Assistive smartphone for people with special needs: the personal social assistant

C

Digital recording of performing arts: formats and conversion

B

Intelligent pre-processing for fast-moving object detection

Detection and segmentation of objects of interest in image sequences is the first major processing step in visual surveillance applications. The outcome is used for further processing, such as object tracking, interpretation, and classification of objects and their trajectories. To speed up the algorithms for moving object detection, many applications use techniques such as frame rate reduction. However, temporal consistency is an important feature in the analysis of surveillance video, especially for tracking objects. Another technique is the downscaling of the images before analysis, after which the images are up-sampled to regain the original size. This method, however, increases the effect of false detections. We propose a different pre-processing step in which we use a checkerboard-like mask to decide which pixels to process. For each frame the mask is inverted to avoid that certain pixel positions are never analyzed. In a post-processing step we use spatial interpolation to predict the detection results for the pixels which were not analyzed. To evaluate our system we have combined it with a background subtraction technique based on a mixture of Gaussian models. Results show that the models do not get corrupted by using our mask and we can reduce the processing time with over 45% while achieving similar detection results as the conventional technique.Detection and segmentation of objects of interest in image sequences is the first major processing step in visual surveillance applications. The outcome is used for further processing, such as object tracking, interpretation, and classification of objects and their trajectories. To speed up the algorithms for moving object detection, many applications use techniques such as frame rate reduction. However, temporal consistency is an important feature in the analysis of surveillance video, especially for tracking objects. Another technique is the downscaling of the images before analysis, after which the images are up-sampled to regain the original size. This method, however, increases the effect of false detections. We propose a different pre-processing step in which we use a checkerboard-like mask to decide which pixels to process. For each frame the mask is inverted to avoid that certain pixel positions are never analyzed. In a post-processing step we use spatial interpolation to predict the detection results for the pixels which were not analyzed. To evaluate our system we have combined it with a background subtraction technique based on a mixture of Gaussian models. Results show that the models do not get corrupted by using our mask and we can reduce the processing time with over 45% while achieving similar detection results as the conventional technique.P

Compression and reconstruction of sorted PET listmode data

C

Motion Compensation and Reconstruction of H.264/AVC Video Bitstreams using the GPU

Abstract — Most modern computers are equipped with powerful yet cost-effective Graphics Processing Units (GPUs) to accelerate graphics operations. Although programmable shaders on these GPUs were designed for the creation of 3-D rendering effects, they can also be used as generic processing units for vector data. This paper proposes a hardware renderer capable of executing motion compensation, reconstruction, and visualization entirely on the GPU by the use of vertex and pixel shaders. Our measurements show that a speedup of 297 % can be achieved by relying on the processing power of the GPU, relative to the CPU. As an example, real-time playback of high-definition video (1080p) was achieved at 62.0 frames per second, consuming only 68.2 % of all CPU cycles on a modern machine. I

Genome sequences as media files

C

Calculating numerical observer performance for list mode PET using the bootstrap method

C

Hybrid path planning for massive crowd simulation on the GPU

In modern day games, it is often desirable to have many agents navigating intelligently through detailed environments. However, intelligent navigation remains a computationally expensive and complicated problem. In the past, the continuum crowds algorithm demonstrated the value of using a dynamic potential field to guide many agents to a common goal location. However this algorithm is prohibitively resource intensive for real time applications using large and detailed virtual worlds. In this paper, we propose a novel hybrid system that first uses a coarse A* path finding step. This helps to eliminate unnecessary work during the potential field generation by excluding areas of the world from the potential field calculation. Additionally, we show how an optimized potential field solver can be implemented on the GPU using the concepts of persistent threads and inter-block communication. Results show that our system achieves considerable speedups compared to existing path planning systems and that up to 100,000 agents can be simulated and rendered in real time on a mainstream GPU.In modern day games, it is often desirable to have many agents navigating intelligently through detailed environments. However, intelligent navigation remains a computationally expensive and complicated problem. In the past, the continuum crowds algorithm demonstrated the value of using a dynamic potential field to guide many agents to a common goal location. However this algorithm is prohibitively resource intensive for real time applications using large and detailed virtual worlds. In this paper, we propose a novel hybrid system that first uses a coarse A* path finding step. This helps to eliminate unnecessary work during the potential field generation by excluding areas of the world from the potential field calculation. Additionally, we show how an optimized potential field solver can be implemented on the GPU using the concepts of persistent threads and inter-block communication. Results show that our system achieves considerable speedups compared to existing path planning systems and that up to 100,000 agents can be simulated and rendered in real time on a mainstream GPU.C