?? ???? ??? ??? ??
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2D ?????? ?? ??. ?????? ??? ????? ??/?? ??? ?????? ??? ?? ?? ??. ???(Python)? ?????/???? ?????? scikit-image? ?? ?????? ??? ?? ??. R???? ??? (2014.5.30) ?.
![Pixel Operation
? Get pixel value
? Put Pixel Value
? Area Operation
>> print img[ i ][ j ][ k ]
for i in range(img.shape[0]):
for j in range(img.shape[1]):
for k in range(img.shape[2]):
if i>=200 and i<300:
if j>=200 and j<300:
img[i][j][k] = 0 * img[i][j][k]
>> img[ i ][ j ][ k ] = 0
>> img[200:300][200:300] =
0 * img[200:300][200:300]
or Simply,](https://image.slidesharecdn.com/random-140530093559-phpapp01/85/-16-320.jpg)
![Polygon Drawing
? ??? ???
>> from skimage.draw import polygon
>> img = np.zeros((width, height), dtype=np.uint8)
>> x = np.array([x1, x2, x3, x4, ¡])
>> y = np.array([y1, y2, y3, y4, ¡])
>> rr, cc = polygon(y, x)
>> img[rr, cc] = 1](https://image.slidesharecdn.com/random-140530093559-phpapp01/85/-18-320.jpg)
![Picking Points (¡°ginput¡±)
? ??? ??? ??? ??? ??
>> from PIL import Image
>> from pylab import *
>> img = array(Image.open('C:mandrill.bmp'))
>> imshow(img)
>> print 'Please click 3 points'
>> x = ginput(3)
>> print 'you clicked:',x
>> show()
>> x
you clicked: [(16.737903225806448, 18.705645161
290249), (406.85887096774195, 23.2419354838709
18), (227.22177419354841, 242.79838709677415)]
¡ù ??? ??? MATLAB? ?? ??
(Graphical INPUT)??.
?? ?? ??? ???? ?????? ?.](https://image.slidesharecdn.com/random-140530093559-phpapp01/85/-19-320.jpg)
![Contour Finding
? Contour Finding
>> import numpy as np
>> import matplotlib.pyplot as plt
>> from skimage import measure
>> x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j]
>> r = np.sin(np.exp((np.sin(x)**3 + np.cos(y)**2)))
>> contours = measure.find_contours(r, 0.8)
>> fig, ax = plt.subplots()
>> ax.imshow(r, interpolation='nearest', cmap=plt.cm.gray)
>> for n, contour in enumerate(contours):
ax.plot(contour[:, 1], contour[:, 0], linewidth=2)
>> plt.show()
¡ù ???? ? ??? ??? ????](https://image.slidesharecdn.com/random-140530093559-phpapp01/85/-29-320.jpg)
![Hough Transform (2of2)
? Hough Transform
http://www.aiaa-daycin.org/node/430
http://campar.in.tum.de/Students/DaPentenrieder
>>from skimage.transform import (hough_line, hough_l
ine_peaks, probabilistic_hough_line)
>> edges = canny(image, 2, 1, 25)
>> lines = probabilistic_hough_line(edges, threshold=
10, line_length=5, line_gap=3)
>> for line in lines:
p0, p1 = line
plt.plot((p0[0], p1[0]), (p0[1], p1[1]))
>> plt.show()
¡ù rho, theta ??? ? ??? x, y ??? ??
? ??? ???
¡ù ? ?? ?? ?? ??? rho, theta ????
voting?? ????? ???? ??? ???](https://image.slidesharecdn.com/random-140530093559-phpapp01/85/-34-320.jpg)
![Corner Detection (1of2)
? Harris Corner Detection
http://www.krankerkaktus.de/Portfolio/GraphicsProgramming
https://www.ee.iitb.ac.in/~sumantra/courses/ip/assignment_1.html
>> from skimage.feature import corner_harris, corner
_subpix, corner_peaks
>> coords = corner_peaks(corner_harris(image), min_
distance=5)
>> coords_subpix = corner_subpix(image, coords, win
dow_size=13)
>> fig, ax = plt.subplots()
>> ax.imshow(image, interpolation='nearest', cmap=p
lt.cm.gray)
>> ax.plot(coords[:, 1], coords[:, 0], '.b', markersize=3)
>> ax.plot(coords_subpix[:, 1], coords_subpix[:, 0], '+r',
markersize=15)
>> ax.axis((0, 350, 350, 0))
>> plt.show()](https://image.slidesharecdn.com/random-140530093559-phpapp01/85/-35-320.jpg)
![Kalman Filtering
? Kalman Filtering
# Kalman filter example demo in Python
# A Python implementation of the example given in pages 11-15 of "An
# Introduction to the Kalman Filter" by Greg Welch and Gary Bishop,
# University of North Carolina at Chapel Hill, Department of Computer
# Science, TR 95-041,
# http://www.cs.unc.edu/~welch/kalman/kalmanIntro.html
# by Andrew D. Straw
import numpy
import pylab
# intial parameters
n_iter = 50
sz = (n_iter,) # size of array
x = -0.37727 # truth value (typo in example at top of p. 13 calls this z)
z = numpy.random.normal(x,0.1,size=sz) # observations (normal about x, sigma=0.1)
Q = 1e-5 # process variance
# allocate space for arrays
xhat=numpy.zeros(sz) # a posteri estimate of x
P=numpy.zeros(sz) # a posteri error estimate
xhatminus=numpy.zeros(sz) # a priori estimate of x
Pminus=numpy.zeros(sz) # a priori error estimate
K=numpy.zeros(sz) # gain or blending factor
R = 0.1**2 # estimate of measurement variance, change to see effect
# intial guesses
xhat[0] = 0.0
P[0] = 1.0
for k in range(1,n_iter):
# time update
xhatminus[k] = xhat[k-1]
Pminus[k] = P[k-1]+Q
# measurement update
K[k] = Pminus[k]/( Pminus[k]+R )
xhat[k] = xhatminus[k]+K[k]*(z[k]-xhatminus[k])
P[k] = (1-K[k])*Pminus[k]
pylab.figure()
pylab.plot(z,'k+',label='noisy measurements')
pylab.plot(xhat,'b-',label='a posteri estimate')
pylab.axhline(x,color='g',label='truth value')
pylab.legend()
pylab.xlabel('Iteration')
pylab.ylabel('Voltage')
pylab.figure()
valid_iter = range(1,n_iter) # Pminus not valid at step 0
pylab.plot(valid_iter,Pminus[valid_iter],label='a priori error estimate')
pylab.xlabel('Iteration')
pylab.ylabel('$(Voltage)^2$')
pylab.setp(pylab.gca(),'ylim',[0,.01])
pylab.show()
http://wiki.scipy.org/Cookbook/KalmanFiltering
¡ù z : ???? ??? ??
xhat : ??? ??
¡ù ¡°?? ???? ?? ??? ??? ?? ???.¡± (NASA)
¡°Stanley? ??? ?? ????.¡± (???? ??)
¡ù ??? ???? ???
???? ??](https://image.slidesharecdn.com/random-140530093559-phpapp01/85/-46-320.jpg)
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?? ???? ??? ??? ??
- 1. ?? ???? ??? ???? ??? LG CNS Advanced Analytics Center
- 2. ??????? ?? ??? ? ?? ???? ?? ????? ?? ??? ¡ü http://www.datanami.com/2012/12/24/how_object_storage_and_information_dispersal_address_big_data_challenges/
- 3. ??? ?? ? ?????, ???? ?? (??) ? ??? ?? 11?? ¨C ???? ?????? ? ?????, ? ??? (CAD) ¨C LG CNS ???? ?? ? ?????, ????/?? ? ???? ?????/ ????/???? ?? ??? (neuralix@gmail.com) www.facebook.com/groups/cvprml (>2,200?) ¡ù iPad 1, iPhone 5GS ? ??? ??, ??? ????? ? ??? ??+a? ?? ?????
- 4. ?? ??????? ???? ? ????? ?? ??? ????? ???? ??? ???? ??? ??? ?? ???? : ¡ù ?????? ¡®??¡¯? ???? ?? ???? ?? ? ??/?? ?? (¡°???? ?? ???? ???? ??¡±)
- 5. ????? ???? http://www.theguardian.com/technology/2012/sep/30/google-self-driving-car-unemployment http://edn.com/electronics-blogs/automotive-innovation/4402800/Self-driving-car-pushes-sensor-technology ? ?? ???? ??? ¡ù ??? ??? ? ??? ???? ??? LIDAR, ??? ???? RADAR?? ? ?? ?? ??? ???
- 6. ? ????¡ ? Data Type ¨C Image (O) ¨C Video (X) ? Language ¨C Python (2.7.X) ? Library (???) ¨C scikit-image NOT ? GPU(CUDA), 3D, ????, ???? ????, ??? ???? ¡ù ????? ??? ??? ??? ?? ?? ????
- 7. ?? ??? ?? ? Digitizing Figure Credit : Amin Allalou ¡ù ??? ??. ? ??? ?? ??? ?? ???? ????? ?
- 8. ?? ??? ?? ? ????? Figure Credit : Alex Lin 1. ????? ?? 2. ??? ?? ?? ??
- 9. ?? ??? ?? ? 256 ?? ?? Figure Credit : Ruye Wang Weber-Fechner law ¡°? ??? ?? ??? ?? ? ?? ??¡± (??? ??) ?? ??? ????? ? ??? ??
- 10. ?? ?? ?? ? ?? ?? ?? ?? ?? ???? ?? ??? ?? ??? ??? ?? BMP Ÿo ? (????) N/A ? ??? ??, Windows OS? TIFF ??? ?? ??, ?? ??? PNG ? ??? ?? ??? ?? GIF ÓÐ ?? ?? ???? ????, ????? ?? ? ??(:?? GIF) JPG ?? ?? ?? ?? ??? ??? ?? ?? ??? ???? ??? ???
- 11. ? ? ? Basic Processing ? Region Processing ? Edge/Contour ? Point/Line/Corner ? Matching ? Preprocessing ? Color ¡ù ??? ????? ??
- 12. Basic Processing ¡°??? ?? ? ? ? ??.¡± (?, ?????...)
- 13. Load/Save and Display ? PIL (Python Image Library) ¨C Import Module ¨C Load ¨C Display ¨C Save >> from PIL import Image >> from pylab import * >> Img = Image.fromarray(img) >> Img.save('C:mandrill_new.bmp') >> img = array(Image.open('C:mandrill.bmp')) >> imshow(img)
- 14. ?? ?? ? ??? ??? ??? matplotlib? ? ????? pylab? ??? ? ??? ? numpy? ?? ?? ? ? ???, ?? ? ???? ??? ??? ?? ??? ?? ?? ?? ????? ? ?? ? ?? ??? ?? ?? ??? ???? ?? ??? ?? ?? ? ?? ( ?? ?, neuralix@gmail.com ? inform ?? )
- 15. Load/Save and Display ? skimage >> from skimage import io >> io.imsave( filename, logo ) >> imshow( img ) ? opencv >> import numpy as np >> import cv2 >> cv2.imwrite( filename, img) >> cv2.imshow('image', img) >> camera = io.imread( filename ) >> img = cv2.imread( filename, 0 ) ¡ù URL? ??? ??
- 16. Pixel Operation ? Get pixel value ? Put Pixel Value ? Area Operation >> print img[ i ][ j ][ k ] for i in range(img.shape[0]): for j in range(img.shape[1]): for k in range(img.shape[2]): if i>=200 and i<300: if j>=200 and j<300: img[i][j][k] = 0 * img[i][j][k] >> img[ i ][ j ][ k ] = 0 >> img[200:300][200:300] = 0 * img[200:300][200:300] or Simply,
- 17. Line Drawing ? DDA (Digital Differential Analyzer) >> from skimage.draw import line, set_color >> rr, cc = line(x1, y1, x2, y2) >> set_color(img, (rr, cc), 1) >> imshow(img) ¡ù ?? ??? ??? ??
- 18. Polygon Drawing ? ??? ??? >> from skimage.draw import polygon >> img = np.zeros((width, height), dtype=np.uint8) >> x = np.array([x1, x2, x3, x4, ¡]) >> y = np.array([y1, y2, y3, y4, ¡]) >> rr, cc = polygon(y, x) >> img[rr, cc] = 1
- 19. Picking Points (¡°ginput¡±) ? ??? ??? ??? ??? ?? >> from PIL import Image >> from pylab import * >> img = array(Image.open('C:mandrill.bmp')) >> imshow(img) >> print 'Please click 3 points' >> x = ginput(3) >> print 'you clicked:',x >> show() >> x you clicked: [(16.737903225806448, 18.705645161 290249), (406.85887096774195, 23.2419354838709 18), (227.22177419354841, 242.79838709677415)] ¡ù ??? ??? MATLAB? ?? ?? (Graphical INPUT)??. ?? ?? ??? ???? ?????? ?.
- 20. Region Processing ¡ù ??? ??? ??
- 21. Thresholding (Binarizing) ? Otsu¡¯s (Automatic) Thresholding >> from skimage import data >> from skimage.filter import threshold_otsu, threshold_adaptive >> from skimage import color >> img = io.imread('C:Mandrill.bmp') >> img_g = color.rgb2grey(img) >> val_thresh = threshold_otsu(img_g) >> img_bw = im_g > val_thresh >> io.imshow( img_bw ) Figure Credit : https://software.intel.com/sites/products/documentation/hpc/ipp/ippi/ippi_ch7/functn_ComputeThreshold_Otsu.html ¡°Blob¡±
- 22. Labeling ? Connected Components Labeling http://engineersphere.com/biomedical-engineering/biomedical-image-processing-iii.html >> from skimage.morphology import label, closing, square >> from skimage.color import label2rgb ¡ >> val_thresh = threshold_otsu(img) >> img_label = label(val_thresh) >> image_label_rgb = label2rgb(label_image) >> imshow(image_label_rgb) ¡ù Check neighborhood convention first! (8-neighborhood is complete, 4-neighbor is not.)
- 23. Morphological Operation http://www.dspguide.com/ch25/4.htm >> from skimage.morphology import erosion, dilation, opening, closing, white_tophat >> from skimage.morphology import black_tophat, skeletonize, convex_hull_image >> from skimage.morphology import disk ¡ >> selem = disk(6) >> img_eroded = erosion(img, selem) >> imshow(img_eroded, cmap=plt.cm.gray) >> img_dilated = dilation(img, selem) >> imshow(img_dilated, cmap=plt.cm.gray) https://www.cs.auckland.ac.nz/courses/compsci773s1c/lectures/ImageProcessing-html/topic4.htm ? 1) ??? <????/????>, 2) ?? ?? ??? <??/??? ??>, 3) <??? ???/???? ????> ¡ù Structuring element (:selem)? ??? ?? ? ?? ??? ¡ù LIDAR/RADAR ?? ?? ???? ??
- 24. Boolean Operation ? Image Boolean Operations
- 25. Flood Fill ? Filling the holes >> from scipy import ndimage >> from skimage.filter import canny >> img_edges = canny(img/255.) >> img_filled = ndimage.binary_fill_holes(img_edges)
- 26. Segmentation ? Mean Shift >> import cv2 >> import pymeanshift as pms >> img_orig = cv2.imread("example.png") >>(img_segmented, img_label, number_regions) = pms.segment(img_orig, spatial_radius=6, range_radius=4.5, min_density=50) https://code.google.com/p/pymeanshift/ ¡ù ???(Segmentation)? ??? ??, ?? ?? ??? ?? ???? ??, ???? ? ??? ??. GraphCuts, GrabCuts? ??, Mean Shift(:Moving Averaging)? ??, Chan¡¯s Active Contour without Edges? ?? ?? ???
- 27. Edge / Contour ¡ù ??? ?, ???
- 28. Skeletonization ? Skeletonization http://homepages.inf.ed.ac.uk/rbf/HIPR2/thin.htm >> from skimage.morphology import skeletonize >> img_skeleton = skeletonize(image) >> imshow(img_skeleton, cmap=plt.cm.gray)
- 29. Contour Finding ? Contour Finding >> import numpy as np >> import matplotlib.pyplot as plt >> from skimage import measure >> x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j] >> r = np.sin(np.exp((np.sin(x)**3 + np.cos(y)**2))) >> contours = measure.find_contours(r, 0.8) >> fig, ax = plt.subplots() >> ax.imshow(r, interpolation='nearest', cmap=plt.cm.gray) >> for n, contour in enumerate(contours): ax.plot(contour[:, 1], contour[:, 0], linewidth=2) >> plt.show() ¡ù ???? ? ??? ??? ????
- 30. Edge Detection (1of2) ? Canny Edge Detection >> import numpy as np >> import matplotlib.pyplot as plt >> from scipy import ndimage >> from skimage import filter >> img_edges = filter.canny(img, sigma=3) >> imshow(img_edges, cmap=plt.cm.gray) ¡ù ???? ???? ??? ?? ??? ?? ??? ? ??? ???? ??? ????? ???? ?? ? ??? ? ???? (1???? ???). ¡ù ?? ??? Thresholding ? ?? ??? ??
- 31. Edge Detection (2of2) ? Canny Edge Detection http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/MARBLE/low/edges/canny.htm http://news.mynavi.jp/photo/series/computer_vision/040/images/014l.jpg http://homepages.inf.ed.ac.uk/rbf/HIPR2/gsmooth.htm ¡ù ?? ?? ??? ???? ?? (¡°Canny¡±)
- 32. Point / Line / Corner
- 33. Hough Transform (1of2) ? Hough Transform http://docs.opencv.org/modules/imgproc/doc/feature_detection.html
- 34. Hough Transform (2of2) ? Hough Transform http://www.aiaa-daycin.org/node/430 http://campar.in.tum.de/Students/DaPentenrieder >>from skimage.transform import (hough_line, hough_l ine_peaks, probabilistic_hough_line) >> edges = canny(image, 2, 1, 25) >> lines = probabilistic_hough_line(edges, threshold= 10, line_length=5, line_gap=3) >> for line in lines: p0, p1 = line plt.plot((p0[0], p1[0]), (p0[1], p1[1])) >> plt.show() ¡ù rho, theta ??? ? ??? x, y ??? ?? ? ??? ??? ¡ù ? ?? ?? ?? ??? rho, theta ???? voting?? ????? ???? ??? ???
- 35. Corner Detection (1of2) ? Harris Corner Detection http://www.krankerkaktus.de/Portfolio/GraphicsProgramming https://www.ee.iitb.ac.in/~sumantra/courses/ip/assignment_1.html >> from skimage.feature import corner_harris, corner _subpix, corner_peaks >> coords = corner_peaks(corner_harris(image), min_ distance=5) >> coords_subpix = corner_subpix(image, coords, win dow_size=13) >> fig, ax = plt.subplots() >> ax.imshow(image, interpolation='nearest', cmap=p lt.cm.gray) >> ax.plot(coords[:, 1], coords[:, 0], '.b', markersize=3) >> ax.plot(coords_subpix[:, 1], coords_subpix[:, 0], '+r', markersize=15) >> ax.axis((0, 350, 350, 0)) >> plt.show()
- 36. Corner Detection (2of2) ? Harris Corner Detection http://www.mathworks.co.kr/kr/help/images/detect-corners-in-images.html http://miac.unibas.ch/BIA/05-LandmarkBasedRegistration.html ¡ù ??? ??? ????? ?????? ??? ?, ???? ????? lambda 1, 2? ?? 0?? ? ? ?, ???? ?? ???? Corner? ???? ??.
- 38. Image Matching ? Template Matching >>import numpy as np >>import matplotlib.pyplot as plt >>from skimage import data >>from skimage.feature import match_template >>result = match_template( img_whole, img_template )
- 39. Noise Suppression ? Denoising ? Median >> from skimage import data, img_as_float >> from skimage.restoration import denoise_tv_ch ambolle, denoise_bilateral ¡ >> imshow(denoise_bilateral(img, sigma_range=0.1, sigma_spatial=15)) >> from skimage.morphology import disk >> from skimage.filter.rank import median ¡ >> img_med = median(img, disk(5))
- 40. Contrast ? Histogram Equalizing >> from skimage import exposure ¡ >> img_eq = exposure.equalize_hist(img) Figure Credit : James Fishbaugh ¡ù ????? ????? ??? ? ?????? ??? ?? ??? ?? ???. ?? ?? ? ????? ?????? CDF? ??? ????? ????? ??? ?? ?? ?? ?? ? ???? ??? ??? ?????? ???.
- 41. Rotation ? Rotation with Interpolation http://www.darktable.org/2012/06/upcoming-features-new-interpolation-modes-and-better-resize/dt-rotation-grid/ >> from skimage.transform import rotate ¡ >> img = rotate(img, 90, resize=True).shape >> import numpy as np ¡ >> coord_center = tuple(np.array(image.shape)/2) >> rot_mat = cv2.getRotationMatrix2D(coord_center,angle,1.0) >> result = cv2.warpAffine(image, rot_mat, image.shape,flags=cv2.I NTER_LINEAR) ¡ù ?? rotation? resizing? ?? ???? ??? interpolation? ?? ??? ? ?? ?. Skimage? ?? ??? ???? ?? ???? opencv? ??. ?? ima ge.shape,flags ??? ??? ??? ???.
- 42. Color (1of3) ? RGB ? Grey Luminousity (Grey Value) Average LightnessOriginal (Color) Figure Credit : Hohn D. Cook (http://www.johndcook.com/blog/2009/08/24/algorithms-convert-color-grayscale/) >> from skimage.color import rgb2gray >> from skimage import data ¡ >> img_gray = rgb2gray(img) // Luminousity
- 43. Color (2of3) ? Radiometry ? Photometry ? Colorimetry http://www.screentekinc.com/resource-center/photometry-and-colorimetry.shtml http://www.chromapure.com/products-d3pro.asp
- 44. Color (3of3) ? Color Space http://en.wikipedia.org/wiki/File:RGB_sliders.svg https://software.intel.com/sites/products/documentation/hpc/ipp/ippi/ippi_ch6/ch6_color_models.html ¡ù ??????? 1? 255? ?? ¡ù RGB? CMY? ?? (Cyan: R=0, G=255, B=255)
- 45. Kalman Filter (linear quadratic estimation)
- 46. Kalman Filtering ? Kalman Filtering # Kalman filter example demo in Python # A Python implementation of the example given in pages 11-15 of "An # Introduction to the Kalman Filter" by Greg Welch and Gary Bishop, # University of North Carolina at Chapel Hill, Department of Computer # Science, TR 95-041, # http://www.cs.unc.edu/~welch/kalman/kalmanIntro.html # by Andrew D. Straw import numpy import pylab # intial parameters n_iter = 50 sz = (n_iter,) # size of array x = -0.37727 # truth value (typo in example at top of p. 13 calls this z) z = numpy.random.normal(x,0.1,size=sz) # observations (normal about x, sigma=0.1) Q = 1e-5 # process variance # allocate space for arrays xhat=numpy.zeros(sz) # a posteri estimate of x P=numpy.zeros(sz) # a posteri error estimate xhatminus=numpy.zeros(sz) # a priori estimate of x Pminus=numpy.zeros(sz) # a priori error estimate K=numpy.zeros(sz) # gain or blending factor R = 0.1**2 # estimate of measurement variance, change to see effect # intial guesses xhat[0] = 0.0 P[0] = 1.0 for k in range(1,n_iter): # time update xhatminus[k] = xhat[k-1] Pminus[k] = P[k-1]+Q # measurement update K[k] = Pminus[k]/( Pminus[k]+R ) xhat[k] = xhatminus[k]+K[k]*(z[k]-xhatminus[k]) P[k] = (1-K[k])*Pminus[k] pylab.figure() pylab.plot(z,'k+',label='noisy measurements') pylab.plot(xhat,'b-',label='a posteri estimate') pylab.axhline(x,color='g',label='truth value') pylab.legend() pylab.xlabel('Iteration') pylab.ylabel('Voltage') pylab.figure() valid_iter = range(1,n_iter) # Pminus not valid at step 0 pylab.plot(valid_iter,Pminus[valid_iter],label='a priori error estimate') pylab.xlabel('Iteration') pylab.ylabel('$(Voltage)^2$') pylab.setp(pylab.gca(),'ylim',[0,.01]) pylab.show() http://wiki.scipy.org/Cookbook/KalmanFiltering ¡ù z : ???? ??? ?? xhat : ??? ?? ¡ù ¡°?? ???? ?? ??? ??? ?? ???.¡± (NASA) ¡°Stanley? ??? ?? ????.¡± (???? ??) ¡ù ??? ???? ??? ???? ??
- 47. ???? ? ???? Package, Tools ¨C Anaconda Distribution https://store.continuum.io/cshop/anaconda ¡ù opencv, scikit-image, spyder? ?? ?? ¨C Scikit-Image Package ? http://scikit-image.org ? http://scikit-image.org/docs/dev/auto_examples ¨C Pycharm IDE http://www.jetbrains.com/pycharm ¨C ?? Spyder IDE, iPython QTConsole, Sublime Text 2 ? ? ? (?? ? ???) ¨C http://book.naver.com/bookdb/book_detail.nhn?bid=6191572 ¨C http://book.naver.com/bookdb/book_detail.nhn?bid=6802764 ¨C http://book.naver.com/bookdb/book_detail.nhn?bid=6332184 ¨C http://kangcom.com/sub/view.asp?sku=200311040002 (??) ? ? (??) ¨C http://www.amazon.com/Machine-Vision-Ramesh-Jain/dp/0070320187 ¨C http://www.amazon.com/Computer-Vision-Algorithms-Applications- Science/dp/1848829345/ref=sr_1_1?s=books&ie=UTF8&qid=1401379429&sr=1- 1&keywords=szeliski+computer+vision
- 48. ?