Enhance Your Understanding with Computer Vision Programming Concept Cards for quick learning
A field of study that focuses on enabling computers to see, understand, and interpret visual information from digital images or videos.
The manipulation and analysis of digital images to improve their quality, enhance features, or extract useful information.
The process of identifying and extracting meaningful features or patterns from images, such as edges, corners, or textures.
The task of locating and classifying objects of interest within an image or video, often using techniques like Haar cascades or deep learning models.
The process of categorizing images into predefined classes or labels based on their visual content, typically using machine learning algorithms.
The partitioning of an image into multiple segments or regions to simplify its representation and enable more detailed analysis.
A subset of machine learning that utilizes artificial neural networks with multiple layers to learn and extract complex patterns from data.
The process of estimating the intrinsic and extrinsic parameters of a camera to correct for distortions and accurately measure objects in the image.
The task of estimating the motion of objects or the camera in a sequence of images or videos, often used in surveillance or robotics applications.
A technology that overlays virtual objects or information onto the real world, enhancing the user's perception and interaction with the environment.
A type of deep neural network commonly used in computer vision tasks, designed to automatically learn hierarchical representations from image data.
A feature descriptor technique that counts the occurrences of gradient orientations in an image to represent its local shape and texture.
A feature detection and description algorithm that identifies and describes distinctive local features in an image, invariant to scale and rotation.
A real-time object detection system that uses a single neural network to predict bounding boxes and class probabilities directly from full images.
A pixel-level image segmentation task that assigns semantic labels to each pixel, aiming to understand the scene and objects in the image.
A class of deep learning models that consist of a generator network and a discriminator network, trained in an adversarial manner to generate realistic data.
The pattern of apparent motion of objects between consecutive frames in a sequence of images, often used for motion estimation or tracking.
A transformation matrix that relates the perspective projection of points in one image to another image, often used for image registration or stitching.
A technique used in object detection to eliminate overlapping bounding boxes and keep only the most confident predictions.
A technique in deep learning where a pre-trained model is used as a starting point for a new task, often fine-tuning the model on a smaller dataset.
The process of artificially increasing the size and diversity of a training dataset by applying random transformations or modifications to the original data.
A type of neural network commonly used for sequence data, capable of capturing temporal dependencies and context information.
The process of identifying and highlighting the boundaries or edges of objects in an image, often used as a preprocessing step for further analysis.
A technique that identifies regions or blobs in an image based on their properties, such as intensity or color, often used for object tracking or recognition.
A clustering algorithm that iteratively shifts the center of a kernel density estimate towards the mode of the data, used for image segmentation or tracking.
A dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving the most important information.
A supervised learning algorithm that separates data into different classes by finding an optimal hyperplane in a high-dimensional feature space.
A metric commonly used to evaluate the performance of object detection algorithms, measuring the accuracy and robustness of the predictions.
A measure of overlap between two bounding boxes, often used to assess the accuracy of object detection or segmentation algorithms.
A technique used in deep neural networks to normalize the activations of each layer, improving training speed and stability.
The region in the input space that a particular feature or neuron in a convolutional neural network can 'see' or respond to.
A mathematical function applied to the output of a neuron in a neural network, introducing non-linearity and enabling complex mappings.
A function that measures the discrepancy between predicted and target values, used to guide the learning process in a neural network.
An algorithm used to train neural networks by computing the gradients of the loss function with respect to the network's parameters.
A phenomenon in machine learning where a model performs well on the training data but fails to generalize to new, unseen data.
The process of cleaning, transforming, and normalizing raw data to prepare it for analysis or training machine learning models.
A layer in a convolutional neural network that applies a set of learnable filters to the input data, extracting local features and creating feature maps.
A layer in a convolutional neural network that reduces the spatial dimensions of the input data, reducing computational complexity and extracting invariant features.
A regularization technique used in neural networks to prevent overfitting by randomly disabling a fraction of the neurons during training.
A hyperparameter that determines the step size at each iteration during the optimization process of training a neural network.
The number of training examples used in a single iteration of gradient descent during the training of a neural network.
A complete pass through the entire training dataset during the training of a neural network, consisting of multiple iterations.
A mathematical function applied to the output of a neuron in a neural network, introducing non-linearity and enabling complex mappings.
An activation function commonly used in deep neural networks, defined as the positive part of its input.
An adaptive optimization algorithm commonly used to update the parameters of a neural network during the training process.