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This paper compares the performance of topographic analysis and binary analysis for recognition of digits in hydrographic maps. Each of the methods processed the input image by extracting binary print components, recognizing long lines, splitting touching digits, and, finally, recognizing individual symbol candidates. The topographic analysis extracted the information by computing topographic labels for each pixel, while the binary analysis was based on a locally adaptive thresholding of the gray scale image. The performance of each method was measured by the correct classification rate of the final symbol recognition step when processing a complete hydrographic map of size 0.45 × 0.6m 2 with about 35,000 digits. The experimental results indicated that binary analysis had a better performance than topographic analysis. Overall, the performance of the binary analysis was acceptable. Deconvolution of the gray scale hydrographic image did not improve the performance of any of the two methods.

This paper presents a new full-reference image quality metric for objective assessment in spatial domain. This metric empirically combines some standard image metrics for assessment of complete visual distortion. The proposed quality metric (Q) is formulated by modeling an image distortion as a combined effect of structural distortion, contrast distortion and edge distortion, which may occur on account of deteriorations due to noise contamination, contrast manipulations, blurring, rotation or compression. As portrayed by the experimental results, this metric provides a more accurate estimation for the above mentioned deteriorations in comparison to conventional MSE (PSNR) and SSIM approaches in terms of performance with reference to subjective assessment. The novelty of the proposed metric is adjudged by its ability to evaluate across different types of distortions under a single metric for image quality assessment.Keywords-edge performance index, full-reference human visual system, mean square error, image quality metric, structural correlation, SSIM.