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Universidad Nacional Autónoma De México Universidad Nacional Aut´onoma de M´exico Instituto de Investigaciones en Matem´aticas Aplicadas y en Sistemas Facultad de Ingenier´ıa Laboratorio de Biorob´otica ocalizaci´on y seguimiento de objetos a trav´es de sus caracter´ısticas principales Ing� Rommel S´anchez Verdejo [email protected] 2008 a UNAM – Dirección General de Bibliotecas Tesis Digitales Restricciones de uso DERECHOS RESERVADOS © PROHIBIDA SU REPRODUCCIÓN TOTAL O PARCIAL Todo el material contenido en esta tesis esta protegido por la Ley Federal del Derecho de Autor (LFDA) de los Estados Unidos Mexicanos (México). El uso de imágenes, fragmentos de videos, y demás material que sea objeto de protección de los derechos de autor, será exclusivamente para fines educativos e informativos y deberá citar la fuente donde la obtuvo mencionando el autor o autores. Cualquier uso distinto como el lucro, reproducción, edición o modificación, será perseguido y sancionado por el respectivo titular de los Derechos de Autor. Resumen El objetivo de este trabajo es proporcionar al Laboratorio de Biorob´otica del Posgrado de la Facultad de Ingenier´ıa de la Universidad Nacional Aut´onoma de M´exico un sistema de visi´on !ue sea "´acil de modi#car para "uturas generaciones de un conjunto de $erramientas en mantenimiento constante as´ı como per"ectamente documentadas utili%ando implementaciones con su respectivo c´odigo anteriormente propuesto evaluando & mejorando su rendimiento a trav´es de t´ecnicas de optimi%aci´on por $ard'are & una t´ecnica te´orica en este caso( la cuanti%aci´on vectorial) En este trabajo se presenta una comparaci´on entre dos t´ecnicas para la correspondencia de caracter´ısticas locales mencionadas en la bibliogra"´ıa( correspondencia por distancia Euclideana & correspondencia por cuanti%aci´on vectorial utili%ando el algoritmo *U+F ,B-./01 como generador de caracter´ısticas locales llamadas descriptor) b Agradacimientos Pap´as: qui´en me han soportado durante 27 anos sin e!igir nada a cam"io m´as all´ade mi "ienestar constante# Amigos: los que me conocen sa"r´an a qu´eme re$ero: gracias por estar aqu´ı# Universidad: %na de las pocas cosas por las que me puedo sentir orgulloso de ser me!icano: gracias por todos estos anos de preparacion# &racias a todas las personas' maestros y companeros' que hicieron de "uena o mala manera' la persona que soy# (onde quiera que est´e' donde quiera que pueda recordar: Por mi raza hablar´ael esp´ıritu CONACyT : por sostenerme econ´omicamente por )#* anos# Software Libre: +odas las personas que contri"uyen de una u otra ,orma a ´el' ya que de no e!istir' no podr´ıa enca-ar en este mundo# Paul’s Family: .erry / 0ancy : 1ou 2no3' 3ithout your support' I 3ould have never reached this ,ar# A mis sinodales: .es´us Savage 4armona' Francisco &arc´ıa %galde' 5dgar &arduno Angeles'´ Mar´ıa 5lena Mart´ıne6 7´ere6 y 8oris 5scalante 9am´ıre6 quienes pacientemente me-oraron la calidad de estre tra"a-o y con eso mi persona# c ´Indice general Resumen b Agradecimientos c Lista de Tablas f Lista de Figuras g 1� Introducci´on 1 2� Estado del arte 4 2.1. Detectoresdepuntos............................... 5 2.1.1. HarrisyStephens ............................ 6 2.1.1.1. KanadeyTomasi ....................... 7 2.1.2. Detecci´on de m´aximos y m´ınimos en la representaci´on espacio-escala 2.1.2.1. Harris tipo laplaciano . ! 2.1.". #´alculor´apidodelahessiana . 1$ 2.2. Descriptoresdepuntos. ............ .... .... ......... 11 2.2.1. S%&T' Trans(romada de caracter´ısticas in)ariantes a escala . 11 2.2.2. S*+&' #aracter´ısticas ro,ustas y aceleradas . 11 3� Esquema general del sistema de visi´on 13 ".1. -ocali.aci´on.................................... 1" ".1.1. +e/ionesdin´amicas. 10 ".1.2. +e/iones est´aticas . 10 ".2. 1arcado...................................... 16 ".". 2´us3uedadecorrespondencia . 16 ".".1. #orrespondencia4st´andar. 16 ".".2. 5r,olesK-d................................ 17 ".".". #uanti.aci´on )ectorial . 1 d ".0. Se/uimiento.................................... 1! ".0.1. 4l&iltrodeKalman ........................... 1! ".5. Sistemade)isi´on'2lo3ues. 22 4� lataforma t´ecnica 2! 0.1. #apa(´ısica .................................... 26 0.1.1. 43uipodec´omputo ........................... 27 0.1.2. 5d3uisici´ondevideo . .. 2 0.2. #apal´o/ica .................................... 2 0.2.1. Sistema6perati)o ............................ 2 0.2.2. 2i,liotecas ................................ 2! 0.2.2.1. 6pen#7 ............................ 2! 0.2.2.2. %ntel 8er(ormance 8rimiti)es . "$ 0.2.2.". 2i,liotecas internas . "$ "� ruebas # Resultados 31 5.1. 8rue,as+o,ocup2$$ .............................. "1 5.1.1. +eco9eyentre/a ............................. "2 5.1.2. 2uscayencuentra ............................ "2 5.1.". Si/ueme.................................. "2 5.2. %nterpretaci´onderesultados . "" 5.2.1. +esultados num´ericos . "5 !� $onclusiones 42 6.1. Tra,a9o&uturo .................................. 00 A� Instalaci´on del Sistema 4" &losario 4' (ibliograf´ıa 4' e ´Indice de tablas ".1. 5l/oritmoSimplepara7oronoi . 1! ".2. 4cuaciones por actuali.aci´on de tiempo . 21 ".". 4cuaciones de actuali.aci´on para la medici´on . 21 5.1. :´umero de caracter´ısticas por o,9eto . "5 5.2. 1´ınimo n´umero de caracter´ısticas comparadas en cada prue,a . "5 5.". 8rimersecuenciaTama;nonormal. "5 5.0. 8rimersecuenciaTama;noreducido. "6 5.5. Se/undasecuenciaTama;nonormal. "6 5.6. Se/undasecuenciaTama;noreducido. "6 5.7. &alsos positi)os y ne/ati)os para la primer secuencia . "7 5. &alsos positi)os y ne/ati)os para la se/unda secuencia . "7 ( ´Indice de figuras ".1. Di(erencia )isual para la detecci´on de puntos interesantes . 15 ".2. -ocali.aci´on din´amica . 2" ".". 4s3uema /eneral del sistema en la (ase de entrenamiento . 20 ".0. 4s3uema /eneral del sistema en la (ase de reconocimiento . 25 5.1. #omparaci´on en la primer secuencia. Tama;no' 0 $ × "6$........... " 5.2. #omparaci´on en la se/unda secuencia. Tama;no' 0 $ × "6$.......... "! 5.". #omparaci´on en la primer secuencia. Tama;no "2$ × 20$ ........... 0$ 5.0. #omparaci´on en la se/unda secuencia. Tama;no "2$ × 20$ .......... 01 / Cap´ıtulo 1 Introducci´on El campo conocido ahora como visi´on por computadora o visi´on de m´aquina, es una de las ´areas de mayor inter´es en el ´ambito de la ciencia e ingenier´ıa de la computaci´on. Esta rama se consideraba una simple automatizac´on o extensi´on de los temas estudiados por el Procesamiento digital de im´agenes (PDI . !in embargo, el con"ugar estos t´opicos con otros como lo son, la Inteligencia #rti$cial (I# , el %econcimiento de Patrones (%P o la rob´otica, permiten explorar todo un rango de posibilidades, muchas de ellas siendo borde de conocimiento, a´un por explorar, adquierie un sentido de mayor comple"idad y &ormalismo en su estructura y divulgaci´on. El traba"o que se ha desarrollado con mayor intensidad durante los ´ultimos a'nos est´aen&ocado en automatizar de manera e$ciente alguna de las aplicaciones m´as comunes en el campo de la visi´on por computadora( localizaci´on y seguimiento de ob"etos, reconstrucci´on )D de un ob"eto, determinaci´on espacial de la c´amara seg´un su entorno1, reconocimiento de rostros o bien recuperaci´on de la in&omaci´on inscrita en un ob"eto. En un sistema de visi´on por computadora (en adelante s´olo me re&erir´ea ´el como sistema de visi´on la entrada es un con"unto de im´agenes digitales, es decir una representaci´on digital del entorno a trav´es de un transductor, que en este caso es un sensor digital conocido como *oupled *harged Device (**D 2. Estos sensores digitales se encuentran en cualquier c´amara digital+ en todo el proceso de adquisici´on se generan distorsiones, de las que se hablar´acon mayor detalle en los cap´ıtulos subsecuentes. Estas distoriones son generalmente trans&ormaciones de car´acter geom´etrico que pueden ser modeladas de cierta manera permitiendo as´ıgenerar una reconsotrucci´on virtual bastante aproximada a la realidad del ob"eto, ya sea en dos dimensiones o bien en tres dimensiones. 1SLAM, por sus siglas en ingl´es 2sic , El ob"etivo de este traba"o esta en realizar un sistema que permita la localizaci´on autom´atica de ob"etos previamente reconocidos a trav´es de sus as´ıllamadas caracter´ısticas principales, a pesar de las de&ormaciones encontradas en el proceso de adquisici´on. El detalle de c´omo se obtienen estas caracter´ısticas ser´atambi´en materia de este estudio, ya que se implementaron algunas herramientas encontradas en la literatura. *abe mencionar que existe m´as de una manera en la que se puede caracterizar a un con"unto de valores como un vector de caracter´ısticas principales. -´asicamente se otorga este nombre debido a que las caracter´ısticas que se extraen de un punto, pixel, son llamadas invariantes a las de&ormaciones mencionadas+ es decir, a pesar de las trans&ormaciones a$nes o proyectivas as´ıcomo cambios en la iluminaci´on, ´estos valores no tienen un gran intervalo de variaci´on. El algoritmo que ha alcanzado mayor satis&acci´on en la bibliogra&´ıa es( !cale Invariant .eature /rans&orm (!I./ 01o2345 que se revisar´a, de manera general. %ecientemente otro algoritmo usado para la descripci´on de caracter´ısticas locales, que ha captado mayor atenci´on es( !peed67p Robustness .eatures (!7%. 0-/8395, el cual ha sido implementado y utlizado en este traba"o. Estos vectores caracter´ısticos son una respuesta a un con"unto de est´ımulos sobre una regi´on circundante a un pixel en una imagen. #lgunos de estos est´ımulos buscan resaltar ciertas caracter´ısticas, que para los autores de cada algoritmo resultan venta"osas para el estudio de sus propiedades, ya sea la &ormalizaci´on o modelado de una tran&ormada espacio6 escala, o bien la respuesta al con"unto de intensidades acumuladas.
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