Comprehensive Analysis of Edge Detection in Color Image Processing

Various approaches to edge detection for color images, including techniques extended from monochrome edge detection as well as vector space approaches, are examined. In particular, edge detection techniques based on vector order statistic operators and difference vector operators are studied in detail. Numerous edge detectors are obtained as special cases of these two classes of operators. The effect of distance measures on the performance of different color edge detectors is studied by employing distance measures other than the Euclidean norm. Variations are introduced to both the vector order statistic opera-tors and the difference vector operators to improve noise performance. They both demonstrate the ability to attenuate noise with added algorithm complexity. Among them, the difference vector operator with adaptive filtering shows the most promising results. Other vector directional filtering techniques are also introduced and utilized for color edge detection. Both quantitative and subjective tests are performed in evaluating the performance of the edge detectors, and a detailed comparison is presented.<div>Copyright 1999 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.<br></div><div><br></div>

Evaluation of Astigmatic Correction Using Vector Analysis after Combined Femtosecond Laser-Assisted Phacoemulsification and Intrastromal Arcuate Keratotomy

The aim of this study was to evaluate astigmatic correction in patients with mild to moderate astigmatism after combined femtosecond laser-assisted cataract surgery (FLACS) and intrastromal arcuate keratotomy (ISAK), using vector analysis. This retrospective study included patients with corneal astigmatism of 0.5–3.0 diopters (D) who underwent FLACS and ISAK. Vector analyses of astigmatism were performed using the Alpins method, considering three vectors: target-induced astigmatism (TIA), surgically induced astigmatism (SIA), and difference vector (DV). Magnitude of error (ME), angle of error (AE), correction index (CI), and coefficient of adjustment (CA) were calculated. Subgroup analysis according to the axis of astigmatism, patient age, and white to white (WTW) diameter was conducted. In total, for the 79 eyes of 79 patients, the TIA was 1.21 ± 0.52 D, the SIA was 0.76 ± 0.53 D, and the DV was 0.86 ± 0.50 D. The ME (difference between SIA and TIA) was −0.46 ± 0.45 D, and the CI (ratio of SIA and TIA) was 0.62 ± 0.34; both these parameters demonstrated slight undercorrection. The CA (inverse of the CI) was 2.48 ± 2.61. The AE was 4.02° ± 28.7°, and the absolute AE was 21.7° ± 19.0°. In the univariate regression analyses to identify factors that affected the CI, there was a negative correlation between age and the CI ( P = 0.022 ). In conclusion, vector analysis after the combined FLACS and ISAK revealed slight undercorrection, regardless of the astigmatism meridian. The precision of the nomogram should be improved through long-term vector analysis for the results of arcuate keratotomy and through further research on the relationship between patient demographics and CI. Overall, this study has shown that FLACS and ISAK could reduce postoperative corneal astigmatism effectively and safely.

Research on Hyperspectral Image Reconstruction Based on GISMT Compressed Sensing and Interspectral Prediction

Hyperspectral remote-sensing images have the characteristics of large transmission data and high propagation requirements, so they are faced with transmission and preservation problems in the process of transmission. In view of this situation, this paper proposes a spectral image reconstruction algorithm based on GISMT compressed sensing and interspectral prediction. Firstly, according to the high spectral correlation of hyperspectral remote-sensing images, the hyperspectral images are grouped according to the band, and a standard band is determined in each group. The standard band in each group is weighted by the GISMT compressed sensing method. Then, a prediction model of the general band in each group is established to realize the remote-sensing image reconstruction in the general band. Finally, the difference between the actual measured value and the predicted value is calculated. According to the prediction algorithm, the corresponding difference vector is obtained and the predicted measured value is iteratively updated by the difference vector until the hyperspectral reconstructed image of the relevant general band is finally reconstructed. It is shown by experiments that this method can effectively improve the reconstruction effect of hyperspectral images.

Deep-Mining Backtracking Search Optimization Algorithm Guided by Collective Wisdom

The backtracking search optimization algorithm (BSA) is a recently proposed evolutionary algorithm with simple structure and well global exploration capability, which has been widely used to solve optimization problems. However, the exploitation capability of the BSA is poor. This paper proposes a deep-mining backtracking search optimization algorithm guided by collective wisdom (MBSAgC) to improve its performance. The proposed algorithm develops two learning mechanisms, i.e., a novel topological opposition-based learning operator and a linear combination strategy, by deeply mining the winner-tendency of collective wisdom. The topological opposition-based learning operator guides MBSAgC to search the vertices in a hypercube about the best individual. The linear combination strategy contains a difference vector guiding individuals learning from the best individual. In addition, in order to balance the overall performance, MBSAgC simulates the clusterity-tendency strategy of collective wisdom to develop another difference vector in the above linear combination strategy. The vector guides individuals to learn from the mean value of the current generation. The performance of MBSAgC is tested on CEC2005 benchmark functions (including 10-dimension and 30-dimension), CEC2014 benchmark functions, and a test suite composed of five engineering design problems. The experimental results of MBSAgC are very competitive compared with those of the original BSA and state-of-the-art algorithms.