w5 gemini Flashcards
What is the primary goal of image segmentation in computer vision?
To group together elements of an image that ‘belong together’ and segment these elements from all others.
Differentiate between ‘grouping’ and ‘segmenting’ in the context of image analysis.
Grouping refers to bringing together elements that are similar or connected, while segmenting involves isolating these grouped elements from the rest of the image.
List four main categories of methods used for image segmentation.
Thresholding, Region-based methods, Clustering, and Fitting.
Explain the concept of ‘Feature Space’ in the context of image segmentation.
Feature space is a conceptual space where each element of an image (e.g., a pixel) is represented as a point based on its feature values. Similarity between elements is determined by the distance between their points in this space.
Name at least three features that are commonly used in computer vision for determining if elements ‘belong together’.
Location (proximity), Colour/Intensity, Texture, Depth, Motion, Contour, and CNN features.
Explain the role of ‘Similarity Measures’ in feature space for image segmentation.
Similarity measures quantify the resemblance between image elements based on their feature vectors. This helps in grouping elements that are considered alike.
Provide an example of a similarity measure and briefly explain how it works.
Euclidean distance is a common similarity measure. It calculates the straight-line distance between the feature vectors of two elements in feature space; smaller distances indicate higher similarity.
Why is ‘feature scaling’ important when using multiple features for image segmentation?
Features with different scales can disproportionately influence distance calculations. Feature scaling ensures that all features contribute equally to the similarity assessment.
Explain the basic principle of ‘Thresholding’ for image segmentation.
Thresholding segments an image by setting a threshold value. Pixels with intensity values above the threshold are assigned to one group, and those below are assigned to another.
What is the limitation of basic thresholding methods, and how can ‘local thresholding’ address this?
Basic thresholding uses a single threshold for the entire image, which can be ineffective with uneven illumination. Local thresholding applies different thresholds to different regions of the image to adapt to varying lighting conditions.
Describe the ‘Hysteresis thresholding’ technique and its advantage over single-threshold methods.
Hysteresis thresholding uses two thresholds. Pixels above the high threshold are classified as foreground, and those below the low threshold are background. Pixels between the thresholds are classified based on their connectivity to foreground pixels, reducing noisy segmentation.
What are ‘Morphological Operators’, and how are they used in conjunction with thresholding?
Morphological operators (like dilation and erosion) are used to clean up the results of thresholding by filling gaps, removing noise, and smoothing boundaries in binary images.
Explain the ‘Dilation’ operation in morphological image processing.
Dilation expands the area of foreground pixels in a binary image. Background pixels adjacent to foreground pixels are changed to foreground.
Explain the ‘Erosion’ operation in morphological image processing.
Erosion shrinks the area of foreground pixels in a binary image. Foreground pixels adjacent to background pixels are changed to background.
What is the fundamental drawback of thresholding that ‘Region-based’ methods aim to address?
Thresholding does not explicitly consider the spatial relationships between pixels.
Describe the ‘Region Growing’ segmentation method.
- starts with 1 seed pixel
- iteratively adds neighbouring pixels to the region based on a similarity criterion.
- continue until region stops growing
- pick another random seed (from those not assigned to a region) and repeat until all labelled
Explain the ‘Region Merging’ segmentation method.
- starts with each pixel (or small regions) as a separate region
- iteratively merges adjacent regions based on their similarity
- continue merging adjacent regions until can’t anymore, mark region complete
- repeat on remaining until all finals
Explain the ‘Region Splitting and Merging’ segmentation method.
Splitting;
1) begin by splitting image into quadrants
2) for each region- if all pixels are not similar, split into 4 sub quadrants
Continue until each region is homogeneous
Merging;
For each region - merge with similar neighbours
Continue until no more regions can merge
What is a potential problem with ‘Region Merging’ related to the order of merging?
The order in which regions are merged can affect the final segmentation, especially if the properties used for merging are averages of the constituent parts.
What are some general problems associated with region-based segmentation methods?
Meaningful regions may not have uniform properties (e.g., due to lighting variations), and it’s unusual for natural images to be composed of perfectly uniform regions.
What is the core idea behind ‘Clustering-based Segmentation’?
Clustering aims to group image elements into clusters based on the similarity of their feature vectors, without prior knowledge of the number or characteristics of the clusters.
Differentiate between ‘Partitional Clustering’ and ‘Hierarchical Clustering’.
Partitional clustering divides data into non-overlapping subsets, while hierarchical clustering creates a nested hierarchy of clusters.
Explain the ‘K-means Clustering’ algorithm.
K-means randomly initializes cluster centers, assigns each data point to the closest center, recalculates the centers based on the assigned points, and repeats until the centers stabilize.
What is a limitation of the K-means algorithm regarding the initial placement of cluster centers?
The final clustering result in K-means can be sensitive to the initial random placement of cluster centers, potentially leading to different outcomes for different runs.
