Simultaneous navigation and mascon gravity estimation around small bodies

This is an open access article under the CC BY licenseThis manuscript develops a simultaneous navigation and gravity estimation strategy around a small body. The scheme combines dynamical model compensation with a mascon gravity fit. Dynamical compensation adds the unmodeled acceleration to the filter state. Consequently, the navigation filter is able to generate an onorbit position-unmodeled acceleration dataset. The available measurements correspond to the landmarks-based navigation technique. Accordingly, an on-board camera is able to provide landmark pixels. The aforementioned position-unmodeled acceleration dataset serves to train a mascon gravity model on-board while in flight. The training algorithm finds the optimal mass values and locations using Adam gradient descent. By a careful choice of the mascon variables and constraints projection, the masses are ensured to be positive and within the small body shape. The numerical results provide a comprehensive analysis on the global gravity accuracy for different estimation scenarios.Horizon 2020 10102525

Illumination tolerance in facial recognition

In this research work, five different preprocessing techniques were experimented with two different classifiers to find the best match for preprocessor + classifier combination to built an illumination tolerant face recognition system. Hence, a face recognition system is proposed based on illumination normalization techniques and linear subspace model using two distance metrics on three challenging, yet interesting databases. The databases are CAS PEAL database, the Extended Yale B database, and the AT&T database. The research takes the form of experimentation and analysis in which five illumination normalization techniques were compared and analyzed using two different distance metrics. The performances and execution times of the various techniques were recorded and measured for accuracy and efficiency. The illumination normalization techniques were Gamma Intensity Correction (GIC), discrete Cosine Transform (DCT), Histogram Remapping using Normal distribution (HRN), Histogram Remapping using Log-normal distribution (HRL), and Anisotropic Smoothing technique (AS). The linear subspace models utilized were principal component analysis (PCA) and Linear Discriminant Analysis (LDA). The two distance metrics were Euclidean and Cosine distance. The result showed that for databases with both illumination (shadows), and lighting (over-exposure) variations like the CAS PEAL database the Histogram remapping technique with normal distribution produced excellent result when the cosine distance is used as the classifier. The result indicated 65% recognition rate in 15.8 ms/img. Alternatively for databases consisting of pure illumination variation, like the extended Yale B database, the Gamma Intensity Correction (GIC) merged with the Euclidean distance metric gave the most accurate result with 95.4% recognition accuracy in 1ms/img. It was further gathered from the set of experiments that the cosine distance produces more accurate result compared to the Euclidean distance metric. However the Euclidean distance is faster than the cosine distance in all the experiments conducted

Model based methods for locating, enhancing and recognising low resolution objects in video

Visual perception is our most important sense which enables us to detect and recognise objects even in low detail video scenes. While humans are able to perform such object detection and recognition tasks reliably, most computer vision algorithms struggle with wide angle surveillance videos that make automatic processing difficult due to low resolution and poor detail objects. Additional problems arise from varying pose and lighting conditions as well as non-cooperative subjects. All these constraints pose problems for automatic scene interpretation of surveillance video, including object detection, tracking and object recognition.Therefore, the aim of this thesis is to detect, enhance and recognise objects by incorporating a priori information and by using model based approaches. Motivated by the increasing demand for automatic methods for object detection, enhancement and recognition in video surveillance, different aspects of the video processing task are investigated with a focus on human faces. In particular, the challenge of fully automatic face pose and shape estimation by fitting a deformable 3D generic face model under varying pose and lighting conditions is tackled. Principal Component Analysis (PCA) is utilised to build an appearance model that is then used within a particle filter based approach to fit the 3D face mask to the image. This recovers face pose and person-specific shape information simultaneously. Experiments demonstrate the use in different resolution and under varying pose and lighting conditions. Following that, a combined tracking and super resolution approach enhances the quality of poor detail video objects. A 3D object mask is subdivided such that every mask triangle is smaller than a pixel when projected into the image and then used for model based tracking. The mask subdivision then allows for super resolution of the object by combining several video frames. This approach achieves better results than traditional super resolution methods without the use of interpolation or deblurring.Lastly, object recognition is performed in two different ways. The first recognition method is applied to characters and used for license plate recognition. A novel character model is proposed to create different appearances which are then matched with the image of unknown characters for recognition. This allows for simultaneous character segmentation and recognition and high recognition rates are achieved for low resolution characters down to only five pixels in size. While this approach is only feasible for objects with a limited number of different appearances, like characters, the second recognition method is applicable to any object, including human faces. Therefore, a generic 3D face model is automatically fitted to an image of a human face and recognition is performed on a mask level rather than image level. This approach does not require an initial pose estimation nor the selection of feature points, the face alignment is provided implicitly by the mask fitting process

Observation of two-dimensional Fermi surface and Dirac dispersion in YbMnSb2_2

We present the crystal structure, electronic structure, and transport properties of the material YbMnSb$_2$, a candidate system for the investigation of Dirac physics in the presence of magnetic order. Our measurements reveal that this system is a low-carrier-density semimetal with a 2D Fermi surface arising from a Dirac dispersion, consistent with the predictions of density functional theory calculations of the antiferromagnetic system. The low temperature resistivity is very large, suggesting scattering in this system is highly efficient at dissipating momentum despite its Dirac-like nature.Comment: 8 pages, 6 figure

Efficient From-Point Visibility for Global Illumination in Virtual Scenes with Participating Media

Sichtbarkeitsbestimmung ist einer der fundamentalen Bausteine fotorealistischer Bildsynthese. Da die Berechnung der Sichtbarkeit allerdings äußerst kostspielig zu berechnen ist, wird nahezu die gesamte Berechnungszeit darauf verwendet. In dieser Arbeit stellen wir neue Methoden zur Speicherung, Berechnung und Approximation von Sichtbarkeit in Szenen mit streuenden Medien vor, die die Berechnung erheblich beschleunigen, dabei trotzdem qualitativ hochwertige und artefaktfreie Ergebnisse liefern