We are developing an Alternative and Augmentative Communication (AAC) portable device called PictoBar which is used in speech rehabilitation therapy. PictoBar recognizes barcodes and images, such as pictograms and pictures. Then it plays a sound message associated with the recognized barcode or image. This paper describes the development of the image recognition algorithm and its implementation using Codec Engine framework on a DM6446 DaVinci processor

Ayyalasomayajula, Pradyumna

Deurin, Nicolas

Farine, Pierre-André

Grassi Pauletti, Sara

Gueguen, Thierry

Infoscience - École polytechnique fédérale de Lausanne

IMPLEMENTATION OF AN IMAGE RECOGNITION ALGORITHM ON THEDM6446 DAVINCI PROCESSORP. Ayyalasomayajula 1, S. Grassi 1, N. Deurin 2, P.-A. Farine 1 and T. Gue´guen 21 Ecole Polytechnique Fe´de´rale de Lausanne, Institute of Microengineering, Electronics and Signal Processing LaboratoryEPFL STI IMT ESPLAB, Rue A.-L. Breguet 2, 2000, Neuchaˆtel, Switzerlandphone: + (41) 32 718 34 25, fax: + (41) 32 718 34 02, email: pradyumna.ayyalasomayajula@epfl.chweb: http://esplab.epfl.ch2 Epicard S.A., Rue Fernand-Dubois 3, 1890 St-Maurice, Switzerlandweb: www.epicard.chABSTRACTWe are developing an Alternative and Augmentative Com-munication (AAC) portable device called PictoBar which isused in speech rehabilitation therapy. PictoBar recognizesbarcodes and images, such as pictograms and pictures. Thenit plays a sound message associated with the recognized bar-code or image. This paper describes the development of theimage recognition algorithm and its implementation usingCodec Engine framework on a DM6446 DaVinci processor.1. INTRODUCTIONThe field of electronic aid for disabled people is growing con-stantly and many innovations are added every year. In partic-ular, there is an increasing need for electronic aids in Alterna-tive and Augmentative Communication (AAC). Further, theuse of picture symbols (pictograms) has proved successful inclinical studies to improve the communication in people withmild-to-severe speech-impairments. Communication boards,i.e., arrangements on a physical support of pictograms thatconvey graphical messages, are widely used for this purpose.The Swiss Foundation for Rehabilitation technology [1]together with speech therapists has identified the need for aportable, rugged and user friendly device, able to recognizea pictogram and play a sound or phrase associated to it. Thisdevice will be used to improve speech therapy using AAC.In this paper we describe the development of such a de-vice, called “PictoBar”, using Texas Instruments DaVincitechnology. Emphasis is placed in the description of thedevelopment of image recognition algorithm and its imple-mentation on a DM6446 DaVinci processor, using Texas In-struments Codec Engine framework. The paper is organizedas follows. The PictoBar device is described in Section 2.The development of the image recognition algorithm is ex-plained in Section 3. Section 4 describes the software frame-work used for the DSP implementation. Section 5 explainsthe DSP implementation. Current status and Future work arepresented in Section 6. Finally conclusions are drawn in Sec-tion 7.2. DEVICE DESCRIPTIONPictoBar is a portable and rugged AAC device with image(pictures and pictograms) and barcode recognition capabili-ties, used as communication aid and for therapy of speech-impaired people. Once an image or barcode is recognized,PictoBar plays a pre-recorded sound message, associated toFigure 1: B.A.Bar, a present generation AAC device and ex-amples of its utilizationProceedings of the 4th European DSP in Education and Research Conference (EDERC 2010), Nice, France, December 1-2, 2010.the recognized image or barcode. In order to be recognized,an image (picture or pictogram) must have been previouslystored into the device database. PictoBar is an improved ver-sion of B.A.Bar [2], a present generation AAC device whichrecognizes barcodes and plays sound messages associated tothem. Figure 1 shows B.A.Bar and its utilization.2.1 Device hardwareThe hardware block diagram of PictoBar is shown inFigure 2. Main components are:• Micro-camera: used to acquire the images or barcodesprinted on communication boards. The camera is afixed focus OV7675 from Omnivision [3] with VGAresolution (640x480 pixels).• Processing unit: is a DaVinci DM6446 dual coreprocessor from Texas Instruments [4]. This processorhas an ARM926EJ-S core and a fixed point C64x+ DSPcore. The application runs on the ARM core, featuringuser interface, barcode recognition algorithm, peripheralcontrol and overall system management. The imagerecognition algorithm runs on the DSP core.• Audio I/O Interface: consisting of an Audio IntegratedCircuit (IC TLV320AIC26) [5], a microphone for audioacquisition and a speaker for audio playback. The AICcontains AD/DA converters used in 16-bit mode, 16 kHzsampling frequency.• USB Interface: used to connect PictoBar to an externalPC, for firmware updates and to train new images intoPictoBar database.• Memory: PictoBar contains built-in memory (FlashNAND and DDR2-SDRAM) as well as detachablememory (SD card). The FLASH NAND is used tostore the programs. The SD Card is used to store thedatabase containing the JPEG compressed images andtheir pre-extracted features, together with the voicemessages associated to each image.Figure 2: Hardware block diagram of PictoBar3. IMAGE RECOGNITION ALGORITHMFigure 3 shows the block diagram of the image recognitionalgorithm, developed at ESPLAB, that is used in PictoBar.The constitutive blocks of the recognition algorithm are ex-plained as follows:• Border Recognition, Reconstruction and image Prepro-cessing (BRRP):This block is used to align and crop the input “queryimage” i.e., image to be recognized which is acquiredwith the camera. The images in the communicationboard are, by convention, printed with a rectangularborder enclosing its content. The enclosing border isdetected using line detection, based on modified Houghtransform. The query image is then aligned and cropped.Then, image pre-processing such as histogram stretchingis applied.• Color Density Circular Crop (CDCC) preselection algo-rithm:To decrease the complexity while keeping a good perfor-mance, image recognition is done in two stages. In thefirst stage we use a less complex but also less robust al-gorithm called “Color Density Circular Crop” (CDCC).This algorithm searches the database and preselects the50 images which are closest to the query image. In thesecond stage, a more robust but computationally more ex-pensive algorithm called “DCT phase match” (DCTPM)is used. This algorithm searches the closest match to thequery within the reduced database of the 50 images pres-elected in the first stage.The CDCC preselection algorithm searches the closestimages in the database using as feature the color pro-portions within concentric circular zones of the images.Feature calculation requires edge detection to determinethese zones. As the features are pre-calculated andstored in the image database, the CDCC algorithm islow in complexity. Additionally, this algorithm is robustto rotation, translation and scaling, but is sensitive tovariations in lighting.• DCT Phase Match (DCTPM):The base recognition algorithm, which is referred to asDCT phase match [6], searches for the best match ofthe query image into a database using correlation onthe DCT phase of the 8x8 blocks of the images. Thisalgorithm is compatible with the JPEG compressionstandard. The diagram of the DCT phase match algo-rithm is shown in Figure 4. This algorithm is accurateand robust to lighting variation but computationallyexpensive. To reduce its complexity, DCT phase matchis only performed on the reduced database of imagespreselected by the CDCC algorithm.3.1 Camera Settings and image database for testingThe database of pictograms used during development is asubset of 1500 pictograms randomly selected from the Pic-ture Communication Symbols set [7]. We refer to this set of1500 pictograms as the “clean database” and use it as the ref-erence database in which the query image is searched. Res-Proceedings of the 4th European DSP in Education and Research Conference (EDERC 2010), Nice, France, December 1-2, 2010.Figure 3: Image recognition algorithmFigure 4: DCT phase match algorithmolution of this database was adjusted to be 320x240 pixels(QVGA).To reproduce the conditions in the final device, the fixedfocus OV7675 camera was fixed at a distance of 9.2 cm fromthe image. Under this condition, a VGA resolution acquiredimage corresponds to a rectangle of 6 x 8 cm. A LED crownwas mounted on the camera to provide uniform lighting. Thisis required as the CDCC preselection algorithm is not ro-bust to lighting variation. A subset of 50 representative pic-tograms, selected from the 1500 of the “clean database” wasprinted on paper with size of 3 x 4 cm. To provide visualreference for manual alignment and cropping, two enclosingborders (3 x 4 cm and 6 x 8 cm) were printed as shown inFigure 6. These pictograms were acquired with the camera,from MATLAB, manually aligned using the printed bordersas visual help, and cropped to 3 x 4 cm (320x240 pixels), thusremoving both borders. These 50 acquired images constitutethe “real condition database” and are used as query imagesfor the tests.The camera setup including the LED crown is shown inFigure 5, and an example of printed pictogram for testing isshown in Figure 6.Figure 5: Camera setup with LED crownFigure 6: Example of printed pictogram3.2 Algorithm development and testingThe algorithm was implemented and tested in MATLAB us-ing Image processing, Signal processing and Image acquisi-Proceedings of the 4th European DSP in Education and Research Conference (EDERC 2010), Nice, France, December 1-2, 2010.Figure 7: Codec Engine software componentstion toolboxes. The algorithm was tested initially with queryimages from the “clean database”. After this first validation,the algorithm was tested with query images acquired fromthe camera (from the “real condition database”).4. SOFTWARE FRAMEWORK FOR DSPIMPLEMENTATIONThe DM6446 contains an ARM9 core and a fixed pointC64x+ DSP core. The ARM9 core uses Embedded Linuxas RTOS. The application running on the ARM core fea-tures user interface, barcode recognition algorithm, periph-eral control and overall system management. Additionally,the ARM application controls the remote execution of algo-rithms on the DSP core using Codec Engine framework asexplained below. DSP core uses DSP / BIOSTM [8] as real-time kernel. The ARM and DSP communicate with eachother through DSP / BIOSTM LINK.To implement the recognition algorithm we use theCodec Engine (CE) [9] framework whose software compo-nents are shown in Figure 7. The application runs on theARM core and handles I/O and application processing. Toprocess the signals the application uses the VISA APIs whichare provided by the Codec Engine along with a generic APIcalled IUNIVERSAL. Codec Engine is divided into a CE Re-mote Server which runs on the DSP and a CE Client whichruns on the ARM.In order to fit in the Codec Engine framework, the recog-nition algorithms have to be compatible with xDAIS / xDM[10] standards form Texas Instruments, so that algorithm re-source allocation is controlled by the application and that theapplication controls and uses the algorithms through a uni-form set of APIs.5. DSP IMPLEMENTATIONFirst the complete image recognition algorithm was im-plemented and tested in MATLAB as explained in sec-tion 3.2. This MATLAB implementation is used as referencethroughout the DSP implementation on DM6446 using theEVMDM6446 from Spectrum Digital [11] as developmentboard.We have implemented the different blocks of the algo-rithm in C language using fixed point arithmetic. This waspartly done under Code Composer Studio (CCS v4) fromTexas Instruments along with Blackhawk USB-JTAG emula-tor (BH-USB-560BP) [12] as debugging tool. To implementthe CDCC and DCTPM blocks, we used as much as possiblethe functions from the Image library (IMGLIB2) and the Vi-sion library (VLIB) provided by Texas Instruments [13, 14]such as Canny edge detection, 8x8 block DCT calculation(IMG fdct 8x8) and IMG quantize for JPEG quantization.The algorithm blocks to be implemented in the softwareframework are (see section 3) BRRP, CDCC, DCTPM andJPEG decoder. Each of the blocks was implemented as aseparate Codec Engine “codec”. The BRRP, CDCC andDCTPM codecs were implemented using the IUNIVERSALxDM interface. The JPEG decoder which is provided byTexas Instruments is delivered as IIMGDEC1 xDM codec[15]. A single Codec Engine server configured to includethese four codecs is made available to the application. Wehave used the Codec Engine GenCodecPkg wizard [16] togenerate each codec package containing starter code and thenwe have added the C code of the algorithm blocks into it. Thetesting application for each codec using File I/O was writtenby modifying the universal copy example delivered with theCodec Engine software.Proceedings of the 4th European DSP in Education and Research Conference (EDERC 2010), Nice, France, December 1-2, 2010.5.1 TestingWe have put the file system of the board on a server with afixed IP address and mounted it using NFS. The board, con-figured to boot from Flash NAND, was also connected to thenetwork and was assigned a fixed IP address. This allowedthe access from remote location to the file system via Sambaor NFS and facilitated the remote login and execution on theboard using SSH or Telnet. This setup allows sharing of theboard between users and the possibility to perform extensivetesting automatically from MATLAB: first the input files arewritten from MATLAB on the board filesystem, execution ofthe application on the board is controlled from MATLAB us-ing SSH, and the output files are read by MATLAB from theboard filesystem to analyze the results.Using this development setup, we have extensively testedthe codecs with images from the “clean database” and the“real condition database” (see section 3.1). By comparing theresults from the DSP implementation with the results fromthe MATLAB implementation, we have verified the properfunctioning of the DSP implementation, and that the lossesdue to the fixed-point arithmetic of the C64x+ do not affectsignificantly the performance.6. CURRENT STATUS AND FUTUREWORKWe have completed the MATLAB implementation of theimage recognition algorithm. Concerning the implementa-tion on DSP, each of the blocks of the algorithm, namely,BRRP, CDCC, DCTPM and JPEG decoder have been sepa-rately implemented and tested. Ongoing and future work is inconcatenating these blocks performing extensive testing, andmeasuring if the overall performance of the implemented al-gorithm is within the initial device specifications, especiallythe response time which should not exceed 0.5 second. How-ever, given the measured performance of each of the blocks,we expect the overall performance to be well within thesespecifications. On the ARM side, we have implemented thedevice management, the barcode detection algorithm, the im-age acquisition and the audio acquisition and playback. Ahardware prototype has been designed and delivered to themanufacturer. Once this prototype is ready, the completefirmware including application and DSP server will be testedon it.7. CONCLUSIONSWe have presented the development and the implementationof an image recognition algorithm on DM6446 using theCodec Engine framework, outlining the steps to go from thealgorithm design down to the DSP implementation. This im-plementation is part of the development of PictoBar, the nextgeneration of Alternative and Augmentative Communicationportable device for improved speech rehabilitation therapy.By using Davinci technology, and by including the re-quirements of low complexity for portability throughout allstages of the development, we could successfully implementfast response image recognition task on a portable device.8. ACKNOWLEDGEMENTSThis work was partly supported by the Swiss Federal Officefor Professional Education and Technology (OPET) throughthe Innovation Promotion Agency (CTI) under the Grant CTI8811.2 PFNM-NM (“PictoBar”project)[17]. We are gratefulto all the people who have contributed to this work: Mr.Christian Vaucher, Mr. Michel Guinand, Mr. Yvan Magninand Mr. Yves Mu¨hlebach from FST; Mr. Mario Aellen andMr. Timothe´e Carron from Gigatec S.A. and Mr. PascalBach who has implemented the CDCC algorithm during hispractical semester work carried out at EPFL-ESPLAB.REFERENCES[1] www.fst.ch[2] www.fst.ch/babar/[3] www.ovt.com[4] TMS320DM6446 Digital Media System-on-Chip -http://focus.ti.com/docs/prod/folders/print/tms320dm6446.html[5] http://focus.ti.com/docs/prod/folders/print/tlv320aic26.html[6] J. Bracamonte, M. Ansorge, F. Pellandini and P.-A.Farine, “Low complexity Image Matching in the com-pressed domain by using DCT-phase”, Proc. of 6thCOST 276 workshop, 88-93, 2004.[7] www.mayer-johnson.com/education/symbols/pcs/[8] http://focus.ti.com/docs/toolsw/folders/print/dspbios5.html[9] TI Codec Engine Application Developer Users Guide,Texas Instruments, edition d, September 2007.[10] TI xDAIS-DM (Digital Media) User Guide, Texas In-struments, edition b, January 2007.[11] Spectrum Digital Inc. DaVinci-DM644x EvaluationModule Technical Reference, Spectrum Digital Inc., edi-tion e, March 2007.[12] Usb560-bp jtag emulator. - www.blackhawk-dsp.com/products/USB560BP.aspx.[13] TI TMS320C64x+ DSP Image/Video Processing Li-brary (v2.0.1), Texas Instruments, edition a, May 2008.[14] TI Vision Library (VLIB) Application Programming In-terface Reference Guide, Texas Instruments, edition c,November 2009.[15] http://focus.ti.com/docs/toolsw/folders/print/tmdjpegd.html[16] http://processors.wiki.ti.com/index.php/Codec EngineGenCodecPkg Wizard FAQ[17] PictoBar: Portable augmentative and alternativecommunication device with pictogram recogni-tion functionality for the speech handicapped. -www.aramis.admin.chProceedings of the 4th European DSP in Education and Research Conference (EDERC 2010), Nice, France, December 1-2, 2010.

Implementation of an image recognition algorithm on the DM6446 DaVinci Processor

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Implementation of an image recognition algorithm on the DM6446 DaVinci Processor

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