Introduction to Computer Vision - The Local Binary Pattern - IT Assignment Help

Assignment Task:

1) Transformations 

Rotation, translation and skew are useful operations for matching, tracking, and data augmentation. 

a) Write a function that takes as input an image I, rotates it by an angle θ1 and horizontally skews it by an angle, θ2. Write the matrix formulation for image rotation R(.) and skewing S(.). Define all the  variables. Note that the origin of the coordinate system of the programming environment you use might be different from the one shown in the lectures.  

b) Create an image that contains your name written in Arial, point 72, capital letters. Rotate clockwise the image you created by 30, 60, 120 and -50 degrees. 

Skew the same image by 10, 40 and 60 degrees.  

Complete the process so that all the pixels have a value. Discuss in the report the advantages and  disadvantages of different approaches. 

c) Analyse the results when you change the order of the two operators, that is R(S(I)) and S(R(I)). i) Rotate the image by θ1 = 20 clockwise and then skew the result by θ2 = 50.  

ii) Skew the image by θ2 = 50 and then rotate the result by θ1 = 20 clockwise. 

Are the results of (i) and (ii) the same? Why?

Texture Descriptors and Classification (Use Datasets A and B) 

The Local binary pattern (LBP) operator describes the surroundings of a pixel by generating a bit-code  from the binary derivatives of a pixel. 

a) Write a function that divides an input (greyscale) image into equally sized non-overlapping windows  and returns the feature descriptor for each window as distribution of LBP codes. For each pixel in the  window, compare the pixel to each of its 8 neighbours. Convert the resulting bit-codes (base 2) to  decimals (base 10 numbers) and compute their histogram over the window. Normalize the histogram.  The histogram is now a feature descriptor representing the window. Show in the report the resulting  images and discuss how you handled the image border. 

b) Come up with a descriptor that represents the whole image as consisting of multiple windows. For  example, you could combine several local descriptions into a global description by concatenation. Discuss in the report alternative approaches. Using the global descriptor you created, implement a  classification process that separates the images in the dataset into two categories: face images and non-face images (You could use simple histogram similarities). Comment the results in the report. Is  the global descriptor able to represent whole images of different types (e.g. faces vs. cars)? Identify  problems (if any), discuss them in the report and suggest solutions. 

c) Decrease the window size and perform classification again. Comment the results in the report.  d) Increase the window size and perform classification again. Comment the results in the report.  e) Discuss how LBP can be used or modified for the analysis of dynamic textures in a video. 

Object segmentation and counting (Use Dataset C) 

Moving objects captured by fixed cameras are the focus of several computer vision applications. 

a) Write a function that performs pixel-by-pixel frame differencing using, as reference frame, the first  frame of an image sequence. Apply a classification threshold and save the results to disk. 

b) Repeat the exercise using the previous frame as reference frame (use frame It-1 as reference frame  for frame It, for each t). Comment the results in the report. 

c) Write a function that generates a reference frame (background) for the sequence using for example  frame differencing and a weighted temporal averaging algorithm.  

d) Write a function that counts the number of moving objects in each frame of a sequence. Generate a  bar plot that visualizes the number of objects for each frame of the whole sequence. Discuss in the  report the implemented solution, including advantages and disadvantages. How could the  disadvantages be overcome?

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