Discover @j-manansala Open Source projects

J. Manansala (@j-manansala)

j-manansala

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Location 🇵🇭 Philippines
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11

image-warping

In this notebook, we will learn how we can apply the homography matrix to adjust the camera perspective in images. We will show the potential and the limitations of the homography matrix in warping images.

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image-differencing

In this notebook, we will learn how we can apply the image differencing to detect changes and movement in each frame of a video. We will also apply homography matrix and template matching to enhance our analysis of this problem further.

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white-balancing

In this notebook, we will explore how the colors in the images can be corrected using white balancing techniques such as the white patch algorithm, gray world algorithm, and ground truth algorithm.

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connected-components

In this notebook, we will explore how to automatically detect, label, and measure objects in images using connected components. This method improves on blob detection methods by grouping pixels based on their connectivity.

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thresholding

In this notebook, we will explore how to conduct image segmentation using trial and error thresholding and Otsu’s method. We will also explore how the RGB and HSV color spaces can help pinpoint and segment objects in images.

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object-detection

In this notebook, we will learn how we can find an object in an input image using template matching. We will also explore how the homography matrix can help address the template matching algorithm’s limitations by warping the image to achieve similar scales and orientations for the objects.

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morphological

In this notebook, we will explore how to clean, prepare and enhance images using morphological operations. The operations like erosion, dilation, opening, closing, area_opening, and area_closing will be demonstrated.

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histogram-manipulation

In this notebook, we will explore how the colors in the images can be corrected using histogram manipulation techniques. Various distribution functions will be implemented to see how they can affect the distribution of colors in the resulting image

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blob-detection

In this notebook, we will explore how to automatically detect blobs in an image using the Laplacian of Gaussian (LoG), Difference of Gaussian (DoG), and Determinant of Hessian (DoH). This can be particularly useful when counting multiple repeating objects in an image.

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