This library implements the KLT Tracking algorithm [2004] for Feature Tracking in Video useful in computer vision tasks like object recognition, image indexing, tracking and structure from motion. This implementation uses programmable Graphics Hardware to achieve considerable speedup in the running time of the gpu-Based implementation.
This library implements the KLT Tracking algorithm [2004] for Feature Tracking in Video useful in computer vision tasks like object recognition, image indexing, tracking and structure from motion. This implementation uses programmable Graphics Hardware to achieve considerable speedup in the running time of the gpu-Based implementation.
Behavioral models are used in games and computer graphics for
realistic simulation of massive crowds. In this paper, we present a
GPU based implementation of Reynolds [1987] algorithm for simulating
flocks of birds and propose an extension to consider environment
self occlusion. We performed several experiments and
the results showed that the proposed approach runs up to three
times faster than the original algorithm when simulating high density
crowds, without compromising significantly the original crowd
behavior.
Implementation of GPU (Graphics Processing Unit) that rendered triangle based models. Our goal was to generate complex models with a movable camera. We wanted to be able to render complex images that consisted of hundreds to thousands of triangles. We wanted to apply interpolated shading on the objects, so that they appeared more
smooth and realisitc, and to have a camera that orbitted around the object, which allowed us to
look arond the object with a stationary light source. We chose to do this in hardware, because our initial implementation using running software on the NIOS II processor was too slow. Implementing parallelism in hardware is also easier to do than in software, which allows for more efficiency. We used Professor Land s floating point hardware, which allowed us to do calculations efficiency, which is essential to graphics.
3D reconstruction, medical image processing from colons, using intel image processing for based class. This source code. Some code missing but I think you can understand it. Development version. This source code is very interesting for learning segmentation and registration from dataset. This code also has some technique about GPU image processing for ray tracing. Also learn many filter apply for transform from spatial domain to frequency domain.
SiftGPU is an implementation of SIFT [1] for GPU. SiftGPU processes pixels parallely to build Gaussian pyramids and detect DoG Keypoints. Based on GPU list generation, SiftGPU then uses a GPU/CPU mixed method to efficiently build compact keypoint lists. Finally keypoints are processed parallely to get their orientations and descriptors.
