Spectral Residual Based Underwater Animal Detection

In this paper, the spectral residual method is applied in the underwater image data for detecting the animals. The system is designed to assist the underwater monitor system survey operations, specialized to the task of animal detection. Firstly, the regularity for the frequency spectrum of the images collected in the underwater world is discovered by the statistical analysis. Then we transform the input image into the spatial frequency domain and singularities including in the frequency curve is extracted by average filtering. Finally, these singularities are inverse transformed from the frequency domain into spatial domain and the saliency area is detected. Experimental results, which have been performed on a set of real underwater images acquired in different environments, demonstrate the robustness and the accuracy of the proposed system in the task of underwater animal detection.

Download Full-text

Image Statistics Preserving Encrypt-then-Compress Scheme Dedicated for JPEG Compression Standard

Entropy ◽

10.3390/e23040421 ◽

2021 ◽

Vol 23 (4) ◽

pp. 421

Author(s):

Dariusz Puchala ◽

Kamil Stokfiszewski ◽

Mykhaylo Yatsymirskyy

Keyword(s):

Statistical Analysis ◽

Image Encryption ◽

Experimental Studies ◽

Quality Measures ◽

Input Image ◽

Jpeg Compression ◽

Image Statistics ◽

Compression Stage ◽

Wide Range ◽

The Impact

In this paper, the authors analyze in more details an image encryption scheme, proposed by the authors in their earlier work, which preserves input image statistics and can be used in connection with the JPEG compression standard. The image encryption process takes advantage of fast linear transforms parametrized with private keys and is carried out prior to the compression stage in a way that does not alter those statistical characteristics of the input image that are crucial from the point of view of the subsequent compression. This feature makes the encryption process transparent to the compression stage and enables the JPEG algorithm to maintain its full compression capabilities even though it operates on the encrypted image data. The main advantage of the considered approach is the fact that the JPEG algorithm can be used without any modifications as a part of the encrypt-then-compress image processing framework. The paper includes a detailed mathematical model of the examined scheme allowing for theoretical analysis of the impact of the image encryption step on the effectiveness of the compression process. The combinatorial and statistical analysis of the encryption process is also included and it allows to evaluate its cryptographic strength. In addition, the paper considers several practical use-case scenarios with different characteristics of the compression and encryption stages. The final part of the paper contains the additional results of the experimental studies regarding general effectiveness of the presented scheme. The results show that for a wide range of compression ratios the considered scheme performs comparably to the JPEG algorithm alone, that is, without the encryption stage, in terms of the quality measures of reconstructed images. Moreover, the results of statistical analysis as well as those obtained with generally approved quality measures of image cryptographic systems, prove high strength and efficiency of the scheme’s encryption stage.

Download Full-text

The Spectrum Segmentation Algorithm of Multimode Vibration Signal Based on Spectral Clustering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.2372 ◽

2011 ◽

Vol 121-126 ◽

pp. 2372-2376

Author(s):

Dan Dan Wang ◽

Yu Zhou ◽

Qing Wei Ye ◽

Xiao Dong Wang

Keyword(s):

Wave Packet ◽

Frequency Domain ◽

Spectral Clustering ◽

Clustering Algorithm ◽

Wave Packets ◽

Vibration Signal ◽

Segmentation Algorithm ◽

Frequency Curve ◽

Macroscopic Observation ◽

Spectral Clustering Algorithm

The mode peaks in frequency domain of vibration signal are strongly interfered by strong noise, causing the inaccuracy mode parameters. According to this situation, this paper comes up with the thought of mode-peak segmentation based on the spectral clustering algorithm. First, according to the concept of wave packet, the amplitude-frequency of vibration signal is divided into wave packets. Taking each wave packet as a sample of clustering algorithm, the spectral clustering algorithm is used to classify these wave packets. The amplitude-frequency curve of a mode peak becomes a big wave packet in macroscopic. The experiment to simulation signals indicates that this spectral clustering algorithm could accord with the macroscopic observation of mode segmentation effectively, and has outstanding performance especially in strong noise.

Download Full-text