An FPGA System for the High Speed Extraction, Normalization and Classification of Moment Descriptors

We propose a new FPGA system for the high speed extraction, normalization and classification of moment descriptors. Moments are extensively used in computer vision, most recently in the MPEG-7 standard for the region shape descriptor. The computational complexity of such methods has been partially addressed by the proposal of custom hardware architectures for the fast computation of moments. However, a complete system for the extraction, normalization and classification of moment descriptors has not yet been suggested. Our system is a hybrid, relying partly on a very fast parallel processing structure and partly on a custom built, low cost, reprogrammable processing unit. Within the latter, we also propose FPGA circuits for low cost double precision floating-point arithmetic. Our system achieves the extraction and classification of invariant descriptors for hundreds or even thousands of intensity or color images per second and is ideal for high speed and/or volume applications. An FPGA System for the High Speed Extraction, Normalization and Classification of Moment Descriptors Stavros Paschalakis1, Peter Lee, Miroslaw Bober1 1 Mitsubishi Electric ITE-VIL, The Surrey Research Park 20 Frederick Sanger Road, Guildford, Surrey GU2 7YD, UK {Stavros.Paschalakis, Miroslaw.Bober}@vil.ite.mee.com 2 Department of Electronics, University of Kent at Canterbury Canterbury, Kent CT2 7NT, UK [email protected] Abstract. We propose a new FPGA system for the high speed extraction, normalization and classification of moment descriptors. Moments are extensively used in computer vision, most recently in the MPEG-7 standard for the region shape descriptor. The computational complexity of such methods has been partially addressed by the proposal of custom hardware architectures for the fast computation of moments. However, a complete system for the extraction, normalization and classification of moment descriptors has not yet been suggested. Our system is a hybrid, relying partly on a very fast parallel processing structure and partly on a custom built, low cost, reprogrammable processing unit. Within the latter, we also propose FPGA circuits for low cost double precision floating-point arithmetic. Our system achieves the extraction and classification of invariant descriptors for hundreds or even thousands of intensity or color images per second and is ideal for high speed and/or volume applications. We propose a new FPGA system for the high speed extraction, normalization and classification of moment descriptors. Moments are extensively used in computer vision, most recently in the MPEG-7 standard for the region shape descriptor. The computational complexity of such methods has been partially addressed by the proposal of custom hardware architectures for the fast computation of moments. However, a complete system for the extraction, normalization and classification of moment descriptors has not yet been suggested. Our system is a hybrid, relying partly on a very fast parallel processing structure and partly on a custom built, low cost, reprogrammable processing unit. Within the latter, we also propose FPGA circuits for low cost double precision floating-point arithmetic. Our system achieves the extraction and classification of invariant descriptors for hundreds or even thousands of intensity or color images per second and is ideal for high speed and/or volume applications.