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This paper proposes a blind, inaudible, and robust audio watermarking scheme based on singular spectrum analysis (SSA) and the psychoacoustic model 1 (ISO/IEC 11172-3). In this work, SSA is used to analyze the host signals and to extract the singular spectra. A watermark is embedded into the host signals by modifying the singular spectra which are in the convex part of the singular spectrum curve so that this part becomes concave. This modification certainly affects the inaudibility and robustness properties of the watermarking scheme. To satisfy both properties, the modified part of the singular spectrum is determined by a novel parameter selection method based on the psychoacoustic model. The test results showed that the proposed scheme achieves not only inaudibility and robustness but also blindness. In addition, this work showed that the extraction process of a variant of the proposed scheme can extract the watermark without assuming to know the frame positions in advance and without embedding additional synchronization code into the audio content.

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We propose vessel vector-based phase portrait analysis (VVPPA) and a hybrid between VVPPA and a clustering method proposed earlier for automatic optic disk (OD) detection called the vessel transform (VT). The algorithms are based primarily on the location and direction of retinal blood vessels and work equally well on fine and poor quality images. To localize the OD, the direction vectors derived from the vessel network are constructed, and points of convergence of the resulting vector field are examined by phase portrait analysis. The hybrid method (HM) uses a set of rules acquired from the decision model to alternate the use of VVPPA and VT. To identify the OD contour, the scale space (SS) approach is integrated with VVPPA, HM, and the circular approximation (SSVVPPAC and SSHMC). We test the proposed combination against state-of-the-art OD detection methods. The results show that the proposed algorithms outperform the benchmark methods, especially on poor quality images. Specifically, the HM gets the highest accuracy of 98% for localization of the OD regardless of the image quality. Testing the segmentation routines SSVVPPAC and SSHMC against the conventional methods shows that SSHMC performs better than the existing methods, achieving the highest PPV of 71.81% and the highest sensitivity of 70.67% for poor quality images. Furthermore, the HM can supplement practically any segmentation model as long as it offers multiple OD candidates. In order to prove this claim, we test the efficiency of the HM in detecting retinal abnormalities in a real clinical setting. The images have been obtained by portable lens connected to a smart phone. In detecting the abnormalities related to diabetic retinopathy (DR), the algorithm provided 94.67 and 98.13% for true negatives and true positives, respectively.