Maximum Entropy Image Segmentation Research Articles

Maximum entropy multi-threshold image segmentation based on improved particle swarm optimization

To solve the problem of low speed and low precision of traditional maximum entropy image segmentation algorithm, a multi-threshold image segmentation algorithm based on improved particle swarm optimization algorithm is proposed. Taking the optimal threshold optimization problem in multi-threshold image segmentation as the research object, the optimal objective function is obtained by using the maximum entropy multi-threshold segmentation method, and then the maximum entropy method and particle swarm optimization algorithm are fused. In order to solve the problem that particle swarm optimization (PSO) is prone to fall into local optimization in the later iteration process, the PSO is improved and the expansion model is added. Finally, the maximum entropy multi-threshold image segmentation method based on the standard particle swarm optimization algorithm and the maximum entropy multi-threshold image segmentation method based on the improved particle swarm optimization algorithm segment the h-component image (hue, saturation, value) of HSV. Images are converted from images. The segmentation results of the two algorithms are evaluated by running time, and the structural similarity of the algorithms is evaluated. evaluation result. Experimental results show that the improved maximum entropy multi-threshold image segmentation algorithm based on particle swarm optimization can better achieve complex image segmentation, and the algorithm has stronger real-time performance.

Image Segmentation Method with Maximum Entropy Optimized by Wolf Pack Algorithm

In order to improve the accuracy and efficiency of image segmentation, the wolf pack algorithm is introduced into the maximum entropy image segmentation method. The wolf pack algorithm is applied to the calculation of the maximum entropy threshold, and the image segmentation is realized by finding the optimal threshold. Simulation results show that this method can quickly and accurately find the segmentation threshold, obtain better segmentation accuracy, and improve the image segmentation effect to a certain extent.

Automatic gas leak detection system

With the development of submarine oil and gas resources, people pay more and more attention to the safety of pipeline transportation. Once an oil and gas leak occurs in the submarine transportation pipeline, it will not only cause direct economic loss but also damage the marine ecology. Therefore, it is particularly important for underwater gas leak detection research. Due to the difficult underwater detection operation and the high labor cost, an embedded module with automatic gas leak detection function is designed and completed in this paper. This paper analyzes the imaging characteristics of multi-beam sounding sonar images, and designs a set of underwater gas leak detection algorithms. The algorithm mainly includes: median filtering, bottom detection, top-hat processing, maximum entropy image segmentation, connected area labeling, nearest neighbor interpolation based on backward mapping, linear upward trend estimation based on Hough transform, and testing of 120 images, With an accuracy rate of more than 98%.

Maximum Entropy Image Segmentation Method Based On Improved Firefly Algorithm

An adaptive parametric firefly algorithm is proposed for the premature convergence of the firefly algorithm itself and the late oscillation of the algorithm iteration. In the image threshold segmentation test experiment, the algorithm is used to optimize the maximum entropy image segmentation, and the maximum entropy and the basic firefly improved maximum entropy algorithm are compared. At the same time, two important indicators of image segmentation evaluation index regional consistency are used to evaluate the results. It shows that the experimental results of the algorithm have better intra-regional consistency and noise immunity.

The vision guidance and image processing of AGV

Firstly, the principle of AGV vision guidance is introduced and the deviation and deflection angle are measured by image coordinate system. The visual guidance image processing platform is introduced. In view of the fact that the AGV guidance image contains more noise, the image has already been smoothed by a statistical sorting. By using AGV sampling way to obtain image guidance, because the image has the best and different threshold segmentation points. In view of this situation, the method of two-dimensional maximum entropy image segmentation is used to solve the problem. We extract the foreground image in the target band by calculating the contour area method and obtain the centre line with the least square fitting algorithm. With the help of image and physical coordinates, we can obtain the guidance information.

The Application of Particle Swarm Algorithm based on the Global Optimal Position Mutation in Maximum Entropy Image Segmentation

The Application of Particle Swarm Algorithm based on the Global Optimal Position Mutation in Maximum Entropy Image Segmentation

Evaluation of single and multi-thresholdentropy-based algorithms for folded substrate analysis

This paper presents a detailed evaluation of two variants of Maximum Entropy image segmentation algorithm (single and multi-thresholding) with respect to their performance on segmenting test images showing folded substrates. The segmentation quality was determined by evaluating values of four different measures: misclassification error, modified Hausdorff distance, relative foreground area error and positive-negative false detection ratio. New normalization methods were proposed in order to combine all parameters into a unique algorithm evaluation rating. The segmentation algorithms were tested on images obtained by three different digitalisation methods covering four different surface textures. In addition, the methods were also tested on three images presenting a perfect fold. The obtained results showed that Multi-Maximum Entropy algorithm is better suited for the analysis of images showing folded substrates.