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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]
In the fields of computer vision and image analysis, the Harris affine region detector belongs to the category of feature detection.Feature detection is a preprocessing step of several algorithms that rely on identifying characteristic points or interest points so to make correspondences between images, recognize textures, categorize objects or build panoramas.
An edge in an image may point in a variety of directions, so the Canny algorithm uses four filters to detect horizontal, vertical and diagonal edges in the blurred image. The edge detection operator (such as Roberts, Prewitt, or Sobel) returns a value for the first derivative in the horizontal direction (G x) and the vertical direction (G y ...
The Marr-Hildreth edge detector [26] is distinguished by its use of the Laplacian of Gaussian (LoG) operator for edge detection in digital images. Unlike other edge detection methods, the LoG approach combines Gaussian smoothing with second derivative operations, allowing for simultaneous noise reduction and edge enhancement.
In contrast to the classic SIFT approach, Wagner et al. use the FAST corner detector for feature detection. The algorithm also distinguishes between the off-line preparation phase where features are created at different scale levels and the on-line phase where features are only created at the current fixed scale level of the phone's camera image.
A color picture of an engine The Sobel operator applied to that image. The Sobel operator, sometimes called the Sobel–Feldman operator or Sobel filter, is used in image processing and computer vision, particularly within edge detection algorithms where it creates an image emphasising edges.
Oriented FAST and rotated BRIEF (ORB) is a fast robust local feature detector, first presented by Ethan Rublee et al. in 2011, [1] that can be used in computer vision tasks like object recognition or 3D reconstruction.
The Haar features used in the Viola-Jones algorithm are a subset of the more general Haar basis functions, which have been used previously in the realm of image-based object detection. [4] While crude compared to alternatives such as steerable filters, Haar features are sufficiently complex to match features of typical human faces. For example: