The Cell Processor Computer Vision Project utilizes Cell processors for image processing and computer vision tasks. The project aims to develop software that maximizes the potential of the Cell processor and provides a reliable platform for real-time video analysis.
If you're looking for a powerful computer vision project, look no further than the CellCV software. This project is designed to provide parallel image processing using the STI Cell Processor. It doesn't matter if you're working with image sequences in real-time or from file, the CellCV project is perfect for all your needs.
One of the great things about the Cell Processor is its CBEA architecture. This architecture allows users to split input images into vertical bands using the PPE Power Processor Element. Each sub-image is analyzed by computer vision algorithms, which work in parallel on the subimages and return bounding box results to the PPE. The output sub-images are then joined together into a single image by the PPE.
The PPE also collects a list of boxes and tracks their location evolution over time, associating each with a unique ID. Finally, the resulting output image with the regions of interest outlined is exported to disk.
Thanks to the SPE Synergistic Processing Elements' embedded SRAM, you can run multiple compute-intensive computer vision algorithms simultaneously. This parallelism results in a significant reduction in image processing time. This speedup can be measured statistically by comparing the frames per second of the serial execution of the algorithm with the fps of the parallel one.
The CellCV project, built on a YellowDog 5.0 Linux distribution for PowerPC Processor, is perfect for anyone looking to harness the power of the Cell Processor. With the Cell SDK 2.0 installed, this software offers an unparalleled level of parallelism and efficiency.
One of the great things about the Cell Processor is its CBEA architecture. This architecture allows users to split input images into vertical bands using the PPE Power Processor Element. Each sub-image is analyzed by computer vision algorithms, which work in parallel on the subimages and return bounding box results to the PPE. The output sub-images are then joined together into a single image by the PPE.
The PPE also collects a list of boxes and tracks their location evolution over time, associating each with a unique ID. Finally, the resulting output image with the regions of interest outlined is exported to disk.
Thanks to the SPE Synergistic Processing Elements' embedded SRAM, you can run multiple compute-intensive computer vision algorithms simultaneously. This parallelism results in a significant reduction in image processing time. This speedup can be measured statistically by comparing the frames per second of the serial execution of the algorithm with the fps of the parallel one.
The CellCV project, built on a YellowDog 5.0 Linux distribution for PowerPC Processor, is perfect for anyone looking to harness the power of the Cell Processor. With the Cell SDK 2.0 installed, this software offers an unparalleled level of parallelism and efficiency.
What's New
Version 0.51: N/A