Intelligent classification of sketch strokes

This paper presents an intelligent method for classifying pen strokes in an on-line sketching system. The method, based on adaptive threshold and fuzzy knowledge with respect to curve's linearity and convexity, can identify sketch strokes (curves) into lines, circles, arcs, ellipses, elliptical arcs, loop lines, spring lines and free-form B-spline curves. The proposed method has proven to be fast, suitable for real-time classification and identification

Direct Least-Squares Ellipse Fitting

Many biological and astronomical forms can be best represented by ellipses. While some more complex curves might represent the shape more accurately, ellipses have the advantage that they are easily parameterised and define the location, orientation and dimensions of the data more clearly. In this paper, we present a method of direct least-squares ellipse fitting by solving a generalised eigensystem. This is more efficient and more accurate than many alternative approaches to the ellipse-fitting problem such as fuzzy c-shells clustering and Hough transforms. This method was developed for human body modelling as part of a larger project to design a marker-free gait analysis system which is being undertaken at the National Rehabilitation Hospital, Dublin

Face Detection Using Randomized Hough Transform (RHT) with Various Ellipses Segmentations

Face detection is one of earlier phase in face recognition process. This research aims to get the faces area on digital image without being affected by face orientation, lights condition, background and the expression. The detected face area is usually shaped by a rectangle. Many pixels on the rectangle are not part of face, especially at the four of the image corners. This research use an ellipse as replacement a rectangle. The detected face is shaped by ellipses with various sizes and orientations. The digital image segmentations is used to detect face candidates area. The ellipse is formed by using Randomized Hough Transform (RHT) method, which is influenced by the center point of ellipse candidates. RHT found three random pixels on segmented image. The rate of success of RHT is determined by segmentation results. The research result is tested by using various thresholds, and get the best accuracy at 74.4%. The rate of accuracy is measured by comparing between RHT ellipses shape and circle shape on OpenCV library as ground truth

Ellipsoid fitting with the Cayley transform

We introduce an algorithm, Cayley transform ellipsoid fitting (CTEF), that uses the Cayley transform to fit ellipsoids to noisy data in any dimension. Unlike many ellipsoid fitting methods, CTEF is ellipsoid specific -- meaning it always returns elliptic solutions -- and can fit arbitrary ellipsoids. It also outperforms other fitting methods when data are not uniformly distributed over the surface of an ellipsoid. Inspired by calls for interpretable and reproducible methods in machine learning, we apply CTEF to dimension reduction, data visualization, and clustering. Since CTEF captures global curvature, it is able to extract nonlinear features in data that other methods fail to identify. This is illustrated in the context of dimension reduction on human cell cycle data, and in the context of clustering on classical toy examples. In the latter case, CTEF outperforms 10 popular clustering algorithms

Identifying the necrotic zone boundary in tumour spheroids with pair-correlation functions

Automatic identification of the necrotic zone boundary is important in the assessment of treatments on in vitro tumour spheroids. This has been difficult especially when the difference in cell density between the necrotic and viable zones of a tumour spheroid is small. To help overcome this problem, we developed novel one-dimensional pair-correlation functions (PCFs) to provide quantitative estimates of the radial distance of the necrotic zone boundary from the centre of a tumour spheroid. We validate our approach on synthetic tumour spheroids in which the position of the necrotic zone boundary is known a priori. It is then applied to nine real tumour spheroids imaged with light sheet-based fluorescence microscopy. PCF estimates of the necrotic zone boundary are compared with those of a human expert and an existing standard computational method.S. Dini, B. J. Binder, S. C. Fischer, C. Mattheyer, A. Schmitz, E. H. K. Stelzer, N. G. Bean and J. E. F. Gree

Recognition of quadrics from 3d point clouds generated by scanning of rotational parts

This paper presents a method for recognition of second order surfaces (quadrics) from point clouds containing information about scanned rotational parts. The method is region growing method that exploits the scatter of data during least squares fitting of quadrics as a region growing criterion. The presented procedure is convenient for segmentation of regions with high (G1 or higher) continuity. Besides, the region seed point is automatically selected which is its comparative advantage to a number of existing methods. The applicability of the proposed method is evaluated using two case studies; the first case study refers to a synthesized signal, and the second presents the applicability of the method on a real world example.*Ovaj rad je izabran sa konferencije 12th International Scientific Conference MMA 2015 - Flexible Technologies, i publikovan u casopisu Journal of Production Engineering