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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]
SURF descriptors have been used to locate and recognize objects, people or faces, to reconstruct 3D scenes, to track objects and to extract points of interest. SURF was first published by Herbert Bay , Tinne Tuytelaars, and Luc Van Gool, and presented at the 2006 European Conference on Computer Vision .
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.
Foreground detection is one of the major tasks in the field of computer vision and image processing whose aim is to detect changes in image sequences. Background subtraction is any technique which allows an image's foreground to be extracted for further processing (object recognition etc.).
To recognize an object in an arbitrary input image, the paper detects features, and then uses RANSAC to find the affine projection matrix which best fits the unified object model to the 2D scene. If this RANSAC approach has sufficiently low error, then on success, the algorithm both recognizes the object and gives the object's pose in terms of ...
This theory heavily relied on the development of feature-efficient learning approach. The goal of this algorithm is to learn an object represented by some geometric features in an image. The input is a feature vector and the output is 1 which means successfully detect the object or 0 otherwise. The main point of this learning approach is ...
In computer vision, the problem of object categorization from image search is the problem of training a classifier to recognize categories of objects, using only the images retrieved automatically with an Internet search engine. Ideally, automatic image collection would allow classifiers to be trained with nothing but the category names as input.
Moving object detection is to recognize the physical movement of an object in a given place or region. [2] By acting segmentation among moving objects and stationary area or region, [3] the moving objects' motion can be tracked and thus analyzed later. To achieve this, consider a video is a structure built upon single frames, moving object ...