Sentence examples for computer vision methods from inspiring English sources
From the experiments done by Rublee et al. [9], ORB produced high performance and computational efficiency when compared to other similar computer vision methods such as SIFT and SURF.
Modern surveillance systems can monitor people, cars or objects by using computer vision methods.
While at Oxford University, his research was devoted to the development of computer vision methods for the autumatic analysis of seismic imagery.
In contrast to earlier computer vision methods, deep learning methods are known for steadily improving their performance as they are fed more data.
Hence, number of research groups have focused on adapting perceptual, computer graphics and computer vision methods to several video coding platforms.
Facial landmark estimation techniques are a family of computer vision methods which automatically locate key anatomical points of the face, outputting X and Y coordinates of these landmarks in each video frame.
In the present work, we developed a computer vision method that allows us to quantify urban change at high spatial resolutions using image time-series from Google Street View.
Since the rheological behavior of fruits are important for any processing operation, the viscoelastic characteristics of date fruits were studied at six moisture content, and four temperature levels of product, using a computer vision method.
Gellan gel was formulated and selected as the model food to simulate the real foods processed by the MAP system; heating patterns and cold spots of the pouched samples were detected by a chemical-marker based computer vision method.
To address this issue, in the present study, we present a computer vision method that contains three machine learning models for the large-scale and automatic evaluation on the qualities of the urban environment by leveraging state-of-the-art machine learning techniques and wide-coverage street view images.
By processing images extracted from Google Street View API– using a computer vision method based on joint Histograms of Oriented Gradients and Color– traffic signs were detected and classified into four categories of regulatory, warning, stop, and yield signs.
