Programmable media processors have been emerging to meet the continuously increasing computational demand in complex digital media applications, such as HDTV and MPEG-4, at an affordable cost. These media processors provide the flexibility to implement various image computing algorithms along with high performance, unlike the hardwired approach that has provided high performance for a particular algorithm, but lacks flexibility. However, to achieve high performance on these media processors, a careful and sometimes innovative design of algorithms is essential. In addition, programming techniques, e.g., software pipelining and loop unrolling, are needed to speed up the computations while the data flow can be optimized using a programmable DMA controller. In this paper, we describe an algorithm for two-dimensional convolution, which can be implemented efficiently on many media processors. Implemented on a new media processor called the MAP1000, it takes 7.9 ms to convolve a 512ϫ512 image with a 7ϫ7 kernel, which is much faster than the previously reported software-based convolution and is comparable with the hardwired implementations. High performance in two-dimensional convolution and other algorithms on the MAP1000 clearly demonstrates the feasibility of software-based solutions in demanding imaging and video applications. © 2000 SPIE and IS&T. [S1017-9909(00)

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We present an integrated research environment (RAVEN) that we have developed for the purpose of developing and testing object tracking algorithms. As a Windows application, RAVEN provides a user interface for loading and viewing video sequences and interacting with the segmentation and object tracking algorithms, which are included at run time as plug-ins. The plug-ins interact with RAVEN via a programming interface, enabling algorithm developers to concentrate on their ideas rather than on the user interface. Over the past two years, RAVEN has greatly enhanced the productivity of our researchers, enabling them to create a variety of new algorithms and extend RAVEN's capabilities via plug-ins. Examples include several object tracking algorithms, a live-wire segmentation algorithm, a methodology for the evaluation of segmentation quality, and even a mediaprocessor implementation of an object tracker. After implementing an algorithm, RAVEN makes it easy to present the results since it provides several mask display modes and output options for both image and video. We have found that RAVEN facilitates the entire research process, from prototyping an algorithm to visualization of the results to a mediaprocessor implementation.