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Systém pro využití technologie rozšířené reality v muzeích a galeriích / System for Augmented Reality Utilisation in Museums and GalleriesMüller, Frederik January 2020 (has links)
The aim of the master thesis is to provide visitors of various types of objects - typically galleries, museums, etc. with additional information about exhibited objects with emphasis on visual display in augmented reality. It includes an analysis of already existing solutions, design and implementation of the entire system needed for deployment. From the point of view of the content creator (administrator), the system represents a complex solution enabling the creation of an interactive walk - scanning 3D objects, scanning 2D objects and adding content to the given objects. On the other hand, the tool for visitors (users) who are part of an interactive walk, provides additional information about objects primarily located near the user, which are automatically detected by the camera on the mobile phone. Solution includes augmented reality, which is implemented using ARKit technology, so the final application is built on the iOS platform. The work addresses the issue of detection of 3D objects and their subsequent recognition, along with the way to work with this information, how to store it and then use it for purposes of this thesis. In the final solution, emphasis is placed on the simplicity of the usage (guide marks, hints...) and overall user experience.
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Návrh kamerového systému s průmyslovým robotem Kuka / Design of a vision system with Kuka robotRusnák, Jakub January 2011 (has links)
Diploma thesis deals with applications of vision system with KUKA robot in field of identification and sorting bigger amount of different objects. Introductory and theoretical part of the thesis describes present situation on industrial vision systems market and their usage. Diploma thesis include practical application of object (coin) recognition with SICK IVC 2D vision system and their sorting by industrial robot KUKA KR 3. Application is also concerned with network communication between camera and robot via PLC, programming in KRL language and programm for object recognition in IVC Studio.
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Rozšířená realita v reklamě / Augmented Reality for CommercialsAngelov, Michael January 2011 (has links)
Master's thesis presents a possible application of augmented reality in domain of commerci- als. It presents designed architecture of a mobile application that is able to detect and track specific objects (e.g. printed commercials, logos) in mobile';s phone camera in real time and provide some extra information about the detected object towards the user. Thesis also provides a review of contemporary used techniques in object recognition, object tracking and image retrieval from image databases.
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Biologicky inspirované metody rozpoznávání objektů / Biologically Inspired Methods of Object RecognitionVaľko, Tomáš January 2011 (has links)
Object recognition is one of many tasks in which the computer is still behind the human. Therefore, development in this area takes inspiration from nature and especially from the function of the human brain. This work focuses on object recognition based on extracting relevant information from images, features. Features are obtained in a similar way as the human brain processes visual stimuli. Subsequently, these features are used to train classifiers for object recognition (e.g. SVM, k-NN, ANN). This work examines the feature extraction stage. Its aim is to improve the feature extraction and thereby increase performance of object recognition by computer.
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Monkey see, monkey touch, monkey do: Influence of visual and tactile input on the fronto-parietal grasping networkBuchwald, Daniela 13 March 2020 (has links)
No description available.
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Components of Embodied Visual Object Recognition : Object Perception and Learning on a Robotic PlatformWallenberg, Marcus January 2013 (has links)
Object recognition is a skill we as humans often take for granted. Due to our formidable object learning, recognition and generalisation skills, it is sometimes hard to see the multitude of obstacles that need to be overcome in order to replicate this skill in an artificial system. Object recognition is also one of the classical areas of computer vision, and many ways of approaching the problem have been proposed. Recently, visually capable robots and autonomous vehicles have increased the focus on embodied recognition systems and active visual search. These applications demand that systems can learn and adapt to their surroundings, and arrive at decisions in a reasonable amount of time, while maintaining high object recognition performance. Active visual search also means that mechanisms for attention and gaze control are integral to the object recognition procedure. This thesis describes work done on the components necessary for creating an embodied recognition system, specifically in the areas of decision uncertainty estimation, object segmentation from multiple cues, adaptation of stereo vision to a specific platform and setting, and the implementation of the system itself. Contributions include the evaluation of methods and measures for predicting the potential uncertainty reduction that can be obtained from additional views of an object, allowing for adaptive target observations. Also, in order to separate a specific object from other parts of a scene, it is often necessary to combine multiple cues such as colour and depth in order to obtain satisfactory results. Therefore, a method for combining these using channel coding has been evaluated. Finally, in order to make use of three-dimensional spatial structure in recognition, a novel stereo vision algorithm extension along with a framework for automatic stereo tuning have also been investigated. All of these components have been tested and evaluated on a purpose-built embodied recognition platform known as Eddie the Embodied. / Embodied Visual Object Recognition
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Scene Understanding For Real Time Processing Of Queries Over Big Data Streaming VideoAved, Alexander 01 January 2013 (has links)
With heightened security concerns across the globe and the increasing need to monitor, preserve and protect infrastructure and public spaces to ensure proper operation, quality assurance and safety, numerous video cameras have been deployed. Accordingly, they also need to be monitored effectively and efficiently. However, relying on human operators to constantly monitor all the video streams is not scalable or cost effective. Humans can become subjective, fatigued, even exhibit bias and it is difficult to maintain high levels of vigilance when capturing, searching and recognizing events that occur infrequently or in isolation. These limitations are addressed in the Live Video Database Management System (LVDBMS), a framework for managing and processing live motion imagery data. It enables rapid development of video surveillance software much like traditional database applications are developed today. Such developed video stream processing applications and ad hoc queries are able to "reuse" advanced image processing techniques that have been developed. This results in lower software development and maintenance costs. Furthermore, the LVDBMS can be intensively tested to ensure consistent quality across all associated video database applications. Its intrinsic privacy framework facilitates a formalized approach to the specification and enforcement of verifiable privacy policies. This is an important step towards enabling a general privacy certification for video surveillance systems by leveraging a standardized privacy specification language. With the potential to impact many important fields ranging from security and assembly line monitoring to wildlife studies and the environment, the broader impact of this work is clear. The privacy framework protects the general public from abusive use of surveillance technology; iii success in addressing the "trust" issue will enable many new surveillance-related applications. Although this research focuses on video surveillance, the proposed framework has the potential to support many video-based analytical applications.
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Evolutionary Learning of Boosted Features for Visual Inspection AutomationZhang, Meng 01 March 2018 (has links)
Feature extraction is one of the major challenges in object recognition. Features that are extracted from one type of objects cannot always be used directly for a different type of objects, therefore limiting the performance of feature extraction. Having an automatic feature learning algorithm could be a big advantage for an object recognition algorithm. This research first introduces several improvements on a fully automatic feature construction method called Evolution COnstructed Feature (ECO-Feature). These improvements are developed to construct more robust features and make the training process more efficient than the original version. The main weakness of the original ECO-Feature algorithm is that it is designed only for binary classification and cannot be directly applied to multi-class cases. We also observe that the recognition performance depends heavily on the size of the feature pool from which features can be selected and the ability of selecting the best features. For these reasons, we have developed an enhanced evolutionary learning method for multi-class object classification to address these challenges. Our method is called Evolutionary Learning of Boosted Features (ECO-Boost). ECO-Boost method is an efficient evolutionary learning algorithm developed to automatically construct highly discriminative image features from the training image for multi-class image classification. This unique method constructs image features that are often overlooked by humans, and is robust to minor image distortion and geometric transformations. We evaluate this algorithm with a few visual inspection datasets including specialty crops, fruits and road surface conditions. Results from extensive experiments confirm that ECO-Boost performs closely comparable to other methods and achieves a good balance between accuracy and simplicity for real-time multi-class object classification applications. It is a hardware-friendly algorithm that can be optimized for hardware implementation in an FPGA for real-time embedded visual inspection applications.
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Visual Search for Objects with Straight LinesMelikian, Simon Haig January 2006 (has links)
No description available.
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Fast Recognition and Pose Estimation for the Purpose of Bin-Picking RoboticsLonsberry, Alexander J. January 2011 (has links)
No description available.
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