"ippr" 태그로 연결된 3개 게시물개의 게시물이 있습니다.

Point Cloud​

• The simplest form of a 3D model, a collection of individual points plotted in 3D space.
• Photogrammetry: science of making measurements from photographs
  • it uses photos of an object taking a different locations
• Vertices -> Edges -> Faces -> Polygons -> Surfaces
• PCL: Point Cloud Library

NeRFs​

• Neural Radiance Fields
• NeRF Studio

Gaussian Splatting​

• Instead of building objects using polygons, it represents everything using millions of tiny soft 3D shapes called Gaussians.
• Less computational costs, more efficient.
• Run in parallel using GPU rasterization.

1. First, it builds a rough point cloud from images
2. Then, replaces those points with these Gaussian blobs
3. It will optimize them until it match original photos as closely as possible.

Edge Detectors​

• Roberts, Sobel, Prewitt
  • Simple and fast
  • Must verify if they are adequate for the application
  • Sobel is often used
• LoG, Canny
  • More sophisticated
  • LoG uses the total gradient magnitude and direction to find edges
  • Canny uses the 2nd derivative magnitude in the gradient direction
  • Canny is more accurate and most often used

Binary Morphology​

• Taking binary images and modifying them systematically to extract information about the shapes in the image.
• A system of algebraic operations
  • conveniently process binary objects
  • elimate object shape distortions, typically due to acquisition noise
  • decomposing objects into simpler objects for easier shape characterization
• Dilation, Erosion, Closing, Opening, Shrinking, Skeletonization, and Thinning

Dilation​

$A \oplus B = \{ c \in E^N | c = a + b, a \in A, b \in B \}$

# A
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 1 0 0 0
0 0 0 1 0 0 0
0 0 0 1 1 0 0
0 0 1 0 0 0 0 
0 0 0 0 0 0 0

# B
1 1 1
1 1 1
1 1 1

# like stamping

# A \oplus B
0 0 0 0 0 0 0
0 0 X X X 0 0
0 0 X X X 0 0
0 0 X X X X 0
0 X X X X X 0
0 X X X 0 0 0

# A
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 1 0 0 0
0 0 0 1 0 0 0
0 0 0 1 1 0 0
0 0 1 0 0 0 0
0 0 0 0 0 0 0

# B
0 1 0
1 0 1
0 1 0

# A \oplus B
0 0 0 0 0 0 0
0 0 0 X 0 0 0
0 0 X X X 0 0
0 0 X X X 0 0
0 0 X X X X 0
0 X 0 X X 0 0
0 0 X 0 0 0 0

Erosion​

$A \ominus B = \{ x \in E^N | x + b \in A, \forall b \in B \}$

• It reducs the image based on the structing element B.
• Simple way of computing the erosion is to translate the initial image in the directions opposite of B 1s and AND the results.
• It checks the neighboring pixels and keeps only the pixels where the entire structuring element fits within the foreground.

Opening and Closing​

$A \circ B = (A \ominus B) \oplus B$

• $A \circ K \neq A$

$A \bullet B = (A \oplus B) \ominus B$

• $A \bullet K \neq A$

Controlled Erosions​

• It doesn't result in the complete removal of the object.
• Shrinking: Repeatedly reduces an object until each connected component becomes a single point or a minimal shape.
• Skeletonization: Reduces an object to a one-pixel-wide skeleton while preserving its overall topology and structural shape.
• Thinning: Reduces the thickness of an object while preserving its connectivity and general shape.

Object geometrical properties​

• Area
• Centroid
• Perimeter pixels
• Perimeter length
• Circularity
  • Haralick circularity
• Bouding box
• Spatial moments

riceim = imread('rice.png')
imshow(riceim);

level = graythresh(riceim);
bw = imbinarize(riceim, level);
rice_level = bwlabel(bw);

rice_level_rgb = label2rgb(rice_level);
imshow(rice_level_rgb);

pl_im = imread("Alaska_Airlines_Boeing_737-898.jpg")
pl_im = imresize(pl_im, 0.25);
pl_grey = rgb2gray(pl_im);
imshow(pl_grey);

se = strel('square', 3);
pl_erode = imerode(pl_BW, se);
pl_erode = imerode(pl_erode, se);
pl_erode = imerode(pl_erode, se);
figure(2);
imshow(pl_erode);

pl_skel = bwmorph(pl_BW, 'skel', Inf);
imshow(pl_skel);

pl_thin = bwmorph(pl_BW, 'thin', Inf);
imshow(pl_thin);

se_close = strel('disk', 20);
pl_close = imclose(pl_BW, se_close);
imshow(pl_close);

pl_skel2 = bwmorph(pl_close, 'skel', Inf);
imshow(pl_skel2);

cell_im = imread('cell.tif');
imshow(cell_im);

cell_edge = edge(cell_im, 'Sobel');
imshow(cell_edge);

se_close = strel('disk', 7);
cell_edge_close = imclose(cell_edge, se_close);
imshow(cell_edge_close);

cell_edge_close_clean = imclearborder(cell_edge_close);
imshow(cell_edge_close_clean);
figure(3);
imshow(labeloverlay(cell_im, cell_edge_close_clean));

Matching, Finding or Tracking Objects​

1. Detect invarient features of the image
2. Describe the local area around each feature
3. Match patterns of the local feature descriptions

Corners​

• Invariant to rotation, translation and scaling
• Harris corner detector is a popular method

Features​

• Detectors: detects the location of the features in an image or video
• Descriptors: summarizes the apperance of the neighborhood.
• Used in many applications: Tracking, object matching, stero vision, object and action recognition.

SIFT​

Scale-Invariant Feature Transform

1. Build a scale-space pyramid of Differences of Gaussians (DoG) and detect minima/maxima.
2. Localize Keypoints
3. Assign key point and orientation and scale
4. Compute the SIFT descriptor at the assigned orientation and scale.

Image Processing Operations​

Point Operation​

$b[m, n] = f(a[m, n])$

• It only depends on the value of the pixel itself, not on the values of its neighbors.
• e.g. current pixel + 20.
• to increase the brightness of an image, adjust contrast, or apply a threshold to create a binary image.

Local Operation​

$b[m, n] = f(a[m - 1, n - 1], a[m - 1, n], a[m - 1, n + 1], a[m, n - 1], a[m, n], a[m, n + 1], a[m + 1, n - 1], a[m + 1, n], a[m + 1, n + 1])$

• It depends on the values of the pixel and its neighbors.
• e.g. current pixel + average of 8 neighbors.
• to blur an image, sharpen an image, detect edges, or convolution with a kernel.
• The most common type of neighborhoods are:
  • 4-neighbors:
    • top, bottom, left, right.
  • 8-neighbors:
    • top, bottom, left, right, and the 4 diagonal neighbors.

Global Operation​

$b[m, n] = f(a[0, 0], a[0, 1], ..., a[M - 1, N - 1])$

• It depends on the values of all pixels in the image.
• e.g. current pixel + average of all pixels in the image.
• to compute the histogram equalization, apply a global threshold, or perform a Fourier transform.

Image Histogram​

• It is a graph showing how many pixels in an image have each possible intensity value.
  • Intensity value: the brightness of a pixel.
• e.g. 8-bit grayscale image has 256 possible intensity values (0-255).
  • The histogram will graphically display 256 numbers showing the distribution of pixels among those gray-scale values.

Histogram Equalization​

0                     255
|------████████--------|
       80~140에 몰림

0                     255
|--██--██--██--██--██--|

• It spreads out the intensity values that are concentrated in a narrow range, increasing the contrast of the image.
• It is useful when the images have been acquired under poor lighting conditions or have low contrast (different circumstances).

Noise​

• Any undesired information that contaminatest the image.
• During the analog-to-digital conversion process, it is a side effect of the physical conversion of patterns of light energy into electrical patterns.
• The shape of distribution of noise types used to describe many of them and is related closely to the histogram.

Gaussian Noise​

frequency
 ^
 |             █
 |          █████
 |       █████████
 |    █████████████
 +----------------------> noise gray level
        -20  0  +20

• The most common type of noise, with a bell-shaped distribution.
• Natural noise process such as electronic noise in the image acquisition system.

Uniform Noise​

frequency
 ^
 |       ┌───────────────┐
 |       │               │
 |       │               │
 +-------┴───────────────┴------> noise intensity
         a               b

• A type of noise with a distribution that is constant across the range of intensity values.
• The gray-level values of noise are evenly distributed across a specific range.
• It can be used to generate any toehr type of noise distribution, often used to degrade images for the evaluation of image restoration algorithms.
  • it provides the most unbiased or neutral noise model.

Salt-and-pepper noise​

frequency
 ^
 | █                       █
 | █                       █
 | █                       █
 +----------------------------> gray level
   0                         255

• A distribution that has two spikes at the minimum and maximum intensity values.
• The presence of single dark pixels in bright regions, or single bright pixels in dark regions.
  • Typically affects a small set of pixels.
• It is usually quantified by the percentage of pixels which are corrupted by noise.
• It is typically caused by errors in data transmission, faulty memory locations, or malfunctioning pixel elements in camera sensors.

Signal-to-Noise Ratio​

$SNR = 10 \log_{10} \frac{P_{signal}}{P_{noise}}$

• SNR
• The ratio between the power of the signal and that of the noise.
• In a perfect image, the ratio of signal to noise is infinite.

Noise Elimination​

• Restore the true value of the pixels as much as possibole.
• It may undesirably reduce image information.
• Averaging the pixel with its neighbours will smooth the noise or other types of image filters can be applied to reduce noise.

Filters​

• Linear filters: low pass, high pass
• Non-linear filters: median
• Filters are used to improve an image
  • if the image is destined for human viewing, to make it more pleasant to look it or more readable.
  • if the image is the input to a pattern recognition process, to facilitate the following steps of automated image analysis.

Convolution​

$I(r, c) \otimes F = \sum_{i=1}^{2M + 1} \sum_{j=1}^{2M+1} I(r+i-(M+1), c + j-(M+1)) F(i, j)$

• Multiply the pixels of a neighborhood of $(r, c)$ by the corresponding coefficients of the filter $F$, and add them all together.

Low Pass Filter​

• Smoothing or softening, employes to remove high spatial frequency noise from a disital image.
• It replace each pixwel with a weighted sum of each pixel's neighbors.
• It is used to remove noise, might have the side-effect of generally smoothing or blurring images and reducing edge information.
• Local averaging: take the local average of the pixels in a neighborhood and replace the center pixel with that value.

Gaussian Filter​

$H_{ij} = \frac{1}{2\pi\sigma^2} e^{-\frac{i^2 + j^2}{2\sigma^2}}$

• yields a $2k+1 \times 2k+1$ kernel, where $k$ is the size of the filter and $\sigma$ is the standard deviation of the Gaussian distribution.
• A smoothing filter that computes a weighted average of neighboring pixels, giving larger weights to pixels closer to the center.

1   4   7   4   1
4  16  26  16   4
7  26  41  26   7
4  16  26  16   4
1   4   7   4   1

• Smaller $\sigma$ values result in a more localized filter, which means weak smoothing and less blurring of the image.
• Larger $\sigma$ values result in a more spread-out filter, which means stronger smoothing and more blurring of the image.

Median Filter​

• A non-linear filter that replaces a pixel with the median of its neighbors.
• It is effective at removing salt-and-pepper noise and other isolated noise compared to low-pass linear filters.
• Less blurred, edges remain sharp, removes single pixel erros completely, but slower requires sorting the pixels in the neighborhood.

10  11  10
12 255  11
10  12  11

# 255 is salt-and-pepper noise, the median of the 9 pixels is 11, so the center pixel is replaced with 11.
10, 10, 11, 11, 11, 12, 12, 255

# to-be
10, 10, 11, 11, 11, 12, 12, 11

High Pass Filter​

• It extracts high-frequency components, such as edges and fine details, by subtracting a low-pass filtered image from the original image.
• Sometimes, it is desired to enhance the high frequencies without removing the low frequencies.

Sharpened Image = Original Image + High-frequency component
  = Origial Image + (Original Image - Low-pass filtered Image)

Conclusion​

• Low-pass filter → smooth / blur
• High-pass filter → edge / detail
• High-pass + original → sharpening