Efficient Image Processing Algorithms Research Articles

Quantifying Nesting Behavior Metrics of Broiler Breeder Hens with Computationally Efficient Image Processing Algorithms and Big Data Analytics

Quantifying Nesting Behavior Metrics of Broiler Breeder Hens with Computationally Efficient Image Processing Algorithms and Big Data Analytics

Detecting Counterfeit Currency with Image Processing

Detecting Counterfeit currency with Image Processing focuses on leveraging image processing techniques to identify counterfeit currency. Currency plays a crucial role in economic transactions, functioning as a means of trade, standard measure of value, and reservoir of wealth. Ensuring the integrity of currency is crucial for maintaining trust in financial systems, preventing economic disruptions, and protecting individuals and businesses from financial losses. The need for currency detection arises in the situation of counterfeit activities, which pose serious threats to the stability of economy. Counterfeit currency can lead to financial fraud, loss of confidence in monetary systems, and can negatively impact businesses and individuals. By employing efficient image processing algorithms, this paper aims to enhance the accuracy and efficiency of counterfeit currency detection, providing a robust tool for financial institutions, businesses, and law enforcement agencies to safeguard against economic threats.

Plant Leaf Disease Detection Using Efficient Image Processing and Machine Learning Algorithms

India is often described as a country of villages, where a majority of the population depends on agriculture for their livelihood. The landscape of Indian agriculture is approximately 159.7 million hectares. Agriculture plays a pivotal role in India's Gross Domestic Product (GDP), accounting for about 18% of the nation's economic output. Diseases and pests can have detrimental effects on crops, leading to reduced yields. These challenges can include the spread of plant diseases, infestations by insects or other pests, and the overall degradation of crop health. Early detection of diseases in crops is crucial for several reasons. Detecting diseases at an early stage allows for prompt intervention, such as applying appropriate pesticides or taking preventive measures. The main aim of this study is to develop a highly effective method for plant leaf disease detection using computer vision techniques. Here, leaf disease detection comprises histogram equalization, denoising, image color threshold masking, feature descriptors such as Haralick textures, Hu moments, and color histograms to extract the salient features of leaf images. These features are then used to classify the images by training Logistic Regression, Linear Discriminant Analysis, K-nearest neighbor, decision tree, Random Forest, and Support Vector Machine algorithms using K-fold validation. K-fold validation is used to separate the validation samples from the training samples, and the K indicates the number of times this is repeated for the generalization. The training and validation processes are performed in two approaches. The first approach uses default hyperparameters with segmented and non-segmented images. In the second approach, all hyperparameters of the models are optimized to train segmented datasets. The classification accuracy improved by 2.19% by utilizing segmentation and hyperparameter tuning further improved by 0.48%. The highest average classification accuracy of 97.92% is achieved using the Random Forest classifier to classify 40 classes of 10 different plant species. Accurate detection of plant disease leads to the sustained growth of plants throughout the growing span of the plants.

Computationally efficient low-power sigma delta modulation-based image processing algorithm

Digital Image Processing has dominated Digital Signal Processing at the cost of more memory, resources, and high computational power. In image processing, filtering transformations and other operations need complex multiplications, and the multiplier is one of the most resources consuming elements. Recently, mitigating the multiplier complexity in the digital signal processing (DSP) algorithms sigma-delta modulation based general purpose and adaptive DSP algorithms are developed in MATLAB and compared with its counterpart multi-bit algorithms for functionality and area-performance-power in FPGA. The contemporary multiplier algorithms are also optimized to overcome the multiplier complexity challenge as computation becomes simple and fast. This paper extends the reported work by investigating the sigma-delta modulation approaches for developing a computationally efficient low-power image processing algorithm. The proposed model is designed, developed, and simulated in MATLAB. The simulation results are analyzed using SNR, MSE, and Peak SNR. The simulation results show that the proposed system can better mitigate the noise effect, making it robust for noisy environment.

P‐29: Noise reduction and defect detection algorithm in white‐light scanning interferometry for display panel inspection

White‐light scanning interferometry (WSI) has been widely used for the inspection of various display panels by 3D surface imaging from the micrometer scale to the nanometer scale. However, WSI often fails to sense light reflected from towering defects due to diffused reflection, represented by near‐zero heights, called black spot (b‐spot). It is challenging to detect the b‐spot defect especially in display pad area with varying heights. In addition, vibration of WSI facility hinders a defect spot due to harsh noise amplification. To resolve the issues, we proposed a novel and efficient image processing algorithm which consists of five steps. The first step is image denoising by median filtering with rectangle kernels. The second is pad area preprocessing to avoid misdetection. The third is to identify the presence of b‐spot defect using curvature of 1D projection array of an image. The fourth is defect detection through minimum value finding method. The last is Laplace fitting to infer the height of towering b‐spot defect. We conducted an experimental study using WSI raw data of defective display panels to evaluate the defect detection rate and the reliability of inferred height. We also calculated the correlation between the inferred height by Laplace fitting and measured height by atomic force microscopy (AFM) as a gold standard. We have shown that the proposed method can effectively detect a towering b‐spot defect position with much improved detection rate and infer the height with high precision.

Development of a wide-field EMCCD camera-based alpha-particle imaging system

We demonstrate a wide-field alpha-particle imaging system based on the detection of alpha-induced scintillation light from ZnS:Ag screen using an EMCCD camera. We develop an efficient image processing algorithm to remove cosmic and thermal noise and to locate alpha particle interactions in EMCCD images. This algorithm is particularly suited to imaging low-activity samples where long image acquisition periods entail a large number of frames with no detected alpha particle. Our system has a relative counting efficiency of ∼50 % compared to a PMT-based alpha counter. It can detect alpha particles at a resolution of <480 µm over an imaging area of ∼45 × 45 mm. We demonstrate the performance of this system by imaging a granite sample. Correlation between the resulting alpha image and the micro XRF-determined element map demonstrates that most of the alpha activity in our sample is held in dark-coloured Ba-rich veins and inclusions at mineral grain boundaries.

An Intelligent Tongue Diagnosis System via Deep Learning on the Android Platform.

To quickly and accurately identify the pathological features of the tongue, we developed an intelligent tongue diagnosis system that uses deep learning on a mobile terminal. We also propose an efficient and accurate tongue image processing algorithm framework to infer the category of the tongue. First, a software system integrating registration, login, account management, tongue image recognition, and doctor–patient dialogue was developed based on the Android platform. Then, the deep learning models, based on the official benchmark models, were trained by using the tongue image datasets. The tongue diagnosis algorithm framework includes the YOLOv5s6, U-Net, and MobileNetV3 networks, which are employed for tongue recognition, tongue region segmentation, and tongue feature classification (tooth marks, spots, and fissures), respectively. The experimental results demonstrate that the performance of the tongue diagnosis model was satisfying, and the accuracy of the final classification of tooth marks, spots, and fissures was 93.33%, 89.60%, and 97.67%, respectively. The construction of this system has a certain reference value for the objectification and intelligence of tongue diagnosis.

Multivariate neural network interpolation operators

In this paper, we introduce and study fractional neural network interpolation operators activated by a sigmoidal function belonging to the extended class of multivariate sigmoidal functions. We examine the rates of approximation by the operators in Lp-spaces using the modulus of continuity. Moreover, we give some special examples with graphics for the extended class of multivariate sigmoidal functions, and present some illustrative examples to demonstrate the interpolation quality of the operators based on various activation functions. Finally, as an application, we present an efficient image processing algorithm by the proposed neural network interpolation operators for both general and medical gray-level images.

Toward implementing efficient image processing algorithms on quantum computers

Toward implementing efficient image processing algorithms on quantum computers

Dizajn i razvoj sistema robotskih ruku za klasifikaciju i sortiranje primenom mašinskog vida

The main focus of this paper is to design and develop a system of two robot arms for classifying and sorting objects based on shape and size using machine vision. The system uses a low-cost and high-performance hierarchical control system including one master and two slaves. Each slave is a robot controller based on a microcontroller that receives commands from the master to control the robot arm independently. The master is an embedded computer used for image processing, kinematic calculations, and communication. A simple and efficient image processing algorithm is proposed that can be implemented in real-time, helping to shorten the time of the sorting process. The proposed method uses a series of algorithms including contour finding, border extraction, centroid algorithm, and shape threshold to recognize objects and eliminate noise. The 3D coordinates of objects are estimated just by solving a linear equation system. Movements of the robot's joints are planned to follow a trapezoidal profile with the acceleration/deceleration phase, thus helping the robots move smoothly and reduce vibration. Experimental evaluation reveals the effectiveness and accuracy of the robotic vision system in the sorting process. The system can be used in the industrial process to reduce the required time to achieve the task of the production line, leading to improve the performance of the production line.

Application of neuromorphic resistive random access memory in image processing

With the increasing demands for processing images and videos at edge terminals, complementary metal oxide semiconductor (CMOS) hardware systems based on conventional Von Neumann architectures are facing challenges in terms of energy consumption, speed, and footprint. Neuromorphic devices, including resistive random access memory with integrated storage-computation characteristic and optoelectronic resistive random access memory with highly integrated in-sensor computing characteristic, show great potential applications in image processing due to their high similarity to biological neural systems and advantages of high energy efficiency, high integration level, and wide bandwidth. These devices can be used not only to accelerate large numbers of computational tasks in conventional image preprocessing and higher-level image processing algorithms, but also to implement highly efficient biomimetic image processing algorithms. In this paper, we first introduce the state-of-the-art neuromorphic resistive random access memory and optoelectronic neuromorphic resistive random access memory, then review the hardware implementation of and challenges to image processing based on these devices, and finally provide perspectives of their future developments.

Yield estimation of citrus fruit using rapid image processing in natural background

Yield estimation can provide critical information for high-value crop growers. It helps them to plan and coordinate the logistics of harvesting operations. Most existing yield estimation techniques are based on manual sampling and statistical approaches, which are complex, labor-intensive, and time-consuming. In recent years, advances in image processing and the ubiquitous use of autonomous platforms, such as unmanned aerial platforms, have provided an opportunity for faster and more accurate yield estimation techniques for different crops. A new rapid, efficient, and accurate image processing algorithm was proposed in this study. The image segmentation technique was able to separate mature citrus trees and natural background in CIE (Commission International Eclairage) L*a*b* (i.e., Lab) color space. The effect of camera distance to object, affected resolution, and detection accuracy were evaluated and discussed. The results showed that the distance between 1.524 meters (5 feet) and 2.134 meters (7 feet) could provide high detection accuracy. The number of correctly counted fruit reached 91.69% at the average processing time of 1.1 s per picture for 132 pictures. A correlation coefficient (R2) of 0.98 was obtained between the citrus counting algorithm and counting performed through human observation of 66 sample trees. The comparative analysis of the proposed method showed a higher accuracy rate and robustness compared to other similar studies.

IoT Based an Efficient Image Processing Algorithm for Capture Image in Museum using Localization Service for User Involvement

The emergence of new technologies makes everything in today’s world to be smarter. The innovation focused toward IoT leads to the enhancement of the domestic environment. The smart environment provides advanced services to the user by allowing the environment address to user and user address to the world. The Museum is a place where work of art, scientific specimens or other object kept permanent and displayed. This paper, to design a smart museum that brings comfort to visitors through wearable device, interacting with IoT based smart environment to act as a museum guide. The localization service is provided by the Beacon device which is installed in the museum. The user has the wearable device, which capture the image of user interested artwork and the image processing algorithm analysis the captured image. Once the image is recognized, then the result is forwarded to the proceeding center along with this localization information. The proceeding center retrieves this content from outside world of the cloud and sent them to the user mobile devices. This system improves the experience of user by delivering the content to the user three times faster than the system not using localization service.

Vision-based Size Estimation and Centroid Positioning of Partially Occluded Fruits

The objective of this study was to propose a simple and efficient image processing algorithm for estimating the size and centroid of partially occluded round fruits. In the proposed method, the size and centroid of partially occluded fruit were estimated based on the mathematical properties of the arc-radius. The experimental tests were conducted in a laboratory with orange, Sunkist, apple, and tomato fruits by setting different occlusion conditions. The occlusion percentage was varied between 0% and 90%. The accuracy and processing time of the proposed method were compared with that of the widely-used conic-section circle fitting method. The results showed that the proposed method resulted in an overall accuracy of 95.1% and processing time of 0.66 s, as opposed to 60.2% and 0.68 s, respectively, using the conic-section equation. Compared with the conic-section equation, the proposed method was able to give a more robust prediction, even for low resolution images.

Study on image processing method to classify objects on dynamic conveyor

Controlling a robotic arms for applications such as detection and classification moving object using the vision sensor is a trend in the field of industrial robots. In particular, the vision sensor is the "eye" of the robot. To solve this problem, we need an efficient image processing algorithm for object identification to optimize the speed. Our classification principle based on the color of the object to be classified first, then separating contour to classify according to the shape of the object. In addition, our paper also propose a classification method that rarely mentioned in the relevant documents that classify based on object's characteristic. In fact, the product packaging not only has one color, but also includes complex color and patterns. Being able to classify these products shows the practicality of the proposed method. For complex colors and patterns object, the PCASIFT algorithm is useful, where SIFT extracts the local characteristics of the object and PCA reduces the number of dimensionality and retain only the best characteristics for identification. To picking object, a proposed design with the optimal requirements of picking order so that picking time is the shortest to minimize the delay for the next picking. The other outstanding advantage is a system of robotic arm to perform pick-up and sorting. This helps to verify good running algorithms in real time. The items are randomly released and the rotation of items is random. The speed of the conveyor is 5cm/s, an average of more than 2 seconds to pick up an object and robot arm processing precisely at high speed. The experimental results using camera Logitech C270, Yamaha Scara YK-400X robotic arm, LabVolt conveyor and OpenCV library are satisfactory, reliable and applicable.

Vision based volume estimation method for automatic mango grading system

In horticulture, measuring, sorting by shape, and determining the size and volume of fruits are all essential processes for meeting market quality standards and increasing market value. Fruit sorting and grading processes are very laborious and time-consuming task but machine vision-based fruit grading systems have the potential to replace human labour. However, a great challenge in vision-based fruit grading system is the recognition of different features such as shape, size, skin flaw and sometimes even three-dimensional (3D) shape. In this study, a simple and efficient image processing algorithm is proposed for estimating volume and 3D shape of mango fruit. The width and length of mango fruit are obtained from two-dimensional (2D) colour image. Then, fruit thickness is estimated based on light intensity distribution in 2D (top view) of mango fruit and maximum width–thickness correlation. The 3D shape of the mango fruit is then reconstructed. The accuracy of proposed method was compare with two existing volume prediction methods. Estimated volumes were compared with measured measurements using water-displacement method and the reconstructed 3D shapes were compared with measured structures of mango fruits. For a total of 150 mango samples, the results show that the proposed method gave an accuracy of 96.8% whilst the two other methods gave 91.7% and 91.5% respectively. The reconstructed mango shapes were therefore in close agreement with measured shapes.

An efficient algorithm for overlapping bubbles segmentation

Image processing is an effective method for characterizing various two-phase gas/liquid flow systems. However, bubbly flows at a high void fraction impose significant challenges such as diverse bubble shapes and sizes, large overlapping bubble clusters occurrence, as well as out-of-focus bubbles. This study describes an efficient multi-level image processing algorithm for highly overlapping bubbles recognition. The proposed approach performs mainly in three steps: overlapping bubbles classification, contour segmentation and arcs grouping for bubble reconstruction. In the first step, we classify bubbles in the image into a solitary bubble and overlapping bubbles. The purpose of the second step is overlapping bubbles segmentation. This step is performed in two subsequent steps: at first, we classify bubble clusters into touching and communicating bubbles. Then, the boundaries of communicating bubbles are split into segments based on concave point extraction. The last step in our algorithm addresses segments grouping to merge all contour segments that belong to the same bubble and circle/ellipse fitting to reconstruct the missing part of each bubble. An application of the proposed technique to computer generated and high-speed real air bubble images is used to assess our algorithm. The developed method provides an accurate and computationally effective way for overlapping bubbles segmentation. The accuracy rate of well segmented bubbles we achieved is greater than 90 % in all cases. Moreover, a computation time equal to 12 seconds for a typical image (1 Mpx, 150 overlapping bubbles) is reached.

VORONOI-BASED SKELETONIZATION ALGORITHM FOR SEGMENTING THE NETWORK OF BIOLOGICAL NEURONS

Context. The problem of automated processing and analysis of microscopy image data is of high relevance due to its extreme impact on the research and recent developments in the field of biology and medicine. Efficient image processing algorithms facilitate the development of new medical diagnostic tools and therapy processes. They help us to broaden our knowledge of underlying mechanisms and processes inside living organisms. The primal focus of this paper is the processing of the microscopy images of the biological neural network. This aims to facilitate further studies of biological neural network that would lead to the development of better methods for diagnosis, prevention and cure of the related deceases. Objective. The goal of the work is to development of an efficient image processing algorithm for segmenting the network of biological neurons based on the fluorescent microscopy image data. Method. The introduced algorithm for segmenting the network of biological neurons comprises several steps. Firstly, we apply image processing routines, which aim to enhance the quality of the image data and extract the contours of the biological neural network. Then we construct the skeleton of the network applying the Voronoi diagram for line segments extracted from the object’s contours. We employ Voronoi skeleton to identify the cellular somas and differentiate them from axons and dendrites. Results. The developed Voronoi-based algorithm allows us to segment individual neurons, localize their somas, axons and dendrites and extract graph representation of the neural network. The underlying Voronoi diagram data structure allows us to compute such graph efficiently in O(N log N) operations (where N is a number of contour points). The proposed segmentation method was implemented in the C++/Python programming language and evaluated on the fluorescent images from CellImageLibrary (CIL). Conclusions. The proposed segmentation method aims to facilitate studies of biological neural networks. It computes segmentation of the network of biological neurons in O(N log N) operations using the Voronoi diagram data structure. This data structure, in turn, gives us an attributed graph representation of the segmented network. Therefore, classical graph processing algorithms can be applied to analyze the neural and compute such network’s characteristics as the number of connections between individual neurons, the shortest signal transduction path between two neurons, etc.

Safety-Enhanced Model-Free Visual Servoing for Continuum Tubular Robots Through Singularity Avoidance in Confined Environments

Minimally invasive procedures have gained ever-increasing popularity due to their advantages of smaller incisions, faster recoveries, fewer complications, and reduced scarring to name a few. With the force exertion and curvilinear flexibility at their distal end effectors, the continuum tubular robots have the potential to perform robot-assisted trans-orifice minimally invasive surgery, such as transnasal and transoral operations. During these procedures, it is important and challenging for the continuum tubular robot to automatically adjust its pose according to the target surgical sites and compensate for undesired disturbance, such as respiratory motions. In this paper, a singularity-avoidance visual servoing algorithm based on Jacobian Optimization has been proposed to improve safety and control continuum tubular robots based on intra-operative visual feedback in confined environments. Without the prior knowledge of robot kinematics or hand-eye calibration between the robot and the endoscope, the proposed model-free eye-in-hand visual servoing technique is capable of accomplishing the targeting tasks by adopting the safety-enhanced singularity avoidance mechanism and an efficient image-processing algorithm. The multiple experiments, including simulations, experiments conducted in a confined environment, and cadaveric studies, have been implemented and demonstrated to illustrate the superiority of the proposed singularity-avoidance visual servoing method.

A Cross-border Detection Algorithm for Agricultural Spraying UAV

Abstract.In the agriculture sector, an essential task of spraying uncrewed aerial vehicles (UAVs) is to return as soon as the farmland border is reached. Initially, they need to be manually controlled which is a tedious job. This article presents an efficient image processing algorithm to automatically detect farmland borders based on the images received from the airborne cameras. First, the steerable-filter-based surrounded inhibition method was adopted to detect major borders, and then the images were thinned and binarized using non-maxima suppression (NMS) and hysteresis thresholding, respectively. Secondly, the results with different inhibition coefficients were fused, and the burrs were trimmed. Then the breakpoints were connected using a seed growing method. Finally, an improved Markov Random Field (MRF) model based on line segments was proposed to screen out fake borders. The result of classification depends on the maximum length of the retained segment. The experimental results and offline field tests showed that the proposed algorithm could accurately detect farm borders of different types from a complex farmland image. The average detection accuracy and completeness of the proposed algorithm is 85.6% and 83.6%, respectively. Compared with other methods, the proposed algorithm is highly reliable, robust, and scalable to other applications. Keywords: Agricultural spraying UAVs, Cross-border detection, Markov Random Field (MRF), Steerable filters, Surround suppression.