Framework for extracting and solving combination puzzles

Selles töös uuritakse, kuidas arvuti nägemisega seotud algoritme on võimalik rakendada objektide tuvastuse probleemile. Täpsemalt, kas arvuti nägemist on võimalik kasutada päris maailma kombinatoorsete probleemide lahendamiseks. Idee kasutada arvuti rakendust probleemide lahendamiseks, tulenes tähelepanekust, et probleemide lahenduse protsessid on kõik enamasti algoritmid. Sellest võib järeldada, et arvutid sobivad algoritmiliste probleemide lahendamiseks paremini kui inimesed, kellel võib sama ülesande peale kuluda kordades kauem. Siiski ei vaatle arvutid probleeme samamoodi nagu inimesed ehk nad ei saa probleeme analüüsida. Niisiis selle töö panuseks saab olema erinevate arvuti nägemise algoritmide uurimine, mille eesmärgiks on päris maailma kombinatoorsete probleemide tõlgendamine abstraktseteks struktuurideks, mida arvuti on võimeline mõistma ning lahendama.Praegu on antud valdkonnas vähe materiali, mis annab hea võimaluse panustada sellesse valdkonda. Seda saavutatakse läbi empiirilise uurimise testide kogumiku kujul selleks, et veenduda millised lähenemised on kõige paremad. Nende eesmärkide saavutamiseks töötati läbi suur hulk arvuti nägemisega seotud materjale ning teooriat. Lisaks võeti ka arvesse reaalaja toimingute tähtsus, mida võib näha erinevate liikumisest struktuuri eraldavate algoritmide(SLAM, PTAM) õpingutest, mida hiljem edukalt kasutati navigatsiooni ja liitreaalsuse probleemide lahendamiseks. Siiski tuleb mainida, et neid algoritme ei kasutatud objektide omaduste tuvastamiseks.See töö uurib, kuidas saab erinevaid lähenemisi kasutada selleks, et aidata vähekogenud kasutajaid kombinatoorsete päris maailma probleemide lahendamisel. Lisaks tekib selle töö tulemusena võimalus tuvastada objektide liikumist (translatsioon, pöörlemine), mida saab kasutada koos virutaalse probleemi mudeliga, et parandada kasutaja kogemust.This thesis describes and investigates how computer vision algorithms and stereo vision algorithms may be applied to the problem of object detection. In particular, if computer vision can aid on puzzle solving. The idea to use computer application for puzzle solving came from the fact that all solution techniques are algorithms in the end. This fact leads to the conclusion that algorithms are well solved by machines, for instance, a machine requires milliseconds to compute the solution while a human can handle this in minutes or hours. Unfortunately, machines cannot see puzzles from human perspective thus cannot analyze them. Hence, the contribution of this thesis is to study different computer vision approaches from non-related solutions applied to the problem of translating the physical puzzle model into the abstract structure that can be understood and solved by a machine.Currently, there is a little written on this subject, therefore, there is a great chance to contribute. This is achieved through empirical research represented as a set of experiments in order to ensure which approaches are suitable. To accomplish these goals huge amount of computer vision theory has been studied. In addition, the relevance of real-time operations was taken into account. This was manifested through the Different real-time Structure from Motion algorithms (SLAM, PTAM) studies that were successfully applied for navigation or augmented reality problems; however, none of them for object characteristics extraction.This thesis examines how these different approaches can be applied to the given problem to help inexperienced users solve the combination puzzles. Moreover, it produces a side effect which is a possibility to track objects movement (rotation, translation) that can be used for manipulating a rendered game puzzle and increase interactivity and engagement of the user

Proceedings of the 4th field robot event 2006, Stuttgart/Hohenheim, Germany, 23-24th June 2006

Zeer uitgebreid verslag van het 4e Fieldrobotevent, dat gehouden werd op 23 en 24 juni 2006 in Stuttgart/Hohenhei

Video based vehicle detection for advance warning Intelligent Transportation System

Video based vehicle detection and surveillance technologies are an integral part of Intelligent Transportation System (ITS), due to its non-intrusiveness and capability or capturing global and specific vehicle behavior data. The initial goal of this thesis is to develop an efficient advance warning ITS system for detection of congestion at work zones and special events based on video detection. The goals accomplished by this thesis are: (1) successfully developed the advance warning ITS system using off-the-shelf components and, (2) Develop and evaluate an improved vehicle detection and tracking algorithm. The advance warning ITS system developed includes many off-the-shelf equipments like Autoscope (video based vehicle detector), Digital Video Recorders, RF transceivers, high gain Yagi antennas, variable message signs and interface processors. The video based detection system used requires calibration and fine tuning of configuration parameters for accurate results. Therefore, an in-house video based vehicle detection system was developed using the Corner Harris algorithm to eliminate the need of complex calibration and contrasts modifications. The algorithm was implemented using OpenCV library on a Arcom\u27s Olympus Windows XP Embedded development kit running WinXPE operating system. The algorithm performance is for accuracy in vehicle speed and count is evaluated. The performance of the proposed algorithm is equivalent or better to the Autoscope system without any modifications to calibration and lamination adjustments

Industrial inspection and reverse engineering

Journal ArticleWe propose a new design for inspection and reverse engineering environments. We have designed and experimented with such an environment for capturing sense data of mechanical parts in an intelligent way. We construct a sensing ? CAD interface for the automatic reconstruction of parts from visual data. We briefly discuss the use of the dynamic recursive finite state machine (DRFSM) as a new discrete event dynamic system (DEDS) tool for controlling inspection and exploration. We also implement a graphical interface for designing DRFSM DEDS controllers

Use of Automatic Chinese Character Decomposition and Human Gestures for Chinese Calligraphy Robots

Conventional Chinese calligraphy robots often suffer from the limited sizes of predefined font databases, which prevent the robots from writing new characters. This paper presents a robotic handwriting system to address such limitations, which extracts Chinese characters from textbooks and uses a robot’s manipulator to write the characters in a different style. The key technologies of the proposed approach include the following: (1) automatically decomposing Chinese characters into strokes using Harris corner detection technology and (2) matching the decomposed strokes to robotic writing trajectories learned from human gestures. Briefly, the system first decomposes a given Chinese character into a set of strokes and obtains the stroke trajectory writing ability by following the gestures performed by a human demonstrator. Then, it applies a stroke classification method that recognizes the decomposed strokes as robotic writing trajectories. Finally, the robot arm is driven to follow the trajectories and thus write the Chinese character. Seven common Chinese characters have been used in an experiment for system validation and evaluation. The experimental results demonstrate the power of the proposed system, given that the robot successfully wrote all the testing characters in the given Chinese calligraphic style