Browsing by Subject "Computer vision."

Now showing 1 - 20 of 25

Open Access
3-dimensional median-based algorithms in image sequence processing
(1990) Alp, Münire Bilge
This thesis introduces new 3-dimensional median-based algorithms to be used in two of the main research areas in image sequence proc(',ssi,ng; image sequence enhancement and image sequence coding. Two new nonlinear filters are developed in the field of image sequence enhancement. The motion performances and the output statistics of these filters are evaluated. The simulations show that the filters improve the image quality to a large extent compared to other examples from the literature. The second field addressed is image sequence coding. A new 3-dimensional median-based coding and decoding method is developed for stationary images with the aim of good slow motion performance. All the algorithms developed are simulated on real image sequences using a video sequencer.
Open Access
Cepstral methods for image feature extraction
(2010) Çakır, Serdar
Image feature extraction is one of the most vital tasks in computer vision and pattern recognition applications due to its importance in the preparation of data extracted from images. In this thesis, 2D cepstrum based methods (2D mel- and Mellin-cepstrum) are proposed for image feature extraction. The proposed feature extraction schemes are used in face recognition and target detection applications. The cepstral features are invariant to amplitude and translation changes. In addition, the features extracted using 2D Mellin-cepstrum method are rotation invariant. Due to these merits, the proposed techniques can be used in various feature extraction problems. The feature matrices extracted using the cepstral methods are classified by Common Matrix Approach (CMA) and multi-class Support Vector Machine (SVM). Experimental results show that the success rates obtained using cepstral feature extraction algorithms are higher than the rates obtained using standard baselines (PCA, Fourier-Mellin Transform, Fourier LDA approach). Moreover, it is observed that the features extracted by cepstral methods are computationally more efficient than the standard baselines. In target detection task, the proposed feature extraction methods are used in the detection and discrimination stages of a typical Automatic Target Recognition (ATR) system. The feature matrices obtained from the cepstral techniques are applied to the SVM classifier. The simulation results show that 2D cepstral feature extraction techniques can be used in the target detection in SAR images.
Open Access
A comparative study on human activity classification with miniature inertial and magnetic sensors
(2011) Yüksek, Murat Cihan
This study provides a comparative assessment on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques compared in this study are: naive Bayesian (NB) classifier, artificial neural networks (ANNs), dissimilarity-based classifier (DBC), various decision-tree methods, Gaussian mixture model (GMM), and support vector machines (SVM). The algorithms for these techniques are provided on two commonly used open source environments: Waikato environment for knowledge analysis (WEKA), a Java-based software; and pattern recognition toolbox (PRTools), a MATLAB toolbox. Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. A feature set extracted from the raw sensor data using principal component analysis (PCA) is used in the classification process. Three different cross-validation techniques are employed to validate the classifiers. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost. The methods that result in the highest correct differentiation rates are found to be ANN (99.2%), SVM (99.2%), and GMM (99.1%). The magnetometer is the best type of sensor to be used in classification whereas gyroscope is the least useful. Considering the locations of the sensor units on body, the sensors worn on the legs seem to provide the most valuable information.
Open Access
Comparison of multi-scale directional feature extraction methods for image processing
(2013) Bozkurt, Alican
Almost all images that are presented in classification problems regardless of area of application, have directional information embedded into its texture. Although there are many algorithms developed to extract this information, there is no ‘golden’ method that works the best every image. In order to evaluate performance of these developed algorithms, we consider 7 different multi-scale directional feature extraction algorithms along with our own multi-scale directional filtering framework. We perform tests on several problems from diverse areas of application such as font/style recognition on English, Arabic, Farsi, Chinese, and Ottoman texts, grading of follicular lymphoma images, and stratum corneum thickness calculation. We present performance metrics such as k-fold cross validation accuracies and times to extract feature from one sample, and compare with the respective state of art on each problem. Our multi-resolution computationally efficient directional approach provides results on a par with the state of the art directional feature extraction methods.
Open Access
Computer vision based behavior analysis
(2009) Yücel, Zeynep
In this thesis, recognition and understanding of behavior based on visual inputs and automated decision schemes are investigated. Behavior analysis is carried out on a wide scope ranging from animal behavior to human behavior. Due to this extensive coverage, we present our work in two main parts. Part I of the thesis investigates locomotor behavior of lab animals with particular focus on drug screening experiments, and Part II investigates analysis of behavior in humans, with specific focus on visual attention. The animal behavior analysis method presented in Part I, is composed of motion tracking based on background subtraction, determination of discriminative behavioral characteristics from the extracted path and speed information, summarization of these characteristics in terms of feature vectors and classification of feature vectors. The experiments presented in Part I indicate that the proposed animal behavior analysis system proves very useful in behavioral and neuropharmacological studies as well as in drug screening and toxicology studies. This is due to the superior capability of the proposed method in detecting discriminative behavioral alterations in response to pharmacological manipulations. The human behavior analysis scheme presented in Part II proposes an efficient method to resolve attention fixation points in unconstrained settings adopting a developmental psychology point of view. The head of the experimenter is modeled as an elliptic cylinder. The head model is tracked using Lucas-Kanade optical flow method and the pose values are estimated accordingly. The resolved poses are then transformed into the gaze direction and the depth of the attended object through two Gaussian regressors. The regression outputs are superposed to find the initial estimates for object center locations. These estimates are pooled to mimic human saccades realistically and saliency is computed in the prospective region to determine the final estimates for attention fixation points. Verifying the extensive generalization capabilities of the human behavior analysis method given in Part II, we propose that rapid gaze estimation can be achieved for establishing joint attention in interaction-driven robot communication as well.
Open Access
CUDA based implementation of flame detection algorithms in day and infrared camera videos
(2011) Hamzaçebi, Hasan
Automatic fire detection in videos is an important task but it is a challenging problem. Video based high performance fire detection algorithms are important for the detection of forest fires. The usage area of fire detection algorithms can further be extended to the places like state and heritage buildings, in which surveillance cameras are installed. In uncontrolled fires, early detection is crucial to extinguish the fire immediately. However, most of the current fire detection algorithms either suffer from high false alarm rates or low detection rates due to the optimization constraints for real-time performance. This problem is also aggravated by the high computational complexity in large areas, where multicamera surveillance is required. In this study, our aim is to speed up the existing color video fire detection algorithms by implementing in CUDA, which uses the parallel computational power of Graphics Processing Units (GPU). Our method does not only speed up the existing algorithms but it can also reduce the optimization constraints for real-time performance to increase detection probability without affecting false alarm rates. In addition, we have studied several methods that detect flames in infrared video and proposed an improvement for the algorithm to decrease the false alarm rate and increase the detection rate of the fire.
Open Access
Detection and classification of objects and texture
(2009) Tuna, Hakan
Object and texture recognition are two important subjects in computer vision. An efficient and fast algorithm to compute a short and efficient feature vector for classification of images is crucial for smart video surveillance systems. In this thesis, feature extraction methods for object and texture classification are investigated, compared and developed. A method for object classification based on shape characteristics is developed. Object silhouettes are extracted from videos by using the background subtraction method. Contour of the objects are obtained from these silhouettes and this 2-D contour signals are transformed into 1-D signals by using a type of radial transformation. Discrete cosine transformation is used to acquire the frequency characteristics of these signals and a support vector machine (SVM) is employed for classification of objects according to this frequency information. This method is implemented and integrated into a real time system together with object tracking. For texture recognition problem, we defined a new computationally efficient operator forming a semigroup on real numbers. The new operator does not require any multiplications. The codifference matrix based on the new operator is defined and an image descriptor using the codifference matrix is developed. Texture recognition and license plate identification examples based on the new descriptor are presented. We compared our method with regular covariance matrix method. Our method has lower computational complexity and it is experimentally shown that it performs as well as the regular covariance method.
Open Access
Detection of tree trunks as visual landmarks in outdoor environments
(2010) Yıldız, Tuğba
One of the basic problems to be addressed for a robot navigating in an outdoor environment is the tracking of its position and state. A fundamental first step in using algorithms for solving this problem, such as various visual Simultaneous Localization and Mapping (SLAM) strategies, is the extraction and identification of suitable stationary “landmarks” in the environment. This is particularly challenging in the outdoors geometrically consistent features such as lines are not frequent. In this thesis, we focus on using trees as persistent visual landmark features in outdoor settings. Existing work to this end only uses intensity information in images and does not work well in low-contrast settings. In contrast, we propose a novel method to incorporate both color and intensity information as well as regional attributes in an image towards robust of detection of tree trunks. We describe both extensions to the well-known edge-flow method as well as complementary Gabor-based edge detection methods to extract dominant edges in the vertical direction. The final stages of our algorithm then group these vertical edges into potential tree trunks using the integration of perceptual organization and all available image features. We characterize the detection performance of our algorithm for two different datasets, one homogeneous dataset with different images of the same tree types and a heterogeneous dataset with images taken from a much more diverse set of trees under more dramatic variations in illumination, viewpoint and background conditions. Our experiments show that our algorithm correctly finds up to 90% of trees with a false-positive rate lower than 15% in both datasets. These results establish that the integration of all available color, intensity and structure information results in a high performance tree trunk detection system that is suitable for use within a SLAM framework that outperforms other methods that only use image intensity information.
Open Access
Dynamic texture analysis in video with application to flame, smoke and volatile organic compound vapor detection
(2009) Günay, Osman
Dynamic textures are moving image sequences that exhibit stationary characteristics in time such as fire, smoke, volatile organic compound (VOC) plumes, waves, etc. Most surveillance applications already have motion detection and recognition capability, but dynamic texture detection algorithms are not integral part of these applications. In this thesis, image processing based algorithms for detection of specific dynamic textures are developed. Our methods can be developed in practical surveillance applications to detect VOC leaks, fire and smoke. The method developed for VOC emission detection in infrared videos uses a change detection algorithm to find the rising VOC plume. The rising characteristic of the plume is detected using a hidden Markov model (HMM). The dark regions that are formed on the leaking equipment are found using a background subtraction algorithm. Another method is developed based on an active learning algorithm that is used to detect wild fires at night and close range flames. The active learning algorithm is based on the Least-Mean-Square (LMS) method. Decisions from the sub-algorithms, each of which characterize a certain property of the texture to be detected, are combined using the LMS algorithm to reach a final decision. Another image processing method is developed to detect fire and smoke from moving camera video sequences. The global motion of the camera is compensated by finding an affine transformation between the frames using optical flow and RANSAC. Three frame change detection methods with motion compensation are used for fire detection with a moving camera. A background subtraction algorithm with global motion estimation is developed for smoke detection.
Open Access
Fire and flame detection methods in images and videos
(2010) Habiboğlu, Yusuf Hakan
In this thesis, automatic fire detection methods are studied in color domain, spatial domain and temporal domain. We first investigated fire and flame colors of pixels. Chromatic Model, Fisher’s linear discriminant, Gaussian mixture color model and artificial neural networks are implemented and tested for flame color modeling. For images a system that extracts patches and classifies them using textural features is proposed. Performance of this system is given according to different thresholds and different features. A real-time detection system that uses information in color, spatial and temporal domains is proposed for videos. This system, which is develop by modifying previously implemented systems, divides video into spatiotemporal blocks and uses features extracted from these blocks to detect fire.
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
Naming faces on the web
(2010) Zitouni, Hilal
In this study, we introduce a method to name less-frequently appearing people on the web via naming frequently appearing ones first. Current image search engines are widely used for querying a person, however; retrievals are based on textual content; therefore, the results are not satisfactory. On the other hand, although; face recognition is a long standing problem; it is tested for limited sizes and successful results are acquired just for face images captured under controlled environments. Faces on the web, contrarily are huge in amount and vary in pose, illumination, occlusion and facial attributes. Recent researches on the area, suggest not to use simply the visual or textual content alone, but to combine them both. With this approach, face recognition problem is simplified to a face-name association problem. Following these approaches, in our method textual and visual information is combined to name faces. We divide the problem into two sub problems, first the more frequently appearing faces, then the less-frequently appearing faces on the web images are named. A supervised algorithm is used for naming a specified number of categories belonging to more frequently appearing faces. The faces that are not matched with any category are then considered to be the less-frequently appearing faces and labeled using the textual content. We extracted all the names from textual contents, and then eliminate the ones used to label frequentlyappearing faces before. The remaining names are the candidate categories for lessfrequently appearing faces. Each detected less-frequently appearing face finally matched to the names extracted from their corresponding textual content. In order to prune the irrelevant face images, finally, the most similar faces among this collection are found to be matched with their corresponding category. In our experiments, the method is applied on two different datasets. Bothdatasets are constructed from the images captured in realistic environments, varying in pose, illumination, facial expressions, occlusions and etc. The results of the experiments proved that the combination of textual and visual contents on realistic face images outperforms the methods that use either one of them. Besides, handling the face recognition problem as a face-name association, improves the results for the face images collected from uncontrolled environments.
Open Access
Nearest-neighbor based metric functions for indoor scene recognition
(2011) Çakır, Fatih
Indoor scene recognition is a challenging problem in the classical scene recognition domain due to the severe intra-class variations and inter-class similarities of man-made indoor structures. State-of-the-art scene recognition techniques such as capturing holistic representations of an image demonstrate low performance on indoor scenes. Other methods that introduce intermediate steps such as identifying objects and associating them with scenes have the handicap of successfully localizing and recognizing the objects in a highly cluttered and sophisticated environment. We propose a classi cation method that can handle such di culties of the problem domain by employing a metric function based on the nearest-neighbor classi cation procedure using the bag-of-visual words scheme, the so-called codebooks. Considering the codebook construction as a Voronoi tessellation of the feature space, we have observed that, given an image, a learned weighted distance of the extracted feature vectors to the center of the Voronoi cells gives a strong indication of the image's category. Our method outperforms state-of-the-art approaches on an indoor scene recognition benchmark and achieves competitive results on a general scene dataset, using a single type of descriptor. In this study although our primary focus is indoor scene categorization, we also employ the proposed metric function to create a baseline implementation for the auto-annotation problem. With the growing amount of digital media, the problem of auto-annotating images with semantic labels has received signi cant interest from researches in the last decade. Traditional approaches where such content is manually tagged has been found to be too tedious and a time-consuming process. Hence, succesfully labeling images with keywords describing the semantics is a crucial task yet to be accomplished.
Open Access
An object recognition framework using contextual interactions among objects
(2009) Kalaycılar, Fırat
Object recognition is one of the fundamental tasks in computer vision. The main endeavor in object recognition research is to devise techniques that make computers understand what they see as precise as human beings. The state of the art recognition methods utilize low-level image features (color, texture, etc.), interest points/regions, filter responses, etc. to find and identify objects in the scene. Although these work well for specific object classes, the results are not satisfactory enough to accept these techniques as universal solutions. Thus, the current trend is to make use of the context embedded in the scene. Context defines the rules for object - object and object - scene interactions. A scene configuration generated by some object recognizers can sometimes be inconsistent with the scene context. For example, observing a car in a kitchen is not likely in terms of the kitchen context. In this case, knowledge of kitchen can be used to correct this inconsistent recognition. Motivated by the benefits of contextual information, we introduce an object recognition framework that utilizes contextual interactions between individually detected objects to improve the overall recognition performance. Our first contribution arises in the object detector design. We define three methods for object detection. Two of these methods, shape based and pixel classification based object detection, mainly use the techniques presented in the literature. However, we also describe another method called surface orientation based object detection. The goal of this novel detection technique is to find objects whose shape, color and texture features are not discriminative while their surface orientations (horizontality or verticality) are consistent across different instances. Wall, table top, and road are typical examples for such objects. The second contribution is a probabilistic contextual interaction model for objects based on their spatial relationships. In order to represent the spatial relationships between objects, we propose three features that encode the relative position/location, scale and orientation of a given object pair. Using these features and our object interaction likelihood model, we achieve to encode the semantic, spatial, and pose context of a scene concurrently. Our third main contribution is a contextual agreement maximization framework that assigns final labels to the detected objects by maximizing a scene probability function that is defined jointly using both the individual object labels and their pairwise contextual interactions. The most consistent scene configuration is obtained by solving the maximization problem using linear optimization. We performed experiments on the LabelMe [27] and Bilkent data sets by both utilizing and not utilizing the scene type (indoor or outdoor) information. While the average F2 score increased from 0.09 to 0.20 without the scene type assumption, it increased from 0.17 to 0.25 when the scene type is known on the LabelMe dataset. The results are similar for the experiments performed on the Bilkent data set. F2 score increased from 0.16 to 0.36 when the scene type information is not available and it increased from 0.31 to 0.44 when this additional information is used. It is clear that the incorporation of the contextual interactions improves the overall recognition performance.
Open Access
Pose sentences : a new representation for understanding human actions
(2008) Hatun, Kardelen
In this thesis we address the problem of human action recognition from video sequences. Our main contribution to the literature is the compact use of poses while representing videos and most importantly considering actions as pose-sentences and exploit string matching approaches for classification. We focus on single actions, where the actor performs one simple action through the video sequence. We represent actions as documents consisting of words, where a word refers to a pose in a frame. We think pose information is a powerful source for describing actions. In search of a robust pose descriptor, we make use of four well-known techniques to extract pose information, Histogram of Oriented Gradients, k-Adjacent Segments, Shape Context and Optical Flow Histograms. To represent actions, first we generate a codebook which will act as a dictionary for our action dataset. Action sequences are then represented using a sequence of pose-words, as posesentences. The similarity between two actions are obtained using string matching techniques. We also apply a bag-of-poses approach for comparison purposes and show the superiority of pose-sentences. We test the efficiency of our method with two widely used benchmark datasets, Weizmann and KTH. We show that pose is indeed very descriptive while representing actions, and without having to examine complex dynamic characteristics of actions, one can apply simple techniques with equally successful results.
Open Access
Real time physics-based augmented fitting room using time-of-flight cameras
(2013) Gültepe, Umut
This thesis proposes a framework for a real-time physically-based augmented cloth tting environment. The required 3D meshes for the human avatar and apparels are modeled with speci c constraints. The models are then animated in real-time using input from a user tracked by a depth sensor. A set of motion lters are introduced in order to improve the quality of the simulation. The physical e ects such as inertia, external and forces and collision are imposed on the apparel meshes. The avatar and the apparels can be customized according to the user. The system runs in real-time on a high-end consumer PC with realistic rendering results.
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
Segmentation of colon glands by object graphs
(2008) Kandemir, Melih
Histopathological examination is the most frequently used technique for clinical diagnosis of a large group of diseases including cancer. In order to reduce the observer variability and the manual effort involving in this visual examination, many computational methods have been proposed. These methods represent a tissue with a set of mathematical features and use these features in further analysis of the biopsy. For the tissue types that contain glandular structures, one of these analyses is to examine the changes in these glandular structures. For such analyses, the very first step is to segment the tissue into its glands. In this thesis, we present an object-based method for the segmentation of colon glands. In this method, we propose to decompose the image into a set of primitive objects and use the spatial distribution of these objects to determine the locations of glands. In the proposed method, pixels are first clustered into different histological structures with respect to their color intensities. Then, the clustered image is decomposed into a set of circular primitive objects (white objects for luminal regions and black objects for nuclear regions) and a graph is constructed on these primitive objects to quantify their spatial distribution. Next, the features are extracted from this graph and these features are used to determine the seed points of gland candidates. Starting from these seed points, the inner glandular regions are grown considering the locations of black objects. Finally, false glands are eliminated based on another set of features extracted from the identified inner regions and exact boundaries of the remaining true glands are determined considering the black objects that are located near the inner glandular regions. Our experiments on the images of colon biopsies have demonstrated that our proposed method leads to high sensitivity, specificity, and accuracy rates.and that it greatly improves the performance of the previous pixel-based gland segmentation algorithms. Our experiments have also shown that the object-based structure of the method provides tolerance to artifacts resulting from variances in biopsy staining and sectioning procedures. This proposed method offers an infrastructure for further analysis of glands for the purpose of automated cancer diagnosis and grading.
Open Access
Spatial subdivision for parallel ray casting/tracing
(1995) İşler, Veysi
Ray casting/tracing has been extensively studied for a long time, since it is an elegant way of producing realistic images. However, it is a computationally intensive algorithm. In this study, a taxonomy of parallel ray casting/tracing algorithms is presented cind the primary parallel ray casting/tracing systems are discussed and criticized. This work mainly focuses on the utilization of spatial subdivision technique for ray casting/tracing on a distributed-memory MIMD parallel computer. In this research, the reason for the use of parallel computers is not only the processing power but also the large memory space provided by them. The spatial subdivision technique has been adapted to parallel ray casting/tracing to decompose a three-dimensional complex scene that may not fit into the local memory of a single processor. The decomposition method achieves an even distribution of scene objects while allowing to exploit graphical coherence. Additionally, the decomposition method produces three-dimensional volumes which are mapped inexpensively to the processors so that the objects within adjacent volumes are stored in the local memories of close processors to decrease interprocessor communication cost. Then, the developed decomposition and mapping methods have been parallelized efficiently to reduce the preprocessing overhead. Finally, a splitting plane concept (called “jaggy splitting plane”) has been proposed to accomplish full utilization of the memory space of processors. Jaggy splitting plane avoids the shared objects which are the major sources of inefficient utilization of both memory and processing power. The proposed parallel algorithms have been implemented on the Intel iPSC/2 hypercube multicomputer (distributed-memory MIMD).