CRM - Betrachtung wichtiger Prozesse im Customer Relationship Management (German Edition)
Classifying and gathering additional information about an unknown 3D objects is dependent on having a large amount of learning data. We propose to use procedural models as data foundation for this task. In our method we semi- automatically define parameters for a procedural model constructed with a modeling tool. Then we use the procedural models to classify an object and also automatically estimate the best parameters.
We use a standard convolutional neural network and three different object similarity measures to estimate the best parameters at each degree of detail. We evaluate all steps of our approach using several procedural models and show that we can achieve high classification accuracy and meaningful parameters for unknown objects. Detection of objects from satellite optical remote sensing images is very important for many commercial and governmental applications.
With the development of deep convolutional neural networks deep CNNs , the field of object detection has seen tremendous advances. Currently, objects in satellite remote sensing images can be detected using deep CNNs. In general, optical remote sensing images contain many dense and small objects, and the use of the original Faster Regional CNN framework does not yield a suitably high precision. Therefore, after careful analysis we adopt dense convoluted networks, a multi-scale representation and various combinations of improvement schemes to enhance the structure of the base VGGNet for improving the precision.
We propose an approach to reduce the test-time detection time and memory requirements. To validate the effectiveness of our approach, we perform experiments using satellite remote sensing image datasets of aircraft and automobiles.
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The results show that the improved network structure can detect objects in satellite optical remote sensing images more accurately and efficiently. We propose a general approach for absolute pose problems including the well known perspective-n-point PnP problem, its generalized variant GPnP with and without scale, and the pose from 2D line correspondences PnL. These have received a tremendous attention in the computer vision community during the last decades. However, it was only recently that efficient, globally optimal, closed-form solutions have been proposed, which can handle arbitrary numbers of correspondences including minimal configurations as well as over-constrained cases with linear complexity.
We follow the general scheme by eliminating the linear parameters first, which results in a least squares error function that only depends on the non-linear rotation and a small symmetric coefficient matrix of fixed size. We propose a unified formulation based on a representation with orthogonal complements which allows to combine different types of constraints elegantly in one single framework.
We show that with our unified formulation existing polynomial solvers can be interchangeably applied to problem instances other than those they were originally proposed for. It becomes possible to compare them on various registrations problems with respect to accuracy, numerical stability, and computational speed. Our compression procedure not only preserves linear complexity, it is even faster than previous formulations.
For the second step we also derive an own algebraic equation solver, which can additionally handle the registration from 3D point-to-point correspondences, where other rotation solvers fail. Finally, we also present a marker-based SLAM approach with automatic registration to a target coordinate system based on partial and distributed reference information. It represents an application example that goes beyond classical camera pose estimation from image measurements and also serves for evaluation on real data. This paper presents a comprehensive defect detection method for two common fabric defects groups.
Most existing systems require textiles to be spread out in order to detect defects. This method can be applied when the textiles are not spread out and does not require any pre- processing.
The deep learning architecture we present is based on transfer learning and localizes and recognizes cuts, holes and stain defects. Classification and localization is combined into a single system combining two different networks.
The experiments this paper presents show that even without adding depth information, the network was able to distinguish between stain and shadow. This method has been successful even for textiles in voluminous shape and is less computationally intensive than other state-of-the-art methods.
Kabir, Ahmedul; Kuijper, Arjan [1. The demand of medical image is increasing with every day that contains crucial information for diagnosis, treatment planning, disease monitoring, image-guided surgery, educating medical students through different medical cases and for many other research purposes in medical science. This information can be gathered through image classifications, registration, and segmentation. Machine learning algorithms are used for gathering information through medical image classification, registration or segmentation.
However, these algorithms need a large set of training data in their initial stages to learn from.
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Medical experts provides the annotation of these medical images. This is where the problem lies. Annotating these medical images is a very time consuming, monotonous and expensive process. The medical experts lack motivation and are always occupied with their daily important clinician stuff.
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Thus, we need a solution that will be fast, accurate and cost-effective. This is where crowdsourcing comes into play. Yes, crowdsourcing is the best way to speed up these annotations tasks. But there are still questions whether these crowd workers are good enough to generate the initial training data sets for these algorithms and whether they are good enough to replace the experts.
The scope of our thesis is to analyze and compare the perceived limits and pose estimation of crowd workers for the annotation of x-ray images. To our prior knowledge, previously there has been no research done on this. We have the x-ray images of different parts of the body that contains bone surgeries with a screw in it.
We have put two wireframed screws, one in red and the other in green color and put both beside the ground-truth screw. And the objective of this thesis is to study whether these crowds are able to interpret the x-ray image and then able to classify which screw is close to the original ground-truth screw.
We ran the first x-ray classification experiment with our Master students as a controlled group. Therefore, we can easily say crowd workers can replace medical experts for generating training data for algorithms in medical x-ray image classifications with a perception limit of 1px with respect to Euclidean distance. However, we failed to do our second experiment which was about post estimation of crowd workers for the registration tasks in the x-ray images.
We managed to build the prototype but we failed to map the projection data of the ground truth screw with our model screw. But the prototype can be used in future to run the experiment once the projection matrix could be mapped out. Biometric recognition is the automated recognition of individuals based on their behavioral or biological characteristics. Beside forensic applications, this technology aims at replacing the outdated and attack prone, physical and knowledge-based, proofs of identity.
Choosing one biometric characteristic is a tradeoff between universality, acceptability, and permanence, among other factors. Moreover, the accuracy cap of the chosen characteristic may limit the scalability and usability for some applications. The use of multiple biometric sources within a unified frame, i.
This work aims at presenting application-driven advances in multi-biometrics by addressing different elements of the multi-biometric system work-flow. At first, practical oriented pre-fusion issues regarding missing data imputation and score normalization are discussed.
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This includes presenting a novel performance anchored score normalization technique that aligns certain performance-related score values in the fused biometric sources leading to more accurate multi-biometric decisions when compared to conventional normalization approaches. Missing data imputation within scorelevel multi-biometric fusion is also addressed by analyzing the behavior of different approaches under different operational scenarios.
Within the multi-biometric fusion process, different information sources can have different degrees of reliability. This is usually influenced in the fusion process by assigning relative weights to the fused sources. This work presents a number of weighting approaches aiming at optimizing the decision made by the multi-biometric system. First, weights that try to capture the overall performance of the biometric source, as well as an indication of its confidence, are proposed and proved to outperform the state-of-the-art weighting approaches.
The work also introduces a set of weights derived from the identification performance representation, the cumulative match characteristics. The effect of these weights is analyzed under the verification and identification scenarios. To further optimize the multi-biometric process, information besides the similarity between two biometric captures can be considered. Previously, the quality measures of biometric captures were successfully integrated, which requires accessing and processing raw captures.
In this work, supplementary information that can be reasoned from the comparison scores are in focus. First, the relative relation between different biometric comparisons is discussed and integrated in the fusion process resulting in a large reduction in the error rates. Secondly, the coherence between scores of multi-biometric sources in the same comparison is defined and integrated into the fusion process leading to a reduction in the error rates, especially when processing noisy data.
Large-scale biometric deployments are faced by the huge computational costs of running biometric searches and duplicate enrollment checks.
Data indexing can limit the search domain leading to faster searches. Multibiometrics provides richer information that can enhance the retrieval performance. This work provides an optimizable and configurable multi-biometric data retrieval solution that combines and enhances the robustness of rank-level solutions and the performance of feature-level solutions. Furthermore, this work presents biometric solutions that complement and utilize multi-biometric fusion.
The first solution captures behavioral and physical biometric characteristics to assure a continuous user authentication. Later, the practical use of presentation attack detection is discussed by investigating the more realistic scenario of cross-database evaluation and presenting a state-of-the-art performance comparison. Finally, the use of multibiometric fusion to create face references from videos is addressed. Face selection, feature-level fusion, and score-level fusion approaches are evaluated under the scenario of face recognition in videos. Deshmukh, Akshay Madhav; Kuijper, Arjan [1.
Gutachten]; Burkhardt, Dirk [2. In today's world, computers are tightly coupled with the internet and play a vital role in the development of business and various aspects of human lives. Hence, developing a quality user-computer interface has become a major challenge. Well-designed programs that are easily usable by users are moulded through a regress development life cycle.
To ensure a user friendly interface, the interface has to be well designed and need to support smart interaction features. User interface can become an Archilles heel in a developed system because of the simple design mistakes which causes critical interaction problems which eventually leads to massive loss of attractiveness in the system.
To overcome this problem, regular and consistent user evaluations have to be carried out to ensure the usability of the system. The importance of an evaluation for the development of a system is well known.