TRAFFIC SIGN DETECTION USING PCA AND ANN

The traffic signs are strained using the scale condition to reject unacceptable objects and they have taken proper care of into three classes by mapping their shapes for that samples. During this research, the effective recognition method while using morphological analysis may be used. During this paper, a manuscript technique is suggested for the Traffic Sign Recognition when using the Principle Component Analysis along with the Multi-Layer Perception network. Designed for the suggested morphological classification method, the candidate signs are individually detected from two chrome areas of the YCbCr space then classified into three shape classes: circle, square, and triangular according to computing the rotated version correlations. Just like a good method, the Eigen-based Traffic Sign Recognition applied a PCA formula to extract the key areas of the input images for categorization. Some weights were calculated from best eigenvectors in the database then unknown objects may be classified when using the Euclidean distances. The PCA-based highlights of these sign objects is going to be helpful for that PCNNs because the training system much like formerly determined class. This method not just cuts lower at about the time but in addition improves the performance within the recognition process

Prioritizing Content of Interest in Multimedia Data Compression

Image and video compression techniques make data transmission and storage in digital multimedia systems more efficient and feasible for the system's limited storage and bandwidth. Many generic image and video compression techniques such as JPEG and H.264/AVC have been standardized and are now widely adopted. Despite their great success, we observe that these standard compression techniques are not the best solution for data compression in special types of multimedia systems such as microscopy videos and low-power wireless broadcast systems. In these application-specific systems where the content of interest in the multimedia data is known and well-defined, we should re-think the design of a data compression pipeline. We hypothesize that by identifying and prioritizing multimedia data's content of interest, new compression methods can be invented that are far more effective than standard techniques. In this dissertation, a set of new data compression methods based on the idea of prioritizing the content of interest has been proposed for three different kinds of multimedia systems. I will show that the key to designing efficient compression techniques in these three cases is to prioritize the content of interest in the data. The definition of the content of interest of multimedia data depends on the application. First, I show that for microscopy videos, the content of interest is defined as the spatial regions in the video frame with pixels that don't only contain noise. Keeping data in those regions with high quality and throwing out other information yields to a novel microscopy video compression technique. Second, I show that for a Bluetooth low energy beacon based system, practical multimedia data storage and transmission is possible by prioritizing content of interest. I designed custom image compression techniques that preserve edges in a binary image, or foreground regions of a color image of indoor or outdoor objects. Last, I present a new indoor Bluetooth low energy beacon based augmented reality system that integrates a 3D moving object compression method that prioritizes the content of interest.Doctor of Philosoph

Remote Sensing in Applications of Geoinformation

Remote sensing, especially from satellites, is a source of invaluable data which can be used to generate synoptic information for virtually all parts of the Earth, including the atmosphere, land, and ocean. In the last few decades, such data have evolved as a basis for accurate information about the Earth, leading to a wealth of geoscientific analysis focusing on diverse applications. Geoinformation systems based on remote sensing are increasingly becoming an integral part of the current information and communication society. The integration of remote sensing and geoinformation essentially involves combining data provided from both, in a consistent and sensible manner. This process has been accelerated by technologically advanced tools and methods for remote sensing data access and integration, paving the way for scientific advances in a broadening range of remote sensing exploitations in applications of geoinformation. This volume hosts original research focusing on the exploitation of remote sensing in applications of geoinformation. The emphasis is on a wide range of applications, such as the mapping of soil nutrients, detection of plastic litter in oceans, urban microclimate, seafloor morphology, urban forest ecosystems, real estate appraisal, inundation mapping, and solar potential analysis

New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty

Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced datasets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present thesis introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images.Comment: 218 pages, 58 figures, PhD thesis, Department of Mechanical Engineering, Karlsruhe Institute of Technology, published online with KITopen (License: CC BY-SA 3.0, http://dx.doi.org/10.5445/IR/1000057821

New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty

Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images

Extracting Physical and Environmental Information of Irish Roads Using Airborne and Mobile Sensors

Airborne sensors including LiDAR and digital cameras are now used extensively for capturing topographical information as these are often more economical and efficient as compared to the traditional photogrammetric and land surveying techniques. Data captured using airborne sensors can be used to extract 3D information important for, inter alia, city modelling, land use classification and urban planning. According to the EU noise directive (2002/49/EC), the National Road Authority (NRA) in Ireland is responsible for generating noise models for all roads which are used by more than 8,000 vehicles per day. Accordingly, the NRA has to cover approximately 4,000 km of road, 500m on each side. These noise models have to be updated every 5 years. Important inputs to noise model are digital terrain model (DTM), 3D building data, road width, road centre line, ground surface type and noise barriers. The objective of this research was to extract these objects and topographical information using nationally available datasets acquired from the Ordnance Survey of Ireland (OSI). The OSI uses ALS50-II LiDAR and ADS40 digital sensors for capturing ground information. Both sensors rely on direct georeferencing, minimizing the need for ground control points. Before exploiting the complementary nature of both datasets for information extraction, their planimetric and vertical accuracies were evaluated using independent ground control points. A new method was also developed for registration in case of any mismatch. DSMs from LiDAR and aerial images were used to find common points to determine the parameters of 2D conformal transformation. The developed method was also evaluated by the EuroSDR in a project which involved a number of partners. These measures were taken to ensure that the inputs to the noise model were of acceptable accuracy as recommended in the report (Assessment of Exposure to Noise, 2006) by the European Working Group. A combination of image classification techniques was used to extract information by the fusion of LiDAR and aerial images. The developed method has two phases, viz. object classification and object reconstruction. Buildings and vegetation were classified based on Normalized Difference Vegetation Index (NDVI) and a normalized digital surface model (nDSM). Holes in building segments were filled by object-oriented multiresolution segmentation. Vegetation that remained amongst buildings was classified using cues obtained from LiDAR. The short comings there in were overcome by developing an additional classification cue using multiple returns. The building extents were extracted and assigned a single height value generated from LiDAR nDSM. The extracted height was verified against the ground truth data acquired using terrestrial survey techniques. Vegetation was further classified into three categories, viz. trees, hedges and tree clusters based on shape parameter (for hedges) and distance from neighbouring trees (for clusters). The ground was classified into three surface types i.e. roads and parking area, exposed surface and grass. This was done using LiDAR intensity, NDVI and nDSM. Mobile Laser Scanning (MLS) data was used to extract walls and purpose built noise barriers, since these objects were not extractable from the available airborne sensor data. Principal Component Analysis (PCA) was used to filter points belonging to such objects. A line was then fitted to these points using robust least square fitting. The developed object extraction method was tested objectively in two independent areas namely the Test Area-1 and the Test Area-2. The results were thoroughly investigated by three different accuracy assessment methods using the OSI vector data. The acceptance of any developed method for commercial applications requires completeness and correctness values of 85% and 70% respectively. Accuracy measures obtained using the developed method of object extraction recommend its applicability for noise modellin

Інтелектуальна система розпізнавання елементів дорожнього руху

У роботі розглянуто проблему розпізнавання елементів дорожнього руху у відео потоці, проведено аналіз наявних проблем та складнощів в існуючих методах розпізнавання елементів та порівняння їхніх характеристик точності та швидкодії, переваг та недоліків. Розроблено інтелектуальну систему розпізнавання елементів дорожнього руху за допомогою алгоритмів машинного навчання та нейронних мереж. Система може бути використана у відео реєстраторах та у системах пасивної безпеки автомобіля. Загалом в роботі розкрито питання призначення та доцільність використання нейронної мережі та представлена програмна реалізація системи за допомогою мови програмування C# та бібліотеки Accord.NET, основними вимогами якої є: прийнятна точність розпізнавання, можливість використання відео потоку в якості вхідних даних, знайдені елементи повинні бути інтуїтивно виділені серед інших елементів та простота в налагоджені. Окремо було приділено увагу локальним результатам експериментів, що дають уявлення про характеристики запропонованої системи. Ключові слова: інтелектуальна система, нейронна мережа, машинне навчання, алгоритм, комп’ютерний зір, дорожній рух. Розмір пояснювальної записки – 81 аркушів, містить 23 ілюстрацій, 28 таблиць, 6 додатків.Examines the problem of recognition of traffic elements in the video stream, analyzes the existing problems and complexities in the existing methods of recognition of the elements and compares their characteristics of accuracy and speed, advantages and disadvantages. An intelligent system for recognizing traffic elements is using machine learning algorithms and neural networks. The system can be used in video recorders and passive vehicle security systems. In general, the paper addresses the purpose and feasibility of using a neural network and presents the software implementation of the system using the C# programming language and the Accord.NET library. The main requirements of which are: acceptable recognition accuracy, the ability to use video stream as input, found elements should be intuitive highlighted among other elements and simplicity in configuring. Special attention was paid to the local results of the experiments, which give an idea of the characteristics of the proposed system. Explanatory note size – 81 pages, contains 23 illustrations, 28 tables, 6 applications