Kristine Faith Roque's AP 186

Hello!  In this activity we will try to process a video of a kinematic event in order to extract information such as constants, frequencies, etc. For our group, we took a video of a 3D spring pendulum which we observed in one plane. We would like to trace its path and then try to determine its phase-space plot. The mass was covered in masking tape with the bottom colored red to facilitate easier segmentation. The video was taken using a Canon D10 camera at frame rate of 30fps.  Media 1. Video of the spring pendulum (first 50 frames only) The frames of the video were then extracted using Avidemux 2.5. The mass was then segmented from each frame using parametric segmentation. The patch of the region of interest (ROI) used for color segmentation is shown in Figure 1. Figure 1. Patch used to segment ROI  Using morphological operations, particularly Open and Close operations, the segmented images were cleaned. The extracted frames for different observation...

In image segmentation, we want to segment or separate a region of interest (ROI) from the entire image. We usually do this to extract useful information or identify objects from the image. The segmentation is done based on the features unique to the ROI.  In this activity, we want to segment objects from the background based on their color information. But real 3D objects in images, although monochromatic, may have shading variations. Hence, it is better to use the normalized chromaticity coordinates (NCC) instead of the RGB color space to enable the separation of brightness and pure color information.  To do this, we consider each pixel and the image and let the total intensity,  I, for that pixel be  I = R + G + B. Then for that pixel, the normalized chromaticity coordinates are computed as: r = R/I;                g = G/I;                   b = B/I The sum of all thre...

When doing image-based measurements, we often want to separate the region of interest (ROI) from the background. One way to do this is by representing the ROIs as blobs. Binarizing the image using the optimum threshold obtained from the image histogram simplifies the task of segmenting the ROI. Usually, we want to examine or process several ROIs in one image. We solve this by looping through the subimages and processing each. The binarized images may be cleaned using morphological operations.  In this activity, we want to be able to distinguish simulated "normal cells" from simulated "cancer cells" by comparing their areas. We do this by taking the best estimate of the area of a "normal cell" and making it our reference.  Figure 1 shows a scanned image of scattered punched papers which we imagine to be cells examined under the microscope. These will be the "normal cells." Figure 1. Scattered punched paper digitized using flatbe...

For this activity, we will try to read a sheet music via image processing and play the music using Scilab. A sheet music is basically black and white so it is easy to separate the notes from the background by thresholding and applying morphological operations.  The first task for this is to find a sheet music. I used the song Clementine and the sheet music is found in Figure 1. Figure 1. Sheet music for Celementine To make processing easier we divide the sheet into different lines. In this case we have four lines as shown in Figure 2. Figure 2. The 4 lines in the sheet music of Clementine were separated  (line 1 to 4 from top to bottom) We then binarize the images with a threshold of 0.95 and invert them such that the background is black and the foreground is white. We disregard the area of the image where the clef is located. Using morphological operations, specifically CloseImage(),OpenImage() and ErodeImage() we reduce the notes into small blobs...

In this activity, we aim to be able to extract text (handwritten or typed) using image processing techniques that we have learned. The image in Figure 1 is the source of the text we need to extract.   Figure 1. Image of a document from which text will be extracted The image is tilted so it was rotated using Gimp 2.8. Using the same software, I selected a portion of the image and cropped it (Figure 2a). The image was then loaded in Scilab 4.1.2 and converted to grayscale for image processing. The grayscale image is shown in Figure 2b.   Figure 2. (a) Cropped portion from the rotated image of the document  (b) grayscale version of the patch in (a) The first task was to remove the lines, to do this, I took the fftshifted FT of the grayscale image and multiplied it by a mask to filter out the higher order frequencies that contribute to the lines. I then took the inverse FT to get the image with the lines removed. I then binarized the image and inverted it...

When talking about morphology, what immediately comes to mind are the forms and structures or shapes of objects. Hence, performing morphological operations imply that the shape or form of an object is altered.       In this activity, we will perform morphological operations on binary images. In particular, we make use of erosion and dilation . Erosion and dilation were performed on the following: 1. A 5×5 square 2. A triangle, base = 4 boxes, height = 3 boxes 3. A hollow 10×10 square, 2 boxes thick 4. A plus sign, one box thick, 5 boxes along each line Using each of the structuring elements below: 1. 2×2 ones 2. 2×1 ones 3. 1×2 ones 4. cross, 3 pixels long, one pixel thick. 5. A diagonal line, two boxes long, i.e. [[0 1],[1 0] ].      When performing these operations, it is important to note the “anchor” or “origin” of the structuring element in order to give an accurate prediction of the result. For the 2x2 ones, 2...