Explore the vast range of titles available.

The visual system of sighted animals plays a critical role in providing information about the environment, including motion details necessary for survival. Over the past few years, numerous studies have explored the mechanism of motion direction detection in the visual system for binary images, including the Hassenstein–Reichardt model (HRC model) and the HRC-based artificial visual system (AVS). In this paper, we introduced a contrast-response system based on previous research on amacrine cells in the visual system of Drosophila and other species. We combined this system with the HRC-based AVS to construct a motion-direction-detection system for gray-scale images. Our experiments verified the effectiveness of our model in detecting the motion direction in gray-scale images, achieving at least 99% accuracy in all experiments and a remarkable 100% accuracy in several circumstances. Furthermore, we developed two convolutional neural networks (CNNs) for comparison to demonstrate the practicality of our model.

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques.

Normally, the optic disc detection of retinal images is useful during the treatment of glaucoma and diabetic retinopathy. In this paper, the novel preprocessing of a retinal image with a bat algorithm (BA) optimization is proposed to detect the optic disc of the retinal image. As the optic disk is a bright area and the vessels that emerge from it are dark, these facts lead to the selected segments being regions with a great diversity of intensity, which does not usually happen in pathological regions. First, in the preprocessing stage, the image is fully converted into a gray image using a gray scale conversion, and then morphological operations are implemented in order to remove dark elements such as blood vessels, from the images. In the next stage, a bat algorithm (BA) is used to find the optimum threshold value for the optic disc location. In order to improve the accuracy and to obtain the best result for the segmented optic disc, the ellipse fitting approach was used in the last stage to enhance and smooth the segmented optic disc boundary region. The ellipse fitting is carried out using the least square distance approach. The efficiency of the proposed method was tested on six publicly available datasets, MESSIDOR, DRIVE, DIARETDB1, DIARETDB0, STARE, and DRIONS-DB. The optic disc segmentation average overlaps and accuracy was in the range of 78.5–88.2% and 96.6–99.91% in these six databases. The optic disk of the retinal images was segmented in less than 2.1 s per image. The use of the proposed method improved the optic disc segmentation results for healthy and pathological retinal images in a low computation time.

When side-scan sonars collect data, sonar energy attenuation, the residual of time varying gain, beam patterns, angular responses, and sonar altitude variations occur, which lead to an uneven gray level in side-scan sonar images. Therefore, gray scale correction is needed before further processing of side-scan sonar images. In this paper, we introduce the causes of gray distortion in side-scan sonar images and the commonly used optical and side-scan sonar gray scale correction methods. As existing methods cannot effectively correct distortion, we propose a simple, yet effective gray scale correction method for side-scan sonar images based on Retinex given the characteristics of side-scan sonar images. Firstly, we smooth the original image and add a constant as an illumination map. Then, we divide the original image by the illumination map to produce the reflection map. Finally, we perform element-wise multiplication between the reflection map and a constant coefficient to produce the final enhanced image. Two different schemes are used to implement our algorithm. For gray scale correction of side-scan sonar images, the proposed method is more effective than the latest similar methods based on the Retinex theory, and the proposed method is faster. Experiments prove the validity of the proposed method.

Lead‐acid batteries, widely used in energy storage and power systems, are susceptible to performance degradation due to metal impurities such as iron, copper, and tin. Traditional methods like ICP‐OES are accurate but costly and complex. Therefore, this study proposes a new method based on lens‐free digital holography (LDH) to detect and distinguish metal impurities in lead‐acid battery electrolytes. The study uses a compact setup with a green LED light source and a CMOS camera to capture holographic images of impurities. The technique reconstructs clear particle images using angular spectrum algorithms and analyzes their reconstruction distances and gray‐scale differences for identification. Results show that copper particles have a reconstruction distance of 1406.4 µm and a total gray‐scale difference of 4.4681; tin particles have a reconstruction distance of 1414.85 µm and a total gray‐scale difference of 0.5344; iron hydroxide has a reconstruction distance of 1647.85 µm and the total gray‐scale difference of 13.5789. By combining these parameters, the method effectively distinguishes between copper, tin, and iron particles, even in mixed solutions. This cost‐effective and efficient method offers a promising alternative for rapid and accurate detection of metal impurities in lead‐acid battery electrolytes, with potential applications in battery maintenance and quality control. This study introduces a lens‐free digital holography (LDH) method for detecting metal impurities in lead‐acid battery electrolytes. Using a green LED and CMOS camera, the technique captures holograms and analyzes reconstruction distances and gray‐scale differences. It effectively differentiates copper, tin, and iron particles in lead‐acid battery electrolytes, offering a cost‐effective and efficient alternative to traditional methods like ICP‐OES.

Leather is a critical material in the fashion industry, where it is required to meet specific customer demands for color, specifications, and performance, especially regarding color fastness. Traditional methods for assessing color fastness rely on subjective evaluations conducted by professional experts using grey scale standards. However, human evaluation can be inconsistent due to various factors, such as lighting conditions and individual perception. In this study, leather samples were first subjected to expert evaluations and scored using the grey scale system. These evaluations were then compared with color measurement data obtained through a spectrophotometer, which was processed using custom-designed software (written in the Python programming language). This software provided precise grey scale values based on the color measurements, enabling accurate digital assessments. The results of the comparative analysis showed that the computer-assisted grey scale assessment could be completed in a significantly shorter time frame with a minimal margin of error, offering a more reliable and efficient alternative to traditional evaluation methods. This approach not only enhances the accuracy of color assessments but also streamlines the evaluation process in the leather industry.

A Digital image watermarking is a cutting-edge problem that deals with copyright protection, content authentication and ownership identification. Precisely due to this reason, it is quite clear to the media industry. Particularly for images and videos, falling under un-compressed and compressed domain, it deals with minimizing the trade-off between two essential performance evaluation metrics – Visual Quality of the signal and the Robustness criteria. Although several metaheuristic technqiues have been applied to this problem, we are yet to apply new nature inspired techniques to develop watermarking applications for images and video. In this paper, we propose a novel watermark embedding scheme for gray-scale images using Harmony Search Algorithm (HSA). The HSA optimizes the Objective Function which in turn produces the best Multiple Scaling Factors (MSFs) to be used for embedding the watermark coefficients in the most suitable image coefficients in hybrid transform domain. On signed and attacked images, the PSNR show that their visual quality is very good. This scheme is also found to be very robust against common image processing operations except cropping attack of different variants. It is concluded that the proposed scheme is well optimized in terms of aforesaid performance evaluation metrics and proves improvement over other similar state of the art methods.

The key problem of visible watermarking is how to balance the watermark visibility, the security and the quality of marked image. In this paper, an adaptive visible watermarking scheme in document images using two-stage Mamdani fuzzy inference system (FIS) is presented. Firstly, four attribute parameters of document images including the neighborhood gray-scale value (G), skewness (Sk), entropy value (En) and standard deviation (Std) are defined as visible watermark embedding criteria. Secondly, the FIS1 and FIS2 are designed with different input and output parameters to get the adaptive intensity factors. In order to avoid the visible watermark being removed by the binary removal attack, the gray-scale uniform distribution method is used to remove the peak of the probability logarithmic histogram after the FIS1 stage. Finally, according to the results of FIS2 stage, the change of histogram is not obvious. To evaluate and analyze the performance of this scheme, the proposed scheme is compared with other previous visible watermarking schemes, and experiment results show that the presented one has better visual effect and less distortion.

Road detection on aerial and remote sensing vague images is a hard task. In this paper, an automatic road detection method for the vague images is proposed. The method firstly uses an improved MSR algorithm to enhance image, and it automatically takes different scales in different image regions, based on the image depths obtained by the dark channel prior algorithm. Then the enhanced image is roughly segmented on the principle of the local gray scale consistency, in that, an eight-neighborhood template is considered as a processing unit in which a threshold is utilized for all the neighboring pixels of the detecting pixel. Finally, the Dempster-Shafer (D-S) evidence for road features is applied to finalize road tracing in the binary image, where, the road features include length, width, aspect ratio and fullness rate, all the parameters are obtained in the least external rectangle of a road segment, and then the detected roads are regulated. In experiments, 300 vague road images were selected for testing, by comparing to several traditional algorithms and recent semantic methods, the testing results show that the new method is satisfactory, and the detection accuracy is up to 89%.

In image inpainting, the identification and inpainting of local detail features and the preservation of global features are crucial. Models based on fractional-order partial differential equations exhibit rich evolutionary behaviors. These behaviors enable them to effectively comprehend image details. Additionally, these models possess a certain sharpening effect in image inpainting. However, they are also prone to issues such as inaccurate identification of large-scale features and over-sharpening. The optimal control model proposed in this paper uses the total variation energy of image global features as the objective function. It also employs the spatial fractional-order vector-valued Cahn–Hilliard equation as the constraint, aiming to achieve a balanced effect between local detail restoration and preservation of global features. The paper aims to optimize the objective function by designing numerical computation schemes for non-convex constraint conditions using L 2 gradient flow, H - 1 gradient flow, and convex splitting. The Split Bregman method is used for further optimization, and a dynamic grayscale adjustment strategy is introduced to maintain grayscale discrimination capability while enhancing computational efficiency. Numerical experiments demonstrate that the new model exhibits certain advantages over other inpainting methods in terms of PSNR values. It also shows strong competitiveness in terms of SSIM values, especially in severely damaged images where it demonstrates greater stability. Compared to traditional fractional-order equation models, the proposed model captures global features and incorporates the dynamic grayscale adjustment strategy, resulting in significantly reduced computation time.