Time complexity: The basic Watershed algorithm has a time complexity of O(N log N), where N is the number of pixels in the image. This complexity arises from the sorting operations involved in processing the image gradient. Space complexity: The …
The overall purpose of these steps is to preprocess the image and create a binary image (sure_bg) that serves as a basis for further steps in the watershed algorithm. It helps to distinguish the background from potential foreground objects, contributing …
In this example code, we will implement the following steps: Let’s read the video data from the input directory, extract the frames for the video, and then print the original video frame: video_path = “/datasets/Ch9/Kinetics/dance/dance3.mp4”Check if the file existsif os.path.exists(video_path):cap …
The Watershed algorithm is a popular technique used for image segmentation, and it can be adapted to label video data as well. It is particularly effective in segmenting complex images with irregular boundaries and overlapping objects. Inspired by the natural …
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Using a pre-trained autoencoder model to extract representations from new data can be considered a form of transfer learning. In transfer learning, knowledge gained from training on one task or dataset is applied to a different but related task or …
In this example, a threshold value of 0.5 is used. Pixels with values greater than the threshold are considered part of the foreground, while those below the threshold are considered part of the background. The resulting binary frames provide a …
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The choice of loss function, whether it’s binary cross-entropy (BCE) or mean squared error (MSE), depends on the nature of the problem you’re trying to solve with an autoencoder. BCE is commonly used when the output of the autoencoder is …
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Let’s see some example Python code to label the video data, using a sample dataset: Let’s import the libraries and define the functions: import cv2import numpy as npimport cv2import osfrom tensorflow import keras Let us write a function to load …
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Autoencoders are a powerful class of neural networks widely used for unsupervised learning tasks, particularly in the field of deep learning. They are a fundamental tool in data representation and compression, and they have gained significant attention in various domains, …
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Here is the output: Figure 9.1 – CNN model loss and accuracy This code snippet calculates the test loss and accuracy of the model on the test set, using the evaluate function. The results will provide insights into how well …
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