Maximum Entropy Threshold Segmentation Research Articles

Robust Crater Detection Algorithm Based on Maximum Entropy Threshold Segmentation

For future lunar exploration and planetary missions, the digital elevation model (DEM) of the target object may not be well prepared before the mission, so developing a new robust crater detection algorithm (CDA) without prepared high-precision DEM is needed to meet the requirements of a high-reliability and high-precision detection and navigation system. In this paper, we presented a new robust lunar CDA method based on maximum entropy threshold segmentation. By calculating the entropy distribution of the ternary image, the threshold for retaining the maximum amount of image information is selected adaptively, a variety of evaluation indicators are proposed, and a multiple-indicator constraint matrix is constructed to realize the extraction and fitting of the craters. The proposed method has the following advantages: (1) it has strong robustness and is capable of extracting complete craters under multiple illumination conditions, which makes it suitable for the extraction of large-scale planetary and lunar images; (2) the extracted crater edges are clear and complete and do not merge with the surrounding environment edge; and (3) it avoids the problem of parameter sensitivity that is present in a traditional CDA algorithm. The proposed method was verified using an image taken by the Chang’e-2 lunar probe, and a comparison with the traditional method based on morphology and adaptive Canny edge detection shows that the number of craters detected increases by more than 35%, while the computational efficiency is improved by more than 40%.

Method for wheat ear counting based on frequency domain decomposition of MSVF-ISCT

Wheat ear counting is a prerequisite for the evaluation of wheat yield. A wheat ear counting method based on frequency domain decomposition is proposed in this study to improve the accuracy of wheat yield estimation. The frequency domain decomposition of wheat ear image is completed by multiscale support value filter (MSVF) combined with improved sampled contourlet transform (ISCT). Support Vector Machine (SVM) is the classic classification and regression algorithm of machine learning. MSVF based on this has strong frequency domain filtering and generalization ability, which can effectively remove the complex background, while the multi-direction characteristics of ISCT enable it to represent the contour and texture information of wheat ears. In order to improve the level of wheat yield prediction, MSVF-ISCT method is used to decompose the ear image in multi-scale and multi direction in frequency domain, reduce the interference of irrelevant information, and generate the sub-band image with more abundant information components of ear feature information. Then, the ear feature is extracted by morphological operation and maximum entropy threshold segmentation, and the skeleton thinning and corner detection algorithms are used to count the results. The number of wheat ears in the image can be accurately counted. Experiments show that compared with the traditional algorithms based on spatial domain, this method significantly improves the accuracy of wheat ear counting, which can provide guidance and application for the field of agricultural precision yield estimation.

Application of Multiple Random Forest Algorithm in Image Segmentation of Nanoparticles

Because of its large specific surface area, small particle size, high surface energy, and unique nanoeffect, the morphological characteristics of nanoparticles are the key factors affecting the properties of materials. How to detect and evaluate the morphological characteristics of nanoparticles is the first problem to be solved in the preparation and application of nanomaterials. The main purpose of this paper is to use TEM to recognize the image features of nanoparticles and introduce the transmission electron microscope and image edge segmentation method and random forest algorithm. A method integrating the in situ characterization of modern electron microscopy and the measurement of the electrical properties of nanomonomers was developed. In this paper, a multielectrode TEM in situ electrical measurement platform is prepared, which improves the contact during the integration of nanomaterials and improves the electrical measurement accuracy of the TEM in situ electrical method. In this paper, based on the random forest algorithm, a multirandom forest algorithm is proposed. Due to the different gray levels of images referenced by the multirandom forest algorithm, the segmentation results are processed by FCM clustering algorithm. Experimental results show that in terms of image segmentation accuracy, the minimum Jaccard coefficient obtained by multiple random forest algorithm is 89% and 95%, respectively, which is obviously better than watershed segmentation method and maximum entropy threshold segmentation. In the aspect of automatic image segmentation of nanoparticles, the image segmentation accuracy is the highest when the sample block size and the number of sample blocks selected in the multiple random forest algorithm are 5∗5, 7500, and 35, respectively. Therefore, the multirandom forest algorithm has achieved high accuracy in image segmentation of nanoparticles, which provides valuable information for the preparation and application of nanomaterials. A new type of TEM dark‐field imaging diaphragm was prepared, which greatly improved the imaging quality of weak‐phase bulk materials represented by graphene and nonspiral biological samples represented by intracellular polyvesicles.

Investigation of Spatter Characteristics in Selective Laser Melting Based on Maximum Entropy Threshold Segmentation Algorithm

Investigation of Spatter Characteristics in Selective Laser Melting Based on Maximum Entropy Threshold Segmentation Algorithm

A Novel Video Copyright Protection Scheme Based on Blockchain and Double Watermarking

With the continuous development of multimedia, more and more digital works such as videos are spread, stored, and used in the network. In recent years, digital copyright infringement disputes have occurred frequently. The traditional copyright protection system has some problems, such as difficulty confirming copyright, monitoring infringement, and obtaining evidence for rights protection. To this end, we have designed and implemented a novel video copyright protection scheme based on the blockchain and double watermarking technology. We use the image correlation coefficient method to extract video keyframes. And we combine with Contourlet Transform domain, QR decomposition, and SIFT algorithm to improve the robustness of watermark against geometric attacks on the premise of invisibility. After that, we use Arnold Transformation (Cat Map) based on the Maximum Entropy Threshold Segmentation to encrypt the robust watermark and strengthen the security. Moreover, based on the characteristics of the fragile watermarking, we accurately locate the attacked video’s tamper position and complete the integrity authentication of the watermarked video. In addition, the hash digest of the video watermark and the user ID of the copyright owner is signed by SM2 and uploaded to the blockchain. The user can register the copyright after passing the identity authentication. We conduct tests and security analysis on the blockchain performance of the system, the performance of the commercial cryptography algorithm, and the security of the watermarking system. The experimental results show that the blockchain used in this system conforms to the industry standard, the performance of SM2 and SM3 is better than ECC-256 and SHA-256, and the system security is well guaranteed.

Bayesian prediction of the stress concentration effect on high-strength wires with corrosion pits

This paper investigated the relationship between the morphology of corrosion pits and the stress concentration factor. Several images of corroded wire with different corrosion durations were obtained based on corrosion-accelerated test under the coupling effect of alternating load and salt-spray environment, then the morphology and size of pits were identified by the maximum entropy threshold segmentation method. A finite-element model of high-strength wire with corrosion pits was constructed and analyzed to explore the stress concentration factor close to the corrosion pits. Subsequently, a Bayesian prediction model of the stress concentration effect on wires with corrosion pits was proposed. In conclusion, the morphology of corrosion pits was described as “ellipsoidal”, with its length and width obeying the lognormal distribution and its depth obeying the Weibull distribution. Bayesian analysis indicated that the stress concentration factors around a corrosion pit were affected by the length–width and width-depth ratios of the pit. The stress concentration effect close to corrosion pits with narrow or deep appearance was more dramatic than that of other pits.

A Method for Segmentation of Transformer Oil Level Region Based on Infrared Image

With the rapid development of computer and artificial intelligence technology, infrared images are playing a higher and higher value in the state monitoring of electrical equipment in substations. Aiming at the detection of the oil level area of the oil conservator in the substation, in order to accurately obtain the oil level information, this paper proposes a transformer oil level detection method combining maximum entropy threshold segmentation algorithm and SLIC (simple linear iterativeclustering) segmentation algorithm. First, the original infrared image is pre-processed by image cutting technology to obtain the infrared image of the oil pillow area; the green channel image is extracted, and then the infrared image is segmented by the maximum entropy threshold segmentation algorithm to remove the background interference and obtain the target area of the electrical equipment. Finally, SLIC is used to super the pixel segmentation algorithm performs segmentation to obtain the oil level area. Experiments show that the algorithm can clearly segment the oil level area of the oil pillow, and it has certain practical value.

Image Characteristic Extraction of Ice-Covered Outdoor Insulator for Monitoring Icing Degree

Serious ice accretion will cause structural problems and ice flashover accidents, which result in outdoor insulator string operating problems in winter conditions. Previous investigations have revealed that the thicker and longer insulators are covered with ice, the icing degree becomes worse and icing accident probability increases. Therefore, an image processing method was proposed to extract the characteristics of the icicle length and Rg (ratio of the air gap length to the insulator length) of ice-covered insulators for monitoring the operation of iced outdoor insulator strings. The tests were conducted at the artificial climate room of CIGELE Laboratories recommended by IEEE Standard 1783/2009. The surface phenomena of the insulator during the ice accretion process were recorded by using a high-speed video camera. In the view of the ice in the background of the picture of fuzzy features and high image noise, a direct equalization algorithm is used to enhance the grayscale iced image contrast. The median filtering method is conducted for reducing image noise and sharpening the image edge. The maximum entropy threshold segmentation algorithm is put forward to extract the insulators and its surface ice from the background. Then, the modified Canny operator edge detection algorithm is selected to trace the boundaries of objects through the extraction of information about attributes of the endpoints of edges. After we obtained the improved Canny edge detection image for both of the ice-covered insulators and non-iced insulators, the icing thickness can be obtained by calculating the difference between the edge of the non-iced insulators image and the edge of the iced insulator image. Besides, in order to identify the icing degree of the insulators more accurately, this paper determines the location of icicles by using the region growth method. After that, the icicle length and Rg can be obtained to monitor the icing degree of the insulator. It will be helpful to improve the ability to judge the accident risk of insulators in power systems.

Retinal Vessel Segmentation Based on Multiscale Matched Filtering

Retinal vascular function is complex, morphological structure varies from person to person, and is susceptible to vascular diseases and systemic vascular diseases. Its accurate segmentation is of great significance for disease diagnosis and identification. In this paper, a multi-scale matching filtering algorithm is proposed for the uneven size of retinal blood vessels. On the basis of the traditional singlescale Gaussian matching filter, multiscale Gaussian matched filters with two sizes are used to enhance grayscale images. Enhancement is performed, and the superimposed image is binarized using a twodimensional maximum entropy threshold segmentation algorithm. The algorithm is tested in the DRIVE database with sensitivity, specificity and accuracy of 0.803, 0.959, 0.981, respectively. Comparing with the traditional algorithm, the algorithm has high sensitivity, fast running speed and rich details of segmentation results.

Information Entropy Multi-Decision Attribute Reduction Fuzzy Rough Set for Dust Particulate Imagery Characteristic Extraction

High-precision extraction of particulate characteristic modes is essential for dust explosion safety measurements, such as particulate concentration and size distribution. A new solution based on information entropy multi-decision attribute reduction fuzzy rough set is proposed to analyse the particulate morphology characteristics, which effectively avoids the shortcomings of traditional technology (low accuracy, stochasticity, etc.). The proposed approach consists of three stages: membership function modelling, attribute reduction, and maximum information entropy threshold segmentation. The membership coefficient was determined with a multi-segment function by developing the fuzzy degree of the membership model for dust image pixels. The fuzzy dependence of the conditional attribute was determined to extract the fuzzy attribute reduction. Finally, the model of coal dust particulates with information entropy was improved to extract the maximum segmentation threshold, which is significant for classification. The proposed methods were evaluated over a sequence of 30 image sets. The unclassified rate evaluation reached 0.978 for particle sizes $\ge200~\mu \text{m}$ , 0.958 for [75 $\mu \text{m}$ , 200 $\mu \text{m}$ ] and 0.950 for particle sizes $ . The proposed reduction approach offered a performance improvement in terms of more important attribute implementation. The paper demonstrated that the maximum information entropy reduction model can remove the redundant attributes without compromising the precision.

Foreground detection of group-housed pigs based on the combination of Mixture of Gaussians using prediction mechanism and threshold segmentation

In this paper, a foreground detection method to obtain the foreground objects of pigs in overhead views of group-housed environments is proposed. The method is based on the combination of Mixture of Gaussians (MoG) using prediction mechanism (PM) and threshold segmentation algorithm. First, the “valid region” is manually set according to a priori knowledge. Second, the foreground objects of pigs are detected using the PM-MoG algorithm. The algorithm uses the detected binary image of the previous frame to predict the current frame in the valid region for pixels that fulfil background updating conditions. Different update strategies are used to update the background for different circumstances. Third, the maximum entropy threshold segmentation algorithm is used according to the colour information of foreground objects. Finally, the results of the two previous steps of foreground detection are fused. The experimental results show that the method is effective and can extract relatively complete foreground objects of pigs in complex scenes. These complex scenes include light changes, the influence of ground urine stains, water stains, manure, and other sundries, pigs' slow movement patterns, and varying colours of foreground objects. The average foreground detection rate is approximately 92%. The experimental results set the foundation for further exploration of individual identification of group-housed pigs, their behaviour analysis, and other objectives.

Maximum Entropy Threshold Segmentation for Target Matching Using Speeded-Up Robust Features

This paper proposes a 2-dimensional (2D) maximum entropy threshold segmentation (2DMETS) based speeded-up robust features (SURF) approach for image target matching. First of all, based on the gray level of each pixel and the average gray level of its neighboring pixels, we construct a 2D gray histogram. Second, by the target and background segmentation, we localize the feature points at the interest points which have the local extremum of box filter responses. Third, from the 2D Haar wavelet responses, we generate the 64-dimensional (64D) feature point descriptor vectors. Finally, we perform the target matching according to the comparisons of the 64D feature point descriptor vectors. Experimental results show that our proposed approach can effectively enhance the target matching performance, as well as preserving the real-time capacity.

An Improved Ray Casting Algorithm Based on 2D Maximum Entropy Threshold Segmentation

Abstract This paper presents an improved ray casting algorithm, in order to solve the problems of powerful computing capacity and slow rendering velocity. In the course of ray casting volume rendering, an algorithm of ICT slice images original data field 2D maximum entropy threshold segmentation prior preprocessing is proposed. It is used to define the extent of threshold, and research the optimal combination of the parameters. The area threshold removing method is combined applied to filter the noise before the binary processing of image. The results of reconstruction show that this algorithm can greatly reduce reconstruction volume data and improve the efficiency of ray casting.

An Novel Grayscale Image Segmentation Method based on 2D Entropy and Particle Swarm Optimization

The one-dimension entropy method only considers gray information. Spatial information of pixels is ignored. Therefore the spatial conjoint pixels can’t take into account in image. Otherwise, it has poor anti-noise capability. Two-dimension entropy method considers the spatial information and gray information, but the computing quantity is large. Improved 2D maximum entropy threshold segmentation method based on PSO is called PSO-SDAIVE algorithm. This algorithm not only considers the spatial information, but also considers the gray information and decreases the computing quantity. Otherwise, the neighboring pixel control parameter is set. The overly-smoothness of images can be avoided.