The vocabulary used in this guide is designed to help readers understand the common and specialized terms often encountered in digital image processing. While these definitions align with general usage in the field, they are not necessarily standardized. Instead, they reflect the commonly accepted meanings found in published books on image processing and computer vision.
Algebraic operation refers to a type of image processing technique that involves mathematical operations such as addition, subtraction, multiplication, and division between corresponding pixels of two images.
Aliasing occurs when an image has too few pixels compared to the detail present, resulting in visible artifacts or false patterns.
An arc is a sequence of connected pixels that forms a curve, often used to represent edges or boundaries in an image.
A binary image contains only two levels of grayscale—typically black and white—making it useful for object segmentation and analysis.
Blurring refers to the loss of sharpness in an image caused by factors such as out-of-focus lenses, motion, or low-pass filtering.
The border of an image refers to its outermost rows or columns, often used as a reference for image processing tasks.
Boundary chain code is a method used to encode the direction of an object's boundary, helping in shape analysis and recognition.
A boundary pixel is one that is adjacent to at least one background pixel, distinguishing it from internal pixels within an object.
Boundary tracking is a technique used in image segmentation to trace the edges of objects by following their boundaries pixel by pixel.
Brightness is a measure of the light intensity emitted or reflected from a specific point in an image.
Change detection is a method used to identify differences between two similar images by comparing pixel values, often through subtraction or other techniques.
Class refers to a group of objects that share common characteristics, used in pattern recognition and classification systems.
A closed curve is a continuous line that starts and ends at the same point, forming a loop without any breaks.
A cluster is a group of points that are close to each other in space, often used in clustering algorithms for data analysis.
Cluster analysis is the process of identifying and describing groups of similar data points within a dataset.
Concave describes an object that has an indentation, where at least two points inside the object cannot be connected without crossing outside the object (the opposite of convex).
Connected means that pixels or regions are joined together, forming a continuous structure.
Contour encoding is a compression technique that encodes only the boundaries of regions with uniform color or intensity, reducing data size.
Contrast measures the difference in brightness or gray level between an object and its surrounding background.
Contrast stretch is a linear transformation that expands the range of gray levels in an image to enhance visibility.
Convex describes an object where any line segment connecting two points within the object lies entirely inside it.
Convolution is a mathematical operation used in image processing that combines two functions to produce a third, often applied to filter or transform images.
A convolution kernel is a small matrix used in image processing to apply filters, such as blurring or sharpening, through convolution operations.
A continuous path is a sequence of pixels that form a smooth line, often used to describe edges or curves in an image.
Deblurring is the process of removing blur from an image to restore clarity, often used in image restoration and enhancement.
A decision rule is a set of criteria used in pattern recognition to assign objects to specific classes based on their features.
A digital image is a representation of a scene using discrete numerical values, typically stored as a grid of pixels.
Digital image processing involves the manipulation and analysis of images using computational methods, often for enhancement, restoration, or interpretation.
Digitization is the process of converting analog images into digital format for storage, processing, and analysis.
Edge refers to the region in an image where there is a sudden change in brightness, often indicating the boundary of an object.
Edge detection is a technique used to identify the edges of objects by analyzing the local variations in pixel intensity.
Edge enhancement improves the visibility of edges in an image by increasing the contrast between adjacent pixels.
An edge image is a binary representation where each pixel is labeled as either part of an edge or not.
Edge linking is a process that connects individual edge pixels into continuous edges, improving the accuracy of edge detection.
An edge operator is a filter used to detect edges in an image by examining the neighborhood of each pixel.
An edge pixel is a pixel located along the boundary of an object, showing a significant change in brightness compared to neighboring pixels.
Enhance refers to the process of improving the visual quality of an image, such as increasing contrast or clarity.
An exterior pixel is a pixel in a binary image that lies outside the object, contrasting with interior pixels that are inside the object.
False negative occurs when an object is incorrectly classified as not belonging to a certain class in pattern recognition.
False positive occurs when an object is incorrectly classified as belonging to a certain class when it does not.
Feature is a measurable property of an object, such as size, texture, or shape, used to classify or recognize it.
Feature extraction is the process of identifying and calculating relevant properties of objects in an image for further analysis.
Feature selection is the step in pattern recognition where the most relevant features are chosen to improve classification accuracy.
Feature space is a multidimensional space where each dimension corresponds to a feature, allowing objects to be represented and compared.
Fourier transform is a mathematical tool that converts an image from the spatial domain to the frequency domain, revealing its spectral components.
Geometric correction is a technique used to correct distortions in an image by applying geometric transformations.
Gray level represents the brightness of a pixel in a digital image, ranging from black to white.
Gray scale refers to the range of possible gray levels in a digital image, typically from 0 (black) to 255 (white).
Gray-scale transformation is a function that maps input gray levels to output gray levels, often used for image enhancement.
Hankel transform is a mathematical transformation used in signal and image processing, particularly for circularly symmetric functions.
Harmonic signal is a complex signal composed of cosine and sine waves of the same frequency, used in signal analysis.
Hermite function is a complex-valued function with even real and odd imaginary parts, used in various signal and image processing applications.
High-pass filtering enhances high-frequency components of an image, such as edges and fine details, while suppressing low-frequency components.
A hole is a region in a binary image that is surrounded by object pixels but is itself part of the background.
An image is a representation of a physical scene or another image, captured and stored digitally for processing or display.
Image compression reduces the amount of data needed to represent an image, either losslessly or with some loss of detail.
Image coding transforms an image into a different form, such as compressed or encoded data, for efficient storage or transmission.
Image enhancement improves the visual quality of an image, making it more suitable for analysis or display.
Image matching is the process of comparing two images to determine their similarity, often used in object recognition and tracking.
Image-processing operation refers to a series of steps that transform an input image into an output image, such as filtering or segmentation.
Image reconstruction is the process of recovering an image from incomplete or distorted data, such as in medical imaging or remote sensing.
Image registration aligns multiple images of the same scene to ensure that corresponding features are in the same position.
Image restoration is the process of reversing the effects of image degradation, such as noise or blur, to recover the original image.
Image segmentation divides an image into distinct regions, often corresponding to objects or background, for further analysis.
An interior pixel is a pixel located inside an object in a binary image, as opposed to boundary or exterior pixels.
Interpolation is the process of estimating missing pixel values based on known samples, often used in resizing or resampling images.
Kernel is another term for convolution kernel, used in filtering and image processing operations.
Line detection is a technique used to identify straight lines in an image by analyzing pixel neighborhoods.
A line pixel is a pixel that lies along a straight line, often used in edge detection and shape analysis.
Local operation is a type of image processing that considers the neighborhood of a pixel to determine its output value, as opposed to point operations.
Local property refers to a characteristic that varies across different regions of an image, such as brightness or temperature.
Lossless image compression allows the original image to be fully reconstructed without any loss of information.
Lossy image compression reduces file size by discarding some information, resulting in a loss of detail but better compression ratios.
Matched filtering is a technique used to detect specific patterns or objects in an image by correlating with a known template.
Measurement space is a mathematical space used in pattern recognition to represent and compare feature vectors.
Misclassification occurs when an object is assigned to the wrong class during pattern recognition, leading to errors in identification.
Multi-spectral image consists of multiple images of the same scene, each captured in a different band of the electromagnetic spectrum.
Neighborhood refers to the set of pixels surrounding a given pixel, used in many image processing algorithms.
Neighborhood operation is a type of image processing that considers the surrounding pixels to compute the output value for a given pixel.
Noise is unwanted variation in pixel values that can obscure meaningful information in an image.
Noise reduction is the process of minimizing or eliminating noise from an image to improve its quality.
An object is a group of connected pixels in a binary image, representing a distinct entity in the scene.
An optical image is formed by focusing light from a scene onto a surface, such as a camera sensor or film.
Pattern refers to a recurring structure or regularity that can be used to identify and classify objects.
A pattern class is a group of objects that share common characteristics and can be distinguished from others.
Pattern classification is the process of assigning objects to predefined classes based on their features.
Pattern recognition is the automated or semi-automated process of detecting, measuring, and classifying objects in an image.
Pel is an abbreviation for pixel element, referring to the smallest unit of a digital image.
Perimeter is the total length of the boundary of an object, often used in shape analysis.
Picture element is the basic unit of a digital image, also known as a pixel.
Pixel is an abbreviation for Picture Element, the smallest addressable unit in a digital image.
Point operation is a type of image processing that modifies the value of a single pixel based on its own value, without considering its neighbors.
Quantitative image analysis involves extracting numerical data from an image for further processing or interpretation.
Quantization is the process of mapping continuous image values to a finite set of discrete levels, often used in digital image representation.
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