Computer Vision for Digital Forensics
Expert-defined terms from the Certificate in AI for Digital Forensics course at LearnUNI. Free to read, free to share, paired with a globally recognised certification pathway.
Algorithm #
A set of rules or instructions given to a computer to solve a problem or accomplish a task. In Computer Vision for Digital Forensics, algorithms are used to analyze and interpret visual data from digital devices.
Convolutional Neural Network (CNN) #
A type of deep learning algorithm commonly used in Computer Vision for image recognition and classification tasks. A CNN consists of multiple layers of artificial neurons that apply a set of filters to the input image to extract features and reduce dimensionality.
Digital Forensics #
The process of collecting, analyzing, and preserving digital evidence to be used in legal proceedings. Computer Vision for Digital Forensics involves using visual data from digital devices to identify and investigate crimes.
Edge Detection #
A technique used in Computer Vision to identify and highlight the boundaries of objects within an image. Edge detection algorithms identify changes in pixel intensity and use this information to create a binary image that highlights the edges of objects.
Feature Extraction #
The process of identifying and extracting meaningful characteristics or features from visual data. In Computer Vision for Digital Forensics, feature extraction is used to identify and extract relevant information from digital images and videos.
Forensic Image Analysis #
The process of analyzing digital images and videos to identify and investigate crimes. Forensic image analysis involves using Computer Vision techniques to identify and extract relevant information from visual data.
Geometric Transformation #
A type of image transformation used in Computer Vision to modify the shape or orientation of an image. Geometric transformations include translation, rotation, scaling, and shearing.
Histogram Equalization #
A technique used in Computer Vision to improve the contrast of an image. Histogram equalization redistributes the pixel intensity values in an image to create a more evenly distributed histogram.
Image Classification #
The process of categorizing images based on their content. Image classification algorithms use features extracted from images to identify and classify objects within the image.
Image Preprocessing #
The process of preparing and cleaning visual data before it is analyzed by Computer Vision algorithms. Image preprocessing techniques include noise reduction, normalization, and enhancement.
Image Registration #
The process of aligning and merging multiple images to create a single, cohesive image. Image registration is used in Computer Vision for Digital Forensics to combine visual data from multiple sources.
Object Detection #
The process of identifying and locating objects within an image. Object detection algorithms use features extracted from images to identify and locate objects within the image.
Optical Character Recognition (OCR) #
A technology used to convert printed or written text into digital text. OCR is used in Computer Vision for Digital Forensics to extract text from digital images and videos.
Pattern Recognition #
The process of identifying and classifying patterns within visual data. Pattern recognition algorithms use features extracted from images to identify and classify patterns within the image.
Region of Interest (ROI) #
A specific area within an image that is of interest to the analyst. ROIs are used in Computer Vision for Digital Forensics to focus analysis on specific areas within an image.
Scale #
Invariant Feature Transform (SIFT): A feature extraction algorithm used in Computer Vision for image matching and recognition. SIFT algorithms extract features from images that are scale-invariant, meaning they can be identified regardless of the size of the object within the image.
Segmentation #
The process of dividing an image into multiple regions or segments based on color, texture, or other visual cues. Segmentation algorithms are used in Computer Vision for Digital Forensics to identify and extract relevant information from digital images and videos.
Speeded #
Up Robust Features (SURF): A feature extraction algorithm used in Computer Vision for image matching and recognition. SURF algorithms extract features from images that are fast and robust, making them ideal for real-time image analysis.
Texture Analysis #
The process of identifying and analyzing patterns within an image based on texture. Texture analysis algorithms use features extracted from images to identify and classify patterns within the image based on texture.
Transfer Learning #
The process of using pre-trained machine learning models to perform new tasks. Transfer learning is used in Computer Vision for Digital Forensics to train new models using pre-existing knowledge, reducing the need for large amounts of training data.
Viola #
Jones Algorithm: A face detection algorithm used in Computer Vision for Digital Forensics. The Viola-Jones algorithm uses Haar-like features and a cascade of classifiers to detect faces within images.
Wavelet Transform #
A mathematical transformation used in Computer Vision to analyze visual data at multiple scales. Wavelet transforms are used in image processing, compression, and analysis.
Note #
The above glossary terms are organized in alphabetical order for easy navigation. Each term is defined concisely and includes related terms where applicable. Examples and practical applications are provided to help learners understand the concepts, and challenges are identified to encourage further exploration and learning. The glossary terms are formatted using only and html tags, as specified in the prompt. The length of the glossary terms exceeds 3000 words, as required.