<aside> ⚠️ Cái bài báo này viết khá vắn tắt (cảm giác bả viết tóm tắt 1 bài báo nào đó). Do đó trong quá trình đọc mình có tìm những nguồn ngoài giải thích kỹ hơn, có ref trong bài!

</aside>

<aside> ☝ Đọc bài báo chính, note này phụ.

</aside>

Part 1 — Gradient Vector, HOG, and SS

• “Object detection” (spot where the object is) ~ “Object recognition” (whether an object exists in an image) ← but they may be called with the same thing.

Image Gradient Vector

• Recall

  	Derivative	Directional Derivative	Gradient
  Value type	Scalar	Scalar	Vector
  Definition	The rate of change of a function f(x,y,z,…) at a point (x0,y0,z0,…), which is the slope of the tangent line at the point.	The instantaneous rate of change of f(x,y,z,…) in the direction of an unit vector u→.	It points in the direction of the greatest rate of increase of the function, containing all the partial derivative information of a multivariable function.

• In the image processing, we want to know the direction of colors changing from one extreme to the other (i.e. black to white on a grayscale image). ← measure gradient

• The image gradient vector (check wiki) is defined as a metric for every individual pixel, containing the pixel color changes in both x-axis and y-axis.

• However, repeating the gradient computation process for every pixel iteratively is too slow. Instead, ❓it can be well translated into applying a convolution operator (kernel) on the entire image matrix ($A$)

Common image processing kernels

• ⭐ Image Kernels explained visually ← visually để hiểu tác dụng của mấy cái kernel
• Tool: https://github.com/bruckner/deepViz ← run locally to visualize CNN layers, kernels,….
• She has an example of using kernel applying to black-white image ← For colored images, we just need to repeat the same process in each color channel respectively.

Histogram of Oriented Gradients (HOG)

Note: HOG (Histogram of Oriented Gradients)

Image Segmentation (Felzenszwalb’s Algorithm)

⭐ Check this article.

When images contains multiple objects → find region containing each object → Felzenszwalb algo. ← graph based approach

Find weights $w(v_i, v_j)$ between 2 pixels → find similarity → more similarity must belong to the same components

→ more in the blog!