Image Techniques Research Articles

Image Steganography

Abstract: Steganography is the art of hiding the fact that communication is taking place, by hiding by information in other information. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the Internet. For hiding secret information in images, there exists a large variety of stenographic techniques some are more complex than others and all of them have respective strong and weak points. This project intends to give an overview of image steganography, its uses and techniques. It also supports steganography in Audio files. For a more secure approach, the project encrypts the message using secret key and then sends it to the receiver. The receiver then decrypts the message

Moving Vehicle Number Plate Detection

Abstract: The ability to detect and recognize vehicle license plates is a crucial task in various applications such as traffic management, law enforcement, and parking systems. In this project, we propose a system for moving vehicle license plate detection using image and video processing techniques. The system employs a combination of computer vision algorithms and machine learning models to accurately locate and recognize license plates from moving vehicles in real-time. The input images or video frames are preprocesses to enhance the regions containing license plates and reduce noise. This preprocessing step may include operations such as resizing, grayscale conversion, noise reduction, and contrast enhancement. Next, object detection algorithms such as Haar cascades or deep learning-based techniques like YOLO (You Only Look Once) are utilized to detect candidate regions in the image/frame that potentially contain license plates. After candidate regions are identified, a series of image processing techniques such as morphological operations, contour detection, and character segmentation are employed to isolate and extract the license plate characters. Machine learning models like Support Vector Machines (SVM), Convolutional Neural Networks (CNNs) are then applied to recognize the characters on the license plate.

MULTIMODAL LEARNING TO IMPROVE CARDIAC LATE MECHANICAL ACTIVATION DETECTION FROM CINE MR IMAGES.

This paper presents a multimodal deep learning framework that utilizes advanced image techniques to improve the performance of clinical analysis heavily dependent on routinely acquired standard images. More specifically, we develop a joint learning network that for the first time leverages the accuracy and reproducibility of myocardial strains obtained from Displacement Encoding with Stimulated Echo (DENSE) to guide the analysis of cine cardiac magnetic resonance (CMR) imaging in late mechanical activation (LMA) detection. An image registration network is utilized to acquire the knowledge of cardiac motions, an important feature estimator of strain values, from standard cine CMRs. Our framework consists of two major components: (i) a DENSE-supervised strain network leveraging latent motion features learned from a registration network to predict myocardial strains; and (ii) a LMA network taking advantage of the predicted strain for effective LMA detection. Experimental results show that our proposed work substantially improves the performance of strain analysis and LMA detection from cine CMR images, aligning more closely with the achievements of DENSE.

Synthesis, characterization, and XPS study of woolen-like zeolite X-palladium nanoparticles: its application as a suitable catalyst for carbon-carbon coupling reaction

Here, a green methodology for synthesis of powerful catalyst of zeolite X-palladium nanoparticles (Z-X-Pd NPs) is reported. The physicochemical properties of the samples were carried out by X‐ray diffraction (XRD), X-ray photoelectron spectroscopy, Brunauer Emmett Teller and Fourier-transform infrared spectroscopy, transmission electron microscopy, field emission scanning electron microscopy, energy dispersive X-ray spectroscopy, scanning electron microscopy mapping images techniques with a clear interpretation of each analysis. From the XRD’s calculation, it can demonstrate the improvement of the crystallinity index (92.99%) and D (22.55%) values of Z-X-Pd NPs sample, compared with zeolite X (Z-X) sample. Z-X-Pd NPs not only did not have impure phases, but also a characteristic grooved-like of structures were disclosed with a good distribution of Pd NPs on the Z-X surface, confirmed by other analysis outputs. Afterward, the Suzuki-Miyaura (S-M) reaction was precisely studied by applying the desired conditions. Under the optimized conditions acid derivatives were efficiently reacted with aryl halides with their various conjugates, resulting in good yields of about 99% (particularly in aryl fluorides).

Interval type-2 fuzzy approach for retinopathy detection in fundus images

In the manuscript, an automatic approach for analysis and detection of various stages of retinopathy defects in human eyes has been proposed. The approach consists of a robust preprocessing technique of the retina fundus image to mitigate the effects of noise and poor lightening in the image. To realize a compressive analysis of the defects, methods for extracting blood vessels and optic disc in the fundus image has also been developed. Adaptive Histogram Equalization (AHE), median filtering and Connected Component Analysis techniques were used in separating blood vessels and optic disc from each fundus image. The pre-processing utilizes canny edge detection and Morphological Closing on the fundus image. An interval type-2 fuzzy (IT2F) clustering is applied to segments an input image into four clusters. These four clusters from the fuzzy segmentation are further analyzed to extract various stages of retinopathy abnormalities (e.g., Hemorrhage, hard exudates etc.). The extracted blood vessels and optic disc are removed from the analysis to enhance the defects detection process. Experiments were conducted on DIARETDB1 database. The experimental results obtained are validated using the ground-truth images contained in DIARETDB1 database. Impressive results are recorded throughout the experiment. Hard-Exudates and Hemorrhage were detected from the fundus images and results from similarity indexes such as, accuracy (94.11%) sensitivity (93.03%) and specificity (98.45%) were recorded.

Multi-objective optimization for vector quantization via genetic algorithm

The issue of image compression continues to be a subject of ongoing research within the domain of image processing, particularly in the context of medical applications. The quality of the decompressed image can vary depending on the accuracy of the compression technique, resulting in either fine or distorted details. Therefore, the diagnostic procedure performed by medical professionals is contingent upon the precision of the compression and decompression process. In addition, the compression of medical images serves to decrease the amount of storage required, thereby enabling faster transmission over computer networks through the reduction of their bit size. This paper proposes a hybrid mix of the discrete wavelet transform (DWT) technique and vector quantization (VQ) to improve the compression technique of medical images. The aim of the proposed compression technique is to preserve diagnostic image information while achieving a high compression ratio. First, noise in medical images is caused by salt pepper noise. At the same time, the edges of the images are maintained in sharpness and context. Then, a lossless compression method is applied to the wavelet coefficients of the subband with the lowest frequency, while the thresholding method was used to efficiently construct coefficients for high-frequency sub-bands. This process will produce a traditional VQ, which is estimated via the Genetic Algorithm (GA) with fuzzy clustering. While Arithmetic encoded theory was being utilized to quantize coefficients, the proposed compression technique was evaluated by dividing the image into two levels and three levels of sub-bands, respectively, in two different scenarios. Comparing the decompressed image to the filtered image by means of different evaluation metrics, the proposed method can enhance compression performance and strike a balance between compression ratio and image visual quality.

Automatic Image Captioning using Deep Learning

Image captioning aims to detect this information by describing the image content through image and text processing techniques. Now-a-days image captioning is one of the recent and growing research problem. Day by day various solutions are being introduced for solving the problem. Even though, many solutions are already available, a lot of attention is still required for getting better and precise results. So, we came up with the idea of developing a image captioning model using different combinations of Convolutional Neural Network architecture along with Long Short Term Memory to get better results. We have used three combination of CNN and LSTM for developing the model. The proposed model is trained with three Convolutional Neural Network architecture such as Inception-v3,Xception, ResNet50 for feature extraction and Long ShortTerm Memory for generating the relevant captions. Among these models, the best combination based on the accuracy of the model. It is trained using the Flicker8k dataset

Multi-model feature aggregation for classification of laser welding images with vision transformer

The laser welding technique is quite common in various manufacturing lines, including those for lithium-ion power batteries, due to its remarkable productivity, effectiveness, and flexibility. However, it has been observed that the consistency of the welding quality is not always optimal. This study investigates the adoption of laser welding in the manufacturing of lithium-ion batteries, such as their anode, cathode and safety vent, where consistent weld quality is crucial for battery performance and safety. Therefore, evaluating the laser-welding product is indispensable for industrial production and the public domain. Several techniques have been utilized for laser welding evaluation, both destructive and non-destructive. However, these methods are ineffective and too cumbersome to be adopted in mass manufacturing. In contrast, a machine vision strategy has recently been adopted to distinguish between successful and unsuccessful laser welding items. This opened new perspectives for evaluating the quality of the laser welding product using digital image techniques. However, these methods cannot deliver outstanding performance across multiple laser-welding products and achieve optimal classification accuracy. Deep learning has evolved remarkably in recent years and gained popularity in detecting welding defects. This paper presents the observation of a Hybrid Vision Transformer (HViT) to classify the laser welding images corresponding to their feature patch images based on multi-model feature aggregation. The VGG-16 and MobileNet, which were pre-trained on ImageNet, were utilized as the core models to extract the rich features of the laser welding image. To integrate these features, the squeeze excitation module (SE) was utilized, and a multi-layer perception (MLP) approach with a label smoothing optimizer was used for classification. To determine the effectiveness of the proposed strategy, a comparative analysis was conducted with a multitude of alternative machine learning and deep learning approaches. The results indicate that the proposed strategy surpasses all other methods in terms of classification accuracy and evaluation metrics on the test dataset.

Displacement prediction of inclined existing buildings with a raft foundation restored by compaction grouting in clay

Compaction grouting technique is often used to lift and rehabilitate any settled or tilted existing buildings. Prediction of building displacement prior to the grouting operation is crucial for safe remediation. However, the renovation of settled buildings using compaction grouting technique is generally empirical. In this study, we aim to propose a theoretical model to obtain the displacements of the inclined existing building with a raft foundation restored by compaction grouting in clay. The displacements of model involve two parts, (i) the ground heave displacements caused by compaction grouting, and (ii) the surface settlements resulting from a raft foundation. We estimate the displacements of the part (i) and part (ii) by means of a virtual image technique and the integration of the Boussinesqʼs displacement solutions, respectively. When the stiffness of the raft foundation is not ignored, we combine the solutions of the model with a numerical calculation method considering the interactions between soil and foundation to obtain the displacements of building. Two days of data from a engineering case are used to verify the rationality of the model. Results show that the computed displacements and the monitored displacements are in good agreement. The maximum and minimum deviations between the computed displacements and the monitored displacements are 22.3 % (monitored point S14) and 1.3 % (monitored point S5), respectively. Overall, the proposed model can be used to predict the displacements of inclined existing buildings with a raft foundation restored by compaction grouting in clay, as well as optimize the design of building rehabilitation work.

GHE-Ensemble: Enhanced Hybrid Image Enhancement Model for Night Vision Multi-Object detection in Autonomous Vehicle

Autonomous vehicle is the exponential growing topic in the research filed due its huge demand in the current industry. Where object detection plays the major role to take any major decision. Object detection in the normal vision yields the excellent performance due to deep learning models. But object detection in the night vision does not yield the good performance due to various challenges in it. In the proposed work created a night vision database called BDD-Darko where it contains night vision images with multi objects. Applied novel GHE-Ensemble model for image enhancement technique and trained using deep learning model Yolov5 for the multi-object detection in the night vision. Which resulted in 65.3 % accuracy. Proposed model is yielding better results for image enhancement technique for night vision than the previous existing model and detecting the multi object in the single frame with better performance. This paper proposes (1)A new database called as BDD-Darko which contains night vision image with multiple objects. (2) A Novel GHE-Ensemble model for image enhancement technique for night vision images

A comparative study of pre-processing methods to improve glioma segmentation performance in brain MRI using deep learning

Background: Glioma is the most common brain tumor in adult patients and requires accurate treatment. The delineation of tumor boundaries must be accurate and precise, which is crucial for treatment planning. Currently, delineating boundaries for tumors is a tedious, time-consuming task and may be prone to human error among oncologists. Therefore, artificial intelligence plays a vital role in reducing these problems. Objective: This study aims to find a relationship between improving image enhancement and evaluating the performance of deep learning models for segmenting glioma image data on brain MRI images. Materials and methods: The BraTs2023 dataset was used in this study. The image dataset was converted from three dimensions to two dimensions and then subjected to pre-processing via four image enhancement techniques, including contrast-limited adaptive histogram equalization (CLAHE), gamma correction (GC), non-local mean filter (NLMF), and median and Wiener filter (MWF). Subsequently, it was evaluated for structural similarity index (SSIM) and mean squared error. The deep learning segmentation model was created using the U-Net architecture and assessed for dice similarity coefficient (DSC), accuracy, precision, recall, F1-score, and Jaccard index for tumor segmentation. Results: The performance of enhanced image results for CLAHE, GC, NLMF, and MWF techniques shows SSIM values of 0.912, 0.905, 0.999, and 0.911, respectively. The dice similarity coefficient (DSC) for segmentation without image enhancement was 0.817. The DSC of segmentation for CLAHE, GC, NLMF, and MWF techniques were 0.818, 0.812, 0.820, and 0.797, respectively. Conclusion: The enhanced image technique could affect the performance of tumor segmentation. by the enhanced image for use in a trained model may increase or decrease performance depending on the chosen image enhancement technique and the parameters determined by each method.

Accuracy of Mirror Image Mapping of Lymphatic Tract for High-stage and Reoperative Lymphaticovenular Anastomosis: Intraoperative Analysis and Early Clinical Outcome.

Indocyanine green lymphography (ICGL) generally has a nonlinear pattern in advanced-stage lymphedema. Despite the lack of a linear pattern ICGL, lymphatic vessels have been discovered in several studies. The purpose of this work was to establish lymphatic mapping utilizing information from the contralateral limb and to illustrate the symmetry of lymphatic systems. Data were retrospectively collected from 81 patients who underwent lymphaticovenular anastomosis (LVA) using the contralateral mapping technique during 2018 to 2022. The sensitivity, specificity, accuracy, and negative and positive predictive values of this technique were calculated and analyzed. Lymphatic vessels were identified in 85.2% of the upper and 82.3% of the lower limb presumed sites using the contralateral mapping technique. The positive predictive value for successful LVA anastomosis was 93.8% for upper limb and 92.3% for lower limb cases. This mirror image technique's accuracy was 91.7% and 91.1%, for the upper limb and lower limb group, respectively. Between reoperative and new LVA cases, there was no statistically significant difference in the number, type, or diameter of lymphatic vessels or number of anastomoses. LVA with contralateral mapping technique is an effective method for patients with high-stage lymphedema with a nonlinear pattern on ICGL.

Detection of Megakaryocyte Cell Structure Through Artificial Intelligence Tools

Recent research has focused on analysing megakaryocyte images to extract the information needed to track the progression of nervous system diseases. Segmentation is a fundamental step in describing and analysing the core contents of megakaryocytes, including the cytoplasm and nucleus. In this study, 45 megakaryocyte images were obtained. A new segmentation image technique was proposed, called the updating fuzzy c-means technique, through the intelligent selection of the centres of each cluster to separate cell components. The first step of this technique (fuzzification) was based on a knowledge analysis of the local parameters (entropy, contrast and standard deviation) that had a substantial influence on the grey-level distribution between the cytoplasm and nucleus. The second important step was the construction of fuzzy rules in terms of the variation in these local parameters to control the intelligent pick-out or update the centroid of each cluster and obtain a successful separation of the cytoplasm and nucleus. The final step was defuzzification to obtain the output images. The results revealed the superiority of the proposed method over recent technique. The accuracy of the segmented nucleus was greater than 7.46%; in the case of the cytoplasm, the accuracy was higher at 18%. These results indicated that this technique may be applied on other biomedical images.

A Inteligência artificial generativa como quarta ferida narcísica do humano

The advancement of artificial intelligence, particularly with generative AI models, has provoked intense reactions, whether or not they are based on the logic and functioning of the technology. Different from predictive AI, generative AI produces original content by synthesizing text, image, voice, video and code from large databases, with the potential to significantly impact the creative economy. The article introduces basic concepts of AI and generative AI, including a taxonomy of generative models, and outlines the distinction between image or video production techniques and text production techniques. The central argument of the article is that the cultural fuss is not accidental, defending the hypothesis that the advent of generative AI places humanity in the midst of crossing its fourth narcissistic wound.

Plant Leaf Disease Detection

Abstract: The research presents an automated vision a system that makes use of the processing of images techniques to identify plant diseases in agricultural contexts. In order to monitor vast crop fields and automatically identify disease symptoms as soon that they occur on plant leaves, research on automated identification of plant infections is crucial to the agricultural industry. This method uses segmentation, colour modifications, and masking of green pixels to classify data based on learning from some training examples of that category. The simulated outcome, in the end, demonstrates that the network classifier in use offers reduced training error and increased classification accuracy

Global Cultural Integration Strategies of Korean Entertainment Companies Regarding the Idol Culture Industry: The Case of SM Entertainment

The study focuses on the global cultural integration of Korean entertainment companies regarding the idol culture industry, using SM Entertainment as a case study for in-depth analysis. Idol culture, as a part of Korean pop culture, has achieved great success globally over the past decades. With the rise of digital media, the trend of internationalization in the entertainment industry has become more pronounced. The acceptance of different cultures by audiences in different countries has also become one of the focal points of research. This study will focus on SM Entertainment's successful practices in overseas markets such as Asia, with a special focus on how it has integrated the Korean Wave culture to satisfy the aesthetic and cultural needs of audiences in different countries and regions in order to increase its acceptability in the international market. The company's strategies in terms of artist image, music style, and promotional techniques are examined in depth. Ultimately, this study provides insights into SM Entertainment's success in achieving cultural integration and adapting to multicultural demands, which is important for understanding global cultural communication, the internationalization of the entertainment industry, and the dissemination of local culture in the international market.

Comparison of threshold algorithms for automatic processing of fat crystal microscopic images based on ImageJ

AbstractMicroscopic image analysis is a crucial tool in fat crystallization research, enabling the analysis of crystal size, network structure, fractal dimension and other parameters through binarization. It is essential to seek an appropriate thresholding algorithm to binarize fat crystal images, which plays a vital role in image segmentation. In this article, the effectiveness of 17 thresholding algorithms such as Default, Mean, IsoData, Otsu, Li and Triangle were analyzed in processing fat crystal images with different shapes, background colors and image intensities. This was expected to discover a stable and objective thresholding algorithm for the binarization of fat crystal images. The performance evaluation was conducted according to the peak signal noise ratio (PSNR), structural similarity index (SSIM) and region non‐uniformity (RNU) parameter. Moreover, the comparative analysis of crystal size error, crystal area fraction and intraclass correlation coefficients (ICC) for fractal dimension values would provide a foundation for the selection of thresholding techniques for fat crystal network images. The results indicated that the Default algorithm exhibited remarkable robustness and applicability with high‐quality and stable outputs in fat crystal image processing.

Optimizing Image Enhancement: Feature Engineering for Improved Classification in AI-Assisted Artificial Retinas.

Artificial retinas have revolutionized the lives of many blind people by enabling their ability to perceive vision via an implanted chip. Despite significant advancements, there are some limitations that cannot be ignored. Presenting all objects captured in a scene makes their identification difficult. Addressing this limitation is necessary because the artificial retina can utilize a very limited number of pixels to represent vision information. This problem in a multi-object scenario can be mitigated by enhancing images such that only the major objects are considered to be shown in vision. Although simple techniques like edge detection are used, they fall short in representing identifiable objects in complex scenarios, suggesting the idea of integrating primary object edges. To support this idea, the proposed classification model aims at identifying the primary objects based on a suggested set of selective features. The proposed classification model can then be equipped into the artificial retina system for filtering multiple primary objects to enhance vision. The suitability of handling multi-objects enables the system to cope with real-world complex scenarios. The proposed classification model is based on a multi-label deep neural network, specifically designed to leverage from the selective feature set. Initially, the enhanced images proposed in this research are compared with the ones that utilize an edge detection technique for single, dual, and multi-object images. These enhancements are also verified through an intensity profile analysis. Subsequently, the proposed classification model's performance is evaluated to show the significance of utilizing the suggested features. This includes evaluating the model's ability to correctly classify the top five, four, three, two, and one object(s), with respective accuracies of up to 84.8%, 85.2%, 86.8%, 91.8%, and 96.4%. Several comparisons such as training/validation loss and accuracies, precision, recall, specificity, and area under a curve indicate reliable results. Based on the overall evaluation of this study, it is concluded that using the suggested set of selective features not only improves the classification model's performance, but aligns with the specific problem to address the challenge of correctly identifying objects in multi-object scenarios. Therefore, the proposed classification model designed on the basis of selective features is considered to be a very useful tool in supporting the idea of optimizing image enhancement.

Multi-Sensor Image and Range-Based Techniques for the Geometric Documentation and the Photorealistic 3D Modeling of Complex Architectural Monuments.

The selection of the optimal methodology for the 3D geometric documentation of cultural heritage is a subject of high concern in contemporary scientific research. As a matter of fact, it requires a multi-source data acquisition process and the fusion of datasets from different sensors. This paper aims to demonstrate the workflow for the proper implementation and integration of geodetic, photogrammetric and laser scanning techniques so that high-quality photorealistic 3D models and other documentation products can be generated for a complicated, large-dimensional architectural monument and its surroundings. As a case study, we present the monitoring of the Mehmet Bey Mosque, which is a landmark in the city of Serres and a significant remaining sample of the Ottoman architecture in Greece. The surveying campaign was conducted in the context of the 2022-2023 annual workshop of the Interdepartmental Program of Postgraduate Studies "Protection Conservation and Restoration of Cultural Monuments" of the Aristotle University of Thessaloniki, and it served as a geometric background for interdisciplinary cooperation and decision-making on the monument restoration process. The results of our study encourage the fusion of terrestrial laser scanning and photogrammetric datasets for the 3D modeling of the mosque, as they supplement each other as regards geometry and texture.

A comparative analysis of pairwise image stitching techniques for microscopy images

Stitching of microscopic images is a technique used to combine multiple overlapping images (tiles) from biological samples with a limited field of view and high resolution to create a whole slide image. Image stitching involves two main steps: pairwise registration and global alignment. Most of the computational load and the accuracy of the stitching algorithm depend on the pairwise registration method. Therefore, choosing an efficient, accurate, robust, and fast pairwise registration method is crucial in the whole slide imaging technique. This paper presents a detailed comparative analysis of different pairwise registration techniques in terms of execution time and quality. These techniques included feature-based methods such as Harris, Shi-Thomasi, FAST, ORB, BRISK, SURF, SIFT, KAZE, MSER, and deep learning-based SuperPoint features. Additionally, region-based methods were analyzed, which were based on the normalized cross-correlation (NCC) and the combination of phase correlation and NCC. Investigations have been conducted on microscopy images from different modalities such as bright-field, phase-contrast, and fluorescence. The feature-based methods were highly robust to uneven illumination in tiles. Moreover, some features were found to be more accurate and faster than region-based methods, with the SURF features identified as the most effective technique. This study provides valuable insights into the selection of the most efficient and accurate pairwise registration method for creating whole slide images, which is essential for the advancement of computational pathology and biology.