Efficient Synthesis of Gaussian Filters by Cascaded Uniform Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image selective smoothing and edge detection by nonlinear diffusion
SIAM Journal on Numerical Analysis
Java Native Interface: Programmer's Guide and Reference
Java Native Interface: Programmer's Guide and Reference
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Multiple target detection and tracking with guaranteed framerates on mobile phones
ISMAR '09 Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality
Professional Android 2 Application Development
Professional Android 2 Application Development
From box filtering to fast explicit diffusion
Proceedings of the 32nd DAGM conference on Pattern recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Real-time panoramic mapping and tracking on mobile phones
VR '10 Proceedings of the 2010 IEEE Virtual Reality Conference
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The increasing computing power of modern smartphones opens the door for interesting mobile image analysis applications. In this paper, we explore the arising possibilities but also discuss remaining challenges by implementing linear and nonlinear diffusion filters as well as basic variational optic flow approaches on a modern Android smartphone. To achieve low runtimes, we present a fast method for acquiring images from the built-in camera and focus on efficient solution strategies for the arising partial differential equations (PDEs): Linear diffusion is realised by approximating a Gaussian convolution by means of an iterated box filter. For nonlinear diffusion and optic flow estimation we use the recent fast explicit diffusion (FED) solver. Our experiments on a recent smartphone show that linear/nonlinear diffusion filters can be applied in realtime/near-realtime to images of size 176×144. Computing optic flow fields of a similar resolution requires some seconds, while achieving a reasonable quality.