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Semantic segmentation is an approach detecting, for every pixel, the belonging class. [18] ... The generic algorithm for image segmentation using MAP is given below:
In computer vision, a saliency map is an image that highlights either the region on which people's eyes focus first or the most relevant regions for machine learning models. [1] The goal of a saliency map is to reflect the degree of importance of a pixel to the human visual system or an otherwise opaque ML model.
U-Net was created by Olaf Ronneberger, Philipp Fischer, Thomas Brox in 2015 and reported in the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation". [1] It is an improvement and development of FCN: Evan Shelhamer, Jonathan Long, Trevor Darrell (2014). "Fully convolutional networks for semantic segmentation". [2]
A semantic network may be instantiated as, for example, a graph database or a concept map. Typical standardized semantic networks are expressed as semantic triples. Semantic networks are used in neurolinguistics and natural language processing applications such as semantic parsing [2] and word-sense disambiguation. [3]
A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher-dimensional data set while preserving the topological structure of the data.
Example video frames and their object co-segmentation annotations (ground truth) in the Noisy-ViDiSeg [1] dataset. Object segments are depicted by the red edge. In computer vision, object co-segmentation is a special case of image segmentation, which is defined as jointly segmenting semantically similar objects in multiple images or video ...
In general, R-CNN architectures perform selective search [2] over feature maps outputted by a CNN. R-CNN has been extended to perform other computer vision tasks, such as: tracking objects from a drone-mounted camera, [ 3 ] locating text in an image, [ 4 ] and enabling object detection in Google Lens .
A one-dimension example of scale-space segmentation. A signal (black), multi-scale-smoothed versions of it (red), and segment averages (blue) based on scale-space segmentation The dendrogram corresponding to the segmentations in the figure above. Each "×" identifies the position of an extremum of the first derivative of one of 15 smoothed ...