1,721,164 research outputs found
Estimation of planetary surface ages using image based automatic crater detection algorithms
Full text linkA fully automatic system of crater detection, fusion and age estimation is built and constructed to result in reliable results in comparison with manually long time manually process from experts and professionals. A new idea of an extension of crater detection algorithms (CDA) is the Age Estimation that relied basically on Crater frequency-size distribution (CSFD). The age estimation process for surfaces depends basically on the numbers of the craters detected on the Moon surface and the total area of that surface. It is examined how well a template matching method is suitable for determining the age of different lunar areas. Six artificially lit crater models are used to count the craters in the investigated areas using cross-correlation. A threshold value for the automatic crater detection algorithm has been calculated for each dataset in order to obtain the best reliable results followed by a fusion automatic process for duplicated detections. A new implementation of this approach is provided for estimating the surface age with the possibility of flexible threshold values needed for calibration and evaluation process. With these two above-mentioned automatic steps, this will result in a time reduction and reasonable crater detection and so far precise age values. An automatic age mapping process has been applied to use the optimal threshold value in larger homogenous areas for more efficiency and behavior study.
For the purpose of testing accuracy and efficiency, a dataset from lunar nearside regions has been examined to find out if there is an ideal threshold value for the crater detection process so that the smallest possible errors in the surface ages - derived from manually detected craters – are found in comparison to values from the literature. For this purpose, the optimal threshold value is calculated in five areas of Mare Cognitum on the Moon and then use to determine the age of five other areas in Oceanus Procellarum. By subsequently comparing the calculated ages with those from the literature, the accuracy of the method is examined.
An image-based CDA has been implemented on a different dataset of craters, the first group of the dataset is the LU60645GT catalogue that includes a large number of crater candidates with diameters between 0.7 km and 2.5 km and located in the large craters Alphonsus and Ptolemaeus. The second dataset is a different region on the Moon near the crater Hell Q that includes a limited number of small craters with very small diameters between 3 m and 70 m, while the third group of data contains a list of medium-sized craters (128 m-1000 m) on the morphologically homogeneous floor of the lunar crater Tsiolkovsky.
In an advanced step, an automatic method of detection for secondary crater candidates on the lunar surface has been proposed. To assess the accuracy of the developed method, automatic crater counts were performed for the flat floor of the lunar farside crater Tsiolkovsky by applying the Voronoi tesselation based Secondary Candidate Detection (SCD) to the results of the template matching based crater detector. For a small are on the crater floor, the obtained age of 3.21 Ga is consistent with the age of 3.19 Ga determined by Pasckert et al. (2015). In the next step, the age estimation was expanded to the complete crater floor, resulting in a map of the surface age which is at least partially corrected for the influence of secondary craters
Uncertainty-based image segmentation with unsupervised mixture models
Full text linkIn this thesis, a contribution to explainable artificial intelligence is made. More specifically, the aspect of artificial intelligence which focusses on recreating the human perception is tackled from a previously neglected direction. A variant of human perception is building a mental model of the extents of semantic objects which appear in the field of view. If this task is performed by an algorithm, it is termed image segmentation. Recent methods in this area are mostly trained in a supervised fashion by exploiting an as extensive as possible data set of ground truth segmentations. Further, semantic segmentation is almost exclusively tackled by Deep Neural Networks (DNNs).
Both trends pose several issues. First, the annotations have to be acquired somehow. This is especially inconvenient if, for instance, a new sensor becomes available, new domains are explored, or different quantities become of interest. In each case, the cumbersome and potentially costly labelling of the raw data has to be redone. While annotating keywords to an image can be achieved in a reasonable amount of time, annotating every pixel of an image with its respective ground truth class is an order of magnitudes more time-consuming. Unfortunately, the quality of the labels is an issue as well because fine-grained structures like hair, grass, or the boundaries of biological cells have to be outlined exactly in image segmentation in order to derive meaningful conclusions. Second, DNNs are discriminative models. They simply learn to separate the features of the respective classes. While this works exceptionally well if enough data is provided, quantifying the uncertainty with which a prediction is made is then not directly possible. In order to allow this, the models have to be designed differently. This is achieved through generatively modelling the distribution of the features instead of learning the boundaries between classes. Hence, image segmentation is tackled from a generative perspective in this thesis. By utilizing mixture models which belong to the set of generative models, the quantification of uncertainty is an implicit property. Additionally, the dire need of annotations can be reduced because mixture models are conveniently estimated in the unsupervised setting.
Starting with the computation of the upper bounds of commonly used probability distributions, this knowledge is used to build a novel probability distribution. It is based on flexible marginal distributions and a copula which models the dependence structure of multiple features. This modular approach allows great flexibility and shows excellent performance at image segmentation. After deriving the upper bounds, different ways to reach them in an unsupervised fashion are presented. Including the probable locations of edges in the unsupervised model estimation greatly increases the performance. The proposed models surpass state-of-the-art accuracies in the generative and unsupervised setting and are on-par with many discriminative models. The analyses are conducted following the Bayesian paradigm which allows computing uncertainty estimates of the model parameters. Finally, a novel approach combining a discriminative DNN and a local appearance model in a weakly supervised setting is presented. This combination yields a generative semantic segmentation model with minimal annotation effort
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Metabolic profiling on 2D NMR TOCSY spectra using machine learning
Full text linkDue to the dynamicity of biological cells, the role of metabolic profiling in discovering biological fingerprints of diseases, and their evolution, as well as the cellular pathway of different biological or chemical stimuli is most significant.
Two-dimensional nuclear magnetic resonance (2D NMR) is one of the fundamental and strong analytical instruments for metabolic profiling. Though, total correlation spectroscopy (2D NMR 1H -1H TOCSY) can be used to improve spectral overlap of 1D NMR, strong peak shift, signal overlap, spectral crowding and matrix effects in complex biological mixtures are extremely challenging in 2D NMR analysis.
In this work, we introduce an automated metabolic deconvolution and assignment based on the deconvolution of 2D TOCSY of real breast cancer tissue, in addition to different differentiation pathways of adipose tissue-derived human Mesenchymal Stem cells. A major alternative to the common approaches in NMR based machine learning where images of the spectra are used as an input, our metabolic assignment is based only on the vertical and horizontal frequencies of metabolites in the 1H-1H TOCSY.
One- and multi-class Kernel null foley–Sammon transform, support vector machines, polynomial classifier kernel density estimation, and support vector data description classifiers were tested in semi-supervised learning and novelty detection settings. The classifiers’ performance was evaluated by comparing the conventional human-based methodology and automatic assignments under different initial training sizes settings. The results of our novel metabolic profiling methods demonstrate its suitability, robustness, and speed in automated nontargeted NMR metabolic analysis
Momentum Contrast for Representative Face Presentation Attack Detection
No full textWith the widespread usage of using face recognition systems, they became vulnerable to presentation attacks encountered by attackers. To tackle this issue, face presentation attack detection (PAD) methods are implemented. However, these methods have several shortcomings including the generalizability of unknown attacks. This thesis targets two main problems that face PAD methods. The first problem that this work target to solve is databases annotation problems. Annotating databases with labels is time-consuming, to solve this problem, a representative learning model (MoCo framework in this thesis) is used as it focuses on unsupervised learning databases. The second problem that this work target is the insufficient PAD data. Most PAD databases are manually collected especially presentation attack samples, thus they are labor-intensive and small-scale. This thesis target this problem by training the model on a face recognition database such as CASIA-Web database which is a very large-scale public facial recognition database, not a PAD database, which is collected randomly in the wild where images are diverse from illumination, sensors, identity. This work proves that using face recognition databases to learn face representation, can be adapted to be used in detecting presentation attacks and the model can benefit from using extra existing face recognition data besides the model becomes more familiar with diverse setups and illuminations within face images. Finally, the classification model suggested by the state-of-art MoCo, is extended by applying pseudo labeling to it, which improved the general results
3D shape measurement and reflectance analysis for highly specular and interreflection affected surfaces
Full text linkVariations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
- …
