐곡선을 이루게 되는 것
DIP (Digital Image Processing) - Area Processing 2(Second-Order Differential operator) Lo. G (Laplacian of Gaussian) Gaussian Smoothing을 적용한 후, -> 라플라시안을 적용 공식
DIP (Digital Image Processing) - Area Processing 2(Second-Order Differential operator) Lo. G (Laplacian of Gaussian) 예
DIP (Digital Image Processing) - Area Processing 2(Second-Order Differential operator) Do. G (Differential of Gaussian) Lo. G의 계산 시간을 줄여주기 위해 Lo. G를 근사화함 원리 공식 각 가우시안 연산에 분산값을 서로 다르게 주고 차를 구하 여 에지맵을 구하는 원리
DIP (Digital Image Processing) - Area Processing 2(Second-Order Differential operator) Do. G (Differential of Gaussian) 예
DIP (Digital Image Processing) - Area Processing 2(Second-Order Differential operator) Canny필터 예
DIP (Digital Image Processing) - Area Processing 3(Color Edge Detection) Lo. G 연산자를 이용한 에지 추출
DIP (Digital Image Processing) - Area Processing 3(Mean Filter) 평균 필터 (Mean Filter) 예
DIP (Digital Image Processing) - Area Processing 3(Median Filter) 메디안 필터 (Median Filter) 예 2
DIP (Digital Image Processing) - Area Processing 3(Expansion & Contraction ) Expansion & Contraction 확장(Expansion)과 수축(Contraction)에 의한 이진영상의 잡음 제거 확장 → 수축 MIN → MAX (Opening) 고립된 잡음이 확장할 때 제거된다. 수축 → 확장 MAX → MIN (Closing) 고립된 잡음이 수축할 때 제거된다.
DIP (Digital Image Processing) - Area Processing 3(Ranked Order Filters ) Ranked Order Filters 개념 • window에 포함된 원소를 오름차순 정렬을 하고, rank에 해당하는 순서의 원소값을 선택하여 출력 알고리즘 1. 오름차순 정렬 2. 원하는 번째의 값 rank를 결정하여 해당하는 원소 값을 쓴다 3. if rank=0 then Mask[0] -> Min filter if rank=N/2 then Mask[N/2] -> Median filter if rank=N then Mask[N] -> Max filter
DIP (Digital Image Processing) - Area Processing 3(Ranked Order Filters ) Ranked Order Filters
DIP (Digital Image Processing) - Area Processing 3(α-trimmed mean filter) α-trimmed mean filter
DIP (Digital Image Processing) - Area Processing 3((r-s)-Fold trimmed mean filter) (r-s) Fold trimmed mean filter
DIP (Digital Image Processing) - Area Processing 3(α Fold Winsorized mean filter) α Fold Winsorized mean filter
DIP (Digital Image Processing) - Area Processing 3((r-s) Fold Winsorized mean filter) (r-s) Fold Winsorized mean filter
DIP (Digital Image Processing) - Area Processing 3(K-Nearest Neighbor Filter ) K-Nearest Neighbor Filter
DIP (Digital Image Processing) - Area Processing 3(Modified K-Nearest Neighbor Filter ) Modified K-Nearest Neighbor Filter
DIP (Digital Image Processing) - Area Processing 4(Adaptive Median Filter ) Adaptive Median Filter
DIP (Digital Image Processing) - Area Processing 4(d-Adaptive Median Filter ) d-Adaptive Median Filter
DIP (Digital Image Processing) - Area Processing 4(q-Adaptive Median Filter ) q-Adaptive Median Filter
DIP (Digital Image Processing) - Area Processing 4(k-Adaptive Median Filter ) k-Adaptive Median Filter 알고리즘 1. 잡음표준편차 객체를 구하고 2. if {잡음 표준편차 객체 중 셋 이상 k값 이하} 이면 원 영상 else Median Filter
DIP (Digital Image Processing) - Area Processing 4(k-Adaptive Median Filter ) k-Adaptive Median Filter
DIP (Digital Image Processing) - Area Processing 4(α-Adaptive Median Filter ) α-Adaptive Median Filter
DIP (Digital Image Processing) - Topological Processing(Interpolation) 보간법 (Interpolation)의 개념 • • 완벽한 사상이 되지 않는 경우가 발생하므로 화소들 사이에 있 는 주소값을 생성하는 역할 적절한 보간함수의 선택 : 복잡한 알고리즘은 영상의 질을 향상 시키나 많은 처리 시간(Running Time)을 요구 영상을 2배 확대하는 공식 x source = ( x dest ) / 2 y source = ( y dest ) / 2 보간법 ① Nearest Neighbor interpolation ② Bilinear interpolation ③ Cubic Convolution interpolation ④ B-Spline interpolation
- Histogram processing in digital image processing
- High boost filtering matlab
- Neighborhood processing in digital image processing
- Point processing
- Point processing operations in image processing
- Gonzalez
- Translate
- Noise
- Fundamentals of image compression
- Image segmentation in digital image processing
- Huffman coding example
- Image sharpening and restoration
- Image geometry in digital image processing
- The range of values spanned by the gray scale is called:
- Digital image processing
- Maketform
- Noise
- Sanjay ghemawat
- Mapreduce simplified data processing on large clusters
- Mapreduce simplified data processing on large clusters
- Explain various boundary descriptors
- Representation and description in digital image processing
- Image thresholding matlab
- Segmentation in digital image processing
- Relationship between pixels in digital image processing
- Intensity transformation in digital image processing
- Zooming and shrinking in digital image processing
- Imadjust
- M adjacency in image processing
- Coordinate conventions in digital image processing
- Dam construction in image processing
- Digital image processing java
- Thresholding in digital image processing
- Segmentation in digital image processing
- In digital image processing
- Boundary descriptors in digital image processing
- Optimum global thresholding using otsu's method
- What is boundary descriptors in digital image processing
- Colour slicing
- Power law gamma transformation
- Intensity transformation and spatial filtering
- Fourier transform convolution
- Matlab paddedsize
- Digital image processing
- Haar transform in digital image processing for n=8
- Digital image processing
- Lossy compression in digital image processing
- Digital image processing
- Color levels
- Digital image processing
- Digital image processing
- Color fundamentals in digital image processing
- Image processing
- Digital path in image processing
- Digital path in image processing
- 472
- Gray level slicing in image processing
- Intensity transformation in digital image processing
- Digital image processing
- 매트랩 .* 의미
- Image processing lecture
- For a chain code : 10103322
- Specialized image processing hardware
- Digital image processing
- Full color image processing
- Oerdigital
- Digital image processing
- Digital image processing
- Color transformation in digital image processing
- Origins of digital image processing
- Pattern and pattern classes in image processing
- Raquel anido
- Image processing
- Hadamard transform in digital image processing
- Aliasing image processing
- Shape numbers in digital image processing
- Digital image processing
- Digital image processing
- Coordinate conventions in digital image processing
- Digital image processing
- Digital image processing
- Vertical processing
- Digital image processing
- Digital image processing
- Piecewise linear transformation in digital image processing
- Thresholding
- Introduction to digital image processing with matlab
- Image processing
- Watershed morphology
- Analog image and digital image
- Virtual circuit and datagram networks
- Cognitive approach vs behavioral approach
- Waterfall market entry strategy
- Multiple approach avoidance
- Cognitive approach vs behavioral approach
- What is research approach
- Traditional approach vs object oriented approach
- Tony wagner's seven survival skills
- Digital design: a systems approach
- Digital design: a systems approach
- Information processing theory vs piaget
- The level of processing approach
- Apa yang dimaksud dengan warga digital?
- E-commerce: digital markets, digital goods
- Digital data digital signals
- Data encoding and transmission
- E-commerce: digital markets, digital goods
- Digital encoding schemes
- Luxinnovation logo
- Unique features of digital markets
- Simplified weaning index
- Simplified letter format
- Radical properties
- Solve using the square root property 25v^2=1
- Squared roots
- Simplify square root of 4/9
- Simplifying radicals
- Simplified storage
- Sic addressing modes
- S-des algorithm
- Simplified communications
- Simplifying radicals jeopardy
- Quick heal security simplified
- 34/72 simplified
- Blaise pascal's wager simplified
- Redox reaction
- Navier stokes equation simplified
- Business letters introduction
- Kahoot.itk
- Square root of 150 simplified
- Demos.com graphing
- Whats a constant
- Nernst equation class 12