High-performance image computing with modern microprocessors

Video object segmentation and tracking are essential for content-based video processing. This paper presents a framework for a semiautomatic approach to this problem. A semantic video object is initialized with human assistance in a key frame. The video object is then tracked and segmented automatically in the following frames. A new active contour model, VSnakes, is introduced as a segmentation method in this framework. The active contour energy is defined so as to reflect the energy difference between two contours instead of the energy of a single contour. Multiple-resolution wavelet decomposition is applied in generating the edge energy of the image frame. Contour relaxation is used to deal with the object deformation frame by frame, and the Viterbi algorithm is used to update the contour path during contour relaxation. Compared to the original snakes algorithm, semiautomatic video object segmentation with the VSnakes algorithm resulted in improved performance in terms of video object shape distortion (1.4% versus 2.9% in one experiment), which suggests that it could be a useful tool in many content-based video applications, e.g., MPEG-4 video object generation and medical imaging.

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“…Implementations using new-generation mediaprocessors with a high level of parallelism [19] would greatly speed up edge energy generation.…”

Section: Methodsmentioning

confidence: 99%

Semiautomatic video object segmentation using vsnakes

Sun¹,

Haynor

Kim

2003

IEEE Trans. Circuits Syst. Video Technol.

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“…In fact, there is a clear trend toward high-level language support via C intrinsics with partitioned instructions and an efficient compiler in programmable mediaprocessors [6,15,16]. Also, DMA-based I/O provides an efficient mechanism to hide the memory access latency in most image computing functions.…”

Section: -Bit Destinationmentioning

confidence: 99%

“…More often, it takes remapping and/or a new implementation of the existing algorithm on the target microprocessor to obtain better performance. Furthermore, due to the complexity of the TMS32OC8O architecture, not only programming, but also upgrading and modifying the existing codes in assembly language has been challenging and timeconsuming [6].…”

Section: Introductionmentioning

confidence: 99%

New mediaprocessor and its image computing library

Kim

Stotland

Kim

1999

Medical Imaging 1999: Image Display

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High-performance mediaprocessors that aim at high-level language programming without sacrificing much would be very desirable in many applications including medical imaging. Media Accelerated Processor (MAP) 1000 that is jointly being developed by Hitachi, Ltd. and Equator Technologies, Inc. is one of such next-generation mediaprocessors. We present the two main issues in programming these mediaprocessors, i.e., using C intrinsics (hinting to the compiler a desired assembly language instruction in a C program so that the compiler tries to use the hinted instruction in the compiled code) and data flow control, which still requires a high degree of expertise in the detailed architectural features including many low-level instructions, handling input/output data transfers, and in-depth understanding of the algorithm. To ease the programming burden and allow flexible and efficient deployment of the MAP-based target systems, we have developed the MAP University of Washington Image Computing Library (UWICL) for the MAP1000. Currently, it consists of 105 functions. The UWICL functions effectively decouple the data flow control and data processing in a flexible two-layered software structure, where the upper layer is responsible for the data transfer between on-chip cache and off-chip memory by utilizing the on-chip DMA controller in a double-buffering scheme and the lower layer performs the data processing. This hierarchy allows the flexibility to use the UWICL modules depending on how the application is implemented and/or the level of user's experience in programming the MAP1000 mediaprocessor.

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“…Recently, as more types of media, such as video, audio, graphics and images, become digital and readily available, technology developments in multimedia algorithms and processors have been accelerating to meet the rapidly-increasing processing and computing needs in many existing and emerging media standards and services [3]. Capitalizing on these new technologies, many new products have been introduced or are under development.…”

Section: Introductionmentioning

confidence: 99%

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mentioning

confidence: 99%

<title>Consortium for promoting the use of media processors in multimedia applications</title>

Grow

Kim

et al. 1999

SPIE Proceedings

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Mediaprocessors, such as Philips Trimedia and Hitachi/Equator Technologies MAP, combine the computational power of high-end DSPs with various 110 capabilities in a single programmable chip [1][2][3]. Due to their programmability, mediaprocessors have greater flexibility than ASICs and other special-purpose hardware. Early mediaprocessors, such as Texas Instruments TMS32OC8O (MVP) since its introduction in 1994 [4], have had limited success due to their difficulty in programming, insufficient computational power, and high cost. Fortunately, several newer mediaprocessors, which are available or under development, are easier to program, are less expensive, and/or have more computational power. However, due to the earlier difficulties and inherent uncertainties in the programmable solutions, mediaprocessor user companies (set makers) are often hesitant in adopting the mediaprocessors in their products. Furthermore, set makers still need to expend a lot of time and manpower in making a successful transition from hardwired to mediaprocessor-based products. Therefore, we introduce the Mediaprocessor (MP) Consortium, which aims to remove the barrier to the widespread use of programmable mediaprocessors. Through publications, web site, training courses, software libraries, and objective evaluations of mediaprocessors, the MP Consortium can increase the awareness of the benefits of mediaprocessors over ASICs, make the transition to mediaprocessor-based products easier for set makers, and help them attain full advantage of using mediaprocessors.

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High-performance image computing with modern microprocessors

Cited by 10 publications

References 20 publications

Semiautomatic video object segmentation using vsnakes

Semiautomatic video object segmentation using vsnakes

New mediaprocessor and its image computing library

<title>Consortium for promoting the use of media processors in multimedia applications</title>

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