Image Division Research Articles

An improved infrared image post-processing method for metals and composites

Post-processing of the infrared thermography image is crucial for nondestructive testing, which will reduce noise and disturbance considerably. In this work, an improved image post-processing method based on the temperature ratio is proposed, namely the Thermal Image Division (TID) method. To validate this method, we carried out experiments on different materials such as composite CFRP (carbon fiber reinforced polymer) plates, 304 stainless steel plates and aluminum alloy samples. The results show the proposed method is pretty useful, significantly improves the signal-to-noise ratio of the image by comparing with other post-processing methods. It’s not only beneficial for identifying the edge of the defect, but also facilitates the detection of smaller defects with deeper depth. The signal-to-noise ratio is improved by nearly 117.5 % from practice. The TID method was applied to the defect size detection of pulse thermography and ultrasonic thermography. The results indicate TID method has good robustness, significantly reduces the error of defect size quantification.

Image Division Using Threshold Schemes with Privileges

Threshold schemes are used among cryptographic techniques for splitting visual data. Such methods allow the generation of a number of secret shares, a certain number of which need to be assembled in order to reconstruct the original image. Traditional techniques for partitioning secret information generate equal shares, i.e., each share has the same value when reconstructing the original secret. However, it turns out that it is possible to develop and use partitioning protocols that allow the generation of privileged shares, i.e., those that allow the reconstruction of secret data in even smaller numbers. This paper will therefore describe new information sharing protocols that create privileged shares, which will also use visual authorization codes based on subject knowledge to select privileged shares for secret restoration. For the protocols described, examples of their functioning will be presented, and their complexity and potential for use in practical applications will be determined.

Leukemia detection using Artificial Neural Networks in Images of Human Blood Sample

This article presents a preliminary report that uses minuscule images of blood tests to develop a diagnosis of leukemia. Examining through images is crucial since illnesses can be recognized and examined at an earlier stage using the images. The framework will be centered on leukemia and white blood cell illness. In fact, even the hematologist has trouble organizing the leukemic cells, and manually arranging the platelets takes a long time and is quite loose. In this way, early detection of leukemia recurrence allows the patient to receive the appropriate treatment. In order to address this problem, the framework will make use of the capabilities in small images and examine surface, geometry, shading, and quantifiable investigation modifications. These features' variations will be utilized as the classifier input. has transformed the use of images K proposes that (NN) and agglomeration. Examining a wide range of failure measures and increasing the intricacy of every system, the findings are examined. Utilizing feedforward (NN), image division is accomplished with less noise and a very sluggish conjunction rate. K-means agglomeration and (ANN) are intentionally used in this analysis to create a collection of processes that will work together to produce a much better presentation in (IS). An analysis has been conducted to determine the best rule for (IS).

Vision transformer: To discover the “four secrets” of image patches

Vision Transformer (ViT) is widely used in the field of computer vision, in ViT, there are four main steps, which are “four secrets”, such as patch division, token selection, position encoding addition, attention calculation, the existing research on transformer in computer vision mainly focuses on the above four steps. Therefore, “how to divide patch?”, “how to select token?”, “how to add position encoding?”, and “how to calculate attention?” are crucial to improve ViT performance. But so far, most of the review literatures are summarized from the perspective of application, and there is no corresponding literature to comprehensively summarize these four steps from the technology perspective, which restricts the further development of ViT in some degree. To address the above questions, the 4 major mechanisms and 5 applications of ViT are summarized in this paper, the main innovative works are as follows: Firstly, the basic principle and model structure of ViT are elaborated; Secondly, aiming to “how to divide patch?”, the 5 key techniques of patch division mechanism are summarized: from single-size division to multi-size division, from fixed number division to adaptive number division, from non-overlapping division to overlapping division, from semantic segmentation division to semantic aggregation division, and from original image division to feature map division; Thirdly, aiming to “how to select token?”, the 3 key techniques of token selection mechanism are summarized: token selection based on score, token selection based on merge, token selection based on convolution and pooling; Fourthly, aiming to “how to add position encoding?”, the 5 key techniques of position encoding mechanism are summarized: absolute position encoding, relative position encoding, conditional position encoding, locally-enhanced position encoding, and zero-padding position encoding; Fifthly, aiming to “how to calculate attention?”, 18 attention mechanisms are summarized based on the timeline; Sixthly, these models that Transformer is combined with U-Net, GAN, YOLO, ResNet, and DenseNet are discussed in the medical image processing field; Finally, around these four questions proposed in this paper, we look forward to the future development direction of frontier technologies such as patch division mechanism, token selection mechanism, position encoding mechanism, and attention mechanism et al, which play an important role in the further development of ViT.

Enhancement of Old Historical Document by Image Processing from Gray scale to RGB Scale Conversion

The enhancement of historical documents through image processing stands as a pivotal bridge between the past and the present. In this project, we embark on a journey to rejuvenate these treasured artifacts by utilizing cutting-edge techniques. Central to this endeavor is the conversion of grayscale documents into the vibrant realm of RGB, breathing new life into their visual presentation. The Nonlocal Mean Denoising algorithm takes center stage, diligently removing noise while preserving the document’s authenticity. Accompanied by image division code, we break down these historical scrolls and manuscripts into comprehensible segments, ensuring efficient processing without compromising their essence. At the heart of our methodology lies the profound significance of image histograms. Through these histograms, we delve into the intricacies of pixel intensities, unearthing valuable insights into the document’s characteristics. The outcome of our rigorous efforts is a transformation of these historical gems, making them not only aesthetically pleasing but also accessible to a broader audience, fostering a deeper appreciation of our cultural heritage. As technology and history intersect in this project, we highlight the transformative potential of image processing in preserving and reinvigorating the stories of the past. In an age where the digital realm converges with the analog, this endeavor reiterates the importance of ensuring that history remains alive and tangible for generations to come

A Double Clustering Approach for Color Image Segmentation

One of the significant stages in computer vision is image segmentation which is fundamental for different applications, for example, robot control and military target recognition, as well as image analysis of remote sensing applications. Studies have dealt with the process of improving the classification of all types of data, whether text or audio or images, one of the latest studies in which researchers have worked to build a simple, effective, and high-accuracy model capable of classifying emotions from speech data, while several studies dealt with improving textual grouping. In this study, we seek to improve the classification of image division using a novel approach depending on two methods used to segment the images. The first method used the minimum distance, and the second method used the clustering algorithm called DBSCAN. Both methods were tested with and without reclustering using the self-organizing map (SOM). The result from comparing the images after segmenting them and comparing the time taken to implement the segmentation process shows the effectiveness of these methods when used with SOM.

Linguistic Methods of Image Division for Visual Data Security

This paper defines new classes of algorithms for securing and sharing visual information. Algorithms offering data protection against unauthorised access are cryptographic protocols for data sharing and splitting. These protocols ensure the division of information among a trusted group of secret holders, with every protocol participant being allocated a specified number of shares in the executed algorithm. Proposing and defining new solutions in the field of cryptographic algorithms for data sharing constitutes the main topic of this paper. This paper discusses a new class of algorithms for secret sharing with the use of linguistic formalisms dedicated to the processes of meaning interpretation and linguistic data sharing. Linguistic threshold schemes serve the processes of data protection in distributed systems; they are also used to distribute the shared secret parts in an optimum way, and to perform the meaning analysis and interpretation of various data sets. Semantic analysis as an element of the impact assessment of the meaning of the interpreted and analysed data will make it possible to take into consideration a much wider aspect of description and interpretation of the analysed phenomenon or data set; it will also enable the assessment of the core of the characterised sets in respect to other information with related meaning. The proposed protocols enhance the security of shared data, and allow the generation of any number of secret shares, which is greater than traditional secret sharing methods.

Event-based imaging polarimeter simulation with a single DoFP image.

An event camera is a neuromorphic vision sensor with a high dynamic range (HDR), high temporal resolution (HTR), low latency, and low power consumption. A polarimeter is an instrument for measuring the state of polarization of light. Currently, most imaging polarimeters are limited in dynamic range and frame rate when used with frame-based cameras. We can establish an event-based imaging polarimeter using the principle of the event camera to obtain HDR and HTR polarized event streams for processing polarization information. However, because of the short history and high cost of event cameras, developing an event-based imaging polarimeter requires substantial resources. We propose an event-based imaging polarimeter simulation method with a single division of focal plane image based on the existing research on event simulation. This method can easily convert existing data into a polarized event stream. It is beneficial to lower the requirement of processing polarized event streams and to create large datasets for deep learning.

Prediction of Uncertainty Estimation and Confidence Calibration Using Fully Convolutional Neural Network

Convolution neural networks (CNNs) have proven to be effective clinical imaging methods. This study highlighted some of the key issues within these systems. It is difficult to train these systems in a limited clinical image databases, and many publications present strategies including such learning algorithm. Furthermore, these patterns are known for making a highly reliable prognosis. In addition, normalization of volume and losses of dice have been used effectively to accelerate and stabilize the training. Furthermore, these systems are improperly regulated, resulting in more confident ratings for correct and incorrect classification, which are inaccurate and difficult to understand. This study examines the risk assessment of Fully Convolutional Neural Networks (FCNNs) for clinical image segmentation. Essential contributions have been made to this planned work: 1) dice loss and cross-entropy loss are compared on the basis of segment quality and uncertain assessment of FCNNs; 2) proposal for a group model for assurance measurement of full convolutional neural networks trained with dice loss and group normalization; And 3) the ability of the measured FCNs to evaluate the segment quality of the structures and to identify test examples outside the distribution. To evaluate the study’s contributions, it conducted a series of tests in three clinical image division applications such as heart, brain and prostate. The findings of the study provide significant insights into the predictive ambiguity assessment and a practical strategies for outside-distribution identification and reliable measurement in the clinical image segmentation. The approaches presented in this research significantly enhance the reliability and accuracy rating of CNN-based clinical imaging methods.

ViVGG19: Novel exemplar deep feature extraction-based shoulder rotator cuff tear and biceps tendinosis detection using magnetic resonance images.

Rotator cuff tear (RCT) and biceps tendinosis (BT) are the two most common shoulder disorders worldwide. These disorders can be diagnosed using magnetic resonance imaging (MRI), but the expert interpretation is manual, time-consuming, and subjected to human errors. Therefore, a fixed-size feature extraction model was created to objectively and accurately perform automated binary classification of RCT vs. normal and BT vs. normal on MRI images. We have developed an exemplar deep feature extraction model to diagnose RCT and BT disorders. The model was tested on a new MR image dataset comprising transverse, sagittal, and coronal MRI images of the shoulder that had been organized into three cases. BT was studied on transverse MRI images (Case 1), while RCT was studied on sagittal (Case 2) and coronal MRI images (Case 3). Our model comprised deep feature generation using a pre-trained VGG19, feature selection using iterative neighborhood component analysis (INCA), and classification using shallow standard classifiers k-nearest neighbors (KNN), support vector machine (SVM), and artificial neural network (ANN). In the feature extraction phase, two fully connected layers were used to extract deep features from the original image, and sixteen fixed-size patches obtained by the division of the original image. This model was named Vision VGG19 (ViVGG), analogous to vision transformers (ViT). The feature vector is extracted from the raw image dataset, and 16 feature vectors are extracted from each fixed-size patch. Seventeen feature vectors obtained from each image are obtained from fc6 and fc7 layers of the pre-trained VGG19, are merged to obtain final feature vector. INCA was used to choose the top features from the created features, and the chosen features were classified using shallow classifiers. We defined three cases to evaluate the proposed ViVGG19 to diagnose RT and BCT disorders. Our proposed ViVGG19 model achieved more than 99% accuracy using the KNN classifier. ViVGG19 is a very effective model for detecting RCT and BT disorders on shoulder MRI images. The developed automated system is ready to be tested with a bigger diverse database obtained from different medical centers.

Flexible patch moving modes for pixel-value-ordering based reversible data hiding methods

Pixel-value-ordering (PVO) is one of the most popular frameworks in the research of reversible data hiding (RDH) in recent years. In most PVO-based methods, the cover image is divided into non-overlapped blocks to embed secret data into the maximum and minimum pixels of each block. In these methods, the image division processing is just like moving a patch with constant step lengths in both horizontal and vertical directions. As a result, the block amount, as well as the embedding capacity (EC), is limited by the image size and the patch size. In this paper, we propose one-dimensional and two-dimensional flexible patch moving (FPM) modes to move the patch with adaptive step lengths. After enlarging the EC by employing FPM, an average-difference-based complexity computation is also proposed to cooperate with FPM to further improve the embedding performance. Finally, multiple pairwise embedding schemes are adaptively applied on blocks with different complexities to enhance the embedding performance once again. Experimental results illustrate that the proposed FPM modes work well on many PVO-based methods. Moreover, the proposed method achieves an obvious improvement in fidelity when compared with some state-of-the-art RDH schemes.

SegNet-based left ventricular MRI segmentation for the diagnosis of cardiac hypertrophy and myocardial infarction

ObjectiveA set of cardiac MRI short-axis image dataset is constructed, and an automatic segmentation based on an improved SegNet model is developed to evaluate its performance based on deep learning techniques. MethodsThe Affiliated Hospital of Qingdao University collected 1354 cardiac MRI between 2019 and 2022, and the dataset was divided into four categories: for the diagnosis of cardiac hypertrophy and myocardial infraction and normal control group by manual annotation to establish a cardiac MRI library. On the basis, the training set, validation set and test set were separated. SegNet is a classical deep learning segmentation network, which borrows part of the classical convolutional neural network, that pixelates the region of an object in an image division of levels. Its implementation consists of a convolutional neural network. Aiming at the problems of low accuracy and poor generalization ability of current deep learning frameworks in medical image segmentation, this paper proposes a semantic segmentation method based on deep separable convolutional network to improve the SegNet model, and trains the data set. Tensorflow framework was used to train the model and the experiment detection achieves good results. ResultsIn the validation experiment, the sensitivity and specificity of the improved SegNet model in the segmentation of left ventricular MRI were 0.889, 0.965, Dice coefficient was 0.878, Jaccard coefficient was 0.955, and Hausdorff distance was 10.163 mm, showing good segmentation effect. ConclusionThe segmentation accuracy of the deep learning model developed in this paper can meet the requirements of most clinical medicine applications, and provides technical support for left ventricular identification in cardiac MRI.

Towards Efficient Detection for Small Objects via Attention-Guided Detection Network and Data Augmentation.

Small object detection has always been a difficult direction in the field of object detection, especially the detection of small objects in UAV aerial images. The images captured by UAVs have the characteristics of small objects and dense objects. In order to solve these two problems, this paper improves the performance of object detection from the aspects of data and network structure. In terms of data, the data augmentation strategy and image pyramid mechanism are mainly used. The data augmentation strategy adopts the method of image division, which can greatly increase the number of small objects, making it easier for the algorithm to be fully trained during the training process. Since the object is denser, the image pyramid mechanism is used. During the training process, the divided images are up-sampled into three different sizes, and then sent to three different detectors respectively. Finally, the detection results of the three detectors are fused to obtain the final detection results. The small object itself has few pixels and few features. In order to improve the detection performance, it is necessary to use context. This paper adds attention mechanism to the yolov5 network structure, while adding a detection head to the underlying feature map to make the network structure pay more attention to small objects. By using data augmentation and improved network structure, the detection performance of small objects can be significantly improved. The experiment in this paper is carried out on the Visdrone2019 dataset and DOTA dataset. Through experimental verification, our proposed method can significantly improve the performance of small object detection.

Perspectives of medical students on future work-life balance in Japan: A qualitative study using postlecture comments.

Recently, work-life balance (WLB) has grown in popularity among medical professionals, and an increasing number of institutions are including WLB lectures into medical school curricula. In Japan, medical student lectures concerning WLB were given in at least 50% of universities. However, with these changes in social awareness, it is not fully clear how current medical students view WLB. The purpose of this study is to explore how Japanese medical students think about their future WLB from the perspective of constructivism. We used one of the present authors' work career and life cycle as an example of a physician's career in a lecture for medical students on community medicine in 2020. Students were asked to remark on their thoughts on the talk and their views on future WLB, and we analyzed their comments qualitatively to understand the current male and female medical students' perspectives on future WLB. We used the feedback of 119 participants (71 males and 48 females). Most students, regardless of gender, thought WLB was essential, but the factors they mentioned as being connected to WLB mirrored the continuing traditional notions of gender-based division of labor and doctors' fixed-job image. Male students' views, on the other hand, were more varied, reflecting recent generational shifts. For the improved WLB in medical doctors, it would be required to tackle the problem from several angles, including not just increasing support for WLB but also fostering an awareness of the essence of WLB in pregraduate medical school.

Design of an optimized fuzzy system for edge detection in images

Edge Detection is the first stage in the image division into separate parts. Image division is the partitioning of a digital image to the different zones or the set of pixels. Edge detection is one of the techniques applied in digital image processing often. The purpose of detecting pixels is to match the edges in the image. Filtering, Enhancement, and Detection are three steps in edge detection. Images are usually destroyed by casual changes in intensity intervals called noise or confusion. some noise variations include salt and pepper, pulse, and Gaussian. However, there is a relation between edge detection power and noise reduction. Using filters to the noise reduction causes the loss of edge detection power. For facilitating the edges detection, it is essential for the determination of pixels’ intensity constraints in their neighborhood. Many points in an image have a nontransparent slope, and all of them are not the edges of the joint space. Therefore, some of the linear and nonlinear methods such as Sobel, Prewitt, and Robert have to be used to determine the edge points. The fuzzy logic and the system based on it, is one of the most effective methods for edge detection. This paper presents an optimized rule-based fuzzy inference system and designs the efficiency mask matric. The simulation results for edge detection are presented using the traditional edge detection techniques, including Binary Filter, Sobel Filter, Prewitt Filter, and Robert Filter. Also, it is presented using the fuzzy approach. The simulation results show that the designed fuzzy system has been able to detect the edges of the image more accurately and help to increase the sharpness and quality of the edges. Therefore, the proposed method has more accurate and more reliable results and reduces false edge detection comparison to the traditional methods.

Polarized Intensity Ratio Constraint Demosaicing for the Division of a Focal-Plane Polarimetric Image

Polarization is an independent dimension of light wave information that has broad application prospects in machine vision and remote sensing tasks. Polarization imaging using a division-of-focal-plane (DoFP) polarimetric sensor can meet lightweight and real-time application requirements. Similar to Bayer filter-based color imaging, demosaicing is a basic and important processing step in DoFP polarization imaging. Due to the differences in the physical properties of polarization and the color of light waves, the widely studied color demosaicing method cannot be directly applied to polarization demosaicing. We propose a polarized intensity ratio constraint demosaicing model to efficiently account for the characteristics of polarization detection in this work. First, we discuss the special constraint relationship between the polarization channels. It can be simply described as: for a beam of light, the sum of the intensities detected by any two vertical ideal analyzers should be equal to the total light intensity. Then, based on this constraint relationship and drawing on the concept of guided filtering, a new polarization demosaicing method is developed. A method to directly use raw images captured by the DoFP detector as the ground truth for comparison experiments is then constructed to aid in the convenient collection of experimental data and extensive image scenarios. Results of both qualitative and quantitative experiments illustrate that our method is an effective and practical method to faithfully recover the full polarization information of each pixel from a single mosaic input image.

A Spatial–Spectral Combination Method for Hyperspectral Band Selection

Hyperspectral images are characterized by hundreds of spectral bands and rich information. However, there exists a large amount of information redundancy among adjacent bands. In this study, a spatial–spectral combination method for hyperspectral band selection (SSCBS) is proposed to reduce information redundancy. First, the hyperspectral image is automatically divided into subspaces. Seven algorithms classified as four types are executed and compared. The means algorithm is the most suitable for subspace division of the input hyperspectral image, with the calculation being the fastest. Then, for each subspace, the spatial–spectral combination method is adopted to select the best band. The band with the maximum information and more prominent characteristics between the adjacent bands is selected. The parameters of Euclidean distance and spectral angle parameters are used to measure the intraclass correlation and interclass spectral specificity, respectively. Weight coefficient quantifying the intrinsic spatial–spectral relationship of pixels are constructed, and then the optimal bands are selected by a combination of the weight coefficients and the information entropy. Moreover, an automatic method is proposed in this paper to provide an appropriate number of band sets, which is out of consideration for existing research. The experimental results show, as compared to other competing methods, that the SSCBS approach has the highest classification accuracy on the three benchmark datasets and takes less computation time. These demonstrate that the proposed SSCBS achieves satisfactory performance against state-of-the-art algorithms.

Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection

The aim of this study is to evaluate infected leaf disease images. Precision agriculture's automatic leaf disease detection system employs image acquisition, image processing, image segmentation, feature extraction, and machine learning techniques. An automated disease detection system offers the farmer with a fast and accurate diagnosis of the plant disease. Automation of plant leaf disease detection system is essential for accelerating crop diagnosis. Using machine learning and image processing, this paper describes a framework for detecting leaf illness. An image of a leaf can be used as an input for this framework. To begin, leaf photographs are preprocessed in order to remove noise from their images. The mean filter is used to filter out background noise. Histogram equalization is used to enhance the quality of the image. The division of a single image into multiple portions or segments is referred to as segmentation in photography. It assists in establishing the boundaries of the image. Segmenting the image is accomplished using the K-Means approach. Feature extraction is carried by using the principal component analysis. Following that, images are categorized using techniques such as RBF-SVM, SVM, random forest, and ID3.

Joint uneven channel information network with blend metric loss for person re-identification

Person re-identification, one of the most challenging tasks in the field of computer vision, aims to recognize the same person cross different cameras. The local feature information has been proved that can improve performance efficiently. Image horizontal even division and pose estimation are two popular methods to extract the local feature. However, the former may cause misalignment, the latter needs much calculation. To fill this gap and improve performance, an efficient strategy is proposed in this work. First, a joint uneven channel information network consisting of an uneven channel information extraction network and a channel information fusion network is designed. Different from the traditional image division, the former can divide images horizontally and unevenly with strong alignment based on weak pose estimation, and extract multiple channel information. The latter can joint channel information based on channel validity and generate an efficient similarity descriptor. To optimize the joint uneven channel information network efficiently, this work proposes a blend metric loss. The extra image information is utilized to dynamically adjust the penalty for the sample distance and the distance margin based on the outlier of the hardest sample to construct i-TriHard loss. Besides, softmax loss and center loss are embedded in the blend metric loss, which can guide the network to learn more discriminative features. Our method achieves 89.6% mAP and 95.9% Rank-1 on Market-1501, 79.9% mAP and 89.4% Rank-1 on DukeMTMC. The proposed method also performs excellently on occluded datasets.

Design and Implementation of Image Edge Detection Algorithm on FPGA

Image processing is an important task in data processing systems for applications such as medical sectors, remote sensing, and microscopy tomography. Edge recognition is a sort of image division method that is used to simplify the image records so as to reduce the amount of data to be processed. Edges are considered the most important in image processing because they are used to characterize the boundaries of an image. The performance of the Canny edge recognition algorithm remarkably surpasses the present edge recognition technology in various computer visualization methods. The main drawback of using Canny edge boundary is that it consumes lot of period due to its complex computation. In order to tackle this problem a hybrid edge recognition method is proposed in block stage to locate edges with no loss. It employs the Sobel operator estimate method to calculate the value and direction of the gradient by substituting complex processes by hardware cost savings, traditional non-maximum suppression adaptive thresholding block organization, and conventional hysteresis thresholding. Pipeline was presented to lessen latency. The planned strategy is simulated using Xilinx ISE Design Suite14.2 running on a Xilinx Spartan-6 FPGA board. The synthesized architecture uses less hardware to detect edges and operates at maximum frequency of 935 MHz.