International Association for Pattern Recognition (IAPR)

  • Description
  • The Inter­na­tional Asso­ci­a­tion for Pat­tern Recog­ni­tion (IAPR) is an inter­na­tional asso­ci­a­tion of non-​profit, sci­en­tific or pro­fes­sional orga­ni­za­tions (being national, multi-​national, or inter­na­tional in scope) con­cerned with pat­tern recog­ni­tion, com­puter vision, and image pro­cess­ing in a broad sense. Nor­mally, only one orga­ni­za­tion is admit­ted from any one coun­try, and indi­vid­u­als inter­ested in tak­ing part in IAPR’s activ­i­ties may do so by join­ing their national organization.

    Purpose

    The aims of IAPR are to pro­mote pat­tern recog­ni­tion and the allied branches of engi­neer­ing together with the related arts and sci­ences, to advance inter­na­tional co-​operation in the field of inter­est to stim­u­late research, devel­op­ment, and the appli­ca­tion of pat­tern recog­ni­tion in sci­ence and human activ­ity, to fur­ther the dis­sem­i­na­tion and exchange of infor­ma­tion on pat­tern recog­ni­tion in the broad sense, and to encour­age edu­ca­tion in all aspects of the field of inter­est. In achiev­ing these aims, IAPR ful­fills the need for bet­ter world-​wide com­mu­ni­ca­tion and increases under­stand­ing among prac­ti­tion­ers of all nations in the role that machine intel­li­gence can play in accel­er­at­ing tech­ni­cal and sci­en­tific progress.

    Tech­ni­cal Committees

    Areas of pat­tern recog­ni­tion cur­rently rep­re­sented by tech­ni­cal com­mit­tees are: Sta­tis­ti­cal Pat­tern Recog­ni­tion, Struc­tural and Syn­tac­ti­cal Pat­tern Recog­ni­tion, Neural Net­works and Com­pu­ta­tional Intel­li­gence, Bench­mark­ing and Soft­ware, Spe­cial Hard­ware and Soft­ware Envi­ron­ments, Remote Sens­ing and Map­ping, Machine Vision Appli­ca­tions, Bio­med­ical Appli­ca­tions, Graph­ics Recog­ni­tion, Read­ing Sys­tems, Mul­ti­me­dia and Visual Infor­ma­tion Sys­tems, Pat­tern Recog­ni­tion in Astron­omy and Astro­physics, Sig­nal Analy­sis for Machine Intel­li­gence, Graph Based Rep­re­sen­ta­tions, Alge­braic and Dis­crete Math­e­mat­i­cal Tech­niques, Machine Learn­ing and Data Min­ing, Dis­crete Geom­e­try, Cul­tural Her­itage Appli­ca­tions, and Bioinformatics.

    Publications

    Newsletter

    The prin­ci­pal medium through which IAPR activ­i­ties are pub­li­cised is the IAPR Newslet­ter. As well as list­ing IAPR spon­sored sci­en­tific meet­ings and address infor­ma­tion, review­ing con­fer­ences and books and pub­lish­ing arti­cles of gen­eral inter­est to the IAPR com­mu­nity, the Newslet­ter tries to keep its read­er­ship informed as to the work being car­ried out in the IAPR Committees.

    The IAPR Newslet­ter is pub­lished quar­terly in win­ter, spring, sum­mer and autumn edi­tions by IAPR. The last sev­eral issues are avail­able at the IAPR web­site in the member’s section.

    Pat­tern Recog­ni­tion Letters

    Pat­tern Recog­ni­tion Let­ters main fea­tures are con­cise arti­cles, rapid pub­li­ca­tion and a broad cov­er­age of the pat­tern recog­ni­tion literature.

    The sub­ject mat­ter of PRL cov­ers the inter­ests rep­re­sented in the tech­ni­cal com­mit­tees of IAPR. In those fields, the papers may empha­size the­ory, method­ol­ogy, empir­i­cal stud­ies or appli­ca­tions. PRL is a ref­er­eed journal.

    Orig­i­nal­ity, qual­ity and clar­ity are the cri­te­ria for arti­cle accep­tance. PRL (ISSN 0167 – 8655) is pub­lished monthly by Else­vier Sci­ence B.V. Gen­eral infor­ma­tion for authors is avail­able through the Else­vier Edi­to­r­ial Sys­tem (EES).

    Machine Vision and Applications

    Machine Vision and Appli­ca­tions pub­lishes high-​quality tech­ni­cal con­tri­bu­tions in machine vision research and devel­op­ment. Orig­i­nal con­tri­bu­tions deal­ing with sci­en­tific, com­mer­cial, indus­trial, mil­i­tary, and bio­med­ical appli­ca­tions of machine vision, are within the scope of the journal.

    Par­tic­u­lar empha­sis is placed on engi­neer­ing and tech­nol­ogy aspects (algo­rithms, archi­tec­ture, VLSI imple­men­ta­tions, AI tech­niques and expert sys­tems, lan­guages, fron-​end sens­ing, mul­ti­di­men­sional and mul­ti­sen­sor machine vision, real-​time tech­niques, image data­bases and vir­tual real­ity) of image pro­cess­ing and com­puter vision. MV&A is the IAPR spon­sored pub­li­ca­tion since Jan­u­ary 1995. MV&A is pub­lished bimonthly by Springer Ver­lag.

    Inter­na­tional Jour­nal on Doc­u­ment Analy­sis and Recog­ni­tion (IJDAR)

    IJDAR is focused in pub­lish­ing arti­cles ded­i­cated to doc­u­ment analy­sis and recog­ni­tion. This includes con­tri­bu­tions deal­ing with com­puter recog­ni­tion of char­ac­ters, sym­bols, text, lines, graph­ics, images, hand­writ­ing, sig­na­tures, as well as auto­matic analy­ses of the over­all phys­i­cal and log­i­cal struc­tures of doc­u­ments, with the ulti­mate objec­tive of a high-​level under­stand­ing of their seman­tic content.

    The jour­nal pub­lishes arti­cles of four pri­mary types – orig­i­nal research papers, cor­re­spon­dence, overviews and sum­maries, and sys­tem descrip­tions. Spe­cial issues on active areas of research will be encouraged.

    Pos­si­ble top­ics include:

    • Doc­u­ment Image Processing
    • Doc­u­ment Models
    • Hand­writ­ing Mod­els and Analysis
    • Char­ac­ter and Word Recognition
    • Sym­bol Recognition
    • On-​line Recognition
    • Pen-​based Computing
    • Multi-​lingual Processing
    • Phys­i­cal and Log­i­cal Page Analysis
    • Raster-​to-​Vector Conversion
    • Graph­ics Recognition
    • Map and Line Draw­ing Understanding
    • Inter­pre­ta­tion of Engi­neer­ing Drawings
    • Stor­age and Retrieval of Documents
    • Text Analy­sis and Processing
    • Nat­ural Lan­guage Issues
    • Infor­ma­tion Extrac­tion and Filtering
    • Per­for­mance Evaluation
    • Doc­u­ment Authen­ti­ca­tion and Validation
    • Imple­men­ta­tions, Appli­ca­tions and Systems
    • Pro­cess­ing Text in other Contexts
    • Mul­ti­me­dia and Hyper­me­dia Analysis
    • Time-​Varying Documents
    • Dis­trib­uted Doc­u­ment Col­lec­tions (Dig­i­tal Libraries)

    Details

    Website iapr.org

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