Line DetectionThis technique is used to segment, annotate or identify lines and boundaries in images. For instance, lanes on a city road.AdvantagesThe major advantage of this technique is that pixels that don’t share a common border can be detected and annotated as well. This is ideal to annotate lines that are short or those that are occluded.DisadvantagesIf there are several lines, the process becomes more time-consuming.Overlapping lines or objects could give misleading information and results.Landmark DetectionLandmarks in data annotation don’t mean places of special interests or significance. They are special or essential points in an image that needs to be annotated. This could be facial features, biometrics, or more. This is otherwise known as pose estimation as well.AdvantagesIt is ideal to train neural networks that require precise coordinates of landmark points.DisadvantagesThis is very time-consuming as every minute essential point has to be precisely annotated.SegmentationA complex process, where a single image is classified into multiple segments for the identification of different aspects in them. This includes detection of boundaries, locating objects, and more. To give you a better idea, here’s a list of prominent segmentation techniques:Semantic segmentation: where every single pixel in an image is annotated with detailed information. Crucial for models that require environmental context.Instance segmentation: where each and every instance of an element in an image is annotated for granular information.Panoptic segmentation: where details from semantic and instance segmentation are included and annotated in images.AdvantagesThese techniques bring out the finest pieces of information from objects.They add more context and value for training purposes, ultimately optimizing results.DisadvantagesThese techniques are labor-intensive and tedious.Image Classification
Image classification involves the identification of elements in an object and classifying them into specific object classes. This technique is very much different from the object detection technique. In the latter, objects are merely identified. For instance, an image of a cat could be simply annotated as an animal.However, in image classification, the image is classified as a cat. For images with multiple animals, every animal is detected and classified accordingly.AdvantagesGives machines more details on what objects in datasets are.Helps models accurately differentiate among animals (for example) or any model-specific element.DisadvantagesRequires more time for data annotation experts to carefully identify and classify all image elements.Use Cases of Image Annotation techniques in Computer VisionImage Annotation TechniqueUse Cases2D & 3D bounding boxesIdeal to annotate images of products and goods for machine learning systems to estimate costs, inventory, and more.PolygonsBecause of their ability to annotate irregular objects and shapes, they are ideal for tagging human organs in digital imaging records such as X-Rays, CT scans, and more. They can be used to train systems to detect anomalies and deformities from such reports.Semantic SegmentationUsed in the self-driving car’s space, where every pixel associated with vehicle movement can be tagged precisely. Image classification is applicable in self-driving cars, where data from sensors can be used to detect and differentiate among animals, pedestrians, road objects, lanes, and more.Landmark DetectionUsed to detect and study human emotions and for the development of facial recognition systems.Lines And SplinesUseful in warehouses and manufacturing units, where boundaries could be established for robots to perform automated tasks.Wrapping UpLike you see, computer vision is extremely complex. There are tons of intricacies that need to be taken care of. While these look and sound daunting, additional challenges include the timely availability of quality data, error-free data annotation processes, and workflows, the subject-matter expertise of annotators, and more.That being said, data annotation companies such as Shaip are doing a tremendous job of delivering quality datasets to companies that require them. In the coming months, we could also see evolution in this space, where machine learning systems could accurately annotate datasets by themselves with zero errors.