Automatic landmarking for building biological shape models

We present a new method for automatic landmark extraction from the contours of biological specimens. Our ultimate goal is to enable automatic identification of biological specimens in photographs and drawings held in a database. We propose to use active appearance models for visual indexing of both photographs and drawings. Automatic landmark extraction will assist us in building the models. We describe the results of using our method on drawings and photographs of examples of diatoms, and present an active shape model built using automatically extracted data

Feature Neighbourhood Mutual Information for multi-modal image registration: An application to eye fundus imaging

© 2014 Elsevier Ltd. All rights reserved. Multi-modal image registration is becoming an increasingly powerful tool for medical diagnosis and treatment. The combination of different image modalities facilitates much greater understanding of the underlying condition, resulting in improved patient care. Mutual Information is a popular image similarity measure for performing multi-modal image registration. However, it is recognised that there are limitations with the technique that can compromise the accuracy of the registration, such as the lack of spatial information that is accounted for by the similarity measure. In this paper, we present a two-stage non-rigid registration process using a novel similarity measure, Feature Neighbourhood Mutual Information. The similarity measure efficiently incorporates both spatial and structural image properties that are not traditionally considered by MI. By incorporating such features, we find that this method is capable of achieving much greater registration accuracy when compared to existing methods, whilst also achieving efficient computational runtime. To demonstrate our method, we use a challenging medical image data set consisting of paired retinal fundus photographs and confocal scanning laser ophthalmoscope images. Accurate registration of these image pairs facilitates improved clinical diagnosis, and can be used for the early detection and prevention of glaucoma disease

Separation of Overlapping and Touching Lines within Handwritten Arabic Documents

The original publication is available at www.springerlink.comInternational audienceIn this paper, we propose an approach for the separation of overlapping and touching lines within handwritten Arabic documents. Our approach is based on the morphology analysis of the terminal letters of Arabic words. Starting from 4 categories of possible endings, we use the angular variance to follow the connection and separate the endings. The proposed separation scheme has been evaluated on 100 documents contains 640 overlapping and touching occurrences reaching an accuracy of about 96.88%

Application of the Fisher-Rao metric to ellipse detection

The parameter space for the ellipses in a two dimensional image is a five dimensional manifold, where each point of the manifold corresponds to an ellipse in the image. The parameter space becomes a Riemannian manifold under a Fisher-Rao metric, which is derived from a Gaussian model for the blurring of ellipses in the image. Two points in the parameter space are close together under the Fisher-Rao metric if the corresponding ellipses are close together in the image. The Fisher-Rao metric is accurately approximated by a simpler metric under the assumption that the blurring is small compared with the sizes of the ellipses under consideration. It is shown that the parameter space for the ellipses in the image has a finite volume under the approximation to the Fisher-Rao metric. As a consequence the parameter space can be replaced, for the purpose of ellipse detection, by a finite set of points sampled from it. An efficient algorithm for sampling the parameter space is described. The algorithm uses the fact that the approximating metric is flat, and therefore locally Euclidean, on each three dimensional family of ellipses with a fixed orientation and a fixed eccentricity. Once the sample points have been obtained, ellipses are detected in a given image by checking each sample point in turn to see if the corresponding ellipse is supported by the nearby image pixel values. The resulting algorithm for ellipse detection is implemented. A multiresolution version of the algorithm is also implemented. The experimental results suggest that ellipses can be reliably detected in a given low resolution image and that the number of false detections can be reduced using the multiresolution algorithm

Robust and Precise Circular Arc Detection

The original publication is available at www.springerlink.comInternational audienceIn this paper we present a method to robustly detect circular arcs in a line drawing image. The method is fast, robust and very reliable, and is capable of assessing the quality of its detection. It is based on Random Sample Consensus minimization, and uses techniques that are inspired from object tracking in image sequences. It is based on simple initial guesses, either based on connected line segments, or on elementary mainstream arc detection algorithms. Our method consists of gradually deforming these circular arc candidates as to precisely fit onto the image strokes, or to reject them if the fitting is not possible, this virtually eliminates spurious detections on the one hand, and avoiding non-detections on the other hand