Concise overview of benchmark datasets and evaluation metrics used in detection is also provided along with some of the prominent backbone architectures used in recognition tasks.
The paper presents a comparative study of the performance of Back Propagation and Instance Based Learning Algorithm for classification tasks.
A tracking system that will be used for Augmented Reality (AR) applications has two main requirements: accuracy and frame rate.
In embedded vision systems, parallel computation of the integral image presents several design challenges in terms of hardware resources, speed and power consumption.
The most frequently employed measure for performance characterisation of local feature detectors is repeatability, but it has been observed that this does not necessarily mirror actual performance.