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The Edge detection is a customarily task. Edge detection is the main task to perform as it gives clear information about the images. It is a tremendous device in photograph processing gadgets and computer imaginative and prescient. Previous research has been done on moving window approach and genetic algorithms. In this research paper new technique, Bacterial Foraging Optimization (BFO) is applied which is galvanized through the social foraging conduct of Escherichia coli (E.coli). The Bacterial Foraging Optimization (BFO) has been practice by analysts for clarifying real world optimization problems arising in different areas of engineering and application domains, due to its efficiency. The Brightness preserving bi-histogram equalization (BHEE) is another technique that is used for edge enhancement. The BFO is applied on the low level characteristics on the images to find the pixels of natural images and the values of F-measures, recall(r) and precision (p) are calculated and compared with the previous technique. The enhancement technique i.e. BBHE is carried out to improve the information about the pictures.

The article deals with the issue of stabilizing the frame position of the shot in long time astrophotography. The authors described this issue from the point of view of guiding a telescope following of the sky, and also in terms of the possibility of supporting this process with digital signal processing using paraunitary filters. The procedure for obtaining a 2D paraunitary system from an initial (preset) filter is presented. An attempt is made to verify the data that can be obtained from individual outputs of paraunitary systems. Several example filters were shown and their operation was tested on a selected image of a real astronomical object from the telescope's guide camera. Based on the output images obtained (from three paraunit systems), the possibility of their further use in practice was evaluated. [ABSTRACT FROM AUTHOR]

The mandatory migration from fossil to renewable energy sources requires the characterization of new alternative fuels. One important step in fuel characterization is the test in optical engines, which allows the morphological characterization of flames. This analysis requires the post treatment of images by using segmentation. In many cases, an automatic threshold presents shortcomings as the flames may present different regions with variable luminosity, as also reflections from valves and cylinder liner. Consequently, a time-consuming manual image processing is required and, therefore, an automatic procedure would be welcome. The use of deep learning techniques for image segmentation is a promising alternative for such task, which has showed excellent results in several applications. In this study, two different models were trained to identify flames in images obtained from an optical engine operating at various conditions. The dataset used to train the models was generated by using images from tests with several types of fuels and combustion modes. The effects of image resolution and the generalization capabilities for different fuels and combustion operation were investigated. After analyzing the results, the use of deep learning methods to identify and characterize flames was validated as a mean for improving processing time. [ABSTRACT FROM AUTHOR]

Urine color analysis is one of the most helpful indicators of health status, and any changes in urine color might be a symptom of serious disease, dehydration of the body, or caused by drugs. To get better assistance for urine color detection in the proposed system, a urine color automatic identification has been developed based on computer vision. The proposed system uses a web camera to capture an image in real-time, analyze it, and then classify the color of urine by using the random forest (RF) algorithm and show the result via the Graphical User Interface (GUI). In addition, the proposed system can send the results to the mobile phone of the patient or care provider by using an Arduino microcontroller and GSM module. Moreover, sending a voice message about the color of urine is related to pathological conditions. The results showed that the proposed system has high accuracy (approximately about 97%) in detecting urine color under different light conditions, with low cost, short time, and easy implementation. In the comparison with the current methods the proposed system has maximum accuracy and minimum error rate. This methodology can pave the way for an additional case study in medical applications, particularly in diagnosis, and patient health monitoring. [ABSTRACT FROM AUTHOR]

Urine color analysis is one of the most helpful indicators of health status, and any changes in urine color might be a symptom of serious disease, dehydration of the body, or caused by drugs. To get better assistance for urine color detection in the proposed system, a urine color automatic identification has been developed based on computer vision. The proposed system uses a web camera to capture an image in real-time, analyze it, and then classify the color of urine by using the random forest (RF) algorithm and show the result via the Graphical User Interface (GUI). In addition, the proposed system can send the results to the mobile phone of the patient or care provider by using an Arduino microcontroller and GSM module. Moreover, sending a voice message about the color of urine is related to pathological conditions. The results showed that the proposed system has high accuracy (approximately about 97%) in detecting urine color under different light conditions, with low cost, short time, and easy implementation. In the comparison with the current methods the proposed system has maximum accuracy and minimum error rate. This methodology can pave the way for an additional case study in medical applications, particularly in diagnosis, and patient health monitoring.

Disturbances in balance control lead to movement impairment and severe discomfort, dizziness, vertigo and may also lead to serious accidents. It is important to monitor the level of balance in order to determine the risk of a fall and to evaluate progress during treatment. Some solutions exist, but they are generally restricted to indoor environments. We propose and evaluate a system, based on accelerometers and support vector machines, that indicates the user’s postural balance variation which can be used in indoor and outdoor environments. For the training phase of the system, we used the accelerometer signals acquired from a single subject under monitored conditions of balance and intentional imbalance, and used the scores provided by the SWAY®software for establishing the reference target values. Based on these targets, we trained a support vector machine to classify the signal into n levels of balance and later evaluated the performance using cross validation by random resampling. We also developed a support vector machine approach for estimating the center of pressure, by using as reference targets the results from a force platform. For validation, we performed experiments with a subject who was performing determined movements. Later other experiments were executed, so the different centers of pressure could be computed by our system and compared to the results from the force platform. We also performed tests with a dummy and a John Doe doll, in order to observe the system’s behavior in the presence of a sudden drop or a lack of balance. The results show that the system can classify the acquired signals into two to seven levels of balance, with significant accuracy, and was also able to infer the centroid of each center of pressure region with an error lower than 0.9 cm. The tests performed with the dolls show that the system is able to distinguish between the conditions of a sudden drop and of a recovery of balance after losing one’s balance. The results suggest that the system can be used to detect variations in balance and, therefore, to indicate the risk of a fall even in outdoor environments.

The Edge detection is a customarily task. Edge detection is the main task to perform as it gives clear information about the images. It is a tremendous device in photograph processing gadgets and computer imaginative and prescient. Previous research has been done on moving window approach and genetic algorithms. In this research paper new technique, Bacterial Foraging Optimization (BFO) is applied which is galvanized through the social foraging conduct of Escherichia coli (E.coli). The Bacterial Foraging Optimization (BFO) has been practice by analysts for clarifying real world optimization problems arising in different areas of engineering and application domains, due to its efficiency. The Brightness preserving bi-histogram equalization (BHEE) is another technique that is used for edge enhancement. The BFO is applied on the low level characteristics on the images to find the pixels of natural images and the values of F-measures, recall(r) and precision (p) are calculated and compared with the previous technique. The enhancement technique i.e. BBHE is carried out to improve the information about the pictures. [ABSTRACT FROM AUTHOR]

An efficient method for weed detection and the precise generation of spraying maps is crucial to optimizing weed management strategies and minimizing the costs associated with herbicide use. This study presents a method that leverages low-resolution UAV images to accurately detect and map weeds in sugarcane fields. The proposed approach integrates RGB images, vegetation indices and the HSV colour space, enhancing segmentation through histogram equalization (HE) and object-based image analysis (OBIA). The models developed in this study demonstrate exceptional performance in weed detection, with the most suitable dataset, achieving a detection capability of 95.78% for Broad-leaved Weeds (BLW) and the highest accuracy of 94.45% for Narrow-leaved Weeds (NLW). When considering multiple targets such as BLW, NLW, soil and sugarcane, the models exhibit a detection accuracy of 89.69%. Furthermore, the precision spraying maps generated by the coverage model method (CM) demonstrate remarkable accuracy, reaching 97.50% for weed control using agricultural drones. This method offers an efficient, cost-effective, and timely solution for precise weed detection, leading to improved weed control outcomes by enabling the selection of appropriate chemical substances tailored to each weed species. It reduces repetitive spraying costs and minimises chemical usage through spot spraying. • Developed a method to use low-res UAV imaging for precise weed mapping in sugarcane fields. • Applied histogram equalization to enhance OBIA's accuracy in identifying similar-looking weeds. • Optimized weed type detection using RGB, vegetation indices, and HSV in a machine learning. • Achieved 97.50% accuracy in creating weed spraying maps. • The proposed method offers cost-effectiveness, precision, and rapid implementation. [ABSTRACT FROM AUTHOR]

We are interested in repairing blurry images degraded by additive noise. We present a new filter for image enhancement based on the combination of a shock filter with a fourth order diffusion equation. The algorithm that we propose eliminates not only the blurry but also the noise. The control of the diffusion weight and that of the enhancement of the model is done by a parameter. After experimentation on some test images and on some real images of degraded old documents, the results obtained are satisfactory compared to certain models in the literature.

Image filters has taken attention last few years due to its importance in terms of image processing and applications. Applying image filters on images elements can be affected by the values of image parameters, which resulted from any processing tasks. By applying image filters, we can extent the image processing methods to present higher productivity. In this paper, we compare the effect of applying five image filters on the statistical distribution, which are (Laplacian, Differentiation, LOG, Sharpening, and Gaussian). Our method has been applied for a number of textural images (water texture, wool texture, and wood texture), the images has been divided for three groups according to the texture type. The result of our method proved that some of image filter affects the statistical distribution of image elements which are: (Differentiation, LOG, Sharpening) while other do not affect the parameter distribution (Laplacian, Gaussian). We evaluate our method by calculating the value of (MSE). The method opens the door in front of extending such technique with other image processing aspects. [ABSTRACT FROM AUTHOR]

The complex working environment of picking robots has limited the picking speed and equipment memory resources in the intelligent harvesting of Lingwu long jujubes. Therefore, it is necessary to meet the requirements of lighter network structure and higher detection accuracy, particularly for the visual recognition system. A pre-train model has widely been loaded almost all the object detection at present, due to high initialization performance and convergence speed. However, two challenges are still remained: 1) The network structure cannot be changed on the limited memory resources of the device; 2) There may be great differences between the ImageNet dataset and the dataset to be trained, leading to the low training effect. Taking the SSD model as the basic framework, this research aims to propose a lightweight object detection for the images of Lingwu long jujubes. The excellent performance was achieved without loading the pre-train model. Firstly, data augmentation is performed on the collected 1 000 images to obtain 5 000 images. Data augmentation operations include random cropping, random vertical or horizontal flipping, random brightness adjustment, random contrast adjustment, and random saturation adjustment. Secondly, the Lingwu long jujube dataset was established, including 3 500 training images and 1 500 test images. The resolution of images consisted of 3 016×4 032, 4 068×3 456, and 2 448×3 264. The models of smartphones for image acquisition included HUAWEI TRT-AL00A, Vivo Y79A, and Xiaomi 2014501. The images were uniformly scaled to the resolution of 300×300, in order to meet the input requirements of image size in the SSD object detection. Data augmentation included random cropping, random vertical or horizontal flipping, as well as random adjustment of brightness, contrast, and saturation. The format of the PASCAL VOC dataset was also adopted. Labelling software was used to label the images, and then the marked images were stored in the label folder in XML format. Secondly, the improved DenseNet was utilized the Convolutional Block Attention Modules and two dense blocks with convolution groups of 6 and 8. Taking the improved DenseNet as the backbone network, the improved SSD model was obtained to combine with the multi-level fusion structure, where the first three additional layers were replaced in the SSD model with the Inception module. In the improved SSD model without loading the pre-train model, the mAP was 96.60%, the detection speed was 28.05 frames/s, and the number of parameters was 1.99×106, particularly 2.02 percentage points and 0.05 percentage points higher than that of the SSD and SSD model (pre-train), respectively. Correspondingly, the parameter of the improved SSD model was 11.14×106 lower than the SSD model, fully meeting the requirements of the lightweight network without loading the pre-train model. This finding can provide a strong visual technical support for the intelligent harvesting of Lingwu long jujubes, even medical and multispectral images detection tasks. [ABSTRACT FROM AUTHOR]

Microalgae are widely distributed in the ocean, and some species are prone to causing harmful algal blooms that threaten the marine ecological environment. At present, microscopy is the most common method for microalgae analysis, and the combination of computer vision and microscopy is the mainstream trend in algae morphology classification. However, most methods only focus on the species and quantities of algae, without obtaining contour information that can further analyze their survival status and biomass. Therefore, this article proposes a convolutional neural network (AlgaeSeg-YOLO), which can recognize the species, quantities, and contours of algae. Firstly, a dataset of microalgae microscopic images in microfluidic chips was constructed, which includes a total of 2799 annotated images of 6 harmful microalgae, including 3916 instances. Secondly, the AlgaeSeg-YOLO was constructed with stronger feature fusion and pixel-level spatial information modeling capabilities based on YOLOv8n-seg. Finally, compared to other common methods, the experimental results show that the mAP (mean Average Precision) of AlgaeSeg-YOLO reaches 95.61 %, which is 1.64 %, 1.76 %, 5.28 %, 3.34 %, and 5.39 % higher than YOLOv8n-seg, YOLOv5n-seg, Mask R-CNN, Cascade Mask R-CNN, and SOLOv2, respectively, achieving real-time and accurate segmentation of microalgae in complex backgrounds. Meanwhile, the parameters and computation remain relatively low. This work helps to achieve fully automated analysis of microalgae, reduce labor costs, and provide technical support for long-term monitoring of the ecological environment and further research. • Identification and segmentation of microalgae using deep learning • Microalgae instance segmentation algorithm based on microscopic images • Rapid identification and segmentation of microalgae in complex background • Feature fusion network optimization of microalgae instance segmentation model [ABSTRACT FROM AUTHOR]

Artificial intelligence is a general term that means to accomplish a task mainly by a computer, with the least human beings participation, and it is widely accepted as the invention of robots. With the development of this new technology, artificial intelligence has been one of the most influential information technology revolutions. We searched these English-language studies relative to ophthalmology published on PubMed and Springer databases. The application of artificial intelligence in ophthalmology mainly concentrates on the diseases with a high incidence, such as diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract and few with retinal vein occlusion. According to the above studies, we conclude that the sensitivity of detection and accuracy for proliferative diabetic retinopathy ranged from 75% to 91.7%, for non-proliferative diabetic retinopathy ranged from 75% to 94.7%, for age-related macular degeneration it ranged from 75% to 100%, for retinopathy of prematurity ranged over 95%, for retinal vein occlusion just one study reported ranged over 97%, for glaucoma ranged 63.7% to 93.1%, and for cataract it achieved a more than 70% similarity against clinical grading.

Introduction Peripheral arterial disease (PAD) is a fairly common degenerative vascular condition in diabetic patients that leads to inadequate blood flow (BF), this disease is mainly due to atherosclerosis that causes chronic narrowing of arteries, which can precipitate acute thrombotic events. In patients with diabetes, atherosclerosis is the main reason for reducing life expectancy, as long as diabetic nephropathy and retinopathy are the largest contributors to end-stage renal disease and blindness, respectively. Objective This was an assessment of dilatation of the blood vessels on diabetic patients vs. healthy volunteers by using digital processing of imaging's. Materials and Methods The study subject was ultrasound imaging processing of blood vessels dilation on low extremities of diabetic patients, the results were compared with ultrasound images of healthy subjects. Results The digital images processing suggests that there is a significant difference among images experimental of the diabetic group and healthy volunteers' images, the control group. Discussion The digital imaging processing performed in the Matlab platform is an adequate procedure for blood vessels dilation analysis of the ultrasound images taken from the lower extremities in diabetic patients. [ABSTRACT FROM AUTHOR]

In this paper, we propose an images encryption algorithm based on the quaternion multiplication and the XOR function. The proposed algorithm processes the image in 32 quaternions i.e. block of 32 × 4. For each quaternion, the algorithm exor the ith image quaternion and the ith key quaternion. The key quaternion is changed in each block using the quaternion multiplication. The randomness of the proposed algorithm is evaluated by diehard package. Several other analyses are provided to evaluate the performances of this encryption scheme. These analyses demonstrate that the proposed algorithm is with high security level and fast run speed and can be competitive with some other recently proposed image encryption schemes. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights: • Unsupervised vineyards detection from 3D point-cloud map. • Vineyards features evaluation and mapping from 3D point-cloud map. • Unmanned aerial vehicles (UAV) imagery for vineyards 3D point-cloud modelling. Abstract An effective management of precision viticulture processes relies on robust crop monitoring procedures and, in the near future, to autonomous machine for automatic site-specific crop managing. In this context, the exact detection of vineyards from 3D point-cloud maps, generated from unmanned aerial vehicles (UAV) multispectral imagery, will play a crucial role, e.g. both for achieve enhanced remotely sensed data and to manage path and operation of unmanned vehicles. In this paper, an innovative unsupervised algorithm for vineyard detection and vine-rows features evaluation, based on 3D point-cloud maps processing, is presented. The main results are the automatic detection of the vineyards and the local evaluation of vine rows orientation and of inter-rows spacing. The overall point-cloud processing algorithm can be divided into three mains steps: (1) precise local terrain surface and height evaluation of each point of the cloud, (2) point-cloud scouting and scoring procedure on the basis of a new vineyard likelihood measure, and, finally, (3) detection of vineyard areas and local features evaluation. The algorithm was found to be efficient and robust: reliable results were obtained even in the presence of dense inter-row grassing, many missing plants and steep terrain slopes. Performances of the algorithm were evaluated on vineyard maps at different phenological phase and growth stages. The effectiveness of the developed algorithm does not rely on the presence of rectilinear vine rows, being also able to detect vineyards with curvilinear vine row layouts. [ABSTRACT FROM AUTHOR]

The fast progress in research and development of multifunctional, distributed sensor networks has brought challenges in processing data from a large number of sensors. Using deep learning methods such as convolutional neural networks (CNN), it is possible to build smarter systems to forecasting future situations as well as precisely classify large amounts of data from sensors. Multi-sensor data from atmospheric pollutants measurements that involves five criteria, with the underlying analytic model unknown, need to be categorized, so do the Diabetic Retinopathy (DR) fundus images dataset. In this work, we created automatic classifiers based on a deep convolutional neural network (CNN) with two models, a simpler feedforward model with dual modules and an Inception Resnet v2 model, and various structural tweaks for classifying the data from the two tasks. For segregating multi-sensor data, we trained a deep CNN-based classifier on an image dataset extracted from the data by a novel image generating method. We created two deepened and one reductive feedforward network for DR phase classification. The validation accuracies and visualization results show that increasing deep CNN structure depth or kernels number in convolutional layers will not indefinitely improve the classification quality and that a more sophisticated model does not necessarily achieve higher performance when training datasets are quantitatively limited, while increasing training image resolution can induce higher classification accuracies for trained CNNs. The methodology aims at providing support for devising classification networks powering intelligent sensors.

The research aim is apply algorithm for finding the number of regions to determine the largest number of faces of candidates in the video or detection, which appear in the video depending on the color techniques in the video and to fragment the skin and reject the largest number of regions that are non skin, and reduce the false faces. The algorithm depends on the usage of technique to detect human face and select it in the video after segment it to set of color images, the technique includes two steps for image processing The first is for building a model for skin color, and an outline of the distribution of color and isolate areas of skin and non skin within the image, then identify areas of the skin. The second step includes the template matching. The results proved high accuracy by nearly 85% in the discrimination of the faces in the video and enclosed in geometric and stored in a new video, the algorithms is programmed in MATLAB 7.10.0 (2010) programming language which has many efficient functions in image processing.

Cardiovascular disease is the leading cause of death worldwide. Therefore, techniques for improving diagnosis and treatment in this field have become key areas for research. In particular, approaches for tissue image processing may support education system and medical practice. In this paper, an approach to automatic recognition and classification of fundamental tissues, using morphological information is presented. Taking a 40× or 10× histological image as input, three clusters are created with the k-means algorithm using a structural tensor and the red and the green channels. Loose connective tissue, light regions and cell nuclei are recognised on 40× images. Then, the cell nuclei's features – shape and spatial projection – and light regions are used to recognise and classify epithelial cells and tissue into flat, cubic and cylindrical. In a similar way, light regions, loose connective and muscle tissues are recognised on 10× images. Finally, the tissue's function and composition are used to refine muscle tissue recognition. Experimental validation is then carried out by histologist following expert criteria, along with manually annotated images that are used as a ground-truth. The results revealed that the proposed approach classified the fundamental tissues in a similar way to the conventional method employed by histologists. The proposed automatic recognition approach provides for epithelial tissues a sensitivity of 0.79 for cubic, 0.85 for cylindrical and 0.91 for flat. Furthermore, the experts gave our method an average score of 4.85 out of 5 in the recognition of loose connective tissue and 4.82 out of 5 for muscle tissue recognition. [ABSTRACT FROM AUTHOR]

The present review deploys a bibliographic review on the different topics that intervene in the determination of the red color in digital images. These topics are: how to manage the color in digital images, what are the modes of color, what are the digital images and relation to its format; what is the digital images processing, the alternations that are presented in the digital images and its correction, as well as the color segmentation and the determination. [ABSTRACT FROM AUTHOR]

The reflectance of agricultural cultures and other terrestrial surface "targets" is an intrinsic parameter of these targets, so in many situations, it must be used instead of the values of "gray levels" that is found in the satellite images. In order to get reflectance values, it is necessary to eliminate the atmospheric interference and to make a set of calculations that uses sensor parameters and information regarding the original image. The automation of this procedure has the advantage to speed up the process and to reduce the possibility of errors during calculations. The objective of this paper is to present an electronic spreadsheet that simplifies and automatizes the transformation of the digital numbers of the TM/Landsat-5 and ETM+/Landsat-7 images into reflectance. The method employed for atmospheric correction was the dark object subtraction (DOS). The electronic spreadsheet described here is freely available to users and can be downloaded at the following website: http://www.dsr.inpe.br/Calculo_Reflectancia.xls.

One of the most important task in the field of mobile robotics is obstacle detection. To solve this task, computer vision has been used often, especially monocular vision. This is due to the inherently complexity of stereo vision systems and the increasing development of the research that uses a single camera to detect obstacles. Computer vision and image processing algorithms for obstacles detection require multiple parameters that need to be adjusted to work efficiently according to the characteristics of the robot and the conditions of the environment in which it operates. In the present work, a method for the optimum selection of the parameters of this kind of algorithm for a certain environment is proposed. To achieve that, the obstacle detection problem was modeled as an optimization problem. Besides, an explanation of two of these algorithms based on monocular vision are given. These are used for the validation of the given method. For the solution of the given problem, obtained results with different metaheuristics are included. Finally, the obtained results from using these techniques in different environments are compared. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of an effective methodology to determine the optimum levels of independent variables leading to maximize stability of O/W emulsions containing Arabic gum, as a natural emulsifier and stabilizer. Response surface methodology (RSM) was employed to determine the effect of Arabic gum content (2%, 5%, and 8% (w/w)), homogenization time (5, 12.5, and 20 min) and storage temperature (4, 22, and 40 °C). Image processing was used to determine emulsion stability based on responses including creaming index, centrifugal stability, viscosity, color parameters, and D 32 and D 43 indices. For each response, a second-order polynomial model with high coefficient of determination ( R 2 ) values ranging from 0.95 to 0.989 was developed using multiple linear regression analysis. The optimization results showed that the overall optimum region with the highest stability was found to be at the combined levels of 5.81% (w/w) Arabic gum content, 5 min homogenization time, and 22 °C for storage temperature. [ABSTRACT FROM AUTHOR]