Gray Pixel Research Articles

Crack morphology tailoring and permeability prediction of polyvinyl alcohol -steel hybrid fiber engineered cementitious composites

The intrinsic tensile strain-hardening properties of engineered cementitious composites (ECC) determines its multiple cracking and crack control characteristics. The relatively low permeability of cracked ECC is of special significance for more efficient use of cementitious materials, which contributes to the mitigation of carbon emissions. The crack morphology of ECC plays an important role in tailoring the permeability of cracked ECC, as well as the durability of structures made or strengthened by ECC. In the present study, the characterization of crack morphology is conducted by digital image processing (DIP) method, which mainly involves the grey processing, background elimination, binarization and pixel column labeling of digital image. Crack morphologies of ECC are modified by blending PVA fibers with a volume content of 2.0% and steel fibers with volume contents of 0.0%, 0.3%, and 0.6%. A permeability model for cracked hybrid fiber ECC (HyECC) has been proposed based on the Weibull distribution of crack width and crack number. The results indicate that the addition of steel fibers in ECC optimizes the crack morphology, yielding more tortuous and finer cracks. The average crack width decreases from 67.3-115.8 μm in mono fiber system to 46.0-67.6 μm in hybrid system with steel fiber content of 0.6%. The average crack width and crack number increase with the tensile strain, while the average crack spacing behaves in an opposite manner. The evolution of crack morphology could be described by Weibull distribution as a function of tensile strain. The permeability model derived from the crack morphology and the modified Hagen-Poiseuille law well predicts the permeability of cracked HyECC. It reveals that the addition of steel fiber enhances the impermeability of cracked HyECC, especially at large strains. The flow rate at tensile strain of 1.5% is reduced from 7.75 × 10−7 m3/s to 4.82 × 10−9 m3/s as the steel fiber content increases from 0.0% to 0.6%. The findings are expected to support the material-efficient design, crack control and durability improvement of ECC in structural applications.

Illustration Art Based on Visual Communication in Digital Context

With the rapid advancement of digital media technology in recent years, a digital context has emerged and developed under its influence. Visual communication relies heavily on illustration art. It is widely used in many fields of modern design because it is an important form of visual communication. We propose a visual communication design method for small and weak target images based on temporal and spatial filtering. We calculate the probability of gray pixels appearing using the histogram equalization method, assign thresholds to pixels, and enhance the gray value of images using the equalization process. We analyze the characteristics of each element of the 3D video system and the appropriate visual communication design form after performing calculations and combining them with the basic theory of visual communication design. Create system test links, test indicators, and test results. It has been demonstrated that the restored image integrity of the system is higher than that of the existing system, and that its performance is better.

An efficient hand gesture recognition based on optimal deep embedded hybrid convolutional neural network‐long short term memory network model

AbstractHand gestures are the nonverbal communication done by individuals who cannot represent their thoughts in form of words. It is mainly used during human‐computer interaction (HCI), deaf and mute people interaction, and other robotic interface applications. Gesture recognition is a field of computer science mainly focused on improving the HCI via touch screens, cameras, and kinetic devices. The state‐of‐art systems mainly used computer vision‐based techniques that utilize both the motion sensor and camera to capture the hand gestures in real‐time and interprets them via the usage of the machine learning algorithms. Conventional machine learning algorithms often suffer from the different complexities present in the visible hand gesture images such as skin color, distance, light, hand direction, position, and background. In this article, an adaptive weighted multi‐scale resolution (AWMSR) network with a deep embedded hybrid convolutional neural network and long short term memory network (hybrid CNN‐LSTM) is proposed for identifying the different hand gesture signs with higher recognition accuracy. The proposed methodology is formulated using three steps: input preprocessing, feature extraction, and classification. To improve the complex visual effects present in the input images, a histogram equalization technique is used which improves the size of the gray level pixel in the image and also their occurrence probability. The multi‐block local binary pattern (MB‐LBP) algorithm is employed for feature extraction which extracts the crucial features present in the image such as hand shape structure feature, curvature feature, and invariant movements. The AWMSR with the deep embedded hybrid CNN–LSTM network is applied in the two‐benchmark datasets namely Jochen Triesch static hand posture and NUS hand posture dataset‐II to detect its stability in identifying different hand gestures. The weight function of the deep embedded CNN‐LSTM architecture is optimized using the puzzle optimization algorithm. The efficiency of the proposed methodology is verified in terms of different performance evaluation metrics such as accuracy, loss, confusion matrix, Intersection over the union, and execution time. The proposed methodology offers recognition accuracy of 97.86% and 98.32% for both datasets.

A fuzzy membership based comparison of the grey matter (GM) in cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) using brain images

Grey matter (GM) in human brain contains most of the important cells covering the regions involved in neurophysiological operations such as memory, emotions, decision making, etc. Alzheimer’s disease (AD) is a neurological disease that kills the brain cells in regions which are mostly involved in the neurophysiological operations. Mild Cognitive Impairment (MCI) is a stage between Cognitively Normal (CN) and AD, where a significant cognitive declination can be observed. The destruction of brain cells causes a reduction in the size of GM. Evaluation of changes in GM, may help in studying the overall brain transformations and accurate classification of different stages of AD. In this work, firstly skull of brain images is stripped for 5 different slices, then segmentation of GM is performed. Finally, the average number of pixels in grey region and the average atrophy in grey pixels per year is calculated and compared amongst CN, MCI, and AD patients of various ages and genders. It is observed that, for some subjects (in some particular ages) from different dementia stages, pattern of GM changes is almost identical. To solve this issue, we have used the concept of fuzzy membership functions to classify the dementia stages more accurately. It is observed from the comparison that average difference in the number of pixels between CN and MCI= 10.01%, CN and AD= 19.63%, MCI and AD= 10.72%. It can be also observed from the comparison that, the average atrophy in grey matter per year in CN= 1.92%, MCI= 3.13%, and AD= 4.33%.

Research on Digital Steganography and Image Synthesis Model Based on Improved Wavelet Neural Network.

Network compression coding technology is a research hotspot in the field of digital steganography and image synthesis. How to improve image quality while achieving short compression time is a problem currently faced. Based on the improved wavelet neural network theory, this paper constructs a digital steganography and image synthesis model. The model first tracks the contour of the digit to be recognized, then equalizes and resamples the contour to make it translation-invariant and scaling-invariant, and then uses multi-wavelet neural network clusters to stretch the contour shell to obtain orders of magnitude multi-resolution and its average, and finally, these shell coefficients are fed into a feedforward neural network cluster to identify this handwritten digit, solving the problem of multi-resolution decomposition of contour shells while having a high sampling rate. In the simulation process, the classification model that a single pixel is a text/non-text pixel is trained on the original gray value of the gray pixel and its neighboring pixels, and the classified text pixels are connected to a text area through an adaptive MeanShift method. The experimental results show that it is feasible to use multi-wavelet features for handwritten digit recognition. The model combines the neural network and the genetic algorithm, making full use of the advantages of both, so that the new algorithm has the learning ability and robustness of the neural network. The compression ratio after compression by ordinary wavelet coding, PSNR, MSE, and compression time are 8.4, 25 dB, 210, and 7 s, respectively. The values are 11.7, 24 dB, 207, and 11 s, respectively. At the same time, the peak signal-to-noise ratio is higher and the mean square error is lower, that is, the compression quality is better, and the accuracy of digital steganography and image synthesis is effectively improved.

Evaluation of testicular echotexture with Ecotext as a diagnostic method of testicular dysfunction in stallions

This study aimed to assess if Ecotext, a new software for evaluation of testicular echotexture, is a good method for diagnosis of stallions with testicular dysfunction (TD). Relationships between Ecotext parameters and sperm motility and production, testicular volume, and testicular blood flow were also studied. Ecotext provides a total of six echotexture parameters: Ecotext 1 (black pixels), 2 (white pixels) and 3 (grey pixels), and another 3 parameters related to hypoechogenic areas: Ecotext tubular density (ETD), Ecotext tubular diameter (ETd), and Ecotext tubular area (ETA). Stallions (n = 33) were assessed using proven diagnostic techniques (spermiogram, B-mode and Pulse Doppler ultrasound), and subsequent analysis with Ecotext. Animals were classified as “control stallions” (n:21, acceptable semen quality), and “stallions with TD” (n:12, poor semen quality (TM < 60%, PM < 45% and total nº of sperm with PM < 2000 × 106 spz), that were subdivided into “induced TD group” (immunized, anti-GnRH vaccine) and “acquired TD group”. The acquired TD group showed differences in all Ecotext parameters in relation to controls (Ecotext 1:0.11 ± 0.17 vs 2.82 ± 2.52, Ecotext 2:1584.0 ± 575.8 vs 388 ± 368.2, Ecotext 3:134.2 ± 9.26; ETA: 2.14 ± 0.59 vs 5.40 ± 1.90; ETd: 65.66 ± 6.27 vs 86.93 ± 10.65 and ETD: 92.35 ± 11.24 vs 132.10 ± 16.35, p ≤ 0.001). Results suggest acquired TD stallions were suffering testicular degeneration with loss of architecture and function as all Ecotext parameters were altered in relation to controls. Induced TD horses only showed a reduction in ETD (116.2 ± 8.59 vs 132.10 ± 16.35, p ≤ 0.001), despite all sperm parameters being worse. These findings suggested immunized stallions probably only experience an acute loss of testicular functionality and parenchyma architecture is likely not affected since differences in Ecotext parameters with control stallions were not detected. ETD was the best parameter to identify animals with TD (AUC: 0.84, optimal cut-off value of 124.3 seminiferous tubules/cm2). Correlations were found between ETD and Doppler indices (PI: 0.60; RI: 0.47 p ≤ 0.001), total testicular volume (r: 0.48; p ≤ 0.05) and sperm motility (TM:0.51; and PM:0.54; p ≤ 0.001) and production (r:0.51; p ≤ 0.001). In summary, Ecotext could identify changes in testicular echotexture of stallions with TD. Results open the possibility for new research focused on establishing the relationship between Ecotext parameters and histomorphometry features in stallion testes.

Testicular Ultrasound Analysis as a Predictive Tool of Ram Sperm Quality

Simple SummaryIn animal production, the prediction of male fertility is vital for the success of specific techniques such as artificial insemination. Thus, testicular ultrasound, a non-invasive diagnostic procedure, could be a useful tool. Moreover, recent ultrasound-video analysis and software developments allow the visualization of tissue at the microscopic level. The objective of this work was to establish a possible correlation between testicular ultrasonography and semen quality in rams. For this purpose, the testicles of nine rams were evaluated and the semen was analyzed for one year. The results revealed that the number of white and grey pixels correlated with sperm parameters indicating poor seminal quality. On the other hand, the increase in the seminiferous-tubule density or the lumen area of these tubules was related to a rise in seminal quality. Therefore, ultrasound-video analysis could be a good tool for evaluating the fertility of rams, either for artificial insemination or on the farm.Testicular ultrasound is a non-invasive technique that could be very useful for predicting ram seminal quality. Recent software developments allow macroscopic and microscopic evaluation of testicular parenchyma. Thus, the aim of this study was to evaluate the testicular echotexture using ultrasound-video analysis and investigate its possible correlation with semen quality. Nine rams were evaluated for one year using a portable ultrasound scanner and the echotexture was analyzed with ECOTEXT® software. The number of black (Ec1), white (Ec2), and grey pixels (Ec3), tubular density (TD), lumen area (LA), and lumen diameter (LD) were analyzed. Semen was collected by an artificial vagina the same day and the sperm concentration, morphology, motility, viability, phosphatidylserine (PS) translocation, reactive-oxygen-species (ROS) levels, DNA damage and capacitation state were evaluated. Ec2 and Ec3 correlated positively with “bad quality” sperm parameters (the percentage of spermatozoa with high ROS levels, with PS translocation and proximal cytoplasmic droplets), and negatively with motility. In contrast, TD and LA showed a positive correlation with “good quality” parameters (motility or normal morphology) and a negative correlation with spermatozoa with high ROS levels, with DNA fragmentation, and proximal or distal cytoplasmic droplets. Thus, echotexture analysis by ultrasound-video analysis could be a valuable tool for assessing ram fertility.

Research on Color Image Encryption Algorithm Based on Bit-Plane and Chen Chaotic System.

In response to the problems of high complexity and the large amount of operations of existing color image encryption algorithms, a low-complexity, low-operation color image encryption algorithm based on a combination of bit-plane and chaotic systems is proposed that is interrelated with plaintext information. Firstly, three channels of an RGB image are extracted, and the gray value of each pixel channel can be expressed by an eight-bit binary number. The higher- and lower-four bits of the binary gray value of each pixel are exchanged, and the position of each four-bit binary number is scrambled by a logistic chaotic sequence, and all the four-bit binary numbers are converted into hexadecimal numbers to reduce the computational complexity. Next, the position of the transformed image is scrambled by a logistic chaotic sequence. Then, the Chen chaos sequence is used to permute the gray pixel values of the permuted image. Finally, the gray value of the encrypted image is converted into a decimal number to form a single-channel encrypted image, and the three-channel encrypted image is synthesized into an encrypted color image. Through MATLAB simulation experiments, a security analysis of encryption effects in terms of a histogram, correlation, a differential attack, and information entropy is performed. The results show that the algorithm has a better encryption effect and is resistant to differential attacks.

Assessment of meibomian gland drop-out and visibility through a new quantitative method in scleral lens wearers: A one-year follow-up study

ObjectivesTo validate a previously developed algorithm based on the visibility of meibomian gland images obtained with Cobra fundus camera and to assess the changes in meibomian glands in scleral lens wearers over one year of lens wear. MethodsInfrared meibography was obtained from the upper eyelid using the Cobra fundus camera in forty-three volunteers (34.2 ± 10.1 years). Meibographies were classified into 3 groups: Group 1 = good subjective gland visibility and gland drop-out < 1/3 of the total area; Group 2 = low visibility and gland drop-out < 1/3; and Group 3 = low visibility and gland drop-out > 1/3. Meibomian gland visibility metrics were then calculated using the developed algorithm from the pixel intensity values of meibographies. Repeatability of new metrics and their correlations with gland drop-out were assessed. Meibographies and ocular symptoms were also assessed after 1 year of scleral lens wear in 29 subjects. ResultsGland drop-out percentage was not statistically different between groups 1 and 2 (p = 0.464). Nevertheless, group 1 showed higher grey pixel intensity values than the other groups. Statistically significant correlations were found between gland visibility metrics and gland drop-out percentage. Repeatability was acceptable for all metrics, coefficient of variation achieving values between 0.52 and 3.18. While ocular symptoms decreased with scleral lens wear (p < 0.001), no statistically significant differences were found in gland drop-out percentage (p = 0.157) and gland visibility metrics (p > 0.217). ConclusionsThe proposed method can assess meibomian gland visibility in an objective and repeatable way. Scleral lens wear appears to not adversely affect meibomian gland drop-out and visibility while might improve dry eye symptoms after one year of lens wear. These preliminary results should be confirmed with a control group.

Urban Impervious Surface Automatic Threshold Detection Model Derived From Multitemporal Landsat Images

Accurate mapping urban impervious surfaces (UIS) from satellite images is crucial for understanding urban land cover change directions and addressing UIS change-induced environmental issues. The UIS index is an effective way to reduce the computational cost of processing remote sensing imageries although continuous UIS detection from multitemporal Landsat images, repeated fine-tuning, and reselection for a proper threshold should be considered. In this article, we proposed a novel UIS index and an improved particle swarm optimization (PSO)-based UIS automatic threshold selection model (UISAT) to overcome those shortages when detecting UIS derived from multitemporal Landsat images. The proposed UIS index transformed different temporal Landsat images into index images with distinguishable pixel values for each gray pixel. In this way, the pixel gray values representing UIS were enhanced, and non-UIS was weakened with lower gray scales in the transformed index images. Next, an automatic threshold selection strategy was developed to enhance the applicability of the PSO model in terms of threshold fine-tuning and reselection. Then, we performed comprehensive assessments for the UISAT model, including in comparison to an improved convolutional deep belief network for relative accuracy verification, random points matching with Google Earth images, and comparing to other typical indices. The results demonstrated that the UISAT model provided the best performance in UIS detection from multitemporal Landsat images. The producer’s accuracies (PAs) of UISAT results in Beijing, Hefei, and Guangzhou, China, were 91.73%, 93.87%, and 92.84%, while the user’s accuracies (UA) were at 91.51%, 93.35%, and 92.24%. The proposed model outperformed the existing commonly used UIS indices [e.g., the Biophysical Composition Index (BCI), the Combinational Build-up Index (CBI), the Genetic Algorithm-based Urban Cluster Automatic Threshold (GA-UCAT), and the Gaussian-based Automatic Threshold Modified Normalized Difference Impervious Surface Index (G-MNDISI)] by an average increase of 6.51% in overall accuracy.

Reversible Watermarking Method with Low Distortion for the Secure Transmission of Medical Images

In telemedicine, the realization of reversible watermarking through information security is an emerging research field. However, adding watermarks hinders the distribution of pixels in the cover image because it creates distortions (which lead to an increase in the detection probability). In this article, we introduce a reversible watermarking method that can transmit medical images with minimal distortion and high security. The proposed method selects two adjacent gray pixels whose least significant bit (LSB) is different from the relevant message bit and then calculates the distortion degree. We use the LSB pairing method to embed the secret matrix of patient record into the cover image and exchange pixel values. Experimental results show that the designed method is robust to different attacks and has a high PSNR (peak signal-to-noise ratio) value. The MRI image quality and imperceptibility are verified by embedding a secret matrix of up to 262,688 bits to achieve an average PSNR of 51.657 dB. In addition, the proposed algorithm is tested against the latest technology on standard images, and it is found that the average PSNR of our proposed reversible watermarking technology is higher (i.e., 51.71 dB). Numerical results show that the algorithm can be extended to normal images and medical images.

Modified method of image histogram hyperbolization

One of the methods to improve image quality, which consists in increasing the resolution of image details by contrast enhancement, is to hyperbolize the image histogram. Herewith this increase in local contrast is carried out indirectly. It is due to the nature of the change in the histogram of the transformed image. Usually the histogram of the input image is transformed so that it has a uniform distribution, which illustrates the same contribution of pixels gray level to the image structure. However, there is a method that is based on modeling the human visual system, which is characterized by the logarithmic dependence of the human reaction to light stimulation. It consists in the hyperbolic transformation of the histogram of the image. Then, due to its perception by the visual system, at its output, during the psychophysical perception of the image, an approximately uniform distribution of the histogram of the levels of gray pixels is formed. But the drawback is the lack of effectiveness of this approach for excessively light or dark images. The modified method of image histogram hyperbolization has been developed. It is based on the power transformation of the probability distribution function, which in the discrete version of the images is approximated by a normalized cumulative histogram. The power index is a control parameter of the transformation. to improve the darkened images we use the value of the control parameter less than one, and for light images more than one. The effectiveness of the proposed method is shown by examples.

WS7.7. Overview of quantitative muscle ultrasound

The diagnosis and treatment of a patient with suspected neuromuscular disease requires an integrated approach that utilizes clinical, electrodiagnostic, pathologic and genetic data. Ultrasound complements the clinical and electrophysiology exam by showing the degree of abnormalities in myopathies, as well as spontaneous muscle activities in motor neuron diseases and other disorders. US is particularly well suited for evaluation of neuromuscular disorders as it can be performed at the bedside, has no need for sedation, can rapidly evaluate multiple body regions, and is well tolerated by both children and adults including patients in wheelchairs or those who cannot lay flat or still. Muscle ultrasound can be used as a biomarker for Duchenne and FSH muscular dystrophy and spinal muscular atrophy, and the combination of electromyography and ultrasound increases the diagnostic certainty of amyotrophic lateral sclerosis. Qualitative visual analysis of muscle can be graded using the Heckmatt scale, based on the degree of echogenicity and visualization of the deep boney structures. Quantification of the gray scale levels can be performed by importing the ultrasound image into an image analysis software program such as ImageJ, selecting a region of interest within the muscle belly, and measuring the mean gray scale pixel values within the region of interest. Quantitative analysis allows for more sensitive and specific determinations of muscle pathology than qualitative analysis, with reported sensitivities for neuromuscular pathology around 95%. A drawback of quantitative muscle ultrasound is that it requires that imaging settings be held constant and that normative values have been obtained using identical imaging parameters. A strong advantage of ultrasound over other imaging modalities is that evaluation in real time permits the identification of pathologic muscle movement, particularly fasciculations.

SEGMENTASI CITRA DENGAN HISTOGRAM THRESHOLDING MENGGUNAKAN ANALISIS CLUSTER HIRARKIS

Object segmentation in the image aims to separate the object area from the background area. Before the segmentation process, the image undergoes some initial processing to obtain good object segmentation results. Pre-processing is an image processing operation to improve image quality. An image histogram is a graph depicting the quantitative distribution of the value of the degree of gray pixels in or a certain part of the image. From the table we can see the comparison of the values for thresholding of the 4 methods used in image testing image 1 gets the best results with a value of 59, image 2 with a value of 105, image 3 with a value of 161, image 4 with a value of 121 and image 5 with a value of 102 and from the table value it can be seen that the proposed method gives better ME values than the 3 methods another (otsu, KI, Kwon).

Haze removal with channel-wise scattering coefficient awareness based on grey pixels.

Before being captured by observers, the information carried by light may be attenuated by the transmission medium. According to the atmospheric scattering model, this attenuation is wavelength-dependent and increases with distance. However, most existing haze removal methods ignore this wavelength dependency and therefore cannot handle well the color distortions caused by it. To solve this problem, we propose a scattering coefficient awareness method based on the image formation model. The proposed method first makes an initial transmission estimation by the dark channel prior and then calculates the scattering coefficient ratios based on the initial transmission map and the grey pixels in the image. After that, fine transmission maps in RGB channels are calculated from these ratios and compensated for in sky areas. A global correction is also applied to eliminate the color bias induced by the light source before the final output. Qualitatively and quantitatively compared on synthetic and real images against state-of-the-art methods, the proposed method provides better results for the scenes with either white fog or colorized haze.

Small bounding-box filter for small target detection

To detect small infrared targets under the condition of dense clutters, we propose a single-frame target detection algorithm based on a small bounding-box filter, which is characterized by good adaptability to the position and size of a small target. During the small target detection process, the proposed algorithm first searches for the local maximum gray pixel and then, a set of concentric bounding boxes whose center is the pixel found in the first step is constructed, and the detection thresholds of a neighboring region of this pixel are calculated based on the bounding boxes. Finally, the minimum threshold is used to detect small target pixels in the neighboring region. A fast version of the proposed algorithm is a minimum bounding-box filter, which can be implemented by dividing an image into blocks and using the mid-range and range to assess the concentration trend and dispersion of the background. Simulation and analysis results show that the proposed algorithm can achieve high detection probability and low false alarm rate when detecting small targets in the complex background; while its fast version has high computational efficiency. The proposed algorithm can be used in infrared searching and tracking systems.

Segmentation of human knee osteoarthritis images using unsupervised local center of mass computation technique

In this paper, a recent unsupervised segmentation algorithm based on computation of 2-dimensional (2D) local center of mass (LCM) technique is used to segment X-ray images of Knee Osteoarthritis (KOA) of various stages. Clinically, KOA is classified into four grades ranging from 1 to 4 based on the degradation of ligament in between the two bones fibula and femur and causes suffering with impaired movement. Identifying this space between bones through anterior view of knee X-ray image can indicate the type of KOA and helps in selection of suitable treatment process. Several segmentations methods in scrutinizing the 2D Knee X-ray image for KOA is in practice are supervised segmentation methods. Unsupervised segmentation techniques like watershed, K-means etc., are not sufficient to produce a precise segmentation results on its own and also segmented image takes more memory space. In this research, a new unsupervised segmentation algorithm based on computation of LCM technique is used to segment 2D knee X-ray images to screen the KOA stages. The calculated center of masses (CM) is related to the spatial region of pixels and forms the clusters; hence the segmentation is done based on the information from the whole image rather than only on the neighboring pixels. LCM method of segmentation is tested on 80 clinically categorized X-ray images (20 from each category) of KOA. The results are compared with two popular segmentation methods such as Watershed and Gaussian Mixed Model-Hidden Markov Random Field (GMM-HMRF) segmentation methods, respectively. Evaluated Dice score gives higher value of 0.59 ± 0.02 for the segmented results obtained from LCM computation method. Memory space occupied by LCM based segmented images is around 250 ± 30 kilo bytes (KB) which is less compared to other two watershed and GMM-HMRF methods. Overall, it is observed that LCM method could able to produce a crystal-clear segmented region boundaries and less memory space when compared to other two methods. The CMs calculated also depends on the intensity value of grey scale pixels and this method would be more appropriate for bone mineral density (BMD) analysis.

Symmetric Algorithm for Direction Tracking of Dribble Directional Shooting

Tracking the trajectory of dribble directional shooting in the video image is beneficial to improve the basketball training skills. In this paper, the trajectory tracking method of dribble directional shooting based on the symmetrical algorithm is proposed. The symmetric differential algorithm is used to segment the video image of the dribble directional shooting. In the segmented image sequence, the range of motion of the dribble directional shooting is detected every three consecutive frames. Through the maximum variance ratio threshold method, the motion area of the dribble directional shot is divided. The initial motion coordinates and feature vectors of the dribble directional shooting are calculated, and the characteristic affine transformation equation of the shooting action pixel information is constructed. The Camshift method is used to mark the trajectory contour of the dribble directional shooting. According to the result of the contour extraction, the gray pixel value feature points of the video sequence are fitted, and the trajectory tracking model of the dribble directional shooting is established to accurately track the trajectory of the dribble directional shooting. It is verified that the trajectory of the dribble directional shooting tracked by the proposed method is consistent with the actual situation, and the tracking error average is about 1.9%, that is the reliable trajectory tracking method of dribble directional shooting.

Design of Sanda Action Reconstruction Model Based on 3D Images

In order to improve the training level of Sanda movement, this article uses an image analysis method to reconstruct the detailed characteristics of the movement and apply them to the actual training process. Since the traditional wavelet reconstruction method is affected by the accuracy of the decomposition scale, this paper proposes an improved method of Sanda action based on 3D image reconstruction. First, the method relies on frame adjacent phase compensation and digital image stabilization techniques to perform digital frame operations on the image. Then, scanning and corner detection are used for image reconstruction, where adjacent phase compensation methods are used to match feature points and gray pixels. Image extraction is performed by extracting key feature points of the action 3D image, and a fast frame detection method is used to stabilize the image of the digital image, thereby improving the image quality of image reconstruction. The experimental results show that the method has good image output effect and has a high application value in Sanda guidance and optimization.

Iris-based Image Processing for Cholesterol Level Detection using Gray Level Co-Occurrence Matrix and Support Vector Machine

Serious illnesses such as strokes and heart attacks can be triggered by high levels of cholesterol in human blood that exceeds ideal conditions, where the ideal cholesterol level is below 200 mg/dL. To find out cholesterol levels need a long process because the patient must go through a blood sugar test that requires the patient to undergo fasting for 10–12 hours first before the test. Iridology is a branch of science that studies human iris and its relation to the wellness of human internal organs. The method can be used as an alternative for medical analysis. Iridology thus can be used to assess the conditions of organs, body construction, and other psychological conditions. This paper proposes a cholesterol detection system based on the iris image processing using Gray Level Co-Occurrence Matrix (GLCM) and Support Vector Machine (SVM). GLCM is used as the feature extraction method of the image, while SVM acts as the classifier of the features. In addition to GLCM and SVM, this paper also construct a preprocessing method which consist of image resizing, segmentation, and color image to gray level conversion of the iris image. These steps are necessary before the GLCM feature extraction step can be applied. In principle, the GLCM method is a construction of a matrix containing the information about the proximity position of gray level images pixels. The output of GLCM is fed to the SVM that relies on the best hyperplane. Thus, SVM performs as a separator of two data classes of the input space. From the simulation results, the system built was able to detect excess cholesterol levels through iris image and classify into three classes, namely: non–cholesterol (&lt; 200 ), risk of cholesterol (200–239 ) and high cholesterol (&gt; 240 ). The accuracy rate obtained was 94.67% with an average computation time of 0.0696 . It was using each of the 75 training and test data, with the second-order parameters used are contrast–correlation–energy–homogeneity, pixel distance = 1, quantization level = 8, Polynomial kernel types and One Against One Multiclass. Iris has specific advantages which can record all organ conditions, body construction and psychological conditions. Therefore, Iridology as a science based on the arrangement of iris fibers can be an alternative for medical analysis. In this paper proposes a cholesterol detection system through the iris using Gray Level Co-occurence Matrix and Support Vector Machine. Input system is an iris image that will be processed by pre-processing and then extracted features with the Gray Level Co-Occurrence Matrix method which is a matrix containing information about position the proximity of pixels that have a certain gray level. And then classified with the Support Vector Machine method that relies on the best hyper lane which functions as a separator of two data classes in the input space. From the simulation results, the system built was able to detect excess cholesterol levels through iris image and classify into three classes are: risk of cholesterol, high cholesterol and non–cholesterol with an accuracy rate of 96.47% and average computation time was 0.0696 using each of the 75 training and test data, with the second-order parameters used are contrast–correlation–energy–homogeneity, pixel distance = 1, quantization level = 8, Polynomial kernel types and One Against One Multiclass.