Traditional Image Processing Algorithms Research Articles

A method for cabbage root posture recognition based on YOLOv5s

Efficient, non-destructive cabbage harvesting is crucial for preserving its flavor and quality. Current cabbage harvesting mainly relies on mechanized automatic picking methods. However, a notable deficiency in most existing cabbage harvesting devices is the absence of a root posture recognition system to promptly adjust the root posture, consequently impacting the accuracy of root cutting during harvesting. To address this issue, this study introduces a cabbage root posture recognition method that combines deep learning with traditional image processing algorithms. Preliminary detection of the main root Region of Interest (ROI) areas of the cabbage is carried out through the YOLOv5s deep learning model. Subsequently, traditional image processing methods, the Graham algorithm, and the method of calculating the minimum circumscribed rectangle are employed to specifically detect the inclination angle of cabbage roots. This approach effectively addresses the difficulty in calculating the inclination angle of roots caused by occlusion from outer leaves. The results demonstrate that the precision and recall of this method are 98.7 % and 98.6 %, respectively, with an average absolute error of 0.80° and an average relative error of 1.34 % in posture. Using this method as a reference for mechanical harvesting can effectively mitigate cabbage damage rates.

Scaled aggregation operations over three-dimensional extended intuitionistic fuzzy index matrices

Modern image processing techniques are improving beyond old methods, which include advanced approaches, for example deep learning. Convolutional Neural Networks (CNNs) are excellent at automatic feature extraction, whereas Generative Adversarial Networks (GANs) produce realistic images. Transfer learning uses pre-trained models, whereas semantic segmentation identifies pixels in images. Super-resolution, style transfer, and attention mechanisms can increase the quality of images and understanding. Adversarial defenses address purposeful manipulations, while 3D image processing handles three-dimensional data. These advancements make use of improved computational power and massive datasets to revolutionize image processing capabilities. Traditional image processing algorithms frequently fail to handle the complex and multidimensional structure of color images, particularly when dealing with uncertainty and imprecision. In this study, the 3D-EIFIM frame work is extented and scaled aggregation operations 3D-EIFIM tailored for image data are proposed. By representing each pixel as an entry of 3D-EIFIM and applying aggregation techniques to enable more effective image analysis, manipulation, and enhancement. The practical implications of this research are significant, as it can lead to advancements in fields such as computer vision, medical imaging, and remote sensing.

Evaluation of the corneal topography based on deep learning.

The current study designed a unique type of corneal topography evaluation method based on deep learning and traditional image processing algorithms. The type of corneal topography of patients was evaluated through the segmentation of important medical zones and the calculation of relevant medical indicators of orthokeratology (OK) lenses. The clinical data of 1,302 myopic subjects was collected retrospectively. A series of neural network-based U-Net was used to segment the pupil and the treatment zone in the corneal topography, and the decentration, effective defocusing contact range, and other indicators were calculated according to the image processing algorithm. The type of corneal topography was evaluated according to the evaluation criteria given by the optometrist. Finally, the method described in this article was used to evaluate the type of corneal topography and compare it with the type classified by the optometrist. When the important medical zones in the corneal topography were segmented, the precision and recall of the treatment zone reached 0.9587 and 0.9459, respectively, and the precision and recall of the pupil reached 0.9771 and 0.9712. Finally, the method described in this article was used to evaluate the type of corneal topography. When the reviewed findings based on deep learning and image processing algorithms were compared to the type of corneal topography marked by the professional optometrist, they demonstrated high accuracy with more than 98%. The current study provided an effective and accurate deep learning algorithm to evaluate the type of corneal topography. The deep learning algorithm played an auxiliary role in the OK lens fitting, which could help optometrists select the parameters of OK lenses effectively.

Quantitative analysis of the effect of illumination variations on image processing in machine vision inspection

Abstract When unavoidable light, age, expression, and gesture changes occur, the machine vision detection accuracy will be greatly reduced, especially the light change impact on image processing. Combining the reflection equation and frequency domain analysis to construct a light change model based on spherical harmonic function and process the image from four directions, namely, digital image conversion, median filtering, sharpening processing, and image global segmentation. Aiming at the shortcomings of traditional image processing algorithms, the light compensation based on the improved pulse-coupled neural network model is proposed, and the model is used to analyze the impact of light changes on image processing. Despite the changes in light conditions, this algorithm maintains above 0.95 in each subset and has the best overall performance, as shown by the results. When the light intensity is less than 41.88kLux, using the optimal threshold for background segmentation of adult green peppers with respect to a fixed threshold T=0.3 results in a significant reduction of the background segmentation error. When the light intensity is greater than 41.88kLux, the superiority of the optimal threshold is not obvious. The results of this research not only have a significant role in promoting the development of the field of computer vision but also can provide people with convenient and efficient services in all aspects of social life.

Image feature extraction algorithm based on visual information

Abstract Vision is the main sensory organ for human beings to contact and understand the objective world. The results of various statistical data show that more than 60% of all ways for human beings to obtain external information are through the visual system. Vision is of great significance for human beings to obtain all kinds of information needed for survival, which is the most important sense of human beings. The rapid growth of computer technology, image processing, pattern recognition, and other disciplines have been widely applied. Traditional image processing algorithms have some limitations when dealing with complex images. To solve these problems, some scholars have proposed various new methods. Most of these methods are based on statistical models or artificial neural networks. Although they meet the requirements of modern computer vision systems for feature extraction algorithms with high accuracy, high speed, and low complexity, these algorithms still have many shortcomings. For example, many researchers have used different methods for feature extraction and segmentation to get better segmentation results. Scale-invariant feature transform (SIFT) is a description used in the field of image processing. This description has scale invariance and can detect key points in the image. It is a local feature descriptor. A sparse coding algorithm is an unsupervised learning method, which is used to find a set of “super complete” basis vectors to represent sample data more efficiently. Therefore, combining SIFT and sparse coding, this article proposed an image feature extraction algorithm based on visual information to extract image features. The results showed that the feature extraction time of X algorithm for different targets was within 0.5 s when the other conditions were the same. The feature matching time was within 1 s, and the correct matching rate was more than 90%. The feature extraction time of Y algorithm for different targets was within 2 s. The feature matching time was within 3 s, and the correct matching rate was between 80 and 90%, indicating that the recognition effect of X algorithm was better than that of Y algorithm. It indicates the positive relationship between visual information and image feature extraction algorithm.

Image Processing Algorithm Design of Agricultural Internet of Things Platform Based on Big Data Analysis

Abstract To solve the problems of low signal to noise ratio (SNR), poor processing effect, and long processing times for traditional image processing algorithms, an image processing algorithm for the agricultural Internet of Things platform based on big data analysis was designed. The big data analysis method was used for agricultural low-quality image recognition, and the Internet of Things platform uses sparse representation and a complete dictionary of low-quality image denoising processing with an improved histogram equalization method to enhance the image denoising result and get a high-quality image. Through this, the image processing algorithm design of the agricultural Internet of Things platform was completed. Experimental results show that the algorithm has high SNR and a good image processing effect, and the image processing time is always below 0.7 s, which meets research expectations.

Plume detection and emission estimate for biomass burning plumes from TROPOMI carbon monoxide observations using APE v1.1

Abstract. This paper presents the automated plume detection and emission estimation algorithm (APE), developed to detect CO plumes from isolated biomass burning events and to quantify the corresponding CO emission rate. APE uses the CO product of the Tropospheric Monitoring Instrument (TROPOMI) on board the Copernicus Sentinel-5 Precursor (S5P) satellite, launched in 2017, and collocated active fire data from the Visible Infrared Imaging Radiometer Suite (VIIRS), the latter flying 3 min ahead of S5P. After identifying appropriate fire events using VIIRS data, an automated plume detection algorithm based on traditional image processing algorithms selects plumes for further data interpretation. The approach is based on thresholds optimized for data over the United States in September 2020. Subsequently, the CO emission rate is estimated using the cross-sectional flux method, which requires horizontal wind fields at the plume height. Three different plume heights were considered, and the ECMWF Reanalysis v5 (ERA5) data were used to compute emissions. A varying plume height in the downwind direction based on three-dimensional Lagrangian simulation was considered appropriate. APE is verified for observations over Australia and Siberia. For all fire sources identified by VIIRS, only 16 % of the data corresponded to clear-sky TROPOMI CO data with plume signature. Furthermore, the quality filters of APE resulted in emission estimations for 26 % of the TROPOMI CO data with plume signatures. Visual filtering of the APE's output showed a true-positive confidence level of 97.7 %. Finally, we provide an estimate of the emission uncertainties. The greatest contribution of error comes from the uncertainty in Global Fire Assimilation System (GFAS) injection height that leads to emission errors <100 %, followed by systematic errors in the ERA5 wind data. The assumption of constant emission during plume formation and spatial under-sampling of CO column concentration by TROPOMI yields an error of <20 %. The randomized errors from the ensemble ERA5 wind data are found to be less than 20 % for 97 % of the cases.

Fully automatic deep learning-based lung parenchyma segmentation and boundary correction in thoracic CT scans.

The proposed work aims to develop an algorithm to precisely segment the lung parenchyma in thoracic CT scans. To achieve this goal, the proposed technique utilized a combination of deep learning and traditional image processing algorithms. The initial step utilized a trained convolutional neural network (CNN) to generate preliminary lung masks, followed by the proposed post-processing algorithm for lung boundary correction. First, the proposed method trained an improved 2D U-Net CNN model with Inception-ResNet-v2 as its backbone. The model was trained on 32 CT scans from two different sources: one from the VESSEL12 grand challenge and the other from AIIMS Delhi. Further, the model's performance was evaluated on a test dataset of 16 CT scans with juxta-pleural nodules obtained from AIIMS Delhi and the LUNA16 challenge. The model's performance was assessed using evaluation metrics such as average volumetric dice coefficient (DSCavg), average IoU score (IoUavg), and average F1 score (F1avg). Finally, the proposed post-processing algorithm was implemented to eliminate false positives from the model's prediction and to include juxta-pleural nodules in the final lung masks. The trained model reported a DSCavg of 0.9791 ± 0.008, IoUavg of 0.9624 ± 0.007, and F1avg of 0.9792 ± 0.004 on the test dataset. Applying the post-processing algorithm to the predicted lung masks obtained a DSCavg of 0.9713 ± 0.007, IoUavg of 0.9486 ± 0.007, and F1avg of 0.9701 ± 0.008. The post-processing algorithm successfully included juxta-pleural nodules in the final lung mask. Using a CNN model, the proposed method for lung parenchyma segmentation produced precise segmentation results. Furthermore, the post-processing algorithm addressed false positives and negatives in the model's predictions. Overall, the proposed approach demonstrated promising results for lung parenchyma segmentation. The method has the potential to be valuable in the advancement of computer-aided diagnosis (CAD) systems for automatic nodule detection.

A Recipe for Unbiased Background Modeling in Deep Wide-field Astronomical Images

Unbiased sky background modeling is crucial for the analysis of deep wide-field images, but it remains a major challenge in low surface brightness astronomy. Traditional image processing algorithms are often designed to produce artificially flat backgrounds, erasing astrophysically meaningful structures. In this paper, we present three ideas that can be combined to produce wide-field astronomical data that preserve accurate representations of the background sky: (1) Use of all-sky infrared/submillimeter data to remove the large-scale time-varying components while leaving the scattered light from Galactic cirrus intact, with the assumptions of (a) the underlying background has little power on small scales, and (b) the Galactic cirrus in the field is optically thin on large scales; (2) Censoring of frames contaminated by anomalously prominent wings in the wide-angle point-spread function; and (3) Incorporation of spatial covariance in image stacking that controls the local background consistency. We demonstrate these methods using example data sets obtained with the Dragonfly Telephoto Array, but these general techniques are prospective to be applied to improve sky models in data obtained from other wide-field imaging surveys, including those from the upcoming Vera Rubin Telescope.

A Deep Neural Network-Based Intelligent Detection Model for Manufacturing Defects of Automobile Parts

Image defect detection of casting parts is a key part of the production process in the machinery manufacturing industry. The traditional methods are ineffective because traditional computer image processing methods require a large number of manual features to be set artificially, and the detection time is too long. In order to save human resources and improve the efficiency of image defect detection, this paper proposes a deep learning-based defect detection method for automobile parts. This paper selects EfficientNetB0 as the backbone framework of the target detection network, which significantly reduces the memory usage of the model and shortens the model inference time, while improving the model detection accuracy. Facing the problem of small samples of defect image dataset, we analyze the image characteristics of the dataset and introduce shape transformation and scale scaling as the basic online data enhancement method according to the industrial field image projection law. Then, it is expected to combine the traditional image processing algorithms according to the characteristics of casting parts with different depth distribution and multiple morphological changes, and develop a special image defect data enhancement method. This further improves the performance of the model and increases the detection accuracy of the algorithm by 22.3% without increasing the data.

Impact of Training Data, Ground Truth and Shape Variability in the Deep Learning-Based Semantic Segmentation of HeLa Cells Observed with Electron Microscopy.

This paper investigates the impact of the amount of training data and the shape variability on the segmentation provided by the deep learning architecture U-Net. Further, the correctness of ground truth (GT) was also evaluated. The input data consisted of a three-dimensional set of images of HeLa cells observed with an electron microscope with dimensions 8192×8192×517. From there, a smaller region of interest (ROI) of 2000×2000×300 was cropped and manually delineated to obtain the ground truth necessary for a quantitative evaluation. A qualitative evaluation was performed on the 8192×8192 slices due to the lack of ground truth. Pairs of patches of data and labels for the classes nucleus, nuclear envelope, cell and background were generated to train U-Net architectures from scratch. Several training strategies were followed, and the results were compared against a traditional image processing algorithm. The correctness of GT, that is, the inclusion of one or more nuclei within the region of interest was also evaluated. The impact of the extent of training data was evaluated by comparing results from 36,000 pairs of data and label patches extracted from the odd slices in the central region, to 135,000 patches obtained from every other slice in the set. Then, 135,000 patches from several cells from the 8192×8192 slices were generated automatically using the image processing algorithm. Finally, the two sets of 135,000 pairs were combined to train once more with 270,000 pairs. As would be expected, the accuracy and Jaccard similarity index improved as the number of pairs increased for the ROI. This was also observed qualitatively for the 8192×8192 slices. When the 8192×8192 slices were segmented with U-Nets trained with 135,000 pairs, the architecture trained with automatically generated pairs provided better results than the architecture trained with the pairs from the manually segmented ground truths. This suggests that the pairs that were extracted automatically from many cells provided a better representation of the four classes of the various cells in the 8192×8192 slice than those pairs that were manually segmented from a single cell. Finally, the two sets of 135,000 pairs were combined, and the U-Net trained with these provided the best results.

An Approach for Classification of Alzheimer’s Disease Using Deep Neural Network and Brain Magnetic Resonance Imaging (MRI)

Alzheimer’s disease (AD) is a deadly cognitive condition in which people develop severe dementia symptoms. Neurologists commonly use a series of physical and mental tests to diagnose AD that may not always be effective. Damage to brain cells is the most significant physical change in AD. Proper analysis of brain images may assist in the identification of crucial bio-markers for the disease. Because the development of brain cells is so intricate, traditional image processing algorithms sometimes fail to perceive important bio-markers. The deep neural network (DNN) is a machine learning technique that helps specialists in making appropriate decisions. In this work, we used brain magnetic resonance scans to implement some commonly used DNN models for AD classification. According to the classification results, where the average of multiple metrics is observed, which includes accuracy, precision, recall, and an F1 score, it is found that the DenseNet-121 model achieved the best performance (86.55%). Since DenseNet-121 is a computationally expensive model, we proposed a hybrid technique incorporating LeNet and AlexNet that is light weight and also capable of outperforming DenseNet. To extract important features, we replaced the traditional convolution Layers with three parallel small filters (1×1,3×3, and 5×5). The model functions effectively, with an overall performance rate of 93.58%. Mathematically, it is observed that the proposed model generates significantly fewer convolutional parameters, resulting in a lightweight model that is computationally effective.

Neural Image Style Transfer Using Modified Histogram Matching

Neural Image Style Transfer is an algorithm which was first introduced in 2016 for transferring the style of an image using a CNN. Over the years, a large portion of the research has been devoted in balancing the trade-off between time taken by the algorithm to process the images and the quality of the results generated. In addition, the application of traditional image processing algorithms has been limited. In this paper, we aim to introduce a modification to the traditional method by utilizing a localized histogram matching algorithm combined with Contrast Limited Adaptive Histogram Equalization (CLAHE). The results show that the quality of style transferred and the colour of images obtained by this method is much better than the traditional method for the same number of iterations.

Design and Implementation of 3-D Measurement Method for Container Handling Target

In the process of automatic container terminal loading and unloading, the three-dimensional attitude of the container affects the security of loading and unloading operations, so the three-dimensional attitude positioning of the container is very important. In this paper, a visual non-contact measurement method is used to realize the real-time orientation of the three-dimensional attitude of the container. First, the container corner is coarsely positioned by a small-scale deep learning network. Secondly, the precise position of the container keyhole is obtained by the secondary positioning of the container corner through the traditional image processing algorithm, and the container posture is measured in three dimensions by combining the physical motion model of the container during loading and unloading. After testing, unlike previous measurement methods, the measurement accuracy of this method met the requirements of automatic loading and unloading of container terminals, and the measurement time met the requirements of real-time measurement.

Tongue Contour Tracking and Segmentation in Lingual Ultrasound for Speech Recognition: A Review.

Lingual ultrasound imaging is essential in linguistic research and speech recognition. It has been used widely in different applications as visual feedback to enhance language learning for non-native speakers, study speech-related disorders and remediation, articulation research and analysis, swallowing study, tongue 3D modelling, and silent speech interface. This article provides a comparative analysis and review based on quantitative and qualitative criteria of the two main streams of tongue contour segmentation from ultrasound images. The first stream utilizes traditional computer vision and image processing algorithms for tongue segmentation. The second stream uses machine and deep learning algorithms for tongue segmentation. The results show that tongue tracking using machine learning-based techniques is superior to traditional techniques, considering the performance and algorithm generalization ability. Meanwhile, traditional techniques are helpful for implementing interactive image segmentation to extract valuable features during training and postprocessing. We recommend using a hybrid approach to combine machine learning and traditional techniques to implement a real-time tongue segmentation tool.

A vision-based container position measuring system for ARMG

This paper analyzes the problems of existing container positioning methods and proposed a vision-based container position measuring system to provide precise parameters for container lifting operations. This system uses camera to get container information, then detects container corners by the method that combined with convolutional neural network and traditional image processing algorithm. This system is used to provide specific parameters associated with container lifting operation. In the first of detection, it uses the modified SSD (Single Shot MultiBox Detector) neural network to detect the coarse position of container corners in the image, second stage detection uses the usage of rectangle fitting to detect the precise position of corner holes in the coarse position. In the last step the offset distance and deflection angle were calculated by precise corner position. The experiment shows the detection rate of the proposed system reach 94%. The positioning errors between 14.3 and 19.6 mm for a frame rate of 10 fps are obtained.

基于YOLOv4模型的树上遮挡橙果的识别研究

For accurate recognition of orange fruit targets, a detection algorithm based on YOLOv4 was applied in this research. The results showed that AP (average precision) of YOLOv4 had reached 98.17%, 2.14% and 2.67% respectively higher than SSD and Faster RCNN while recognition rate of traditional image processing algorithms was merely 54.94%. Additionally, the extent of occlusion was proved to have obvious influences on the accuracy of orange detection. The accuracy on slight occlusion conditions appeared to be higher than that on serious occlusion conditions. Generally, YOLOv4 detection algorithm showed its feasibility and superiority on fruit detection in the complex natural environment.

DisCnet: Pixel segmentation based on discrete features

In the process of pixel-level semantic segmentation tasks, traditional image processing algorithms will suffer from the working mechanism of convolutional layers and pooling layers, resulting in the loss of some features, which now leads to inaccurate semantic segmentation accuracy. For such problems, we design a discrete pooling layer by analyzing the distribution and statistical properties of discrete data. Compared with the traditional pooling layer, the discrete pooling layer can not only preserve the spatial information of features, avoid the loss of features, but also can efficiently improve the accurate segmentation of instance images. Then, based on the discrete pooling layer, we design DisCnet in combination with the convolutional layer. Finally, DisCnet is compared with some state-of-the-art algorithms under the Cityscapes dataset. Experiments demonstrate that DisCnet achieves excellent results in both accuracy and speed.

A method for sperm activity analysis based on feature point detection network in deep learning

Sperm motility is an important index to evaluate semen quality. Computer-assisted sperm analysis (CASA) is based on the sperm image, through the image-processing algorithm to detect the position of the sperm target and track tracking, so as to judge the sperm activity. Because of the small and dense sperm targets in sperm images, traditional image-processing algorithms take a long time to detect sperm targets, while target-detection algorithms based on the deep learning have a lot of missed detection problems in the process of sperm target detection. In order to accurately and efficiently analyze sperm activity in the sperm image sequence, this article proposes a sperm activity analysis method based on the deep learning. First, the sperm position is detected through the deep learning feature point detection network based on the improved SuperPoint, then the multi-sperm target tracking is carried out through SORT and the sperm motion trajectory is drawn, and at last the sperm survival is judged through the sperm trajectory to realize the analysis of sperm activity. The experimental results show that this method can effectively analyze the sperm activity in the sperm image sequence. At the same time, the average detection speed of the sperm target detection method in the detection process is 65fps, and the detection accuracy is 92%.

A defect detection method for topological phononic materials based on few-shot learning

Topological phononic materials have been widely used in many fields, such as topological antennas, asymmetric waveguides, and noise insulation. However, due to the limitations of the manufacturing process, topological protection is vulnerable to some severe defects that may affect the application effect. Therefore, the quality inspection of topological materials is essential to ensure reliable results. Due to the low contrast and irregularity of defects and the similarity of topological phononics, they are difficult to recognize by traditional image processing algorithms, so manual detection is still mainstream at present. But manual detection requires experienced inspectors, which is expensive and time-consuming. In addition, topological materials are expensive to produce, and there is no large publicly available dataset, but deep learning usually relies on large datasets for training. To solve the above problems, we propose an automatic deep learning topology structure defect detection method (ADLTSDM) in this work, which could classify not only the structure of topological materials but also detect the defects of topological phononics based on a small dataset. ADLTSDM exploits the prior knowledge of the topological material structure and achieves an augmentation factor of more than 100 times through the random and fixed interval screenshot algorithm, thus enabling the training of deep neural networks with only two raw data. For defect detection, ADLTSDM has an accuracy of more than 97% and improves detection speed by more than 38% compared with manual detection. For structure classification, ADLTSDM can achieve an accuracy of over 99% and seven times faster speed compared with manual classification. Besides, the detection standard of ADLTSDM is unified, so the accuracy will not be affected by the experience of the inspectors, which has more potential in high-throughput industrial applications.