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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.
The position of these rectangles is defined relative to a detection window that acts like a bounding box to the target object (the face in this case). 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 ...
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]
The Viola–Jones object detection framework is a machine learning object detection framework proposed in 2001 by Paul Viola and Michael Jones. [1] [2] It was motivated primarily by the problem of face detection, although it can be adapted to the detection of other object classes. In short, it consists of a sequence of classifiers.
The Hough transform (/ h ĘŚ f /) is a feature extraction technique used in image analysis, computer vision, pattern recognition, and digital image processing. [1] [2] The purpose of the technique is to find imperfect instances of objects within a certain class of shapes by a voting procedure.
The process of Canny edge detection algorithm can be broken down to five different steps: Apply Gaussian filter to smooth the image in order to remove the noise; Find the intensity gradients of the image; Apply gradient magnitude thresholding or lower bound cut-off suppression to get rid of spurious response to edge detection
The Hessian affine region detector is a feature detector used in the fields of computer vision and image analysis.Like other feature detectors, the Hessian affine detector is typically used as a preprocessing step to algorithms that rely on identifiable, characteristic interest points.
The detection and description of local image features can help in object recognition. The SIFT features are local and based on the appearance of the object at particular interest points, and are invariant to image scale and rotation. They are also robust to changes in illumination, noise, and minor changes in viewpoint.