Real-time computer vision with OpenCV

By Kari A. Pulli, A. Baksheev, K. Kornyakov, V. Eruhimov. 2012

363 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs (2016, methodology): “A popular library 4 containing several algorithms, built-in in OpenCV 5 .”
Procedural Content Generation via Machine Learning (PCGML) (2017, background): “gameplay video frame-by-frame with OpenCV [63] and a fan-authored spritesheet, a collection of each image that appears in the game.”
Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering (2014, methodology): “2) Analysis of the Influence of Different Postprocessing Techniques: For the proposed spectral–spatial classification framework, different postprocessing techniques, e.g., other types of edge-preserving smoothing methods or statistics-based method, can be used to refine the binary probability maps.”
Mobile Augmented Reality Survey: From Where We Are to Where We Go (2017, background): “Vision-based applications are almost impossible to run on wearables, and very difficult on smartphones since they require capable GPUs [15], [16].”
Use of SIMD Vector Operations to Accelerate Application Code Performance on Low-Powered ARM and Intel Platforms (2013, methodology): “Several computer vision routines were optimized using NEON and benchmarked on an NVIDIA Tegra 3 device in [23].”