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Aquila optimizer (AO) is an efficient meta-heuristic optimization method, which mimics the hunting style of Aquila in nature. However, the AO algorithm may suffer from immature convergence during the exploitation stage. In this paper, two strategies are elegantly employed into conventional AO, such as random opposition-based learning and nonlinear flexible jumping factor, which can efficiently enhance the performance of conventional AO. Experiments on 1 7 benchmark functions and image segmentation demonstrate the effectiveness of the proposed algorithm. Comparison with several state-of-the-art meta-heuristic optimization techniques indicates the efficacy of the developed method. [ABSTRACT FROM AUTHOR]

Subspace clustering aims to seek a multi-subspace representation that is best suitable for data points taken from a high-dimensional space. Sparse representation and low-rank approximation-based methods have become one of the main melodies for subspace clustering. In the existing methods, nuclear norm is used to approximate rank minimization. However, the common deficiency still exists for nuclear norm, which always over-penalizes large singular values and results in a biased solution. In this paper, we propose a nonlinear subspace clustering model that exploits sparsity and low-rank of data in high dimensional feature space by using Schatten-p norm surrogate (p ∈ (0 , 1)) with learned low-rank kernel. By this manner, the model guarantees that the data mapped in the high-dimensional feature spaces is lower rank and self-expressive. And we show the alternating direction method of multipliers (abbreviated as ADMM) for the corresponding problem in a reproducing kernel Hilbert space. Various experiments on motion segmentation and image clustering display that the proposed model has potentiality in outperforming most of state-of-the-art models in current literature. [ABSTRACT FROM AUTHOR]

It is well known that neurons communicate through synapses in the nervous system, and the size, morphology, and connectivity of synapses determine the functional properties of the neural network. Therefore, synapses have always been one of the key objects of neuroscience. Due to the technical advance in electron microscope (EM), the physical structure of synapses can be observed at high resolution. Nevertheless, to date, the automatic analysis of the synapse in EM images is still a challenging task. In this paper, we proposed a fractal dimension-based segmentation method for synaptic clef of mouse cortex on EM image stack. Our method does not require a lot of groundtruth to train the model, and shows better adaptive anti-noise performance. That should be ascribed to the stability of segmentation-related key parameters in the data from same tissue. In this way, we only need to give initial values, and then gradually adjust these key parameters. Experiments reveal that our method achieves the desired results, and reduces the time in artificial annotating, so that researchers can focus more on the analysis of segmentation results. [ABSTRACT FROM AUTHOR]

Infrared images have several applications such as security, health, passenger monitoring, and so on. The quality of infrared image gets affected by noise, blurring effect, and low illumination environment. Due to the low contrast, blurring, and hazy effects in infrared images, state-of-the-art techniques are frequently unable to achieve appropriate edge details. Thus, an edge detection algorithm is proposed using a modified Von Neumann neighborhood kernel and taxicab geometry-based shortest path method. It has been found to perform in a better manner compared to earlier studies in a similar field. The objective of the proposed method is to produce sharp, less noisy and robust edge lines. First, pre-processing of the image is done for edge-preserving smoothing of an infrared image using a smoothing parameter. Second, image segmentation is done based on a two-level threshold value computed by a modified Von Neumann-based kernel. Then, Fourier transform of the segmented image is done to remove spike noise followed by the inverse Fourier transform to produce the final edge lines. The simulation experiment results show that the proposed method is found to yield robust and sharp edge lines compared to other state-of-the-art methods both numerically and visually. Moreover, the whole process takes less computation time. [ABSTRACT FROM AUTHOR]

Level set method has been widely applied in the field of image segmentation. However, the level set formulation is inevitably affected by the regularization function, in-homogeneity and weak edge in the process of evolution, which often leads to the instability and inaccuracy of image segmentation results. To solve these problems, a new distance regularization term defined by a double-well potential function is proposed to satisfy more ideal characteristics of signed distance property. In addition, a novel edge indicator function is introduced to segment images with uneven intensity or weak edge. Finally, the adaptive adjustment formulas of distance regularization and area parameters are derived to alleviate the difficulty of parameter adjustment. Experimental results show that the proposed model provides better accuracy and versatility, quantitative experiment on Weizmann segmentation evaluation database achieves mean Dice score (96.87%), IoU (94.38%), Hausdorff distance (3.20 mm), Recall (97.68%) and Precision (96.32%), respectively. [ABSTRACT FROM AUTHOR]