Evaluation of Local Feature Detectors for the Comparison of Thermal and Visual Low Altitude Aerial Images

Local features are key regions of an image suitable for applications such as image matching, and fusion. Detection of targets under varying atmospheric conditions, via aerial images is a typical defence application where multi spectral correlation is essential. Focuses on local features for the comparison of thermal and visual aerial images in this study. The state of the art differential and intensity comparison based features are evaluated over the dataset. An improved affine invariant feature is proposed with a new saliency measure. The performances of the existing and the proposed features are measured with a ground truth transformation estimated for each of the image pairs. Among the state of the art local features, Speeded Up Robust Feature exhibited the highest average repeatability of 57 per cent. The proposed detector produces features with average repeatability of 64 per cent. Future works include design of techniques for retrieval of corresponding regions

Two-Key Dependent Permutation for Use in Symmetric Cryptographic System

This paper deals with a two-key based novel approach for generating a permutation table that can be used in a symmetric cryptographic system to cause diffusion. It also discusses how the permutation table generated using the approach can be applied to character based encryption and binary data block produced at intermediate stages by symmetric cipher algorithms. It also describes the effect of our approach on characters of intermediate text as well as on bits of binary data block along with the impact of a single bit change in key information on producing permutation sequences applied to plaintexts to produce ciphertexts. The results are satisfactory and the proposed approach can be employed in any symmetric block cipher algorithm that uses the predefined permutation tables

A method to improve the computational efficiency of the Chan-Vese model for the segmentation of ultrasound images

Purpose Advanced image segmentation techniques like the Chan-Vese (CV) models transform the segmentation problem into a minimization problem which is then solved using the gradient descent (GD) optimization algorithm. This study explores whether the computational efficiency of CV can be improved when GD is replaced by a different optimization method. Methods Two GD variants from the literature (Nesterov accelerated, Barzilai-Borwein) and a newly developed hybrid variant of GD were used to improve the computational efficiency of CV by making GD insensitive to local minima. One more variant of GD from the literature (projected GD) was used to address the issue of maintaining the constraint on boundary evolution in CV which also increases computational cost. A novel modified projected GD (Barzilai-Borwein projected GD) was also used to overcome both problems at the same time. The effect of optimization method selection on processing time and the quality of the output was assessed for 25 musculoskeletal ultrasound images (five anatomical areas). Results The Barzilai-Borwein projected GD method was able to significantly reduce computational time (average(±std.dev.) reduction 95.82 % (±3.60 %)) with the least structural distortion in the delineated output relative to the conventional GD (average(±std.dev.) structural similarity index: 0.91(±0.06)). Conclusion The use of an appropriate optimization method can substantially improve the computational efficiency of CV models. This can open the way for real-time delimitation of anatomical structures to aid the interpretation of clinical ultrasound. Further research on the effect of the optimization method on the accuracy of segmentation is needed

Tropical Cyclone Intensity and Track Prediction in the Bay of Bengal Using LSTM-CSO Method

Tropical cyclones (TC) are extreme weather conditions caused by severe circular storms that originate in warm oceans. They are strong destructive forces that cause disastrous effects on human life and property and lead to economic damage. Therefore, it is necessary to forecast the TC intensity to avoid the issues. This study proposes a TC intensity forecast using Long-Short Term Memory (LSTM) with Cat Swarm Optimization (CSO). The LSTM method was optimized using the Cat Swarm Optimization technique to improve accuracy and reduce prediction errors. In this study, the prediction was carried out using the latitude, longitude, pressure, and wind speed of tropical cyclones from 2003 to 2019 in the Bay of Bengal. The performance of the proposed system was evaluated using the performance metrics, such as accuracy, Root Mean Square Error (RMSE), Average Absolute Position Error, Mean Absolute Error (MAE), and Area Under Receiver Operating Characteristic Curve (AUROC). The performance of the proposed system is compared with the results of other traditional methods, and the results show that the LSTM-CSO method outperforms other methods in TC intensity and track prediction

Partial Discharge Random Noise removal using Hankel Matrix based Fast Singular Value Decomposition

The most effective method for insulation assessment in electrical power apparatus is Partial Discharge (PD) detection. During measurements the interference from background environment hampers the PD signal and reduces its measurement accuracy. This paper discuss on the implementation of a Hankel matrix based Fast Singular Value Decomposition (H-FSVD) technique for removing noise from the PD signals. The data is first represented into Hankel Matrix (HM) structure, with appropriate sampling then using Lanczo process the Hankel matrix size is reduced and through Singular Spectral Analysis thresholds are fixed for noise detection and removal. This algorithm has been examined on simulated as well as PD signals measured on two different laboratory environments from transformers with real and simulated noise.The experiment is part of a series of experiments to detect PD patterns related to realistic transformer defects. The denoising performance of H-FSVD is compared with the wavelet based denoising methods, Empirical Mode Decomposition method and normal SVD. Numerical results show that H-FSVD efficiently removes the noise with less computation time, even for large size data

A concept for movement-based computerized segmentation of connective tissue in ultrasound imaging

This study proposes a novel concept for the computerized segmentation of ultrasound images of connective tissue based on movement. Tendons and ligaments are capable of almost frictionless movement relative to their neighbouring tissues making them good candidates for movement-based segmentation. To demonstrate this concept, a central cross section of the patellar tendon was imaged in the axial plane while movement was generated by manually pulling and pushing the skin close to the imaging area. Maps of internal movement were created for four representative pairs of consecutive images using normalised cross corelation. Thresholding followed by a series of morphological operations (k-clustering, blob extraction, curve fitting) enabled the extraction of the superficial-most tendon boundary. Comparison against manually segmented outputs indicated good agreement against ground truth (average ± STDEV Bhattacharyya distance: 0.170 ± 0.039). In contrast to the more superficial parts of the tissue, the applied method for motion generation did not result in clearly visible movement in the tissue areas deeper in the imaging window. The segmentation of the entire tendon will require movement patterns that involve equally the entire tendon (e.g., generated by a contraction of the in-series muscle). The results of this study demonstrate for the first time that movement mapping can be used for the segmentation of connective tissue. Further research will be needed to identify the optimal way to use motion to complement existing segmentation approaches which are based on signal intensity, texture, and shape features