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Features from accelerated segment test (FAST) is a corner detection method, which could be used to extract feature points and later used to track and map objects in many computer vision tasks. The FAST corner detector was originally developed by Edward Rosten and Tom Drummond, and was published in 2006. [ 1 ]
Description of source object Model size License Comments Utah teapot: 1975 Martin Newell at University of Utah: Melitta teapot 28 Bézier patches (32 with the bottom) [1] Also called the "Newell teapot". One of the first models not to be measured. Cornell box: 1984 Cindy M. Goral, Kenneth E. Torrance, Donald P. Greenberg, Bennett Battaile at ...
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]
ESP32 is a series of low-cost, low-power system-on-chip microcontrollers with integrated Wi-Fi and dual-mode Bluetooth.The ESP32 series employs either a Tensilica Xtensa LX6 microprocessor in both dual-core and single-core variations, an Xtensa LX7 dual-core microprocessor, or a single-core RISC-V microprocessor and includes built-in antenna switches, RF balun, power amplifier, low-noise ...
It can be used for tasks such as object recognition, image registration, classification, or 3D reconstruction. It is partly inspired by the scale-invariant feature transform (SIFT) descriptor. The standard version of SURF is several times faster than SIFT and claimed by its authors to be more robust against different image transformations than ...
At the end of this step, one has a model of the target object, consisting of features projected into a common 3D space. 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 ...
This works better for planar surface recognition than 3D object recognition since the affine model is no longer accurate for 3D objects. In this journal, [25] authors proposed a new approach to use SIFT descriptors for multiple object detection purposes. The proposed multiple object detection approach is tested on aerial and satellite images.
Region-based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision, and specifically object detection and localization. [1] The original goal of R-CNN was to take an input image and produce a set of bounding boxes as output, where each bounding box contains an object and also the category (e.g. car or ...