• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 270
  • 73
  • 23
  • 15
  • 10
  • 7
  • 6
  • 4
  • 2
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • Tagged with
  • 497
  • 497
  • 123
  • 114
  • 102
  • 94
  • 92
  • 91
  • 85
  • 71
  • 69
  • 69
  • 62
  • 61
  • 59
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
41

Statistical mechanical models for image processing

16 October 2001 (has links) (PDF)
No description available.
42

Context-Based Algorithm for Face Detection

Wall, Helene January 2005 (has links)
Face detection has been a research area for more than ten years. It is a complex problem due to the high variability in faces and amongst faces; therefore it is not possible to extract a general pattern to be used for detection. This is what makes the face detection problem a challenge. This thesis gives the reader a background to the face detection problem, where the two main approaches of the problem are described. A face detection algorithm is implemented using a context-based method in combination with an evolving neural network. The algorithm consists of two majors steps: detect possible face areas and within these areas detect faces. This method makes it possible to reduce the search space. The performance of the algorithm is evaluated and analysed. There are several parameters that affect the performance; the feature extraction method, the classifier and the images used. This work resulted in a face detection algorithm and the performance of the algorithm is evaluated and analysed. The analysis of the problems that occurred has provided a deeper understanding for the complexity of the face detection problem.
43

Feature Extraction for Automatic Speech Recognition in Noisy Acoustic Environments / Parameteruttrekning for automatisk talegjenkjenning i støyende omgivelser

Gajic, Bojana January 2002 (has links)
This thesis presents a study of alternative speech feature extraction methods aimed at increasing robustness of automatic speech recognition (ASR) against additive background noise. Spectral peak positions of speech signals remain practically unchanged in presence of additive background noise. Thus, it was expected that emphasizing spectral peak positions in speech feature extraction would result in improved noise robustness of ASR systems. If frequency subbands are properly chosen, dominant subband frequencies can serve as reasonable estimates of spectral peak positions. Thus, different methods for incorporating dominant subband frequencies into speech feature vectors were investigated in this study. To begin with, two earlier proposed feature extraction methods that utilize dominant subband frequency information were examined. The first one uses zero-crossing statistics of the subband signals to estimate dominant subband frequencies, while the second one uses subband spectral centroids. The methods were compared with the standard MFCC feature extraction method on two different recognition tasks in various background conditions. The first method was shown to improve ASR performance on both recognition tasks at sufficiently high noise levels. The improvement was, however, smaller on the more complex recognition task. The second method, on the other hand, led to some reduction in ASR performance in all testing conditions. Next, a new method for incorporating subband spectral centroids into speech feature vectors was proposed, and was shown to be considerably more robust than the standard MFCC method on both ASR tasks. The main difference between the proposed method and the zero-crossing based method is in the way they utilize dominant subband frequency information. It was shown that the performance improvement due to the use of dominant subband frequency information was considerably larger for the proposed method than for the ZCPA method, especially on the more complex recognition task. Finally, the computational complexity of the proposed method is two orders of magnitude lower than that of the zero-crossing based method, and of the same order of magnitude as the standard MFCC method.
44

Evaluation of texture features for analysis of ovarian follicular development

Bian, Na 02 December 2005
Ovarian follicles in women are fluid-filled structures in the ovary that contain oocytes (eggs). A dominant follicle is physiologically selected and ovulates during the menstrual cycle. We examined the echotexture in ultrasonographic images of the follicle wall of dominant ovulatory follicles in women during natural menstrual cycles and dominant anovulatory follicles which developed in women using oral contraceptives (OC). Texture features of follicle wall regions of both ovulatory and anovulatory dominant follicles were evaluated over a period of seven days before ovulation (natural cycles) or peak estradiol concentrations (OC cycles). Differences in echotexture between the two classes of follicles were found for two co-occurrence matrix derived texture features and two edge-frequency based texture features. Co-occurrence energy and homogeneity were significantly lower for ovulatory follicles while edge density and edge contrast were higher for ovulatory follicles. In the each feature space, the two classes of follicle were adequately separable.</p><p>This thesis employed several statistical approaches to analyses of texture features, such as plotting method and the Mann-Kendall method. A distinct change of feature trend was detected 3 or 4 days before the day of ovulation for ovulatory follicles in the two co-occurrence matrix derived texture features and two edge-frequency-based texture features. Anovulatory follicles, exhibited the biggest variation of the feature value 3 or 4 days before the day on which dominant follicles developed to maximum size. This discovery is believed to correspond to the ovarian follicles responding to system hormonal changes leading to presumptive ovulation.</p>
45

Robust feature extractions from geometric data using geometric algebra

Minh Tuan, Pham, Yoshikawa, Tomohiro, Furuhashi, Takeshi, Tachibana, Kaita 11 October 2009 (has links)
No description available.
46

Detection and Tracking of People from Laser Range Data

Mashad Nemati, Hassan January 2010 (has links)
In this thesis report, some of the most promising techniques, in the field of intelligent vehicles and mobile robotics, for detection and tracking of moving objects in an indoor environment are investigated. Kalman filter (KF), extended Kalman filter (EKF), and particle filters (PF) based techniques for the tracking of people are implemented and evaluated. A heuristic method is then proposed to improve the performance of the EKF based tracking in situations where moving objects are hidden by obstacles. The proposed method is based on points of maximum uncertainty (PMU) in occlusion situations and its complexity and accuracy is compared with PF method. The EKF, PF and PMU based methods are examined and compared using experimental data which are extracted by a laser range finder in an indoor environment with predefined hinders and people as the moving objects.
47

Evaluation of texture features for analysis of ovarian follicular development

Bian, Na 02 December 2005 (has links)
Ovarian follicles in women are fluid-filled structures in the ovary that contain oocytes (eggs). A dominant follicle is physiologically selected and ovulates during the menstrual cycle. We examined the echotexture in ultrasonographic images of the follicle wall of dominant ovulatory follicles in women during natural menstrual cycles and dominant anovulatory follicles which developed in women using oral contraceptives (OC). Texture features of follicle wall regions of both ovulatory and anovulatory dominant follicles were evaluated over a period of seven days before ovulation (natural cycles) or peak estradiol concentrations (OC cycles). Differences in echotexture between the two classes of follicles were found for two co-occurrence matrix derived texture features and two edge-frequency based texture features. Co-occurrence energy and homogeneity were significantly lower for ovulatory follicles while edge density and edge contrast were higher for ovulatory follicles. In the each feature space, the two classes of follicle were adequately separable.</p><p>This thesis employed several statistical approaches to analyses of texture features, such as plotting method and the Mann-Kendall method. A distinct change of feature trend was detected 3 or 4 days before the day of ovulation for ovulatory follicles in the two co-occurrence matrix derived texture features and two edge-frequency-based texture features. Anovulatory follicles, exhibited the biggest variation of the feature value 3 or 4 days before the day on which dominant follicles developed to maximum size. This discovery is believed to correspond to the ovarian follicles responding to system hormonal changes leading to presumptive ovulation.</p>
48

Self-Organized Deviation Detection

Kreshchenko, Ivan January 2008 (has links)
A technique to detect deviations in sets of systems in a self-organized way is described in this work. System features are extracted to allow compact representation of the system. Distances between systems are calculated by computing distances between the features. The distances are then stored in an affinity matrix. Deviating systems are detected by assuming a statistical model for the affinities. The key idea is to extract features and and identify deviating systems in a self-organized way, using nonlinear techniques for the feature extraction. The results are compared with those achieved with linear techniques, (principal component analysis). The features are computed with principal curves and an isometric feature mapping. In the case of principal curves the feature is the curve itself. In the case of isometric feature mapping is the feature a set of curves in the embedding space. The similarity measure between two representations is either the Hausdorff distance, or the Frechet distance. The deviation detection is performed by computing the probability of each system to be observed given all the other systems. To perform reliable inference the Bootstrapping technique was used. The technique is demonstrated on simulated and on-road vehicle cooling system data. The results show the applicability and comparison with linear techniques.
49

Dynamic Descriptors in Human Gait Recognition

Amin, Tahir 02 August 2013 (has links)
Feature extraction is the most critical step in any human gait recognition system. Although gait is a dynamic process yet the static body parameters also play an important role in characterizing human gait. A few studies were performed in the past to assess the comparative relevance of static and dynamic gait features. There is, however, a lack of work in comparative performance analysis of dynamic gait features from different parts of the silhouettes in an appearance based setup. This dissertation presents a comparative study of dynamic features extracted from legs, arms and shoulders for gait recognition. Our study partially supports the general notion of leg motion being the most important determining factor in gait recognition. But it is also observed that features extracted from upper arm and shoulder area become more significant in some databases. The usefulness of the study hinges on the fact that lower parts of the leg are generally more noisy due to a variety of variations such as walking surface, occlusion and shadows. Dynamic features extracted from the upper part of the silhouettes posses significantly higher discriminatory power in such situations. In other situations these features can play a complementary role in the gait recognition process. We also propose two new feature extraction methods for gait recognition. The new methods use silhouette area signals which are easy and simple to extract. A significant performance increase is achieved by using the new features over the benchmark method and recognition results compare well to the other current techniques. The simplicity and compactness of the proposed gait features is their major advantage because it entails low computational overhead.
50

Dynamic Descriptors in Human Gait Recognition

Amin, Tahir 02 August 2013 (has links)
Feature extraction is the most critical step in any human gait recognition system. Although gait is a dynamic process yet the static body parameters also play an important role in characterizing human gait. A few studies were performed in the past to assess the comparative relevance of static and dynamic gait features. There is, however, a lack of work in comparative performance analysis of dynamic gait features from different parts of the silhouettes in an appearance based setup. This dissertation presents a comparative study of dynamic features extracted from legs, arms and shoulders for gait recognition. Our study partially supports the general notion of leg motion being the most important determining factor in gait recognition. But it is also observed that features extracted from upper arm and shoulder area become more significant in some databases. The usefulness of the study hinges on the fact that lower parts of the leg are generally more noisy due to a variety of variations such as walking surface, occlusion and shadows. Dynamic features extracted from the upper part of the silhouettes posses significantly higher discriminatory power in such situations. In other situations these features can play a complementary role in the gait recognition process. We also propose two new feature extraction methods for gait recognition. The new methods use silhouette area signals which are easy and simple to extract. A significant performance increase is achieved by using the new features over the benchmark method and recognition results compare well to the other current techniques. The simplicity and compactness of the proposed gait features is their major advantage because it entails low computational overhead.

Page generated in 0.2572 seconds