In this presentation Phineas will demonstrate using dynamic programming to implement an efficient content aware rescaling algorithm. This talk will cover how to integrate python and q to allow us to inspect intermediate results and display them. We’ll cover: • use q to do matrix convolutions quickly • implement the seam carving algorithm • pass image data between q/python • use python to display the data with interactive controls

 

 

The dynamic programming approach to content-aware image resizing is a technique for resizing images while preserving their most important content. The algorithm works by first calculating an energy map for the image. The energy map is a measure of the importance of each pixel in the image. The algorithm then finds the lowest energy seam in the image, which is a path of pixels from top to bottom that has the lowest overall energy. The algorithm then removes the seam from the image, which reduces the width of the image by one pixel. The algorithm repeats this process until the image has the desired width.

The following is a step-by-step overview of the dynamic programming approach to content-aware image resizing:

1. Calculate the energy map for the image.
2. Find the lowest energy seam in the image.
3. Remove the seam from the image.
4. Repeat steps 2 and 3 until the image has the desired width.

The energy map for the image can be calculated using a variety of different methods. One common method is to use the gradient of the image. The gradient of an image is a measure of the change in intensity between adjacent pixels. Pixels with a high gradient are typically more important than pixels with a low gradient.

To find the lowest energy seam in the image, the algorithm uses a dynamic programming approach. The algorithm works by building a table that stores the lowest energy path to each pixel in the image. The algorithm starts at the top of the image and calculates the lowest energy path to each pixel in the first row. The algorithm then moves on to the second row and calculates the lowest energy path to each pixel in the second row, taking into account the lowest energy paths to the pixels in the first row. The algorithm continues this process until it reaches the bottom of the image. The lowest energy seam is the path of pixels from top to bottom that has the lowest overall energy.

To remove the seam from the image, the algorithm simply deletes the pixels in the seam. This reduces the width of the image by one pixel.

The dynamic programming approach to content-aware image resizing is a powerful technique for resizing images while preserving their most important content. The algorithm is efficient and can be used to resize images of any size.

Here are some of the advantages of using the dynamic programming approach to content-aware image resizing:

• The algorithm is efficient and can be used to resize images of any size.
• The algorithm is able to preserve the most important content in the image, even when the image is resized significantly.
• The algorithm is relatively easy to implement.

Here are some of the disadvantages of using the dynamic programming approach to content-aware image resizing:

• The algorithm can be sensitive to the energy map that is used. If the energy map is not calculated accurately, the algorithm may not preserve the most important content in the image.
• The algorithm can be time-consuming to run on large images.

Overall, the dynamic programming approach to content-aware image resizing is a powerful and versatile technique for resizing images while preserving their most important content.