Quality assessment metrics for edge detection and edge-aware filtering: A tutorial review

The quality assessment of edges in an image is an important topic as it helps to benchmark the performance of edge detectors, and edge-aware filters that are used in a wide range of image processing tasks. The most popular image quality metrics such as Mean squared error (MSE), Peak signal-to-noise ratio (PSNR) and Structural similarity (SSIM) metrics for assessing and justifying the quality of edges. However, they do not address the structural and functional accuracy of edges in images with a wide range of natural variabilities. In this review, we provide an overview of all the most relevant performance metrics that can be used to benchmark the quality performance of edges in images. We identify four major groups of metrics and also provide a critical insight into the evaluation protocol and governing equations

Accurate Feature Extraction and Control Point Correction for Camera Calibration with a Mono-Plane Target

The paper addresses two problems related to 3D camera calibration using a single mono-plane calibration target with circular control marks. The first problem is how to compute accurately the locations of the features (ellipses) in images of the target. Since the structure of the control marks is known beforehand, we propose to use a shape-specific searching technique to find the optimal locations of the features. Our experiments have shown this technique generates more accurate feature locations than the state-of-the-art ellipse extraction methods. The second problem is how to refine the control mark locations with unknown manufacturing errors. We demonstrate in a case study, where the control marks are laser printed on a A4 paper, that the manufacturing errors of the control marks can be compensated to a good extent so that the remaining calibration errors are reduced significantly. 1

Multi-frequency Study of the LMC Supernova Remnant (SNR) B0513-692 and New SNR Candidate J051327-6911

We present a new multi-wavelength study of supernova remnant (SNR) B0513-692 in the Large Magellanic Cloud (LMC). The remnant also has a strong, superposed, essentially unresolved, but unrelated radio source at its north-western edge, J051324-691049. This is identified as a likely compact HII region based on related optical imaging and spectroscopy. We use the Australia Telescope Compact Array (ATCA) at 4790 and 8640 MHz to determine the large scale morphology, spectral index and polarization characteristics of B0513-692 for the first time. We detect a strongly polarized region (49%) in the remnant's southern edge. Interestingly we also detect a small (~40 arcsec) moderately bright, but distinct optical, circular shell in our Halpha imagery which is adjacent to the compact HII region and just within the borders of the NE edge of B0513-692. We suggest this is a separate new SNR candidate based on its apparently distinct character in terms of optical morphology in 3 imaged emission lines and indicative SNR optical spectroscopy (including enhanced optical [SII] emission relative to Halpha).Comment: 12 page

Feature fusion for facial landmark detection: A feature descriptors combination approach

Facial landmark detection is a crucial first step in facial analysis for biometrics and numerous other applications. However, it has proved to be a very challenging task due to the numerous sources of variation in 2D and 3D facial data. Although landmark detection based on descriptors of the 2D and 3D appearance of the face has been extensively studied, the fusion of such feature descriptors is a relatively under-studied issue. In this report, a novel generalized framework for combining facial feature descriptors is presented, and several feature fusion schemes are proposed and evaluated. The proposed framework maps each feature into a similarity score, combines the individual similarity scores into a resultant score, used to select the optimal solution for a queried landmark. The evaluation of the proposed fusion schemes for facial landmark detection clearly indicates that a quadratic distance to similarity mapping in conjunction with a root mean square rule for similarity fusion achieves the best performance in accuracy, efficiency, robustness and monotonicity