Face analysis using curve edge maps

This paper proposes an automatic and real-time system for face analysis, usable in visual communication applications. In this approach, faces are represented with Curve Edge Maps, which are collections of polynomial segments with a convex region. The segments are extracted from edge pixels using an adaptive incremental linear-time fitting algorithm, which is based on constructive polynomial fitting. The face analysis system considers face tracking, face recognition and facial feature detection, using Curve Edge Maps driven by histograms of intensities and histograms of relative positions. When applied to different face databases and video sequences, the average face recognition rate is 95.51%, the average facial feature detection rate is 91.92% and the accuracy in location of the facial features is 2.18% in terms of the size of the face, which is comparable with or better than the results in literature. However, our method has the advantages of simplicity, real-time performance and extensibility to the different aspects of face analysis, such as recognition of facial expressions and talking

Eyelid Localization for Iris Identification

This article presents a new eyelid localization algorithm based on a parabolic curve fitting. To deal with eyelashes, low contrast or false detection due to iris texture, we propose a two steps algorithm. First, possible edge candidates are selected by applying edge detection on a restricted area inside the iris. Then, a gradient maximization is applied along every parabola, on a larger area, to refine parameters and select the best one. Experiments have been conducted on a database of 151 iris that have been manually segmented. The performance evaluation is carried out by comparing the segmented images obtained by the proposed method with the manual segmentation. The results are satisfactory in more than 90% of the cases

Computer-aided diagnostic systems for digital mammograms

A computer-aided diagnostic (CAD) system that uses a unique shape-based classification scheme, the Ellipse-Closed Curve Fitting (ECCF) algorithm, is developed for digital mammogram image analysis. The system is developed to work as a post-processing extension to a previously developed CAD system that locates and segments mass lesions, or tumors, found in digital mammograms into separate images. The ECCF system is implemented in the MATLAB mathematical scripting language and is thus capable of running on multiple platforms. The ECCF algorithm detects edges in tumor images and casts them into closed curve functions. Parameters for an ellipse of best fit for a closed curve function are computed in a way analogous to that in linear regression, where a line of best fit is determined to fit a set of data points. In addition to the shape-fitting algorithm, the ECCF system comprises several other independently functioning components, including auxiliary algorithms and techniques that perform image cropping and edge detection, employed initially to prepare the images for efficient processing, and self-test tools that calculate R2, area matching ratios, and a shape conformity value to determine the goodness of fit . Output generated by the ECCF system for sufficiently large image sets may contain correlations between malignant tumors and their shape that may be captured with data mining techniques, the implementation of which may result in an improved integrated CAD system

NerVE: Neural Volumetric Edges for Parametric Curve Extraction from Point Cloud

Extracting parametric edge curves from point clouds is a fundamental problem in 3D vision and geometry processing. Existing approaches mainly rely on keypoint detection, a challenging procedure that tends to generate noisy output, making the subsequent edge extraction error-prone. To address this issue, we propose to directly detect structured edges to circumvent the limitations of the previous point-wise methods. We achieve this goal by presenting NerVE, a novel neural volumetric edge representation that can be easily learned through a volumetric learning framework. NerVE can be seamlessly converted to a versatile piece-wise linear (PWL) curve representation, enabling a unified strategy for learning all types of free-form curves. Furthermore, as NerVE encodes rich structural information, we show that edge extraction based on NerVE can be reduced to a simple graph search problem. After converting NerVE to the PWL representation, parametric curves can be obtained via off-the-shelf spline fitting algorithms. We evaluate our method on the challenging ABC dataset. We show that a simple network based on NerVE can already outperform the previous state-of-the-art methods by a great margin. Project page: https://dongdu3.github.io/projects/2023/NerVE/.Comment: Accepted by CVPR2023. Project page: https://dongdu3.github.io/projects/2023/NerVE

Detecting transit signatures of exoplanetary rings using SOAP3.0

CONTEXT. It is theoretically possible for rings to have formed around extrasolar planets in a similar way to that in which they formed around the giant planets in our solar system. However, no such rings have been detected to date. AIMS: We aim to test the possibility of detecting rings around exoplanets by investigating the photometric and spectroscopic ring signatures in high-precision transit signals. METHODS: The photometric and spectroscopic transit signals of a ringed planet is expected to show deviations from that of a spherical planet. We used these deviations to quantify the detectability of rings. We present SOAP3.0 which is a numerical tool to simulate ringed planet transits and measure ring detectability based on amplitudes of the residuals between the ringed planet signal and best fit ringless model. RESULTS: We find that it is possible to detect the photometric and spectroscopic signature of near edge-on rings especially around planets with high impact parameter. Time resolution $\leq$ 7 mins is required for the photometric detection, while 15 mins is sufficient for the spectroscopic detection. We also show that future instruments like CHEOPS and ESPRESSO, with precisions that allow ring signatures to be well above their noise-level, present good prospects for detecting rings.Comment: 13 pages, 16 figures, 2 tables , accepted for publication in A&