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color image, depth image, object class, bounding boxes, 3D center points Predict bounding boxes 2011, updated 2014 [55] Janoch et al. ShapeNet 3D models. Some are classified into WordNet synsets, like ImageNet. Partially classified into 3,135 categories. 3,000,000 models, 220,000 of which are classified. 3D models, class labels Predict class ...
Computer graphics produces image data from 3D models, and computer vision often produces 3D models from image data. [24] There is also a trend towards a combination of the two disciplines, e.g., as explored in augmented reality. The following characterizations appear relevant but should not be taken as universally accepted:
Image analysis or imagery analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques. [1] Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face .
[1] [2] [3] Computer vision tasks include methods for acquiring digital images (through image sensors), image processing, and image analysis, to reach an understanding of digital images. In general, it deals with the extraction of high-dimensional data from the real world in order to produce numerical or symbolic information that the computer ...
In computer vision and computer graphics, 3D reconstruction is the process of capturing the shape and appearance of real objects. This process can be accomplished either by active or passive methods. [1] If the model is allowed to change its shape in time, this is referred to as non-rigid or spatio-temporal reconstruction. [2]
In computer vision, the bag-of-words model (BoW model) sometimes called bag-of-visual-words model [1] [2] can be applied to image classification or retrieval, by treating image features as words. In document classification, a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary.
General scheme of content-based image retrieval. Content-based image retrieval, also known as query by image content and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases (see this survey [1] for a scientific overview of the CBIR field).
The Bartlane cable picture transmission system generated at both its transmitter and its receiver end a punched data card or tape that was recreated as an image. [4] In 1957, Russell A. Kirsch produced a device that generated digital data that could be stored in a computer; this used a drum scanner and photomultiplier tube. [3]