Pattern Recognition with Embedded Systems Technology: a Survey(Extended Abstract of Invited Talk)

Juan-Carlos Perez-Cortes, Jose-Luis Guardiola, Alberto-Jose Perez-JimenezInstituto Tecnologico de Informatica, Universidad Politecnica de Valencia

Valencia, Spain{jcperez,joguagar,aperez}@iti.upv.es

Keywords- embedded systems; pattern recognition; biomet-rics; computer vision; DSP; GPU; FPGA; multimedia

I. INTRODUCTION

Pattern Recognition (PR) tasks are natural candidatesfor embedded systems, since they usually interact withhumans and other complex processes in the real world. Oftenregarded as the part of Artificial Intelligence (AI) closerto perception, a typical PR application reacts to externalevents that the system perceives through physical sensorsor input devices and produces a response using actuators orinformation display subsystems.

Being usually far from trivial, very demanding from thecomputational point of view, and requiring a fast reactiontime, PR algorithms constitute a real challenge to the em-bedded system designer. In this talk, some of the mainapplication domains and optimization approaches proposedto deal with these relevant issues, along with many openproblems and paths to improvement, are presented.

II. APPLICATIONS

There are many applications of embedded and mobilesystems that require a contribution of PR algorithms. Theycan be classified according to several criteria, being the mostobvious the kind of input they have to process.

A number of PR algorithms deal with an Audio Inputsignal. Among them, speech recognition, speech translationand audio biometrics (speaker recognition and verification).Other tasks take Images as their input (Computer Vision).They are increasingly popular and include advanced fea-tures in digital photography, like face detection or smilerecognition, also in surveillance applications, with intruderdetection, behaviour recognition, biometrics, informationgathering from crowds, vehicles, etc.

Other systems deal with very Diverse Inputs from phys-ical devices or from the environment, as in manufacturing,automotive or aeronautic applications, where complex de-cision capabilities and autonomous function are demandingfor increasingly sophisticated PR and AI subsystems.

Work partially supported by the Spanish Ministerio de Educacion yCiencia under project DPI2006-15542-C04 and Consolider CSD2007-018.

Another classification criterion is the industry sectorwhere the application is used. Typical sectors are: Medical,Security, Manufacturing and Mobility.

A prominent medical application is medical imaging. Itusually deals with signals that are not strictly images, but canbe processed as such, like ultrasound, X-ray or magnetic res-onance. Security applications include face, fingerprints, irisand voicebiometrics, among other, as well as surveillance.In manufacturing, industrial inspection and quality controlare the most usual tasks. And, finally, in the mobility sector,a lot of innovative features are being introduced in the hugemarkets of vehicles, as well as handheld mobile devices.

III. APPROACHES

There are no simple ways to address the sometimesdaunting problem of implanting an elaborate algorithm intoa low-power, resource-limited, closed, autonomous, reliableand self-supporting system, sometimes with real-time re-quirements or at least with a fast response expectation fromthe user. Several lines of research have been proposed toachieve that goal, ranging from the optimization of codefor conventional processors with limited CPU power andmemory, to the design of special purpose hardware:

Using off-the-shelf architectures requires a detailed lowlevel code optimization and sometimes the adaptation ofsome operations to specialized DSP’s, GPU’s and MediaProcessors [1].

Using special architectures like parallel interconnectedprocessors, optical computation devices and other specialdesigns is also possible [2].

Using ad-hoc or reconfigurable hardware to implementcore PR algorithms is another option. The designs canbecome VLSI chips or be loaded into reconfigurable circuitslike FPGA’s [3].

REFERENCES

[1] B. Kisacanin, “Examples of low-level computer vision onmedia processors,” June 2005, pp. 135–135.

[2] W. J. MacLean, “An evaluation of the suitability of fpgas forembedded vision systems.”

[3] F. Yu and D. Gregory, “Optical pattern recognition: architec-tures and techniques,” Proceedings of the IEEE, vol. 84, no. 5,pp. 733–752, May 1996.

20th International Workshop on Database and Expert Systems Application

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DOI 10.1109/DEXA.2009.87

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