Imagine camp, Developing Image Processing app for windows phone platform
Download as PPTX, PDF1 like1,333 views
The document outlines an image processing camp and subsequent hackathon led by instructors Rahat Yasir and Anindo, covering various topics including image processing applications, algorithms, and live demonstrations. It also discusses the technical aspects of digital image processing and computer vision, exploring the goals, challenges, and techniques involved in fields such as recognition and enhancement of images. Additionally, it covers specific methodologies for image manipulation, including compression and restoration, along with practical applications in areas like biometrics and document processing.
![Code to Preview images on Windows Phone
private void ccTaskCompleted(object sender, PhotoResult pr)
{
byte[] imgLocal;
if (pr.ChosenPhoto != null)
{
imgLocal = new byte[(int)pr.ChosenPhoto.Length];
pr.ChosenPhoto.Read(imgLocal, 0, imgLocal.Length);
pr.ChosenPhoto.Seek(0, System.IO.SeekOrigin.Begin);
var bitmapImage = PictureDecoder.DecodeJpeg(pr.ChosenPhoto);
this.imgCaptured.Source = bitmapImage;
}
}](https://image.slidesharecdn.com/imaginecamp-150408025653-conversion-gate01/85/Imagine-camp-Developing-Image-Processing-app-for-windows-phone-platform-74-320.jpg)
![103
Logic Operations on Binary Images
BA (A) AND (B)
AND
A B (A) OR (B)
OR
A B (A) XOR (B)
XOR
A B [NOT (A)] AND (B)
NOT-AND
A NOT (A)
NOT](https://image.slidesharecdn.com/imaginecamp-150408025653-conversion-gate01/85/Imagine-camp-Developing-Image-Processing-app-for-windows-phone-platform-103-320.jpg)
Imagine camp, Developing Image Processing app for windows phone platform
- 1. Imagine Camp: Image Processing Camp followed by 2 weeks Hackathon Conducted by: Rahat Yasir Anindo
- 2. Imagine Camp Overview • Image • Image Processing • Applications of image processing • Image processing algorithms (10-12) • Matlab live demo • Image manipulation in Matlab • Windows Phone Demo • Brain storming for Hackathon • Idea selection (one out of three) • Live presentation on selected idea • Suggestion for converting your idea into an app Time Schedule: 10:30-1:00 & 2:00-3:00 = Image processing discussion & demo 3:00-3:30 = Brain storming for Hackathon idea 3:30-4:00 = Idea selection 4:00-5:00 = presentation on idea & suggestion for development.
- 3. A Picture is Worth 100 Words
- 4. A Picture is Worth 10,000 Words
- 5. A Picture is Worth a Million Words
- 6. Lighting Scene Camera Computer Scene Interpretation Components of a Computer Vision System
- 7. The goal of computer vision • To extract “meaning” from pixels What we see What a computer sees
- 8. The goal of computer vision • To extract “meaning” from pixels Source: “80 million tiny images” by Torralba et al. Humans are remarkably good at this…
- 9. What kind of information can be extracted from an image? • Metric 3D information • Semantic information
- 10. What is a digital image? 10
- 11. Processing of images which are Digital in nature by a Digital computer Common image formats include: – 1 sample per point (B&W or Gray scale) – 3 samples per point (Red, Green, and Blue) – 4 samples per point (Red, Green, Blue, and “Alpha”, Opacity) 11
- 12. Why we need image processing? It is motivated by three major application Improvement of pictorial information for human perception Image processing for autonomous machine application Efficient storage and transmission 12
- 13. Matrix Representation of an Image 13 1,11,10,1 1,10,10,1 1,01,00,0 )1,1()1,1()0,1( )1,1()1,1()0,1( )1,0()1,0()0,0( ),( :imageNAn NMMM N N aaa aaa aaa A NMFMfMf Nfff Nfff yxf M
- 14. Visual image formation-Digital Version 14 3D object Digital Camera Digitizer 2D Digital ImageCCD / CMOS sensor Lens
- 15. Sampling and Quantization, 2D Case 15 Continuous Sampled f(0,0) f(0,1) f(N-1,M-1)
- 16. 16 DIGITAL IMAGES: Digital images are 2D arrays (matrices) of numbers:
- 17. Hybrid Images • A. Oliva, A. Torralba, P.G. Schyns, “Hybrid Images,” SIGGRAPH 2006
- 18. Why do we get different, distance-dependent interpretations of hybrid images? ? Slide: Hoiem
- 19. Why study computer vision? • Vision is useful • Vision is interesting • Vision is difficult • Half of primate cerebral cortex is devoted to visual processing • Achieving human-level visual perception is probably “AI-complete”
- 20. Why is computer vision difficult?
- 21. Challenges: viewpoint variation Michelangelo 1475-1564
- 22. Challenges: illumination image credit: J. Koenderink
- 24. Challenges: deformation Xu, Beihong 1943
- 28. Challenges: object intra-class variation
- 32. Challenges or opportunities? • Images are confusing, but they also reveal the structure of the world through numerous cues • Our job is to interpret the cues! Image source: J. Koenderink
- 33. Biometrics Fingerprint scanners on many new laptops, other devices Face recognition systems now beginning to appear more widely http://www.sensiblevision.com/ Source: S. Seitz
- 34. Biometrics How the Afghan Girl was Identified by Her Iris Patterns Source: S. Seitz
- 35. Vision-based interaction: Xbox Kinect
- 36. . Digitizing an image ( convert an continuous image to a digital one) . Enhancing an image ( Let an image better suit for a specific application) . Restoring an image ( Recover a damaged image) . Compressing an image ( Store it with less bytes ) . Segmenting an image ( Partition objects in an image from background ) . Recognizing an image ( Tell what the objects are in an image ) 36 What can digital image processing do?
- 37. When an image damaged, we can recover it 37 Cracked parts torn Restoring an image
- 38. 2015/4/8 38 Original image 257kb Compressed image 147kb Redundant Info. Compressing an image
- 39. Take car license plate recognition as an example. 2015/4/8 39 Recognizing an image
- 40. EXAMPLE OF DIP •One of the most common uses of DIP techniques: improve quality, remove noise etc
- 41. EXAMPLES: (e) Poorly exposed x-ray image (f) The result from contrast and edge enhancement (g) Image blurred by motion (h) The result of de-blurring
- 42. Object Recognition (CVPR 2006)
- 43. Examples: The Hubble Telescope •Launched in 1990 the Hubble telescope can take images of very distant objects •However, an incorrect mirror made many of Hubble’s images useless •Image processing techniques were used to fix this....
- 44. Finding the outline and shape of image objects, e.g. character recognition.
- 46. FACE TRACKING
- 47. 1)Biological Research: e.g. DNA typing and matching; automatic counting and classification of cell structures in bone and tissue. 2) Defence and Intelligence: e.g. Reconnaissance photo- interpretation of objects in satellite images; target acquisition and missile guidance. 3) Document Processing: e.g. Scanning, archiving and transmission (fax); automatic detection and recognition of printed text (postal sorting office, tax return processing, banking cheques).4) Law Enforcement Forensics: e.g. Photo-ID kits, criminal photo-search, automatic fingerprint matching, DNA matching and fibre analysis . APPLICATIONS OF (DIP):
- 48. 5) Photography: e.g. altering colours, zooming; adding and subtracting objects to a scene; 6) Remote Sensing: e.g. Land cover analysis (water, roads, cities and cultivation), vegetation features (water content and temperature) and crop yield analysis; 3-D terrain rendering from satellite or aircraft data (road and dam planning); fire and smoke detection. 7) Space exploration and Astronomy: satellite navigation and altitude control using star positions. 8) Video and Film Special Effects: Animation,and special APPLICATIONS OF (DIP):
- 49. Image Restoration Morphological Processing Segmentation Object Recognition Representation & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital Image Processing:
- 50. Image Restoration Morphological Processing Segmentation Object Recognition Representation & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital Image Processing: Image Aquisition
- 52. Image Restoration Morphological Processing Segmentation Object Recognition Representation & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital Image Processing: Image Enhancement
- 54. Image Restoration Morphological Processing Segmentation Object Recognition Representation & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital Image restoration:
- 56. Image Restoration Morphological Processing Segmentation Object Recognition Representation & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital Image processing, morphological processing:
- 57. Digital Image Morphological Processing:
- 58. Image Restoration Morphological Processing Segmentation Object Recognition Representation & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital ImageProcessing,segmentation:
- 60. Image Restoration Morphological Processing Segmentation Object Recognition Representation & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital Image Processing object recognition:
- 61. Digital Image Processing Object Recognition:
- 62. Image Restoration Morphological Processing Segmentation Object Recognition Representation & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital Image Processing: Representation & Description:
- 63. Key Stages in Digital Image Processing: Representation & Description:
- 64. Image Restoration Morphological Processing Segmentation Object Recognition Representation & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital Image Processing, Image Compression:
- 66. Image Restoration Morphological Processing Segmentation Object Recognition Representation & Description Image Compression Colour Image Processing Problem Domain Image Acquisition Image Enhancement Key Stages in Digital Image Processing, Colour Image Processing:
- 70. 70 Effect of Sampling and Quantization 250 x 210 samples 256 gray levels 125 x 105 samples 50 x 42 samples 25 x 21 samples 8 gray levels 4 gray levels Binary image16 gray levels
- 71. Windows Phone Camera Basics • We can build a camera application that can capture still photos. • We can query device camera capabilities (where there are multiple cameras on the device (front and/or rear), where it has flash, etc.). • We can get raw camera frames and process them ourselves (encode/decode). • We can programmatically set flash and focus when taking a photo. • We can change the resolution of the photo. • We can continue using the hardware button for the camera shutter to capture a photo and for auto-focus when taking shots outside our application. • A camera need not be present (for example, front facing camera was not a part of the original devices). Your application would need to handle that condition gracefully. • To allow our application to have access to the camera capability, you need to specify ID_CAP_ISV_CAMERA in our application manifest file. • To allow our application to have access to a front facing camera (if one exists), we need to specify ID_HW_FRONTCAMERA in our application manifest file.
- 72. Code to Capture images on Windows Phone using Microsoft.Phone.Tasks; using Microsoft.Phone; private CameraCaptureTask ccTask; public MainPage() { InitializeComponent(); ccTask = new CameraCaptureTask(); } private void btnCamera_Click(object sender, RoutedEventArgs e) { ccTask.Show(); }
- 73. Code to Capture images on Windows Phone using Microsoft.Devices; using Microsoft.Xna.Framework.Media; PhotoCamera myCamera; MediaLibrary mediaLibrary; public MainPage() { InitializeComponent(); mediaLibrary = new MediaLibrary(); } private void buttonStartCamera_Click(object sender, RoutedEventArgs e) { myCamera = new Microsoft.Devices.PhotoCamera(CameraType.Primary); myCamera.CaptureCompleted += new EventHandler<CameraOperationCompletedEventArgs>(camera_CaptureCompleted); myCamera.CaptureImageAvailable += new EventHandler<Microsoft.Devices.ContentReadyEventArgs>(camera_CaptureImageAvailable); viewfinderBrush.SetSource(myCamera); }
- 74. Code to Preview images on Windows Phone private void ccTaskCompleted(object sender, PhotoResult pr) { byte[] imgLocal; if (pr.ChosenPhoto != null) { imgLocal = new byte[(int)pr.ChosenPhoto.Length]; pr.ChosenPhoto.Read(imgLocal, 0, imgLocal.Length); pr.ChosenPhoto.Seek(0, System.IO.SeekOrigin.Begin); var bitmapImage = PictureDecoder.DecodeJpeg(pr.ChosenPhoto); this.imgCaptured.Source = bitmapImage; } }
- 75. Windows phone camera API’s
- 76. Windows phone camera API’s
- 77. 77 Image manipulation Demo on Matlab (Matrix representation )
- 78. RGB to GREY Scale Conversion
- 79. RGB to GREY Scale Conversion
- 80. 80 RGB 2 Grey Matlab & Windows Phone Demo
- 81. 81 Effect of averaging: (1) Signal power unchanged (2) Noise power reduced by 1/M Conditions: (1) Signal and noise uncorrelated (2) Observations are independent (3) Noise in different observations has the same distribution (iid noise model, independent identically distributed random variables) Effect of Image Averaging www.cvcrbd.org
- 82. 82 Moving Window Transform: Example original 3x3 average Another example www.cvcrbd.org
- 83. 83 Moving Window Transform: Example original 3x3 average www.cvcrbd.org
- 84. 84 Moving Window Transform: Example original 3x3 average www.cvcrbd.org
- 85. 85 Image Averaging, Example Averaging 2 imagesOriginal image Averaging 8 images Averaging 16 images Averaging 32 images Averaging 128 images www.cvcrbd.org
- 86. 86 Convolution Examples: Original Images www.cvcrbd.org
- 87. 87 Convolution Examples: 33 Blur 111 111 111 9 1 www.cvcrbd.org
- 88. 88 Convolution Examples: 55 Blur 11111 11111 11111 11111 11111 25 1 www.cvcrbd.org
- 89. 89 Convolution Examples: 99 Blur 111111111 111111111 111111111 111111111 111111111 111111111 111111111 111111111 111111111 81 1 www.cvcrbd.org
- 90. 90 Convolution Examples: 1717 Blur 11111111111111111 11111111111111111 11111111111111111 11111111111111111 11111111111111111 11111111111111111 11111111111111111 11111111111111111 11111111111111111 11111111111111111 11111111111111111 11111111111111111 11111111111111111 11111111111111111 11111111111111111 11111111111111111 11111111111111111 289 1 www.cvcrbd.org
- 91. 91 Convolution Examples: 99 Blur using Gaussian mask 0.01124 0.01164 0.01193 0.01211 0.01217 0.01211 0.01193 0.01164 0.01124 0.01164 0.01205 0.01236 0.01254 0.01261 0.01254 0.01236 0.01205 0.01164 0.01193 0.01236 0.01267 0.01286 0.01292 0.01286 0.01267 0.01236 0.01193 0.01211 0.01254 0.01286 0.01305 0.01312 0.01305 0.01286 0.01254 0.01211 0.01217 0.01261 0.01292 0.01312 0.01319 0.01312 0.01292 0.01261 0.01217 0.01211 0.01254 0.01286 0.01305 0.01312 0.01305 0.01286 0.01254 0.01211 0.01193 0.01236 0.01267 0.01286 0.01292 0.01286 0.01267 0.01236 0.01193 0.01164 0.01205 0.01236 0.01254 0.01261 0.01254 0.01236 0.01205 0.01164 0.01124 0.01164 0.01193 0.01211 0.01217 0.01211 0.01193 0.01164 0.011242 22 2 2 2 1 ),( yx eyxh www.cvcrbd.org
- 93. 93 Average filter, Example 2 Beauty Mirror App of IOS
- 94. 94 Average Filtering Demo (Matlab & Windows Phone)
- 95. 95 MEDIAN FILTER
- 96. 96 Effective for salt and pepper noise - remove noise spikes 987654321 10025,20,20,20,20,20,15,10 100,25,20,20,15,20,20,20,10 Median = 20 ranking Median Filtering-more later www.cvcrbd.org
- 97. 97 Impulse Noise Removal, Example 1 Original image Corrupted image 5 X 5 averaging 5 X 5 median filtering www.cvcrbd.org
- 98. 98 Impulse Noise Removal, Example 2 Original image 3 X 3 averaging 3 X 3 median filtering www.cvcrbd.org
- 99. 99 NOICE REDUCTION DEMO (Matlab + Windows Phone)
- 100. Effect of Reducing Gray Levels 100 256 128 64 32 16 8 4 2
- 101. 101 Set Operations Two Sets Union Intersection Complement Difference
- 102. 102 Set Operations, Example Complement (Negative) Union of Constant Image and Complement Image The Original Image
- 103. 103 Logic Operations on Binary Images BA (A) AND (B) AND A B (A) OR (B) OR A B (A) XOR (B) XOR A B [NOT (A)] AND (B) NOT-AND A NOT (A) NOT
- 104. 104 Windows phone demo of set operation
- 105. 105 rk rk Histogram n n rp k k MNnn k k 10 Lk Image Pixel Intensity Histogram 1 1 3 3 7 1 1 3 3 5 1 0 3 3 2 0 0 2 2 2 0 0 2 2 2 0 4321 5 76 r nk 0 2 4 6 r 0 1 2 3 4 5 6 7 nk 5 5 7 6 0 1 0 1
- 107. 107 Uneven Pixel Intensity Distributions The Histogram of a Grayscale Image Let I be a 1-band (grayscale) image. I(r,c) is an 8-bit integer between 0 and 255. Histogram, hI, of I: – a 256-element array, hI – hI (g), for g = 1, 2, 3, …, 256, is an integer – hI (g) = number of pixels in I that have value g-1.
- 108. Image Histogram 108 16-level (4-bit) image blackmarkspixelswithintensityg lowerRHC:numberofpixelswithintensityg
- 109. 109 Plot of histogram: number of pixels with intensity g Black marks pixels with intensity g
- 110. 110 The Histogram of a Grayscale Image Plot of histogram: number of pixels with intensity g Black marks pixels with intensity g
- 111. 111 Image Histogram (Matlab & Windows Phone Demo)
- 112. 112 .graylevelwith inpixelsof numberthe g I ghI 1 Luminosity The Histogram of a Grayscale Image
- 113. 113 If I is a 3-band image (truecolor, 24-bit) then I(r,c,b) is an integer between 0 and 255. Either I has 3 histograms: – hR(g+1) = # of pixels in I(:,:,1) with intensity value g – hG(g+1) = # of pixels in I(:,:,2) with intensity value g – hB(g+1) = # of pixels in I(:,:,3) with intensity value g or 1 vector-valued histogram, h(g,1,b) where – h(g+1,1,1) = # of pixels in I with red intensity value g – h(g+1,1,2) = # of pixels in I with green intensity value g – h(g+1,1,3) = # of pixels in I with blue intensity value g The Histogram of a Color Image
- 114. 114 There is one histo- gram per color band R, G, & B. Luminosity histogram is from 1 band = (R+G+B)/3 RIh GIh BIh LIh Luminosity The Histogram of a Color Image
- 115. 115 The Histogram of a Color Image
- 116. Histogram Processing • Histogram processing re-scales an image intensities so that the enhanced image histogram follows some desired form. • The modification can take on many forms: • histogram equalization or • histogram shaping • e.g. exponential or hyperbolic histogram 116
- 117. 117 Point Processes: Histogram Equalization after 1255, gPcrJ I before Luminosity
- 118. 118 Histogram EQ- LUT The CDF (cummulative distribution) is the LUT for remapping. CDF LUT HEQ
- 120. 120 10 Image CDF Target CDF LUT Look Up Table for Histogram Matching
- 121. 121 Remap an Image: To Have Two of its Color pdfs Match the Third original G & B R B & R G R & G B
- 122. 122 Example: Histogram Matching original target remapped
- 123. 123 255, 256, 255 ,, , gcrI gcrIgcrI crJ k kk k if if , Point Processes: Increase Brightness 0 127 255 0 127 255 g transform mapping index.bandtheisand 3210 ,,kg
- 124. 124 Point Processes: Decrease Brightness 0 127 255 0 127 255 transform mapping 25 5 -g index.bandtheisand 3210 ,,kg crI gcrI gcrI crJ k k k k , 0, ,, 0 , if if,
- 125. 125 Image Brightness Matlab Demo
- 126. 126 Image Sharpening Matlab Demo
- 127. 127 Point Processes: Increase Contrast .255, ,255,0 ,0, ,255 ,, ,0 , crT crT crT crTcrJ k k k kk if if if 0.1127127,),( acrIacrT kk where,Let 0 127 255 0 127 255 transform mapping 321 ,,k
- 128. 128 Point Processes: Decrease Contrast .3,2,10.10 127127,),( ka crIacrT kk andwhere , 0 127 255 0 127 255 transform mapping
- 129. 129 Point Processes: Contrast Stretch . , , Then, .,max,,min ,,max,,minLet J II I JJ JJ II m mM mcrI mMcrJ crJMcrJm crIMcrIm 0 127 255 0 127 255 transform mapping mI MI mJ MJ
- 130. 130 Information Loss from Contrast Adjustment orig a = 0.5 a = 2.0 lo-c lo-c Restored hi-c hi-c Restored
- 131. 131 Information Loss from Contrast Adjustment orig orig orig lo-c hi-c lo-c hi-c rest rest lo-c hi-c diff diff abbreviations: original low-contrast high-contrast restored difference difference between original and restored low-contrast difference between original and restored high-contrast
- 132. 132 0 127 255 0 127 255 transform mapping 0.1 255 , 255, /1 for crI crJ Point Processes: Increased Gamma
- 133. 133 Point Processes: Decreased Gamma 0 127 255 0 127 255 transform mapping m M 0.1 255 , 255, /1 for crI crJ
- 134. 134 Image Subtraction for Background Removal g(x,y) = f(x,y) – h(x,y) www.cvcrbd.org
- 137. 137 Highboost Filtering, Example 3 (a) Image of whole body bone scene (b) Laplacian of (a) + (d) Sobel gredient of (a) = (c) = (a)+(b) Sharpened image (g) = (a)+(f) Sharpened image (h) power-law transform of (g) www.cvcrbd.org
- 138. 138 Commonly Used Derivative Masks www.cvcrbd.org
- 139. 139 Convolution Examples: Original Images www.cvcrbd.org
- 140. 140 Convolution Examples: Vertical Difference 010 020 010 www.cvcrbd.org
- 141. 141 Convolution Examples: Horizontal Difference 000 121 000 www.cvcrbd.org
- 142. 142 010 141 010 Convolution Examples: H + V Diff. www.cvcrbd.org
- 143. 143 Edge Detection (Sobel Operator) Matlab Demo
- 144. 144 Edge Enhancement, Example 1 Original image Magnitude Gradient Prewitt Mask Original image + Edges with magnitude > 25 Edges magnitude > 25 www.cvcrbd.org
- 145. 145 Hybrid Contrast Enhancement Method, Example Original Image Histogram Equalization Highboot Filtering Highboot Filtering + Histogram Equalization www.cvcrbd.org
- 148. 148 Medical Image Analysis Demo
- 149. 149 Brain Storming for Hackathon
- 150. 150 Idea Selection (One Idea our of Three)
- 151. 151 Presentation on Hackathon App Ideas
- 152. 152 Suggestion for converting the idea into an image processing app
- 153. Q / A ?
- 154. thank you