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Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mathematical foundations and algorithms for recognizing bichromatic two-dimensional graphic codes, regardless of their type (QR codes, DataMatrix, GridMatrix, etc.) are presented. The stages of achieving the result include detecting the code, localizing it within an arbitrary quadrilateral, transforming the quadrilateral to a canonical square, constructing a grid of elements (modules) of the square code, and filling it with a sequence of bits. It is shown that perspective transformation formulas make it possible to transform localized quadrangular regions to canonical squares with an acceptable error level for further processing. A flat grid of square code elements is formed based on the search for extrema of the derivatives of the pixel intensity distribution of the square image along the axes x and y. The algorithm for filling grid cells (code modules) with a sequence of zeros and ones uses information about the average intensity of each such cell. At the end of the paper, the algorithms are tested on a variety of real images of two-dimensional codes, and the limitations of the proposed algorithms are examined. [ABSTRACT FROM AUTHOR]

In the context of agricultural modernization and intelligentization, automated fruit recognition is of significance for improving harvest efficiency and reducing labor costs. The variety of fruits commonly planted in orchards and the fluctuations in market prices require farmers to adjust the types of crops they plant flexibly. However, the differences in size, shape, and color among different types of fruits make fruit recognition quite challenging. If each type of fruit requires a separate visual model, it becomes time-consuming and labor intensive to train and deploy these models, as well as increasing system complexity and maintenance costs. Therefore, developing a general visual model capable of recognizing multiple types of fruits has great application potential. Existing multi-fruit recognition methods mainly include traditional image processing techniques and deep learning models. Traditional methods perform poorly in dealing with complex backgrounds and diverse fruit morphologies, while current deep learning models may struggle to effectively capture and recognize targets of different scales. To address these challenges, this paper proposes a general fruit recognition model based on the Multi-Scale Attention Network (MSA-Net) and a Hough Transform localization compensation mechanism. By generating multi-scale feature maps through a multi-scale attention mechanism, the model enhances feature learning for fruits of different sizes. In addition, the Hough Transform ellipse detection compensation mechanism uses the shape features of fruits and combines them with MSA-Net recognition results to correct the initial positioning of spherical fruits and improve positioning accuracy. Experimental results show that the MSA-Net model achieves a precision of 97.56, a recall of 92.21, and an mAP@0.5 of 94.81 on a comprehensive dataset containing blueberries, lychees, strawberries, and tomatoes, demonstrating the ability to accurately recognize multiple types of fruits. Moreover, the introduction of the Hough Transform mechanism reduces the average localization error by 8.8 pixels and 3.5 pixels for fruit images at different distances, effectively improving the accuracy of fruit localization. [ABSTRACT FROM AUTHOR]

In this paper, a Hough transform method based on image gradient featured extraction is proposed to eliminate the ring artifacts of CT scanning system. Firstly, we use the gradient featured cluster analysis of the polar coordinate image to preliminary select the reconstructed image data. Then the Hough transform is adopted to detect the lines of the recombined data. When the artifacts in the original image are mapped out, the linear artifacts in the polar coordinate image are eliminated by interpolation. Then we can purposefully eliminate the ring artifacts in the reconstructed image. Simulation and experimental results show that with the image gradient featured cluster analysis, the effective number of detected artifact lines is greatly increased, the ring artifacts, arc artifacts and cluster artifacts in the central area of the reconstructed images are effectively removed, and the erroneous detection of lines by traditional Hough transform method is greatly reduced.

Doppler through-wall radar faces two challenges when locating targets concealed behind walls: (1) precisely determining the instantaneous frequency of the target within the frequency aliasing region and (2) reducing the impact of the wall on positioning by determining accurate wall parameters. To address these issues, this paper introduces a target localization algorithm that combines the Hough transform and support vector regression-BP neural network. First, a multiview fusion model framework is proposed for through-wall target detection, which enables the auxiliary estimation of wall parameter information by acquiring target positions from different perspectives. Second, a high-precision extraction and estimation algorithm for the instantaneous frequency curve of the target is proposed by combining the differential evolutionary algorithm and Chebyshev interpolation polynomials. Finally, a target motion trajectory compensation algorithm based on the Back Propagation (BP) neural network is proposed using the estimated wall parameter information, which suppresses the distorting effect of obstacles on target localization results and achieves the accurate localization of the target behind a wall. Experimental results indicate that compared with the conventional short-time Fourier method, the developed algorithm can accurately extract target instantaneous frequency curves within the time-frequency aliasing region. Moreover, it successfully reduces the impact caused by walls, facilitating the precise localization of multiple targets behind walls, and the overall localization accuracy is improved ～85%.

Corneal ulcer is a prevalent ocular condition that requires ophthalmologists to diagnose, assess, and monitor symptoms. During examination, ophthalmologists must identify the corneal ulcer area and evaluate its severity by manually comparing ocular staining images with severity indices. However, manual assessment is time-consuming and may provide inconsistent results. Variations can occur with repeated evaluations of the same images or with grading among different evaluators. To address this problem, we propose an automated corneal ulcer grading system for ocular staining images based on deep learning techniques and the Hough Circle Transform. The algorithm is structured into two components for cornea segmentation and corneal ulcer segmentation. Initially, we apply a deep learning method combined with the Hough Circle Transform to segment cornea areas. Subsequently, we develop the corneal ulcer segmentation model using deep learning methods. In this phase, the predicted cornea areas are utilized as masks for training the corneal ulcer segmentation models during the learning phase. Finally, this algorithm uses the results from these two components to determine two outputs: (1) the percentage of the ulcerated area on the cornea, and (2) the severity degree of the corneal ulcer based on the Type–Grade (TG) grading standard. These methodologies aim to enhance diagnostic efficiency across two key aspects: (1) ensuring consistency by delivering uniform and dependable results, and (2) enhancing robustness by effectively handling variations in eye size. In this research, our proposed method is evaluated using the SUSTech-SYSU public dataset, achieving an Intersection over Union of 89.23% for cornea segmentation and 82.94% for corneal ulcer segmentation, along with a Mean Absolute Error of 2.51% for determining the percentage of the ulcerated area on the cornea and an Accuracy of 86.15% for severity grading. [ABSTRACT FROM AUTHOR]

This work presents a novel approach to enhancing iris recognition systems through a two-module approach focusing on low-level image preprocessing techniques and advanced feature extraction. The primary contributions of this paper include: (i) the development of a robust preprocessing module utilizing the Canny algorithm for edge detection and the circle-based Hough transform for precise iris extraction, and (ii) the implementation of Binary Statistical Image Features (BSIF) with domain-specific filters trained on iris-specific data for improved biometric identification. By combining these advanced image preprocessing techniques, the proposed method addresses key challenges in iris recognition, such as occlusions, varying pigmentation, and textural diversity. Experimental results on the Human-inspired Domain-specific Binarized Image Features (HDBIF) Dataset, consisting of 1892 iris images, confirm the significant enhancements achieved. Moreover, this paper offers a comprehensive and reproducible research framework by providing source codes and access to the testing database through the Notre Dame University dataset website, thereby facilitating further application and study. Future research will focus on exploring adaptive algorithms and integrating machine learning techniques to improve performance across diverse and unpredictable real-world scenarios. [ABSTRACT FROM AUTHOR]

Automatic Modulation Format Identification(AMFI) is an important issue in Visible Light Communication (VLC). There are several factors that affect the performance of vehicular VLC (V-VLC) systems. To accomplish the required communication quality for different traffic scenarios, It needs adaptive modulation techniques. Adaptive modulation requires modulation format identification at the receiver to avoid the overhead required to determine the proper modulation format. In this paper, we propose an AMFI scheme based on Deep Learning (DL) for Infrastructure-to-Vehicle (I2V) VLC. Our scheme utilizes the Hough transform to project the constellation diagram of the received modulated signals onto another space to extract features efficiently. Hough transform is estimated for eight different VLC modulation formats, including Q/8/16-Phase Shift Keying (PSK) and 4/16/32/64-Quadrature Amplitude Modulation (QAM) at signal to noise ratio (SNR) ranging from 5 to 25 dB. Conventional classifiers such as Alexnet, Sequeeznet and Googlenet are used to perform the task of AMFI. A study of the impact of the weather conditions and the communication distance on the accuracy of the used classifiers is introduced. The obtained results prove that the proposed scheme can identify the modulation format with high classification accuracy up to 100% at lower SNR levels and different weather conditions. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper introduces an original method for effective spatial data processing, particularly important for land administration and real estate governance. This approach integrates Unmanned Aerial Vehicle (UAV) data acquisition and processing with Artificial Intelligence (AI) and Geometric Transformation algorithms. The results reveal that: (1) while the separate applications of YOLO and Hough Transform algorithms achieve building detection rates up to 77% and 83%, respectively, (2) a novel methodology is proposed to combine spatial data and assess their quality of the detected buildings by comparing the generated building polygons with existing cadastral maps. The evaluation uses a polygon-based comparison approach, which computes metrics such as Precision, Recall, F1-Score, and Accuracy based on the spatial relationships between predicted and reference building contours, (3) the weighted model showed about 7 % improvement in accuracy compared to cadastral data. This innovative approach substantially improves spatial data processing, aiding in implementing principles for real estate good governance and offering a valuable asset for various land administration applications.

Abstract More accurate methods for crop row detection benefit intelligent operation of agricultural machinery, especially avoiding mishandling or crushing crops. For achieving such a target, a traditional method combining the ExGR exponents, Otsu algorithm, Canny method, Hough transform and DBSCAN clustering analysis is proposed so that centerlines of crop rows can be detected effectively without manual intervention. Specifically, ExGR exponents are first adopted to gray green plants. The threshold of binarization will be further obtained by the Otsu algorithm. Further adopting the edge detection algorithm (Canny), edges of crops can be determined. Finally, combining the Hough transform and DBSCAN clustering analysis, the crop row detection is effectively available. Utilizing these methods, numerical simulation and their comparisons with existing methods are also achieved. For example, the Canny algorithm is relatively accurate than the Suzuki algorithm as well as their combinations with a geometric center extraction method if the density of weed is high. Compared with the K‐means clustering method, the DBSCAN algorithm is more suitable to characterize crop rows optimally in more complex conditions. It is validated from experiments that the combination of Canny algorithm, Hough transform and DBSCAN clustering is better than other mentioned traditional methods.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Medical images are important records to improve people's health care, and their security has become an important issue when they are transmitted through various media especially print and scan equipments. This paper proposes a digital watermarking method for medical images based on live QR code, DWT-SVD, Hough transform and bilinear interpolation. A live QR code including private information was embedded into the SVD blocks of LL3sub-band wavelet of medical image. Adjusting the embedding strength, a trade-off between invisibility and robustness was achieved. We printed and scanned the watermarked medical image with common printers and scanners and get the watermarked images after print-scanning. In extraction method, the Hough transform was used to detect the edge of the watermarked medical image and calculate the rotation angle and scaling ratio, and bilinear interpolation was used to correct geometric distortion of watermarked image. Then we extracted live QR code from corrected image successfully. Experimental results indicated that the proposed method provides sufficient security for medical images against print and scan attacks. [ABSTRACT FROM AUTHOR]

The high contrast degraded underwater images often demand extensive pre-processing to attain reliable information for object detection and pipeline tracking. The prototype system developed employs CLAHE, a variant of adaptive histogram equalization, with an adaptive clip limit based on entropy. To yield an enhanced image, CLAHE is applied to the L* channel alone, resulting in improved L- CLAHE and better results. Validation of the improved L-CLAHE method is carried out using real-life underwater images captured using DTG2 Pro ROV from Kochi backwaters based on PSNR, SSIM, EPI, and PFOM quality measures and outperforms the conventional histogram equalization and adaptive histogram equalization techniques. These images are subjected to intensification and filtering for further enhancement as well as object detection and pipeline tracking applications. The proposed algorithm is compared with CLAHE, MSRCR, and L-CIF methods. The improved L-CIF algorithm has a high entropy value compared to the existing algorithms while the EME metric has been evaluated close to unity indicating the improved performance of the fast-guided filter. The method developed also helps in improving the average pixel intensity of the input images. [ABSTRACT FROM AUTHOR]

The sea-sky line is an important basis for marine unmanned equipment to perceive the sea environment. However, due to the interference of many external factors, such as sea fog, illumination, and sea target occlusion, sea-sky line detection is challenging. Therefore, we present a texture feature-based local adaptive Otsu segmentation and Hough transform for sea-sky line detection. In this method, image texture features and weighted texture quantization are used to determine the sea-sky area. Based on this sea-sky area, a longitudinal block strategy is introduced, and a new adaptive Otsu segmentation method is applied to obtain the binary image of the sea-sky area. The obtained binary image is then post-processed to enhance the sea-sky line edge information. On this basis, a new adaptive Canny edge detector is applied, and the sea-sky line is extracted by the Hough transform. The experimental results show that the proposed sea-sky line detection method has high accuracy and robustness when handling images of complex marine environments. Compared with other algorithms, the detection error and the error standard deviation of the proposed method are relatively small, indicating that this method is more stable and accurate than other algorithms. [ABSTRACT FROM AUTHOR]

iRing is a processing program developed for the more convenient processing of two‐dimensional diffraction images collected by image detectors. During the calibration process, iRing uses the Hough transform to achieve automatic peak finding and calibration. The speed of calibration is greatly improved and the difficulty of calibration is reduced. iRing also provides many supporting functions to help process data. [ABSTRACT FROM AUTHOR]

More accurate methods for crop row detection benefit intelligent operation of agricultural machinery, especially avoiding mishandling or crushing crops. For achieving such a target, a traditional method combining the ExGR exponents, Otsu algorithm, Canny method, Hough transform and DBSCAN clustering analysis is proposed so that centerlines of crop rows can be detected effectively without manual intervention. Specifically, ExGR exponents are first adopted to gray green plants. The threshold of binarization will be further obtained by the Otsu algorithm. Further adopting the edge detection algorithm (Canny), edges of crops can be determined. Finally, combining the Hough transform and DBSCAN clustering analysis, the crop row detection is effectively available. Utilizing these methods, numerical simulation and their comparisons with existing methods are also achieved. For example, the Canny algorithm is relatively accurate than the Suzuki algorithm as well as their combinations with a geometric center extraction method if the density of weed is high. Compared with the K‐means clustering method, the DBSCAN algorithm is more suitable to characterize crop rows optimally in more complex conditions. It is validated from experiments that the combination of Canny algorithm, Hough transform and DBSCAN clustering is better than other mentioned traditional methods. [ABSTRACT FROM AUTHOR]

Medjool date thinning automation is essential for reducing Medjool production labor and improving fruit quality. Thinning automation requires motion planning based on feature extraction from a segmented fruit bunch and its components. Previous research with focused bunch images attained high success in bunch component segmentation but less success in establishing correct association between the two components (a rachis and spikelets) that form one bunch. The current study presents an algorithm for improved component segmentation and association in the presence of occlusions based on integrating deep neural networks, traditional methods building on bunch geometry, and active vision. Following segmentation with Mask-R-CNN, segmented component images are converted to binary images with a Savitzky–Golay filter and an adapted Otsu threshold. Bunch orientation is calculated based on lines found in the binary image with the Hough transform. The orientation is used for associating a rachis with spikelets. If a suitable rachis is not found, bunch orientation is used for selecting a better viewpoint. The method was tested with two databases of bunches in an orchard, one with focused and one with non-focused images. In all images, the spikelets were correctly identified [intersection over union (IoU) 0.5: F1 0.9]. The average orientation errors were 18.15° (SD 12.77°) and 16.44° (SD 11.07°), respectively, for the focused and non-focused databases. For correct rachis selection, precision was very high when incorporating orientation, and when additionally incorporating active vision recall (and therefore F1) was high (IoU 0.5: orientation: precision 0.94, recall 0.44, F1 0.60; addition of active vision: precision 0.96, recall 0.61, F1 0.74). The developed method leads to highly accurate identification of fruit bunches and their spikelets and rachis, making it suitable for integration with a thinning automation system. [ABSTRACT FROM AUTHOR]

In the process of automatic grabbing of bridge segment beams, it is crucial to accurately locate and align the corner points of the crane's boom with the beam's lifting holes. This requires the utilization of image processing techniques to precisely detect and locate the corner points of the crane's boom. Existing feature matching methods face challenges such as low detection accuracy and unsuitability for this specific scenario. This article proposes a novel approach for corner point localization by using the intersection points of lines, facilitating the matching of feature points between left and right images. The method consists of three steps: first, a grayscale difference map is constructed by utilizing the R and G channels of the RGB color space. This enhances the bimodal characteristics of the grayscale histograms between the foreground and background, which facilitates the subsequent binarization process. Additionally, opening and closing operations are employed to remove small artifacts from the Canny edge detection results, effectively reducing noise. Second, an adaptive thresholding method based on the mean and variance of Hough transform voting scores is proposed. This method filters out interference lines from the clustering results by selecting appropriate voting scores. Furthermore, an improved centroid calculation method is introduced, which utilizes weighted formulas based on different proportions of voting scores. These weighted formulas replace the original clustering centroids as the basis for line fitting. Finally, the corner coordinates of the crane's boom are computed based on the line fitting results, and the recognition accuracy is compared under different lighting conditions. Experimental results demonstrate that the proposed algorithm exhibits smaller detection errors and higher robustness compared to other corner detection algorithms, particularly when there are numerous interference edge points in the edge detection results. The computed corner coordinates achieve pixel‐level accuracy. The algorithm performs optimally under strong supplementary lighting conditions, with an average detection error percentage of 97.1% within 0–2 pixels and a recognition accuracy of 98.6%. The recognition success rates under different lighting conditions are all above 92.9%. This method is superior to traditional corner detection methods, meets the requirements for automatic grabbing of the boom, and holds practical engineering value. It provides a basis for addressing the accuracy and robustness challenges of crane algorithms influenced by multiple environmental factors. [ABSTRACT FROM AUTHOR]

ABSTRACT The management of natural and planted forests can be conducted sustainably by implementing techniques that consider the spatial and temporal variability of the plant and soil. In this context, precision silviculture through remote sensing can play a vital role, mainly when using Unmanned Aerial Vehicles (UAVs) equipped with specific sensors. In the present study, an automated computational routine (based on computer vision techniques) was developed and validated to perform forest inventory in commercial Eucalyptus grandis forests, using an orthophoto mosaic obtained with an RGB sensor built-in to a UAV. The developed routine employs computer vision techniques, including template matching to locate plants, Delaunay triangulation to create a mesh and indicate the predominant orientations of the planting rows, and an adaptation of the Hough transform to estimate the analytical parameters of each row. These parameters are refined using linear regression to generate the lines best fitting the input data. Finally, the failure segments on each row are identified by detecting the plants in each row. A simulation of regular point distribution on the segment is then used to identify the planting failure. This process allows the geolocation of each failure point for replanting to be quantified. The routine has significant potential in the forest inventory, allowing the geolocation of failures with an overall accuracy of 0.97 and 0.99, respectively, and a maximum positional error of 0.15 m and 0.20 m, respectively.

People are preoccupied with their own duties in today’s environment, making it tough to oversee and care for disabled people. Paralyzed and disabled people, on the other hand, have a powerful desire to move around freely. Different initiatives have been taken in the past to improve and preserve the self-esteem of such people, and various technologies have also been created to assist them in a better way. The primary advantage of a vision-based autonomous wheelchair is that it can provide greater independence to users with limited mobility. The methodology of other smart wheelchairs varies depending on the specific technology used. Some smart wheelchairs may use sensors to detect obstacles and provide feedback to the user, while others may incorporate GPS and other navigation technologies to assist with indoor and outdoor navigation. Our study proposes a revolutionary implementation of an autonomous system for fully handicapped people, allowing them to drive wheelchairs using just their eyes. The wheelchair’s orientation will be determined by the orientation of the eye. The camera will follow the movement of the eyes. Furthermore, sensors will be installed on the front side of the wheelchair to identify any obstacles along its path. The calibration of the camera with the human eye, without obstructing its vision, is the most challenging task in this research, besides controlling the wheels of the wheelchair.

Recently, with the development of semiconductors and VLSI (Very Large Scale Integrated Circuit), the technology required for autonomous driving is rapidly developing. One of the technologies that cannot be left out is the lane detection function. Lane recognition requires a lot of data from the camera sensor. As a result, the data size increases, making it difficult to process on a lightweight embedded board. This paper proposes a sliding-based parallel segment image processing method to solve this problem. Most boards in autonomous vehicles are lightweight, so the technique has been designed to reduce computation and power consumption. After fetching the image’s pixel data, grayscale conversion, Gaussian smoothing, Sobel operator, non-maximum suppression, and hysteresis are performed in parallel. Lanes were detected by performing a Hough transform operation on an image for which edge detection was completed in parallel. Due to the nature of parallel processing, it is more effective when image input is continuous and numerous than single image processing. This algorithm is written in C language and VHDL (VHSIC Hardware Description Language) for two parts in the board, DE1-SoC, FPGA (Field Programmable Gate Array) and HPS (Hard Processor System. Due to the use of the C language and VHDL, parallel programming uses 3.1 times less time, twice as much memory and slightly more power than sequential programming. For hardware languages such as Verilog, the computation algorithms have been converted to a fixed point. When comparing HPS and FPGA, the FPGA consumed significantly fewer resources, with 18 times shorter run time, 50 times fewer clock cycles, 3 times less power, and 183 times less energy. This provides a substantial benefit.

The objective of this study was to develop an interpretable system that could detect specific lung features in neonates. A challenging aspect of this work was that normal lungs showed the same visual features (as that of Pneumothorax (PTX)). M-mode is typically necessary to differentiate between the two cases, but its generation in clinics is time-consuming and requires expertise for interpretation, which remains limited. Therefore, our system automates M-mode generation by extracting Regions of Interest (ROIs) without human in the loop. Object detection models such as faster Region Based Convolutional Neural Network (fRCNN) and RetinaNet models were employed to detect seven common Lung Ultrasound (LUS) features. fRCNN predictions were then stored and further used to generate M-modes. Beyond static feature extraction, we used a Hough transform based statistical method to detect “lung sliding” in these M-modes. Results showed that fRCNN achieved a greater mean Average Precision (mAP) of 86.57% (Intersection-over-Union (IoU) = 0.2) than RetinaNet, which only displayed a mAP of 61.15%. The calculated accuracy for the generated RoIs was 97.59% for Normal videos and 96.37% for PTX videos. Using this system, we successfully classified 5 PTX and 6 Normal video cases with 100% accuracy. Automating the process of detecting seven prominent LUS features addresses the time-consuming manual evaluation of Lung ultrasound in a fast paced environment. Clinical impact: Our research work provides a significant clinical impact as it provides a more accurate and efficient method for diagnosing lung diseases in neonates.

This paper introduced automated detection and analysis of lunar craters are crucial for advancing lunar research and facilitating mission planning. This study introduces a comprehensive multi-scale approach for crater detection on the lunar surface using LROC (Lunar Reconnaissance Orbiter Camera) Dataset, enabling the identification of craters across varying scales and enhancing overall detection accuracy and efficiency. The process involves preprocessing lunar imagery to enhance features and delineate potential crater regions, utilizing edge detection algorithms to extract crater boundaries. Clustering techniques are then applied to group similar edge points and isolate potential crater candidates. A thresholding mechanism based on statistical edge intensity analysis refines the detection process. To address multiple detections of the same crater at slightly different diameters, radius criteria are implemented, yielding high diameter accuracy of 84% for mare Imbrium (test site 1) and 80% for mare Nubium (test site 2). Subsequently, a depth-diameter analysis validates crater-like characteristics by combining depth measurements with crater diameter estimation, showcasing the authenticity of potential craters. we compare our diameter and depth with different reference papers and we get high accuracy of R2 =0.99 then other methods. The results highlight the potential of this multi-scale approach for automated lunar crater detection, providing deeper insights into the Moon's surface morphology and history for future lunar exploration missions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Primary dendrite information is one of the most important metrics to measure the quality of continuous cast slabs. The contrast of low magnification images is very low under the influence of illumination and sampling devices, so the traditional dendrite detection method has the problem of missed detections. We propose an automatic dendrite detection method based on an improved Hough transform, which effectively improves the accuracy and efficiency of primary dendrite detection. By using the local grayscale features of the image, a genetic algorithm-based local contrast enhancement algorithm is proposed. Compared with the traditional contrast enhancement algorithm, it can retain all the information of the dendrites. Combined with the image binarization method based on Hessian matrix, we can obtain more detailed information about the dendrites. According to the continuity and solidification characteristics of dendrites, the Hough transform is improved to extract dendrite information, which effectively reduces the computational cost of the Hough transform. The experimental results show that the method of this paper has versatility, and the error is four pixels compared with the manual method, which can provide a reliable basis for the subsequent judgement of the quality of cast billets. [ABSTRACT FROM AUTHOR]

Falls are a leading cause of unintentional injury and death and pose a significant risk to individuals living alone, particularly older adults. The World Health Organization reports that approximately 684000 fatal falls occur annually, and that timely fall detection is essential to enable prompt treatment and mitigate harm. To address this challenge, we propose a novel inhome fall detection system that uses the Kinect V2 sensor instead of a wearable device. The system can promptly alert a person's caregivers or family members when it detects a fall, enabling timely assistance. The Kinect V2 sensor captures depth frames and performs skeleton tracking, and body parts in single-depth image pixels are identified using a random forest classifier. v-disparity images, the Hough transform, and the random sample consensus algorithm are used to identify the floor in depth images, and falls are predicted by analyzing the vertical accelerations of 10 joints in the human skeleton. The system can issue an alarm up to 0.5 s before a fall, enabling preventive measures to be taken. In experiments, the proposed system was effective in accurately predicting falls and hence, has potential to improve fall risk management in caregiving environments. [ABSTRACT FROM AUTHOR]

In light of growing metropolitan traffic, safety when driving has grown more and more persuasive. The majority of fatalities on the roads are caused by drivers leaving their lanes while following the required laws. Many of them are a consequence of the driver's disrupted and drowsy attitude. Lane discipline is essential for both driving and protecting pedestrians on the road. The proposed research seeks to detect lane markings in order to produce a safe and advanced traffic system. The proposed device's capabilities include displaying lane line locations to the driver on any exterior display to more complicated programs such as recognizing lane changes within the near future so that injuries caused on the roadways may be avoided. The proposed strategy aims to detect markings for lanes on the roadway by providing a video of the road using computer vision technology to reduce the incidence of accidents. The proposed system was installed in automobiles and cabs to reduce the number of accidents caused by careless driving along the road. In addition, the efficiency of motorists in transportation-related sectors can be tracked in order to detect and report driver irresponsibility and lack of focus on the roads. [ABSTRACT FROM AUTHOR]

Abstract In the process of automatic grabbing of bridge segment beams, it is crucial to accurately locate and align the corner points of the crane's boom with the beam's lifting holes. This requires the utilization of image processing techniques to precisely detect and locate the corner points of the crane's boom. Existing feature matching methods face challenges such as low detection accuracy and unsuitability for this specific scenario. This article proposes a novel approach for corner point localization by using the intersection points of lines, facilitating the matching of feature points between left and right images. The method consists of three steps: first, a grayscale difference map is constructed by utilizing the R and G channels of the RGB color space. This enhances the bimodal characteristics of the grayscale histograms between the foreground and background, which facilitates the subsequent binarization process. Additionally, opening and closing operations are employed to remove small artifacts from the Canny edge detection results, effectively reducing noise. Second, an adaptive thresholding method based on the mean and variance of Hough transform voting scores is proposed. This method filters out interference lines from the clustering results by selecting appropriate voting scores. Furthermore, an improved centroid calculation method is introduced, which utilizes weighted formulas based on different proportions of voting scores. These weighted formulas replace the original clustering centroids as the basis for line fitting. Finally, the corner coordinates of the crane's boom are computed based on the line fitting results, and the recognition accuracy is compared under different lighting conditions. Experimental results demonstrate that the proposed algorithm exhibits smaller detection errors and higher robustness compared to other corner detection algorithms, particularly when there are numerous interference edge points in the edge detection results. The computed corner coordinates achieve pixel‐level accuracy. The algorithm performs optimally under strong supplementary lighting conditions, with an average detection error percentage of 97.1% within 0–2 pixels and a recognition accuracy of 98.6%. The recognition success rates under different lighting conditions are all above 92.9%. This method is superior to traditional corner detection methods, meets the requirements for automatic grabbing of the boom, and holds practical engineering value. It provides a basis for addressing the accuracy and robustness challenges of crane algorithms influenced by multiple environmental factors.

To meet the requirements of autonomous detection of underwater moving targets in a strong reverberation background, a track initiation algorithm based on multiple accumulations of active sonar images was proposed to solve the problem of large amounts of data after space-time processing of active sonar echo signals. Firstly, the underwater multi-target track initiation model in the strong reverberation background of the active sonar was constructed. The range-azimuth-frame time three-dimensional sonar image was condensed into the range-azimuth two-dimensional sonar image by selecting the largest data of sonar images, and the accumulated frame information was retained. The measurement information and energy information of the target were obtained through the centroid clustering point extraction method, and the data preprocessing was completed. Secondly, the energy was accumulated in the parameter space of the Hough transform, and the initial track of multiple targets was obtained by the method of local peak detection. Finally, in order to improve the accuracy of the track, the reverberation and duplicate tracks were eliminated by using the velocity constraint and track merging method. The simulation results show that compared with conventional target tracking methods, this algorithm can effectively reduce the amount of sonar image data in the course of track initiation and can still achieve autonomous detection of underwater multiple targets with high detection probability in a strong reverberation background.

In the context of agricultural modernization and intelligentization, automated fruit recognition is of significance for improving harvest efficiency and reducing labor costs. The variety of fruits commonly planted in orchards and the fluctuations in market prices require farmers to adjust the types of crops they plant flexibly. However, the differences in size, shape, and color among different types of fruits make fruit recognition quite challenging. If each type of fruit requires a separate visual model, it becomes time-consuming and labor intensive to train and deploy these models, as well as increasing system complexity and maintenance costs. Therefore, developing a general visual model capable of recognizing multiple types of fruits has great application potential. Existing multi-fruit recognition methods mainly include traditional image processing techniques and deep learning models. Traditional methods perform poorly in dealing with complex backgrounds and diverse fruit morphologies, while current deep learning models may struggle to effectively capture and recognize targets of different scales. To address these challenges, this paper proposes a general fruit recognition model based on the Multi-Scale Attention Network (MSA-Net) and a Hough Transform localization compensation mechanism. By generating multi-scale feature maps through a multi-scale attention mechanism, the model enhances feature learning for fruits of different sizes. In addition, the Hough Transform ellipse detection compensation mechanism uses the shape features of fruits and combines them with MSA-Net recognition results to correct the initial positioning of spherical fruits and improve positioning accuracy. Experimental results show that the MSA-Net model achieves a precision of 97.56, a recall of 92.21, and an mAP@0.5 of 94.81 on a comprehensive dataset containing blueberries, lychees, strawberries, and tomatoes, demonstrating the ability to accurately recognize multiple types of fruits. Moreover, the introduction of the Hough Transform mechanism reduces the average localization error by 8.8 pixels and 3.5 pixels for fruit images at different distances, effectively improving the accuracy of fruit localization.

Corneal ulcer is a prevalent ocular condition that requires ophthalmologists to diagnose, assess, and monitor symptoms. During examination, ophthalmologists must identify the corneal ulcer area and evaluate its severity by manually comparing ocular staining images with severity indices. However, manual assessment is time-consuming and may provide inconsistent results. Variations can occur with repeated evaluations of the same images or with grading among different evaluators. To address this problem, we propose an automated corneal ulcer grading system for ocular staining images based on deep learning techniques and the Hough Circle Transform. The algorithm is structured into two components for cornea segmentation and corneal ulcer segmentation. Initially, we apply a deep learning method combined with the Hough Circle Transform to segment cornea areas. Subsequently, we develop the corneal ulcer segmentation model using deep learning methods. In this phase, the predicted cornea areas are utilized as masks for training the corneal ulcer segmentation models during the learning phase. Finally, this algorithm uses the results from these two components to determine two outputs: (1) the percentage of the ulcerated area on the cornea, and (2) the severity degree of the corneal ulcer based on the Type–Grade (TG) grading standard. These methodologies aim to enhance diagnostic efficiency across two key aspects: (1) ensuring consistency by delivering uniform and dependable results, and (2) enhancing robustness by effectively handling variations in eye size. In this research, our proposed method is evaluated using the SUSTech-SYSU public dataset, achieving an Intersection over Union of 89.23% for cornea segmentation and 82.94% for corneal ulcer segmentation, along with a Mean Absolute Error of 2.51% for determining the percentage of the ulcerated area on the cornea and an Accuracy of 86.15% for severity grading.

This work presents a novel approach to enhancing iris recognition systems through a two-module approach focusing on low-level image preprocessing techniques and advanced feature extraction. The primary contributions of this paper include: (i) the development of a robust preprocessing module utilizing the Canny algorithm for edge detection and the circle-based Hough transform for precise iris extraction, and (ii) the implementation of Binary Statistical Image Features (BSIF) with domain-specific filters trained on iris-specific data for improved biometric identification. By combining these advanced image preprocessing techniques, the proposed method addresses key challenges in iris recognition, such as occlusions, varying pigmentation, and textural diversity. Experimental results on the Human-inspired Domain-specific Binarized Image Features (HDBIF) Dataset, consisting of 1892 iris images, confirm the significant enhancements achieved. Moreover, this paper offers a comprehensive and reproducible research framework by providing source codes and access to the testing database through the Notre Dame University dataset website, thereby facilitating further application and study. Future research will focus on exploring adaptive algorithms and integrating machine learning techniques to improve performance across diverse and unpredictable real-world scenarios.