Image Processing Techniques Quiz

A method of estimating the value of a pixel at a desired location by considering the values of its four nearest neighbors, forming a rectangle around the target pixel.

A method of estimating the value of a pixel at a desired location by considering the values of its 16 nearest neighbors, forming a larger square around the target pixel.

The process of aligning two or more images to ensure they share the same spatial reference, allowing for meaningful comparison or processing.

Reducing noise in an image by averaging the values of multiple images of the same scene.

A measure of distance used in image processing, where distance is calculated along rows and columns, like moving a chess king.

A technique used to assign approximate intensity values to points in an image where the original data is missing, utilizing nearby pixel values.

The process of reducing the number of distinct values or colors that an image can represent, resulting in a smaller amount of information.

The intensity value of a specific pixel in a digital image, often represented as a number between 0 and 255, where 0 is black and 255 is white.

The maximum value of the function representing the image at a specific point.

The coordinates that represent the location of a pixel in an image.

The number of pixels in an image, and their arrangement in rows and columns.

A group of pixels that are connected to each other based on a specific criterion.

The process of representing the amplitude values of image pixels using digital codes.

A technique used to analyze the contribution of specific bits to the overall image by isolating and visualizing each bit-plane.

A process that aims to create a uniform distribution of pixel values in an image.

A technique that modifies the histogram of an image to match a specified shape.

Functions operating directly on the pixel intensities of an image, often used for image enhancement.

A general form of transformation commonly used to enhance the visibility of low-intensity regions, similar to Gamma correction.

A commonly used technique to enhance edges by emphasizing transitions in pixel intensities.

A blurring filter that averages the pixel values in a neighborhood.

A type of filter that enhances edges and details by emphasizing high-frequency components of the image.

A filter that removes noise and unwanted details from an image by suppressing high-frequency components.

The range of gray levels that an image can represent, from the darkest to the lightest.

A process that enhances specific gray levels in an image by making them brighter or darker, while leaving other gray levels unchanged, making those specific levels stand out.

A transformation that adjusts the brightness of an image by altering the relationship between the input and output pixel intensities, typically used to compensate for differences in lighting.

An algorithm that uses edge linking to differentiate true edges from texture, creating edges with smoother contours and avoiding false positives from textured regions.

A technique for line detection that finds lines by accumulating evidence in a parameter space, but suffers from high memory consumption.

The process of linking edge pixels based on their gradient magnitude and direction, which helps to filter out noise and isolate true edges.

A method for edge detection that uses first-order derivative operators to find rapid intensity changes in an image. It involves calculating the gradient magnitude and direction.

An image processing technique used to remove noise or blur in an image by applying a weighted average to pixel values.

A method for edge detection that utilizes the zero-crossings of the second derivative of the image. It is sensitive to edges.

The space that contains all possible parameters of a geometric primitive, such as lines or circles.

A randomized version of the Hough transform that significantly reduces the computational time required for detecting shapes.

A technique for fitting models to a set of data points by iteratively selecting a random subset, fitting a model to this subset, and counting inliers. It is robust to outliers.

Points in a data set that fit well with the hypothesized model.

The collection of geometric primitives that are extracted from an image, such as lines, circles, or other shapes. This representation provides a higher-level understanding of the scene.

A technique used to enhance the contrast of an image by stretching the range of pixel intensities. It is used to improve visibility of edges and details.

The transformation where the output pixel intensity is the inverse of the input pixel intensity. This creates a reversed image where dark areas become bright and vice versa.

Spatial filtering in image processing aims to selectively modify frequency components in the image, allowing you to retain or eliminate specific information based on its frequency.

Median filters are particularly effective at removing salt-and-pepper noise as they replace each pixel with the median value of its neighboring pixels, effectively eliminating isolated noise points.

Convolution differs from correlation in image processing by rotating the filter mask by 180 degrees before applying it to the image. This difference in orientation significantly impacts the resulting output.

Unsharp masking is a technique used for sharpening images by subtracting a blurred version of the image from the original image. This emphasizes edges and details.

Smoothing filters play a significant role in noise reduction. They operate by averaging pixel values in a neighborhood, smoothing out noisy fluctuations and creating a more uniform image.

Applying a Laplacian filter with a negative center value highlights edges in the image by creating a darker background and grayish edge lines that are brighter than the background.

The Marr-Hildreth edge detection method, while effective, is highly susceptible to noise due to its reliance on the second derivative of the image. This sensitivity can lead to false edges in the output.

Edge detection is advantageous for reducing data dimensionality without losing essential image content. It helps to simplify the image by representing prominent features as edges.

The Sobel operator estimates gradients by utilizing two orthogonal masks, one for the horizontal gradient (x-direction) and another for the vertical gradient (y-direction). These masks effectively compute the change in pixel values in both directions, leading to a gradient estimate.

The Canny edge detection algorithm stands out for its superior detection and localization of edges compared to other methods. It combines gradient analysis, non-maximum suppression, and double thresholding for accurate and precise edge extraction.

In edge detection, regions of high contrast (strong intensity changes), where the gradient is large, are grouped into continuous curves, highlighting the boundaries between objects or regions.

The Laplacian of Gaussian (LoG) operator is used in edge detection to find zero-crossings after applying Gaussian smoothing. These zero-crossings represent significant intensity changes, indicating the presence of edges.

Structure from Motion (SfM) is a technique used in 3D reconstruction to estimate the 3D structure of a scene by analyzing multiple 2D images taken from different viewpoints. It infers the relative motion between the camera and the objects in the scene to reconstruct the 3D geometry.

Increasing the size of the Gaussian kernel in the Canny edge detection algorithm impacts the detection by reducing the number of detected edges in the image. This is because a larger kernel smooths out finer details and potentially suppresses weaker edges.

Simple gradient-based edge detection operators, like Sobel, are susceptible to noise in images. This sensitivity makes them prone to generating false edges, often incomplete or inaccurate, due to the influence of noise fluctuations.

Hysteresis thresholding involves using two thresholds (low and high) to identify edges. A pixel is considered an edge if its gradient is above the high threshold or if it is connected to a pixel above the high threshold, even if its own gradient is below the high threshold.

To enhance edge detection in images with strong lighting gradients, normalizing the image intensity values can be effective. By reducing the impact of lighting variations, the process can focus on actual intensity changes that correspond to edges, leading to better edge detection results.

In the Canny edge detection algorithm, Non-maximum suppression is a step that aims to reduce the thickness of detected edges by thinning them down to single pixel widths. It does this by comparing the gradient magnitude of a pixel to its neighboring pixels along the gradient direction and suppressing non-maximal values.

The Canny edge detection algorithm is often preferred over simple gradient-based methods when applied to images with significant texture because of its ability to better handle complex scenes. It's robust to noise and helps identify edges more accurately in texture-rich regions.