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Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes and scales or even when they are translated or rotated.
Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. [1]
Images, text Object recognition, scene recognition 2014 [15] [16] J. Xiao et al. ImageNet: Labeled object image database, used in the ImageNet Large Scale Visual Recognition Challenge: Labeled objects, bounding boxes, descriptive words, SIFT features 14,197,122 Images, text Object recognition, scene recognition 2009 (2014) [17] [18] [19] J ...
The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million [1] [2] images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. [3]
The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection.The technique counts occurrences of gradient orientation in localized portions of an image.
Examples of such applications are: Image differencing, registration, object recognition, multi-camera tracking, co-segmentation and stereo reconstruction. A photograph of 21st-century London recolored to match an 18th-century painting by Canaletto. Other applications of image color transfer have been suggested.
All templates then cast votes for the center of the detected object proportional to the probability of the match, and the probability the template predicts the center. These votes are all summed and if there are enough of them, well enough clustered, the presence of the object in question (i.e. a face or car) is predicted.
In the detection phase of the Viola–Jones object detection framework, a window of the target size is moved over the input image, and for each subsection of the image the Haar-like feature is calculated. This difference is then compared to a learned threshold that separates non-objects from objects.