Morphological Image Processing Operations Research Articles

Cotton Yield Estimation From Aerial Imagery Using Machine Learning Approaches.

Estimation of cotton yield before harvest offers many benefits to breeding programs, researchers and producers. Remote sensing enables efficient and consistent estimation of cotton yields, as opposed to traditional field measurements and surveys. The overall goal of this study was to develop a data processing pipeline to perform fast and accurate pre-harvest yield predictions of cotton breeding fields from aerial imagery using machine learning techniques. By using only a single plot image extracted from an orthomosaic map, a Support Vector Machine (SVM) classifier with four selected features was trained to identify the cotton pixels present in each plot image. The SVM classifier achieved an accuracy of 89%, a precision of 86%, a recall of 75%, and an F1-score of 80% at recognizing cotton pixels. After performing morphological image processing operations and applying a connected components algorithm, the classified cotton pixels were clustered to predict the number of cotton bolls at the plot level. Our model fitted the ground truth counts with an R2 value of 0.93, a normalized root mean squared error of 0.07, and a mean absolute percentage error of 13.7%. This study demonstrates that aerial imagery with machine learning techniques can be a reliable, efficient, and effective tool for pre-harvest cotton yield prediction.

An Iterative Morphological Fuzzy Rule Based Classifier For Moving Vehicle Recognition

A novel fuzzy rule based classifier to detect and recognize the type of moving vehicle from video frame using iterative morphological image processing operations is presented in this research work. A traffic video for a time period is captured, converted into still frames, pre-processed by iterative morphological filter, foreground objects are extracted by Background Subtractiontechnique,boundaries of the vehicles are extracted by morphological operation and the detected vehicles are isolated by Bounding Box method. Fuzzy Rule based classifier is constructed to categorize and recognize the vehicles into different types(Car, Bike, Bus, Container& Truck) based on the structural features Height, Width and Area of the bounding boxes. Finally the proposed method is evaluated with the classification metrics confusion matrix, precision and accuracy and the experimental results show that the performance of the proposed system goes beyond that of the existing video-based vehicle classification techniques yielding 84% of accuracy.

Spicules and downflows in the solar chromosphere

Context. High-speed downflows have been observed in the solar transition region (TR) and lower corona for many decades. Despite their abundance, it has been hard to find signatures of such downflows in the solar chromosphere. Aims. In this work, we target an enhanced network region which shows ample occurrences of rapid spicular downflows in the Hα spectral line, which could potentially be linked to high-speed TR downflowing counterparts. Methods. We used the k-means algorithm to classify the spectral profiles of on-disk spicules in Hα and Ca II K data observed from the Swedish 1 m Solar Telescope and employed an automated detection method based on advanced morphological image processing operations to detect such downflowing features, in conjunction with rapid blue-shifted and red-shifted excursions (RBEs and RREs). Results. We report the existence of a new category of RREs (termed as downflowing RRE) for the first time that, contrary to earlier interpretation, are associated with chromospheric field aligned downflows moving toward the strong magnetic field regions. Statistical analysis performed on nearly 20 000 RBEs and 15 000 RREs (including the downflowing counterparts), which were detected in our 97 min long dataset, shows that the downflowing RREs are very similar to RBEs and RREs except for their oppositely directed plane-of-sky motion. Furthermore, we also find that RBEs, RREs, and downflowing RREs can be represented by a wide range of spectral profiles with varying Doppler offsets, and Hα line core widths, both along and perpendicular to the spicule axis, that causes them to be associated with multiple substructures which evolve together. Conclusions. We speculate that these rapid plasma downflows could well be the chromospheric counterparts of the commonly observed TR downflows.

Multispectral to Panchromatic Image Fusion Based on Morphological Extended-Half-Gradient

Pansharpening refers to the fusion of remotely sensed multispectral and panchromatic images which are characterized by different levels of spectral–spatial resolutions and acquired for the same location by optical remote sensing satellite sensors. In this paper, we propose a pansharpening algorithm based on morphological extended-half-gradient. Popular quality metrics employing two assessment methods, namely reduced resolution assessment and full resolution assessment, are used for performance measurement. For validating the efficiency of the proposed algorithm, we compare its performance with that of morphological half-gradient-based fusion procedure and a few other popular image fusion algorithms. We also propose the best possible bias factor in the formulation of the proposed algorithm by experimentation on varied values. Three real datasets acquired by WorldView-4, SPOT-6 and QuickBird-2 are used in the experimentation. The results affirm that the proposed algorithm offers improved image fusion than using the morphological half-gradient. This successful demonstration of the proposed algorithm proves the potential of morphological image processing operations to be useful in the achievement of efficient pansharpening. This work also underlines the need for more computational efficiency in image fusion.

Numerical investigation of the impact of washcoat distribution on the filtration performance of gasoline particulate filters

A three-step numerical model sheds light on the impact of three-way catalyst washcoat distribution within the cordierite porous wall of a clean gasoline particulate filter on its filtration performance. The model relies on (1) the numerical reconstruction of porous wall sections with various washcoat distributions and coating amounts, generated using a novel set of morphological image processing operations applied on segmented X-ray computed tomography data, (2) the computation of the flow field using the lattice Boltzmann method, and (3) the prediction of soot capture efficiency by solving the Langevin equation. The impact of washcoat distribution and amount on the pressure drop, permeability, filtration efficiency, and filter quality factor is systematically investigated. For a non-uniform washcoat distribution, an unexpected decrease in filtration efficiency with an increase in washcoat amount is explained and this highlights the complexity of the effects generated by the deposition of washcoat within the porous wall of the filter.

Measurement of smaller colon polyp in CT colonography images using morphological image processing.

Automated measurement of the size and shape of colon polyps is one of the challenges in Computed tomography colonography (CTC). The objective of this retrospective study was to improve the sensitivity and specificity of smaller polyp measurement in CTC using image processing techniques. A domain knowledge-based method has been implemented with hybrid method of colon segmentation, morphological image processing operators for detecting the colonic structures, and the decision-making system for delineating the smaller polyp-based on a priori knowledge. The method was applied on 45 CTC dataset. The key finding was that the smaller polyps were accurately measured. In addition to 6-9mm range, polyps of even <5mm were also detected. The results were validated qualitatively and quantitatively using both 2D MPR and 3D view. Implementation was done on a high-performance computer with parallel processing. It takes [Formula: see text]min for measuring the smaller polyp in a dataset of 500 CTC images. With this method, [Formula: see text] and [Formula: see text] were achieved. The domain-based approach with morphological image processing has given good results. The smaller polyps were measured accurately which helps in making right clinical decisions. Qualitatively and quantitatively the results were acceptable when compared to the ground truth at [Formula: see text].

Fault detection and classification in automated assembly machines using machine vision

Automated assembly machines operate continuously to achieve high production rates. Continuous operation increases the potential for faults such as jams, missing parts, and electromechanical failures of subsystems. The goal of this research project was to develop and validate a machine vision inspection (MVI) system to detect and classify multiple faults using a single camera as a sensor. An industrial automated O-ring assembly machine that places O-rings on to continuously moving plastic carriers at a rate of over 100 assemblies per minute was modified to serve as the test apparatus. An industrial camera with LED panel lights for illumination was used to acquire videos of the machine’s operation. A programmable logic controller (PLC) with a human-machine interface (HMI) allowed for the generation of faults in a controlled fashion. Three MVI methods, based on computer vision techniques available in the literature, were developed for this application. The methods used features extracted from the videos to classify the machine’s condition. The first method was based on Gaussian mixture models (GMMs); the second method used an optical flow approach; and the third method was based on running average and morphological image processing operations. In order to provide a single metric to quantify relative performance, a machine vision performance index (MVPI) was developed with five measures of performance: accuracy, processing time, speed of response, robustness against noise, and ease of tuning. The MVPI for the three MVI methods is reported along with the significance of the results.

Image Processing in Intelligent Medical Robotic Systems

The paper deals with the use of high-performance computing systems with the parallel-operation architecture in intelligent medical systems, such as medical robotic systems, based on a computer vision system, is an automatic control system with the strict requirements, such as high reliability, accuracy and speed of performance. It shows the basic block-diagram of an automatic control system based on a computer vision system. The author considers the possibility of using a reconfigurable computing environment in such systems. The design principles of the reconfigurable computing environment allows to improve a reliability, accuracy and performance of whole system many times. The article contains the brief overview and the theory of the research, demonstrates the use of reconfigurable computing environments for the image preprocessing, namely morphological image processing operations. Present results of the successful simulation of the reconfigurable computing environment and implementation of the morphological image processing operations on the test image in the MATLAB Simulink.

Automated Management of Pothole Related Disasters Using Image Processing and Geotagging

Potholes though seem inconsequential, may cause accidents resulting in loss of human life. In this paper, we present an automated system to efficiently manage the potholes in a ward by deploying geotagging and image processing techniques that overcomes the drawbacks associated with the existing survey-oriented systems. Image processing is used for identification of target pothole regions in the 2D images using edge detection and morphological image processing operations. A method is developed to accurately estimate the dimensions of the potholes from their images, analyze their area and depth, estimate the quantity of filling material required and therefore enabling pothole attendance on a priority basis. This will further enable the government official to have a fully automated system for effectively managing pothole related disasters.

A Hybrid Approach for Separation of the Strong Adhesion Macrophages

The traditional macrophages investigation with microscope is labor-intensive and inefficient. The different degree of adhesion and irregular shapes of macrophages challenge the current image segmentation algorithms, which directly influence the follow-up macrophages analysis. In this paper, we present a hybrid approach for separation of the strong adhesion macrophages, which combines top/bottom hat transformation, gray-scale morphological image processing operations, H-minima transform, watershed algorithm and threshold transform. With strong adhesion macrophages images, we quantitatively demonstrate the performance of our method and provide the comparisons with existing methods. The results show that the hybrid approach can reduce the influence of noise, and obtain precise separation results, because it includes both location and edge information.

Motility flow and growth-cone navigation analysis during in vitro neuronal development by long-term bright-field imaging

A long-term live-imaging workstation to follow the development of cultured neurons during the first few days in vitro (DIV) is developed. In order to monitor neuronal polarization and axonal growth by live imaging, we built a micro-incubator system that provides stable temperature, pH, and osmolarity in the culture dish under the microscope, while preserving environment sterility. We are able to image living neurons at 2 DIVs for 48 h with a temporal resolution of one frame for every 2 min. The main features of this system are its ability to adapt to every cell-culture support, to integrate in any optical microscope, because of the relatively small dimensions (9.5×6.5×2.5 cm) and low weight of the system (<200 g), and to monitor the physiological parameters in situ. Moreover, we developed an image-analysis algorithm to quantify the cell motility, in order to characterize its complex temporal-spatial pattern. The algorithm applies morphological image processing operations on the temporal variations occurring in the inspected region of interest. Here, it is used to automatically detect cellular motility in three distinct morphological regions of the neurons: around the soma, along the neurites, and in the growth cone.

An adaptive fuzzy-genetic algorithm approach for building detection using high-resolution satellite images

We propose a new approach for building detection using high-resolution satellite imagery based on an adaptive fuzzy-genetic algorithm. This novel approach improves object detection accuracy by reducing the premature convergence problem encountered when using genetic algorithms. We integrate the fundamental image processing operators with genetic algorithm concepts such as population, chromosome, gene, crossover and mutation. To initiate the approach, training samples are selected that represent the specified two feature classes, in this case “building” and “non-building”. The image processing operations are carried out on a chromosome-by-chromosome basis to reveal the attribute planes. These planes are then reduced to one hyperplane that is optimal for discriminating between the specified feature classes. For each chromosome, the fitness values are calculated through the analysis of detection and mis-detection rates. This analysis is followed by genetic algorithm operations such as selection, crossover and mutation. At the end of each generation cycle, the adaptive-fuzzy module determines the new (adjusted) probabilities of crossover and mutation. This evolutionary process repeats until a specified number of generations has been reached. To enhance the detected building patches, morphological image processing operations are applied. The approach was tested on ten different test scenes of the Batikent district of the city of Ankara, Turkey using 1m resolution pan-sharpened IKONOS imagery. The kappa statistics computed for the proposed adaptive fuzzy-genetic algorithm approach were between 0.55 and 0.88. The extraction performance of the algorithm was better for urban and suburban buildings than for buildings in rural test scenes.

Retrieving the size of particles with rough and complex surfaces from two-dimensional scattering patterns

Scattered intensity measurement is a commonly used method for determining the size of small particles. However, it requires calibration and is subject to errors due to changes in incident irradiance or detector sensitivity. Analysis of two-dimensional scattering patterns offers an alternative approach. We test morphological image processing operations on patterns from a diverse range of particles with rough surfaces and/or complex structure, including mineral dust, spores, pollen, ice analogs and sphere clusters from 4 to 88μm in size. It is found that the median surface area of intensity peaks is the most robust measure, and it is inversely proportional to particle size. The trend holds well for most particle types, as long as substantial roughness or complexity is present. One important application of this technique is the sizing of atmospheric particles, such as ice crystals.

Retrieving the size of particles with rough surfaces from 2D scattering patterns

Normal 0 Scattered intensity measurement is a commonly used method for determining the size of small particles. However, it requires calibration and is subject to errors due to changes in incident irradiance or detector sensitivity. Analysis of two-dimensional scattering patterns offers an alternative approach. We test possible techniques, including morphological image processing operations, on a diverse range of particles. It is found that the average surface area of intensity peaks is inversely proportional to particle size.

Automatic Facial Feature Extraction and Expression Recognition based on Neural Network

In this paper, an approach to the problem of automatic facial feature extraction from a still frontal posed image and classification and recognition of facial expression and hence emotion and mood of a person is presented. Feed forward back propagation neural network is used as a classifier for classifying the expressions of supplied face into seven basic categories like surprise, neutral, sad, disgust, fear, happy and angry. For face portion segmentation and localization, morphological image processing operations are used. Permanent facial features like eyebrows, eyes, mouth and nose are extracted using SUSAN edge detection operator, facial geometry, edge projection analysis. Experiments are carried out on JAFFE facial expression database and gives better performance in terms of 100% accuracy for training set and 95.26% accuracy for test set.

Linear Extraction of Satellite Imageries using Mathematical Morphology

The methodology used in this paper is based on the use of morphological operators contained in toolbox of Mathematical Morphology developed in the software MATLAB. The application of routines in image processing is aimed initially to improve the visual quality of the features of interest in digital grayscale images, which will then afterwards be extracted. Increasingly seeking to get improvement in quality of the extracted feature, the image was binarized through the binary operator with threshold. Methodology adapted to skeleton scanned images using Mathematical Morphology from remotely sensed data is a challenging issue in the field of digital image processing. Image skeletonization is one of the many morphological image processing operations. skeletonization is very often an intermediate step towards object recognition, it should have a low computational cost. Literature about above said methodology is also mentioned in this paper. General Terms Dilation, erosion, gray scale, image analysis, mathematical morphology, opening, and thresholding.

Automated Analysis of in Situ Canopy Images for the Estimation of Forest Canopy Cover

Abstract Forest canopy cover is a commonly used ecological indicator variable, but its reliable estimation in the field with, e.g., the line intersect sampling method is laborious. One alternative for obtaining this information quickly is to sample a larger area of canopy from each prespecified location within a sample plot by using digital cameras and then use image analysis techniques to extract canopy cover from the images. The first aim of this study was to implement an automated method for the estimation of canopy cover from skyward-looking canopy images obtained with standard digital cameras. The second aim was to compare the estimates obtained with the cameras and the automated analysis technique to control values measured with a vertical tube by using two different plot types and a varying angular field of view (1–50°). The results indicate that the automated analysis technique, involving an automated image thresholding algorithm and morphological image processing operations to eliminate the effect of within-crown gaps, can reliably replace manual postprocessing. With the tested sampling schemes, a 40° angle of view yielded the best approximation of the control.

Real-time morphological pipeline architecture using high-capacity programmable logical devices

Mathematical morphology is a very important image analysis area that uses set theory tools to study shapes. The basic operations in mathematical morphology are dilation and erosion, and these can be used to construct more complex operations. Low-level image processing often uses dedicated computing hardware for repetitive processing over large data structures. High-capacity programmable logic devices (HCPLDs) have increasingly been used for the fast development of real-time image processing systems. In this paper we present a pipeline architecture, using high-capacity programmable logic devices, for real-time mathematical morphology operations. The developed architecture can process (512×512) pixel binary images and has flexible stages that can be reprogrammed according to the shape and size of the structuring elements used in the morphological operations. Tests performed over the architecture demonstrated that it performs well when compared to similar architectures and that it is an efficient choice for dedicated morphological image processing operations.

AUTOMATIC GENERATION OF 3D MACHINING SURFACES WITH TOOL COMPENSATION FROM GRAYLEVEL IMAGE MODELS

The paper presents a theoretical approach and implementing issue for automatic CNC toolpath generation using morphological image processing operators applied to depth map images. This method allows computing cutter compensation for various end mill shapes. Roughing is made using zigzag cuts with a flat end mill, and finishing is done with isoparametric toolpaths using various rounded end mills. An AI-based algorithm and image processing software create CNC machining instructions from depth map image models. The paper also describes a graphical user interface and includes experimental results.

Fingerprints thinning algorithm

During the last years, the authors are designing a high resolution fingerprint identification system based on minutiae extraction. In this sense, a fingerprint thinning algorithm by means of morphological image processing operations is presented, to improve the minutiae map involved. The first step, as in every biometric system, is to obtain an image of the user's fingerprint. After this, a pre-processing algorithm is applied which enables feature extraction steps to obtain the location and type of all minutiae; i.e., the discontinuities in the ridges and valleys of the fingerprint.