Glossary
A glossary for definitions used in our products and documentation.
The nomenclature of any new platform can be overwhelming at first. We have tried to conform to industry naming standards wherever possible in a bid to make the user experience both intuitive and welcoming to new users and experienced practitioners alike. However, computer vision and annotation tooling are relatively recent fields, and as such many terms may be used interchangeably in both the literature and industry.
Do not hesitate to contact [email protected] for clarification on the features supported by Encord.
Note
In the interests of brevity, we will refer to both image and video data as 'frames' in the definitions below.
| Term | Description |
|---|---|
| Attribute | Attributes can be nested into objects and classifications to provide more information on the label. For example, for the object 'cat,' an example attribute would be 'color.' |
| Benchmark function | The function used to review tasks with benchmark QA. The benchmark function works by comparing all labels in the annotator submission of the benchmark task against the gold standard label set in the source project’s task. |
| Benchmark task | An annotation task in a project with benchmark QA, which has a corresponding task in the ‘source project’ that contains gold standard labels. |
| Bounding box | A rectangle used to annotate a feature by drawing the bounds of the feature. |
| Classification | A mutually-exclusive category applied to a frame. |
| Crosshair navigation | A way to navigate in 3D. Clicking on a location in one slice will also change the associated views. |
| Dataset | A collection of videos and/or images. |
| Data Unit | A package of data that constitutes a single annotation task, e.g., a video, a single image, an image group, or a DICOM series. |
| Feature | An object in a frame or a classification applied to a frame. These can be used to identify something in a frame (object: 'this thing is an apple') or to classify the frame itself (classification: 'this frame has apples'). |
| Hanging protocol | An arrangement of views, e.g., Axial, sagittal, and coronal. |
| Hounsfield unit | A linear transformation of the measured attenuation coefficient, e.g., air = -1000 HU, water = 0 HU. |
| Image group | A collection of images presented as one data unit. Grouping images in the image group functionality allows Encord's platform to support enhanced performance on playback and more automated labeling features. Also known as image sequences. |
| Instance | Also known as an instance label in the platform, an instance is a unique instantiation of an ontology entity, which, depending on the data type, may contain many frame labels. For example, in a 100-frame video tracking three cars on a road, there are three instances of 'car' and up to 100 frame labels for each car. |
| Key point | A dot used to annotate a feature by specifying its location. |
| Label | Sometimes denoted as a frame label in the platform, labels note relevant features in a frame and apply to a dataset used in model training. They are an annotation asserting which features in the desired ontology are true. |
| Label Editor | The UI for annotating data and managing labels. |
| Maximum intensity projection (MIP) | A method for 3D data that projects all voxels to a plane. |
| Micro-model | A model specifically trained to label a dataset for training other models. |
| Model | A program with a set of functions and parameters that allow it to recognize features in datasets. Different models have different strengths and weaknesses. |
| Model training | The process of teaching a model an ontology. This is done by algorithmically changing model parameters until it can reliably recognize features that are labeled in a dataset. |
| Model inference | The process of using a trained model to predict the presence of features in new data. |
| Object | Something of interest in a frame. Defined by string together with an annotation. It can be used as part of an ontology to label entities of interest in a dataset used for model training. Examples include bounding boxes and polygons that have been applied to a frame. |
| Object detection | The ability of a model to reliably recognize when a frame contains an object of interest. An application of model inference. |
| Object primitive | A unique object annotation type. Used to create templates of shapes (such as 3D cuboids and pose estimation skeletons) commonly used by your annotation team. |
| Object tracking | The ability of a model to reliably detect and track objects in a sequence of frames over time. An application of model inference. |
| Ontology | A defined set of features and their relationships. This is what a model will be trained to apply to frames. Also known as a 'taxonomy.' |
| Polygon | A polygonal shape used to annotate a feature by drawing the bounds of a feature. |
| Polyline | A line composed of multiple segments. |
| Project | A self-contained, collaborative environment for managing all productivity tasks associated with labeling and modeling one or more datasets. |
| Quality | An assessment of the accuracy of a set of labels. |
| Semantic segmentation | The application of labels to each pixel in a frame in order to classify segments of the frame as part of the same entity. |
| Slice | A single image of a DICOM volume. |
| Task | An action required as part of the labeling workflow. |
| Task Manager | The UI for creating and managing tasks. |
| View | A window displaying a specific viewing direction, e.g., coronal. |
| Volume | A set of images, also called slices or frames. |
| Windowing | Changing the appearance of the image to highlight particular structures. |
Updated 5 months ago