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- Bu səhifədəki naviqasiya:
- II INTERNATIONAL SCIENTIFIC CONFERENCE OF YOUNG RESEARCHERS
- ENABLE MOBILE ROBOT TO RECOGNIZE AND CLASSIFY OBJECTS
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II INTER
versity C-ni izləyir. B bazasında ye etmə sistemlər Neo4j Javada araq Cypher d rilənlər bazası siyadan - düy yasiya verilən zasında isə ik af vb-də isə “E ya və Qraf mo və filmin reji elin relyasiya şı 30-dan böyü R (Relyasiya): mlərdə rol alan RT actor=nod ECT Person.na ie.movie_id = ğunu təhlil edə START
MATC MATC
>(movie) RETUR
m aktyor həm person=node:P erson.name name FROM ROM Director
B yalnız C-ni eni veriləni da rindən istifadə a yazılmış açı dilindən istifad ının relyasiya yün və tillərdə lər bazasına y ki operatordan Erase” (düyün odellərini müq ssoru ola bilər və qraf verilə ük olan insanl : SELECT * F n insanların si de:Person("nam ame, Movie.ti = Actor.movie_ ək. T actor=node: CH (actor)-[:A CH (actor RN actor.nam rejissor olan i Person("name: Person WHE r)
izləyir. C də B axil etmək, re ə olunur. Qra ıqməmbə kod də edilir. Cyph tipli verilənlə ən istifadə olun yeni informas n – “Store” və n) və “Disconn qayisəli təhlil r. ənlər bazasınd arın siyahısı Q FROM Person iyahısı me:*") MATC itle FROM Pe _id
:Person ACTS_IN] r)-[:ACTS_IN me, movie.title insanların siya :*") WHERE RE person_id
588
B-ni izləyir və edaktə etmək f verilənlər ba dlu proqramdır her sorğu dili b ər bazasından nduğu halda r siya əlavə etm ə “Connect” is nect” (til) oper etmək üçün da müqayisəli b Q (CYPHER) n WHERE age CH (actor)-[:A erson JOIN Ac FRO JOIN
Actor.per N]-
JOIN Actor.mo
SEL ahısı
(person)-[:AC d IN ( SELECT D Tweet
Tweet Cari tw
Növb RENCE OF Y ə həmçini D-n
k, sorğu etmək azası idarə etm r və müxtəlif bəsit olmasına əsas nəzəri de relyasiya veril mək üçün 1 o stifadə olunur ratorlarından “Aktyor – film
bəzi sorğuları : START n=n e > 30
ACTS_IN]->(m ctor on Person OM Person N Actor
rson_id N Movie
ovie_id LECT Person.n CTS_IN]->() A T person_id F weet bətitweet YOUNG RES 18-19 A ni izləyir. D isə k, təhlükəsliy mə sistemlərin platformalard a baxmaraq gü ezavantajları i lənlər bazasın operatordan – r. İnformasiya istifadə olunu m” modelinə ı: node:Person(" movie) RETU n.person_id = on Person on Movi
name, Movie.t AND (person) FROM Actor)
ə yalnız A-nı yini təmin etm ndən ən məşh da dəstəklənir. üclü dildir. informasiyanı nda isə 1 konse – “İnsert” isti anı silmək üçü ur. baxaq. Hər b "name:*") WH URN actor.nam Actor.person_ n.person_id ie.movie_id title
)-[:DIRECTE AND person_ RS aku, Azerbai izləyir. mək üçün Ver huru Neo4j tət Neo4j VBİS ı saxlamaq üçü epsiyadan isti ifadə olunur, ün relyasiya vb bir şəxs filmdə HERE n.age>3 me, movie.title _id JOIN Mov = = ED]->() _id IN ( SELE ijan rilən
tbiqi S-də
ün 2 ifadə
qraf b-də
ə rol 30
e vie
ECT II INTERNATIONAL SCIENTIFIC CONFERENCE OF YOUNG RESEARCHERS 589
Qafqaz University 18-19 April 2014, Baku, Azerbaijan 4) Lucy ilə filmdə çəkilən aktyorların siyahısı Q: START lucy=node:Person(name="Lucy Liu") MATCH lucy-[:ACTS_IN]->movie, co_actor-[:ACTS_IN]->movie RETURN DISTINCT co_actor.name R: SELECT DISTINCT co_actor.name FROM Person lucy JOIN Actor a1 ON lucy.person_id = a1.person_id JOIN Actor a2 ON a1.movie_id = a2.movie_id JOIN Person co_actor ON co_actor.person.id = a2.person_id WHERE lucy.name = "Lucy Liu” Lucy-nin çəkildiyi filmlərin rejissorlarının siyahısı Q: START lucy=node:Person(name="Lucy Liu") MATCH lucy- [:ACTS_IN]->movie, director-[:DIRECTED]->movie RETURN director.name, count(*) ORDER BY count(*) desc R: SELECT dir.name, count(*) FROM Person lucy JOIN Actor on Person.person_id = Actor.person_id JOIN Director on Actor.movie_id = Director.movie_id JOIN Person dir on Director.person.id = dir.person_id WHERE lucy.name = "Lucy Liu" GROUP BY dir.name ORDER BY count(*) DESC
Huseyn PASHAYEV, Asad MAMMADOV
Qafqaz University h.pashayev@ictsrcqu.org , a.mammadov@ictsrcqu.org AZƏRBAYCAN
use haptic, statistical or visual information like camera, which returns a cloud of points, according to which these points segmented into clusters, where each cluster is primitive geometric structure and the geometric relationship between these clusters defines the object. If we are talking about recognition and classification of objects the term affordance have to be initially proposed. This term was initially proposed by J. Gibson [1] and now widely used in robotics. So what is the affordance and how it’s used in robotics? For each object there are a number of actions which is possible to perform with it depending on object which is interacting with it. Now let suppose the chair, human can push the chair can sit on it, so there chair affords pushing and sitting on it for human, however mobile robot (like Parallax Eddie – Reference Platform) can only push the chair but not sit, so there the affordance of chair for mobile robot is only pushing. So the robot can recognize such objects then, classify the object in classes and according to affordance of class behave with this object. Why affordance is so important? Suppose a robot which helps us at home. The robot need to be able to recognize object then classify whether this is a chair class, or table class or even door class, then robot have to know how to behave with object, whether to pull it( with chairs class), go across(with tables class) or just pass through it(door class). In such problems main aim is not to detect object preciously, the aim here is to detect the correct class to which this object belong to, such that general properties of affordance for objects in same class are the same. Nowadays the field object recognition and classification is widely investigating and there a lot of works done in this field. General object recognition rely on visual appearance of object, where invariant descriptors used to recognize and classify object. In other words the snapshots of object are taken from different positions, then local and global descriptors are used to summarize data and finally distance between vectors of these descriptors is calculated to model the object. In article [2] the robot has 2D camera and uses local descriptors and SIFT (Scale-invariant feature transform) features. In SIFT the algorithm is to identify local features of training object and then detect this object on image containing this and many other objects. Another main property in SIFT is the solid proportion of selected points as feature. Suppose we take 4 corners of door as one feature of this object, so in case if the proportion between these points will change then this will cause failure. Like if door is opened or closed for each position of the door proportion between these points will change, what will cause failure, that means on flexible and articulated objects this won’t work because geometry changes what causes the proportion change on points which are selected as feature. However on practice SIFT method uses large numbers of features which reduce the error caused by such local variations. In article [3] the robot controlled via internet where user give voice commands to robot like “Grasp the ball” and etc. In this work neural network proposed which help to classify object in low resolution and noise images using invariant descriptors. Another article is [4]. In this article proposed to use multiple perception of object and motor modality. The robot using statistical technique, and using motor function grasps the object then rotating it take snapshots from different points and also while observation listen sounds from object, based on audio-video and haptic information robot recognize and classify object. Another work about classification of objects is “Grounding semantic categories in behavioral interactions: experiments with 100 objects” [5] In this article 100 object taken and divided in 20 categories in order to test the humanoid robot, where for each category different behavior assigned. Robot uses supervised recognition method where he process not only visual information but also taking in account |