143 research outputs found
Π£Π‘Π’ΠΠΠ§ΠΠΠ«Π ΠΠΠ’ΠΠ ΠΠΠ ΠΠΠΠΠΠΠ¦ΠΠ ΠΠ’Π‘ΠΠΠΠΠ ΠΠΠΠΠΠΠΠ ΠΠΠΠΠΠ¬ΠΠ«Π Π£Π‘Π’Π ΠΠΠ‘Π’ΠΠΠ ΠΠΠΠΠ ΠΠΠΠΠΠ― Π¨Π’Π ΠΠ₯ΠΠΠΠ
A robust method of normalizing barcode images scanned with a mobile device based on iterative threshold binarization, forming the edge binary image, Hough transform, correction of the angular position of the boundary points and the projective transform is developed. The results of the computer simulation are presented. The method provides the invariance to conditions of printing and lightening (image rotation invariance in the range from -450 to 450, and noninform barcode image illumination invariance) due to using procedures of preprocessing, boundary corner point localization and geometric distortion compensation of the digital barcode images.Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½ ΠΌΠ΅ΡΠΎΠ΄ Π½ΠΎΡΠΌΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΎΡΡΠΊΠ°Π½ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΡΠΌ ΡΡΡΡΠΎΠΉΡΡΠ²ΠΎΠΌ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΡΡΡΠΈΡ
ΠΊΠΎΠ΄Π°, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΠΉ Π½Π° ΠΈΡΠ΅ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΠΏΠΎΡΠΎΠ³ΠΎΠ²ΠΎΠΉ Π±ΠΈΠ½Π°ΡΠΈΠ·Π°ΡΠΈΠΈ, ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΊΠΎΠ½ΡΡΡΠ½ΠΎΠ³ΠΎ Π±ΠΈΠ½Π°ΡΠ½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ, ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΈ Π₯Π°ΡΠ°, ΠΊΠΎΡΡΠ΅ΠΊΡΠΈΠΈ ΠΏΠΎΠ·ΠΈΡΠΈΠΉ ΡΠ³Π»ΠΎΠ²ΡΡ
Π³ΡΠ°Π½ΠΈΡΠ½ΡΡ
ΡΠΎΡΠ΅ΠΊ ΠΈ ΠΏΡΠΎΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠΌ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΈ ΠΏΠ»ΠΎΡΠΊΠΎΡΡΠΈ. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ. ΠΠ°Π½Π½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°Π΅Ρ ΠΈΠ½Π²Π°ΡΠΈΠ°Π½ΡΠ½ΠΎΡΡΡ ΠΊ ΡΡΠ»ΠΎΠ²ΠΈΡΠΌ ΠΏΠ΅ΡΠ°ΡΠΈ ΠΈ ΡΡΠ΅ΠΌΠΊΠΈ (ΠΊ Π²ΡΠ°ΡΠ΅Π½ΠΈΡ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ Π² Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π΅ ΠΎΡ β45Β° Π΄ΠΎ 45Β° ΠΈ Π½Π΅ΡΠ°Π²Π½ΠΎΠΌΠ΅ΡΠ½ΠΎΠΌΡ ΠΎΡΠ²Π΅ΡΠ΅Π½ΠΈΡ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΡΡΡΠΈΡ
ΠΊΠΎΠ΄Π°) Π·Π° ΡΡΠ΅Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΎΡΠ΅Π΄ΡΡ ΠΏΡΠ΅Π΄Π²Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ, Π»ΠΎΠΊΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π³ΡΠ°Π½ΠΈΡΠ½ΡΡ
ΡΠ³Π»ΠΎΠ²ΡΡ
ΡΠΎΡΠ΅ΠΊ ΠΈ ΠΊΠΎΠΌΠΏΠ΅Π½ΡΠ°ΡΠΈΠΈ Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΡΠΊΠ°ΠΆΠ΅Π½ΠΈΠΉ ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΡΡΡΠΈΡ
ΠΊΠΎΠ΄Π°
Rapid detection of multi-QR codes based on multistage stepwise discrimination and a compressed mobilenet.
Poor real-time performance in multi-QR codes detection has been a bottleneck in QR code decoding based Internet-of-Things (IoT) systems. To tackle this issue, we propose in this paper a rapid detection approach, which consists of Multistage Stepwise Discrimination (MSD) and a Compressed MobileNet. Inspired by the object category determination analysis, the preprocessed QR codes are extracted accurately on a small scale using the MSD. Guided by the small scale of the image and the end-to-end detection model, we obtain a lightweight Compressed MobileNet in a deep weight compression manner to realize rapid inference of multi-QR codes. The Average Detection Precision (ADP), Multiple Box Rate (MBR) and running time are used for quantitative evaluation of the efficacy and efficiency. Compared with a few state-of-the-art methods, our approach has higher detection performance in rapid and accurate extraction of all the QR codes. The approach is conducive to embedded implementation in edge devices along with a bit of overhead computation to further benefit a wide range of real-time IoT applications
Objects extraction and recognition for camera-based interaction : heuristic and statistical approaches
In this thesis, heuristic and probabilistic methods are applied to a number of problems for camera-based interactions. The goal is to provide solutions for a vision based system that is able to extract and analyze interested objects in camera images and to use that information for various interactions for mobile usage. New methods and new attempts of combination of existing methods are developed for different applications, including text extraction from complex scene images, bar code reading performed by camera phones, and face/facial feature detection and facial expression manipulation.
The application-driven problems of camera-based interaction can not be modeled by a uniform and straightforward model that has very strong simplifications of reality. The solutions we learned to be efficient were to apply heuristic but easy of implementation approaches at first to reduce the complexity of the problems and search for possible means, then use developed statistical learning approaches to deal with the remaining difficult but well-defined problems and get much better accuracy. The process can be evolved in some or all of the stages, and the combination of the approaches is problem-dependent.
Contribution of this thesis resides in two aspects: firstly, new features and approaches are proposed either as heuristics or statistical means for concrete applications; secondly engineering design combining seveal methods for system optimization is studied. Geometrical characteristics and the alignment of text, texture features of bar codes, and structures of faces can all be extracted as heuristics for object extraction and further recognition. The boosting algorithm is one of the proper choices to perform probabilistic learning and to achieve desired accuracy. New feature selection techniques are proposed for constructing the weak learner and applying the boosting output in concrete applications. Subspace methods such as manifold learning algorithms are introduced and tailored for facial expression analysis and synthesis. A modified generalized learning vector quantization method is proposed to deal with the blurring of bar code images. Efficient implementations that combine the approaches in a rational joint point are presented and the results are illustrated.reviewe
Geometric, Semantic, and System-Level Scene Understanding for Improved Construction and Operation of the Built Environment
Recent advances in robotics and enabling fields such as computer vision, deep learning, and low-latency data passing offer significant potential for developing efficient and low-cost solutions for improved construction and operation of the built environment. Examples of such potential solutions include the introduction of automation in environment monitoring, infrastructure inspections, asset management, and building performance analyses. In an effort to advance the fundamental computational building blocks for such applications, this dissertation explored three categories of scene understanding capabilities: 1) Localization and mapping for geometric scene understanding that enables a mobile agent (e.g., robot) to locate itself in an environment, map the geometry of the environment, and navigate through it; 2) Object recognition for semantic scene understanding that allows for automatic asset information extraction for asset tracking and resource management; 3) Distributed coupling analysis for system-level scene understanding that allows for discovery of interdependencies between different built-environment processes for system-level performance analyses and response-planning.
First, this dissertation advanced Simultaneous Localization and Mapping (SLAM) techniques for convenient and low-cost locating capabilities compared with previous work. To provide a versatile Real-Time Location System (RTLS), an occupancy grid mapping enhanced visual SLAM (vSLAM) was developed to support path planning and continuous navigation that cannot be implemented directly on vSLAMβs original feature map. The systemβs localization accuracy was experimentally evaluated with a set of visual landmarks. The achieved marker position measurement accuracy ranges from 0.039m to 0.186m, proving the methodβs feasibility and applicability in providing real-time localization for a wide range of applications. In addition, a Self-Adaptive Feature Transform (SAFT) was proposed to improve such an RTLSβs robustness in challenging environments. As an example implementation, the SAFT descriptor was implemented with a learning-based descriptor and integrated into a vSLAM for experimentation. The evaluation results on two public datasets proved the feasibility and effectiveness of SAFT in improving the matching performance of learning-based descriptors for locating applications.
Second, this dissertation explored vision-based 1D barcode marker extraction for automated object recognition and asset tracking that is more convenient and efficient than the traditional methods of using barcode or asset scanners. As an example application in inventory management, a 1D barcode extraction framework was designed to extract 1D barcodes from video scan of a built environment. The performance of the framework was evaluated with video scan data collected from an active logistics warehouse near Detroit Metropolitan Airport (DTW), demonstrating its applicability in automating inventory tracking and management applications.
Finally, this dissertation explored distributed coupling analysis for understanding interdependencies between processes affecting the built environment and its occupants, allowing for accurate performance and response analyses compared with previous research. In this research, a Lightweight Communications and Marshalling (LCM)-based distributed coupling analysis framework and a message wrapper were designed. This proposed framework and message wrapper were tested with analysis models from wind engineering and structural engineering, where they demonstrated the abilities to link analysis models from different domains and reveal key interdependencies between the involved built-environment processes.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155042/1/lichaox_1.pd
ΠΠΠΠΠ ΠΠΠΠ ΠΠΠΠΠ’ΠΠ€ΠΠΠΠ¦ΠΠΠΠΠ«Π₯ ΠΠΠΠΠ«Π₯ Π ΠΠΠΠΠ ΠΠΠΠΠΠ― ΠΠ ΠΠ‘ΠΠΠΠ Π‘ΠΠΠ£Π‘ΠΠΠΠΠΠ¬ΠΠ«Π₯ Π ΠΠ¨ΠΠ’ΠΠ ΠΠΠ― ΠΠΠΠΠΠ¬ΠΠ«Π₯ ΠΠ ΠΠΠΠΠΠΠΠ
A method of optically visualized block watermarking the images based on the proposed models of embedded message, marked image and robust textural correlation message extracting is developed. The results of computer modeling are presented.ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈ Π²ΠΈΠ·ΡΠ°Π»ΠΈΠ·ΠΈΡΡΠ΅ΠΌΠΎΠ³ΠΎ Π±Π»ΠΎΡΠ½ΠΎ-ΡΡΡΡΠΊΡΡΡΠ½ΠΎΠ³ΠΎ ΠΌΠ°ΡΠΊΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΠΉ Π½Π° ΠΌΠΎΠ΄Π΅Π»ΡΡ
Π²Π½Π΅Π΄ΡΡΠ΅ΠΌΠΎΠ³ΠΎ ΡΠΎΠΎΠ±ΡΠ΅Π½ΠΈΡ ΠΈ ΠΌΠ°ΡΠΊΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΈ ΡΠΎΠ±Π°ΡΡΠ½ΠΎΠΌ ΡΠ΅ΠΊΡΡΡΡΠ½ΠΎΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΎΠ½Π½ΠΎΠΌ ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΠΈ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ. ΠΡΠ΅Π΄ΡΡΠ°Π²ΡΡΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ
Information embedding and retrieval in 3D printed objects
Deep learning and convolutional neural networks have become the main tools of computer vision. These techniques are good at using supervised learning to learn complex representations from data. In particular, under limited settings, the image recognition model now performs better than the human baseline. However, computer vision science aims to build machines that can see. It requires the model to be able to extract more valuable information from images and videos than recognition. Generally, it is much more challenging to apply these deep learning models from recognition to other problems in computer vision.
This thesis presents end-to-end deep learning architectures for a new computer vision field: watermark retrieval from 3D printed objects. As it is a new area, there is no state-of-the-art on many challenging benchmarks. Hence, we first define the problems and introduce the traditional approach, Local Binary Pattern method, to set our baseline for further study. Our neural networks seem useful but straightfor- ward, which outperform traditional approaches. What is more, these networks have good generalization. However, because our research field is new, the problems we face are not only various unpredictable parameters but also limited and low-quality training data.
To address this, we make two observations: (i) we do not need to learn everything from scratch, we know a lot about the image segmentation area, and (ii) we cannot know everything from data, our models should be aware what key features they
should learn. This thesis explores these ideas and even explore more. We show how to use end-to-end deep learning models to learn to retrieve watermark bumps and tackle covariates from a few training images data. Secondly, we introduce ideas from synthetic image data and domain randomization to augment training data and understand various covariates that may affect retrieve real-world 3D watermark bumps. We also show how the illumination in synthetic images data to effect and even improve retrieval accuracy for real-world recognization applications
Biometrics
Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about
Self-organising an indoor location system using a paintable amorphous computer
This thesis investigates new methods for self-organising a precisely defined pattern of intertwined number sequences which may be used in the rapid deployment of a passive indoor positioning system's infrastructure.A future hypothetical scenario is used where computing particles are suspended in paint and covered over a ceiling. A spatial pattern is then formed over the covered ceiling. Any small portion of the spatial pattern may be decoded, by a simple camera equipped device, to provide a unique location to support location-aware pervasive computing applications.Such a pattern is established from the interactions of many thousands of locally connected computing particles that are disseminated randomly and densely over a surface, such as a ceiling. Each particle has initially no knowledge of its location
or network topology and shares no synchronous clock or memory with any other particle.The challenge addressed within this thesis is how such a network of computing particles that begin in such an initial state of disarray and ignorance can, without outside intervention or expensive equipment, collaborate to create a relative coordinate system. It shows how the coordinate system can be created to be coherent, even in the face of obstacles, and closely represent the actual shape of the networked surface itself. The precision errors incurred during the propagation of the coordinate system are identified and the distributed algorithms used to avoid this error are explained and demonstrated through simulation.A new perimeter detection algorithm is proposed that discovers network edges and other obstacles without the use of any existing location knowledge. A new distributed localisation algorithm is demonstrated to propagate a relative coordinate system throughout the network and remain free of the error introduced by the network perimeter that is normally seen in non-convex networks. This localisation algorithm operates without prior configuration or calibration, allowing the coordinate system to be deployed without expert manual intervention or on networks that are otherwise inaccessible.The painted ceiling's spatial pattern, when based on the proposed localisation algorithm, is discussed in the context of an indoor positioning system
Software-Defined Lighting.
For much of the past century, indoor lighting has been based on incandescent or gas-discharge technology. But, with LED lighting experiencing a 20x/decade increase in flux density, 10x/decade decrease in cost, and linear improvements in luminous efficiency, solid-state lighting is finally cost-competitive with the status quo. As a result, LED lighting is projected to reach over 70% market penetration by 2030. This dissertation claims that solid-state lightingβs real potential has been barely explored, that now is the time to explore it, and that new lighting platforms and applications can drive lighting far beyond its roots as an illumination technology. Scaling laws make solid-state lighting competitive with conventional lighting, but two key features make solid-state lighting an enabler for many new applications: the high switching speeds possible using LEDs and the color palettes realizable with Red-Green-Blue-White (RGBW) multi-chip assemblies.
For this dissertation, we have explored the post-illumination potential of LED lighting in applications as diverse as visible light communications, indoor positioning, smart dust time synchronization, and embedded device configuration, with an eventual eye toward supporting all of them using a shared lighting infrastructure under a unified system architecture that provides software-control over lighting. To explore the space of software-defined lighting (SDL), we design a compact, flexible, and networked SDL platform to allow researchers to rapidly test new ideas. Using this platform, we demonstrate the viability of several applications, including multi-luminaire synchronized communication to a photodiode receiver, communication to mobile phone cameras, and indoor positioning using unmodified mobile phones. We show that all these applications and many other potential applications can be simultaneously supported by a single lighting infrastructure under software control.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111482/1/samkuo_1.pd
Recent Advances in Indoor Localization Systems and Technologies
Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods
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