Browsing by Subject "pattern recognition"

Now showing 1 - 7 of 7

Open Access
Automatic radar antenna scan analysis in electronic warfare
(2010) Eravcı, Bahaeddin
Estimation of the radar antenna scan period and recognition of the antenna scan type is usually performed by human operators in the Electronic Warfare (EW) world. In this thesis, we propose a robust algorithm to automatize these two critical processes. The proposed algorithm consists of two main parts: antenna scan period estimation and antenna scan type classification. The first part of the algorithm involves estimating the period of the signal using a time-domain approach. After this step, the signal is warped to a single vector with predetermined size (N) by resampling the data according to its period. This process ensures that the extracted features are reliable and are solely the result of the different scan types, since the effect of the different periods in the signal is removed. Four different features are extracted from the signal vector with an understanding of the phenomena behind the received signals. These features are used to train naive Bayes classifiers, decision-tree classifiers, artificial neural networks, and support vector machines. We have developed an Antenna Scan Pattern Simulator (ASPS) that simulates the position of the antenna beam with respect to time and generates synthetic data. These classifiers are trained and tested with the synthetic data and are compared by their confusion matrices, correct classification rates, robustness to noise, and computational complexity. The effect of the value of N and different signal-to-noise ratios (SNRs) on correct classification performance is investigated for each classifier. Decision-tree classifier is found to be the most suitable classifier because of its high classification rate, robustness to noise, and computational ease. Real data acquired by ASELSAN Inc. is also used to validate the algorithm. The results of the real data indicate that the algorithm is ready for deployment in the field and is capable of being robust against practical complications.
Open Access
Human activity classification with miniature inertial sensors
(2009) Tunçel, Orkun
This thesis provides a comparative study on activity recognition using miniature inertial sensors (gyroscopes and accelerometers) and magnetometers worn on the human body. The classification methods used and compared in this study are: a rule-based algorithm (RBA) or decision tree, least-squares method (LSM), k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW- 1 and DTW-2), and support vector machines (SVM). In the first part of this study, eight different leg motions are classified using only two single-axis gyroscopes. In the second part, human activities are classified using five sensor units worn on different parts of the body. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer and a tri-axial magnetometer. Different feature sets extracted from the raw sensor data and these are used in the classification process. A number of feature extraction and reduction techniques (principal component analysis) as well as different cross-validation techniques have been implemented and compared. A performance comparison of these classification methods is provided in terms of their correct differentiation rates, confusion matrices, pre-processing and training times and classification times. Among the classification techniques we have considered and implemented, SVM, in general, gives the highest correct differentiation rate, followed by k-NN. The classification time for RBA is the shortest, followed by SVM or LSM, k-NN or DTW-1, and DTW-2 methods. SVM requires the longest training time, whereas DTW-2 takes the longest amount of classification time. Although there is not a significant difference between the correct differentiation rates obtained by different crossvalidation techniques, repeated random sub-sampling uses the shortest amount of classification time, whereas leave-one-out requires the longest.
Open Access
Intelligent sensing for robot mapping and simultaneous human localization and activity recognition
(2011) Altun, Kerem
We consider three different problems in two different sensing domains, namely ultrasonic sensing and inertial sensing. Since the applications considered in each domain are inherently different, this thesis is composed of two main parts. The approach common to the two parts is that raw data acquired from simple sensors is processed intelligently to extract useful information about the environment. In the first part, we employ active snake contours and Kohonen’s selforganizing feature maps (SOMs) for representing and evaluating discrete point maps of indoor environments efficiently and compactly. We develop a generic error criterion for comparing two different sets of points based on the Euclidean distance measure. The point sets can be chosen as (i) two different sets of map points acquired with different mapping techniques or different sensing modalities, (ii) two sets of fitted curve points to maps extracted by different mapping techniques or sensing modalities, or (iii) a set of extracted map points and a set of fitted curve points. The error criterion makes it possible to compare the accuracy of maps obtained with different techniques among themselves, as well as with an absolute reference. We optimize the parameters of active snake contours and SOMs using uniform sampling of the parameter space and particle swarm optimization. A demonstrative example from ultrasonic mapping is given based on experimental data and compared with a very accurate laser map, considered an absolute reference. Both techniques can fill the erroneous gaps in discrete point maps. Snake curve fitting results in more accurate maps than SOMs because it is more robust to outliers. The two methods and the error criterion are sufficiently general that they can also be applied to discrete point maps acquired with other mapping techniques and other sensing modalities. In the second part, we use body-worn inertial/magnetic sensor units for recognition of daily and sports activities, as well as for human localization in GPSdenied environments. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. The error characteristics of the sensors are modeled using the Allan variance technique, and the parameters of lowand high-frequency error components are estimated. Then, we provide a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. Human activities are classified using five sensor units worn on the chest, the arms, and the legs. We compute a large number of features extracted from the sensor data, and reduce these features using both Principal Components Analysis (PCA) and sequential forward feature selection (SFFS). We consider eight different pattern recognition techniques and provide a comparison in terms of the correct classification rates, computational costs, and their training and storage requirements. Results with sensors mounted on various locations on the body are also provided. The results indicate that if the system is trained by the data of an individual person, it is possible to obtain over 99% correct classification rates with a simple quadratic classifier such as the Bayesian decision method. However, if the training data of that person are not available beforehand, one has to resort to more complex classifiers with an expected correct classification rate of about 85%. We also consider the human localization problem using body-worn inertial/ magnetic sensors. Inertial sensors are characterized by drift error caused by the integration of their rate output to get position information. Because of this drift, the position and orientation data obtained from inertial sensor signals are reliable over only short periods of time. Therefore, position updates from externally referenced sensors are essential. However, if the map of the environment is known, the activity context of the user provides information about position. In particular, the switches in the activity context correspond to discrete locations on the map. By performing activity recognition simultaneously with localization, one can detect the activity context switches and use the corresponding position information as position updates in the localization filter. The localization filter also involves a smoother, which combines the two estimates obtained by running the zero-velocity update (ZUPT) algorithm both forward and backward in time. We performed experiments with eight subjects in an indoor and an outdoor environment involving “walking,” “turning,” and “standing” activities. Using the error criterion in the first part of the thesis, we show that the position errors can be decreased by about 85% on the average. We also present the results of a 3-D experiment performed in a realistic indoor environment and demonstrate that it is possible to achieve over 90% error reduction in position by performing activity recognition simultaneously with localization.
Open Access
Novel methods for microscopic image processing, analysis, classification and compression
(2013) Suhre, Alexander
Microscopic images are frequently used in medicine and molecular biology. Many interesting image processing problems arise after the initial data acquisition step, since image modalities are manifold. In this thesis, we developed several algorithms in order to handle the critical pipeline of microscopic image storage/ compression and analysis/classification more efficiently. The first step in our processing pipeline is image compression. Microscopic images are large in size (e.g. 100K-by-100K pixels), therefore finding efficient ways of compressing such data is necessary for efficient transmission, storage and evaluation. We propose an image compression scheme that uses the color content of a given image, by applying a block-adaptive color transform. Microscopic images of tissues have a very specific color palette due to the staining process they undergo before data acquisition. The proposed color transform takes advantage of this fact and can be incorporated into widely-used compression algorithms such as JPEG and JPEG 2000 without creating any overhead at the receiver due to its DPCM-like structure. We obtained peak signal-to-noise ratio gains up to 0.5 dB when comparing our method with standard JPEG. The next step in our processing pipeline is image analysis. Microscopic image processing techniques can assist in making grading and diagnosis of images reproducible and by providing useful quantitative measures for computer-aided diagnosis. To this end, we developed several novel techniques for efficient feature extraction and classification of microscopic images. We use region co-difference matrices as inputs for the classifier, which have the main advantage of yielding multiplication-free computationally efficient algorithms. The merit of the co-difference framework for performing some important tasks in signal processing is discussed. We also introduce several methods that estimate underlying probability density functions from data. We use sparsity criteria in the Fourier domain to arrive at efficient estimates. The proposed methods can be used for classification in Bayesian frameworks. We evaluated the performance of our algorithms for two image classification problems: Discriminating between different grades of follicular lymphoma, a medical condition of the lymph system, as well as differentiating several cancer cell lines from each another. Classification accuracies over two large data sets (270 images for follicular lymphoma and 280 images for cancer cell lines) were above 98%.
Open Access
On a parameter estimation method for Gibbs-Markov random fields
(IEEE, 1994) Gürelli, M. I.; Onural, L.
This correspondence is about a Gibbs-Markov random field (GMRF) parameter estimation technique proposed by Derin and Elliott. We will refer to this technique as the histogramming (H) method. First, the relation of the H method to the (conditional) maximum likelihood (ML) method is considered. Second, a bias-reduction based modification of the H method is proposed to improve its performance, especially in the case of small amounts of image data.
Open Access
Recognition and classification of human activities using wearable sensors
(2012) Yurtman, Aras
We address the problem of detecting and classifying human activities using two different types of wearable sensors. In the first part of the thesis, a comparative study on the different techniques of classifying human activities using tag-based radio-frequency (RF) localization is provided. Position data of multiple RF tags worn on the human body are acquired asynchronously and non-uniformly. Curves fitted to the data are re-sampled uniformly and then segmented. The effect of varying the relevant system parameters on the system accuracy is investigated. Various curve-fitting, segmentation, and classification techniques are compared and the combination resulting in the best performance is presented. The classifiers are validated through the use of two different cross-validation methods. For the complete classification problem with 11 classes, the proposed system demonstrates an average classification error of 8.67% and 21.30% for 5-fold and subject-based leave-one-out (L1O) cross validation, respectively. When the number of classes is reduced to five by omitting the transition classes, these errors become 1.12% and 6.52%. The system demonstrates acceptable classification performance despite that tag-based RF localization does not provide very accurate position measurements. In the second part, data acquired from five sensory units worn on the human body, each containing a tri-axial accelerometer, a gyroscope, and a magnetometer, during 19 different human activities are used to calculate inter-subject and interactivity variations in the data with different methods. Absolute, Euclidean, and dynamic time-warping (DTW) distances are used to assess the similarity of the signals. The comparisons are made using time-domain data and feature vectors. Different normalization methods are used and compared. The “best” subject is defined and identified according to his/her average distance to the other subjects.Based on one of the similarity criteria proposed here, an autonomous system that detects and evaluates physical therapy exercises using inertial sensors and magnetometers is developed. An algorithm that detects all the occurrences of one or more template signals (exercise movements) in a long signal (physical therapy session) while allowing some distortion is proposed based on DTW. The algorithm classifies the executions in one of the exercises and evaluates them as correct/incorrect, identifying the error type if there is any. To evaluate the performance of the algorithm in physical therapy, a dataset consisting of one template execution and ten test executions of each of the three execution types of eight exercise movements performed by five subjects is recorded, having totally 120 and 1,200 exercise executions in the training and test sets, respectively, as well as many idle time intervals in the test signals. The proposed algorithm detects 1,125 executions in the whole test set. 8.58% of the executions are missed and 4.91% of the idle intervals are incorrectly detected as an execution. The accuracy is 93.46% for exercise classification and 88.65% for both exercise and execution type classification. The proposed system may be used to both estimate the intensity of the physical therapy session and evaluate the executions to provide feedback to the patient and the specialist.
Open Access
Vision based handwritten character recognition
(2003) Öksüz, Özcan
Online automatic recognition of handwritten text has been an ongoing research problem for four decades. It is used in automated postal address and ZIP code and form reading, data acquisition in bank checks, processing of archived institutional records, automatic validation of passports, etc. It has been gaining more interest lately due to the increasing popularity of handheld computers, digital notebooks and advanced cellular phones. Traditionally, human-machine communication has been based on keyboard and pointing devices. Online handwriting recognition promises to provide a dynamic means of communication with computers through a pen like stylus, not just an ordinary keyboard. This seems to be a more natural way of entering data into computers. In this thesis, we develop a character recognition system that combines the advantage of both on-line and off-line systems. Using an USB CCD Camera, positions of the pen-tip between frames are detected as they are written on a sheet of regular paper. Then, these positions are used for calculation of directional information. Finally, handwritten character is characterized by a sequence of writing directions between consecutive frames. The directional information of the pen movement points is used for character pre-classification and positional information is used for fine classification. After characters are recognized they are passed to LaTeX code generation subroutine. Supported LaTeX environments are array construction, citation, section, itemization, equation, verbatim and normal text environments. During experiments a recognition rate of 90% was achieved. The main recognition errors were due to the abnormal writing and ambiguity among similar shaped characters.