Automatic landmark annotation and dense correspondence registration for 3D human facial images

Dense surface registration of three-dimensional (3D) human facial images holds great potential for studies of human trait diversity, disease genetics, and forensics. Non-rigid registration is particularly useful for establishing dense anatomical correspondences between faces. Here we describe a novel non-rigid registration method for fully automatic 3D facial image mapping. This method comprises two steps: first, seventeen facial landmarks are automatically annotated, mainly via PCA-based feature recognition following 3D-to-2D data transformation. Second, an efficient thin-plate spline (TPS) protocol is used to establish the dense anatomical correspondence between facial images, under the guidance of the predefined landmarks. We demonstrate that this method is robust and highly accurate, even for different ethnicities. The average face is calculated for individuals of Han Chinese and Uyghur origins. While fully automatic and computationally efficient, this method enables high-throughput analysis of human facial feature variation.Comment: 33 pages, 6 figures, 1 tabl

Automatic waterline registration of a ship model in waves

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Evaluation of Local Feature Detectors for the Comparison of Thermal and Visual Low Altitude Aerial Images

Local features are key regions of an image suitable for applications such as image matching, and fusion. Detection of targets under varying atmospheric conditions, via aerial images is a typical defence application where multi spectral correlation is essential. Focuses on local features for the comparison of thermal and visual aerial images in this study. The state of the art differential and intensity comparison based features are evaluated over the dataset. An improved affine invariant feature is proposed with a new saliency measure. The performances of the existing and the proposed features are measured with a ground truth transformation estimated for each of the image pairs. Among the state of the art local features, Speeded Up Robust Feature exhibited the highest average repeatability of 57 per cent. The proposed detector produces features with average repeatability of 64 per cent. Future works include design of techniques for retrieval of corresponding regions

Detection of deformable objects in a non-stationary scene

Image registration is the process of determining a mapping between points of interest on separate images to achieve a correspondence. This is a fundamental area of many problems in computer vision including object recognition and motion tracking. This research focuses on applying image registration to identify differences between frames in non-stationary video scenes for the purpose of motion tracking. The major stages for the image registration process include point detection, image correspondence, and an affine transformation. After applying image registration to spatially align the image frames and detect areas of motion segmentation is applied to segment the moving deformable objects in the non-stationary scenes. In this paper, specific techniques are reviewed to implement image registration. First, I will present other work related to image registration for feature point extraction, image correspondence, and spatial transformations. Then I will discuss deformable object recognition. This will be followed by a detailed description on the methods developed for this research and implementation. Included is a discussion on the Harris Corner Detection operator that allows the identification of key points on separate frames based on detecting areas in frames with strong contrasts in intensity values that can be identified as corners. These corners are the feature points that are comparable between frames. Then there will be an explanation on finding point correspondences between two separate video frames using ordinal and orientation measures. When a correspondence is made, the data acquired from the image correspondence calculations will be used to apply translation to align the video frames. With these methods, two frames of video can be properly aligned and then subtracted to detect deformable objects. Finally, areas of motions are segmented using histograms in the HSV color space. The algorithms are implemented using INTEL?s open computer vision library called OpenCV. The results demonstrate that this approach is successful at detecting deformable objects in non-stationary scenes

Radio Galaxy Zoo: Knowledge Transfer Using Rotationally Invariant Self-Organising Maps

With the advent of large scale surveys the manual analysis and classification of individual radio source morphologies is rendered impossible as existing approaches do not scale. The analysis of complex morphological features in the spatial domain is a particularly important task. Here we discuss the challenges of transferring crowdsourced labels obtained from the Radio Galaxy Zoo project and introduce a proper transfer mechanism via quantile random forest regression. By using parallelized rotation and flipping invariant Kohonen-maps, image cubes of Radio Galaxy Zoo selected galaxies formed from the FIRST radio continuum and WISE infrared all sky surveys are first projected down to a two-dimensional embedding in an unsupervised way. This embedding can be seen as a discretised space of shapes with the coordinates reflecting morphological features as expressed by the automatically derived prototypes. We find that these prototypes have reconstructed physically meaningful processes across two channel images at radio and infrared wavelengths in an unsupervised manner. In the second step, images are compared with those prototypes to create a heat-map, which is the morphological fingerprint of each object and the basis for transferring the user generated labels. These heat-maps have reduced the feature space by a factor of 248 and are able to be used as the basis for subsequent ML methods. Using an ensemble of decision trees we achieve upwards of 85.7% and 80.7% accuracy when predicting the number of components and peaks in an image, respectively, using these heat-maps. We also question the currently used discrete classification schema and introduce a continuous scale that better reflects the uncertainty in transition between two classes, caused by sensitivity and resolution limits