Image Vector Research Articles

Image Vectorization: a Review

Image Vectorization: a Review

The Short Video Platform Recommendation Mechanism Based on the Improved Neural Network Algorithm to the Mainstream Media

Currently, the capacity of short videos continues to increase. Video manufacturers hope to enhance user experience and stickiness through recommendation algorithms, while users seek personalized videos to save time and money. Therefore, in order to address the data sparsity and high-dimensional feature extraction, this study proposes a novel short video platform recommendation model. The proposed method utilizes the term frequency inverse document frequency algorithm for text mining, and combines error back propagation neural network for learning to explore the potential connection between users and videos. This method combines natural language processing and image analysis in deep learning to construct accurate user and video models, deeply explore user interests, and improve the accuracy and effectiveness of recommendation systems for user preferences. The research results showed that the recommendation accuracy of this method was 66% and 70% respectively, and the prediction accuracy was 73.50% and 88% respectively. When Num=128, 200 data points were recommended within 0.3678s. The proposed algorithm outperforms the other three methods in terms of recommendation accuracy compared with the ItemCF and UserCF algorithms. This is because the method uses an approach based on image and user vector, combined with relevant features of the video and user. The proposed method can deeply explore the relevant features of videos and users, overcoming the data scarcity in previous collaborative screening, and guiding video recommendation on practical media platforms.

A Data-driven Magnetohydrodynamic Simulation of the 2011 February 15 Coronal Mass Ejection from Active Region NOAA 11158

We present a boundary data-driven magnetohydrodynamic (MHD) simulation of the 2011 February 15 coronal mass ejection (CME) event of Active Region NOAA 11158. The simulation is driven at the lower boundary with an electric field derived from the normal magnetic field and the vertical electric current measured from the Solar Dynamics Observatory (SDO) Helioseismic Magnetic Imager vector magnetograms. The simulation shows the buildup of a pre-eruption coronal magnetic field that is close to the nonlinear force-free field extrapolation, and it subsequently develops multiple eruptions. The sheared/twisted field lines of the pre-eruption magnetic field show qualitative agreement with the brightening loops in the SDO Atmospheric Imaging Assembly (AIA) hot passband images. We find that the eruption is initiated by the tether-cutting reconnection in a highly sheared field above the central polarity inversion line and a magnetic flux rope with dipped field lines forms during the eruption. The modeled erupting magnetic field evolves to develop a complex structure containing two distinct flux ropes and produces an outgoing double-shell feature consistent with the Solar Terrestrial Relations Observatory B/Extreme UltraViolet Imager observation of the CME. The foot points of the erupting field lines are found to correspond well with the dimming regions seen in the SDO/AIA observation of the event. These agreements suggest that the derived electric field is a promising way to drive MHD simulations to establish the realistic pre-eruption coronal field based on the observed vertical electric current and model its subsequent dynamic eruption.

Multimodal Data Mining Based on Self Attention Feature Alignment

This study explores the application of multimodal data mining in text and image vector processing, aiming to improve the depth and breadth of data analysis by integrating information from different data types. We use the FairFace dataset combined with the CLIP model encoding layer to obtain text and image vectors, and use the K-Means clustering algorithm to achieve vector dimensionality reduction. Subsequently, we introduced the bipartite graph matching algorithm to achieve maximum matching between text vectors and image vectors, and calculated the contrastive learning loss and similarity loss. The entire process covers steps such as data preparation, feature extraction, vector dimensionality reduction, matching algorithms, and loss assessment, constructing a complete text and image matching task process. Our research contributions include using K-Means clustering algorithm to achieve vector dimensionality reduction, as well as introducing bipartite graph matching algorithm and calculating two types of losses in text and image vector matching, further improving matching quality.

POLARIZATION-CORRELATION MAPPING OF MICROSCOPIC IMAGES OF BIOLOGICAL TISSUES OF DIFFERENT MORPHOLOGICAL STRUCTURE

The results of polarization-correlation mapping of the fourth parameter of the two-point Stokes vector of microscopic images of histological sections of biological tissues with different architectonics of the optically anisotropic polycrystalline component are presented. The coordinate distributions of randomly generated values representing the modulus of the fourth parameter of the polarization-correlation vector from microscopic images of histological sections of fibrillar tissues (such as skin dermis) and parenchymal tissues (like spleen) have been replicated. The statistical analysis results of algorithmically generated coordinate distributions of random values representing the modulus of the fourth parameter of the polarization-correlation vector from microscopic images of histological sections of biological tissues with varying morphological structures are provided.

Advancements in Peptide Vectors for Cancer Therapy and Tumor Imaging: A Comprehensive Review

The objective of this work is to synthesize novel peptide vectors for cancer therapy and tumor imaging. Cancer is a complex disease characterized by uncontrolled cell proliferation and the ability of cancer cells to evade programmed cell death. It poses a significant health problem globally, with increasing incidence rates observed in both industrialized and developing countries. Early detection of cancer is crucial for effective treatment, and various techniques such as observation, palpation, biopsies, and medical imaging have been employed for this purpose. However, these methods have limitations in terms of early detection, precision, and availability. Therefore, the development of new treatments and diagnostic methods is a pressing need in cancer research. This study focuses on the synthesis of peptide vectors that can serve as effective tools for cancer therapy and tumor imaging. Peptide vectors offer several advantages, including their ability to specifically target cancer cells, their potential for multivalency, and their capacity for non-viral vectorization. The research aims to design and synthesize peptide vectors that target αVβ3 integrin, a protein overexpressed in tumor cells and associated with tumor neo-angiogenesis. The vectors will incorporate the cyclodecapeptide scaffold RAFT as a structural framework and will be functionalized with the -RGD- ligand, known for its affinity to αVβ3 integrin. The incorporation of specific features such as oxime ligation and disulfide bridges will enhance the stability and functionality of the vectors. Additionally, the use of FRET (Förster resonance energy transfer) will enable imaging capabilities for tumor detection. Overall, this work seeks to contribute to the advancement of cancer therapy and tumor imaging by synthesizing novel peptide vectors with targeted specificity and imaging capabilities. The successful development of these peptide vectors could potentially lead to improved early detection, targeted drug delivery, and more effective treatment strategies for cancer patients.

Augmented Attention: Enhancing Morph Detection in Face Recognition

Face Morphing is a technique that involves blending two or more faces to create new often realistic- looking images. These morphed images are generated or created using morphing techniques or photo manipulation tools pose a significant peril to face recognition systems. In this paper, we proposed a method to improve deep learning based face morphing detection systems to be more robust against face morphing attacks. Leveraging deep learning models and algorithms including MTCNN (Multitask Cascaded Convolutional Neural Network) for efficient face extraction from original and morphed faces with a high accuracy and FaceNet for extracting unified embeddings from the faces. The project aims to push the boundaries of face morphing detection capabilities by leveraging feature combination techniques using cosine distance and SSIM(structural similarity index measure) for identifying the similarity between faces and applying spatial attention mechanism which aims to enhance the feature representation learned by the model by focusing on the most informative parts of the image and training support vector machine classifier and voting classifier using the extracted embeddings significantly helps in building a robust face morphing detection system.

An optimal B-spline approach for vectorizing raster image outlines.

Capturing outlines is crucial in vectorization of the digital images. An effective and automatic algorithm is presented for outline vectorizing of planar objects within the digital images using trigonometric B-spline. A soft computing optimization technique Genetic Algorithm (GA) is employed to determine the suitable measures of parameter in the description of proposed B-spline. The anticipated scheme is executed on a few raster (bitmap), digital images to validate the robustness of the algorithm. The procedure of vectorizing outlines encompasses a series of stages like boundary detection, corner recognition, break point identification and curve fitting using the proposed B-spline.

Image-Guided Mesenchymal Stem Cell Sodium Iodide Symporter (NIS) Radionuclide Therapy for Glioblastoma.

Glioblastoma (GBM) is a highly aggressive, invasive, and growth factor-independent grade IV glioma. Survival following the diagnosis is generally poor, with a median survival of approximately 15 months, and it is considered the most aggressive and lethal central nervous system tumor. Conventional treatments based on surgery, chemotherapy, and radiation therapy only delay progression, and death is inevitable. Malignant glioma cells are resistant to traditional therapies, potentially due to a subpopulation of glioma stem cells that are invasive and capable of rapid regrowth. This is a literature review. The systematic retrieval of information was performed on PubMed, Embase, and Google Scholar. Specified keywords were used in PubMed and the articles retrieved were published in peer-reviewed scientific journals and were associated with brain GBM cancer and the sodium iodide symporter (NIS). Additionally, the words 'radionuclide therapy OR mesenchyma, OR radioiodine OR iodine-131 OR molecular imaging OR gene therapy OR translational imaging OR targeted OR theranostic OR symporter OR virus OR solid tumor OR combined therapy OR pituitary OR plasmid AND glioblastoma OR GBM OR GB OR glioma' were also used in the appropriate literature databases of PubMed and Google Scholar. A total of 68,244 articles were found in this search on Mesenchymal Stem Cell Sodium Iodide Symporter and GBM. These articles were found till 2024. To study recent advances, a filter was added to include articles only from 2014 to 2024, duplicates were removed, and articles not related to the title were excluded. These came out to be 78 articles. From these, nine were not retrieved and only seven were selected after the removal of keyword mismatched articles. Appropriate studies were isolated, and important information from each of them was understood and entered into a database from which the information was used in this article. As a result of their natural capacity to identify malignancies, MSCs are employed as tumor therapy vehicles. Because MSCs may be transplanted using several methods, they have been proposed as the ideal vehicles for NIS gene transfer. MSCs have been used as a delivery vector for anticancer drugs in many tumor models due to their capacity to move precisely to malignancies. Also, by directly injecting radiolabeled MSCs into malignant tumors, a therapeutic dosage of beta radiation may be deposited, with the added benefit that the tumor would only localize and not spread to the surrounding healthy tissues. The non-invasive imaging-based detection of glioma stem cells presents an alternate means to monitor the tumor and diagnose and evaluate recurrence. The sodium iodide symporter gene is a specific gene in a variety of human thyroid diseases that functions to move iodine into the cell. In recent years, an increasing number of studies related to the sodium iodide symporter gene have been reported in a variety of tumors and as therapeutic vectors for imaging and therapy. Gene therapy and nuclear medicine therapy for GBM provide a new direction. In all the preclinical studies reviewed, image-guided cell therapy led to greater survival benefits and, therefore, has the potential to be translated into techniques in glioblastoma treatment trials.

NOVEL METHODOLOGY FOR DEVELOPING A MACHINE LEARNING-BASED IMAGING TOOL FOR HYBRID VISUALIZATION OF MACROSCOPIC AND MICROSCOPIC DEGENERATIVE CHANGES IN THE INTERVERTEBRAL DISC

BackgroundMagnetic resonance imaging (MRI) algorithm identifies end stage severely degenerated disc as ‘black’, and a moderately degenerate to non-degenerated disc as ‘white’. MRI is based on signal intensity changes that identifies loss of proteoglycans, water, and general radial bulging but lacks association with microscopic features such as fissure, endplate damage, persistent inflammatory catabolism that facilitates proteoglycan loss leading to ultimate collapse of annulus with neo-innervation and vascularization, as an indicator of pain. Thus, we propose a novel machine learning based imaging tool that combines quantifiable microscopic histopathological features with macroscopic signal intensities changes for hybrid assessment of disc degeneration.Methods100-disc tissue were collected from patients undergoing surgeries and cadaveric controls, age range of 35–75 years. MRI Pfirrmann grades were collected in each case, and each disc specimen were processed to identify the 1) region of interest 2) analytical imaging vector 3) data assimilation, grading and scoring pattern 4) identification of machine learning algorithm 5) predictive learning parameters to form an interface between hardware and software operating system.ResultsKernel algorithm defines non-linear data in xy histogram. X,Y values are scored histological spatial variables that signifies loss of proteoglycans, blood vessels ingrowth, and occurrence of tears or fissures in the inner and outer annulus regions mapped with the dampening and graded series of signal intensity changes.ConclusionTo our knowledge this study is the first to propose a machine learning method between microscopic spatial tissue changes and macroscopic signal intensity grades in the intervertebral disc.No conflict of interest declared. Sources of FundingICMR/5/4-5/3/42/Neuro/2022-NCD-1, Dr TMA PAI SMU/ 131/ REG/ TMA PURK/ 164/2020.A part of the above study was presented as an oral paper at the International Society for the Study of Lumbar Spine (ISSLS) meeting held on 1–5th May 2023, Melbourne, Australia.

Optimization of mechanical and safety properties by designing interface characteristics within energetic composites

The interfacial structure has an important effect on the mechanical properties and safety of the energetic material. In this work, a mesostructure model reflecting the real internal structure of PBX is established through image digital modeling and vectorization processing technology. The microscopic molecular structure model of PBX is constructed by molecular dynamics, and the interface bonding energy is calculated and transferred to the mesostructure model. Numerical simulations are used to study the influence of the interface roughness on the dynamic compression and impact ignition response of PBX, and to regulate and optimize the mechanical properties and safety of the explosive to obtain the optimal design of the surface roughness of the explosive crystal. The results show that the critical hot spot density of PBX ignition under impact loading is 0.68 mm−2. The improvement of crystal surface roughness can improve the mechanical properties of materials, but at the same time it can improve the impact ignition sensitivity and reduce the safety of materials. The optimal friction coefficient range for the crystal surface that satisfies both the mechanical properties and safety of PBX is 0.06–0.12. This work can provide a reference basis for the formulation design and production processing of energetic materials.

Image retrieval based on auto-encoder and clustering with centroid update

Abstract This paper suggests a revolutionary deep learning method using a dynamic deep auto-encoder for improving the performance of indexing the feature vectors of images by centroid updation. Feature vectors such as color, semantic, and spatial local binary pattern are extracted from the images for content-based image retrieval. The owner encrypts the images for protection using elliptic curve cryptography before uploading them to the cloud. A black hole entropic fuzzy clustering with Tversky indexing is used to retrieve similar information. When the new training image is matched with any of the centroid then the centroid gets updated by using dynamic deep auto-encoder. During the auto-encoder phase, the conflicted data points are dedicated to reconstruction and the reliable data points are helpful to centroid updation. The suggested BHE fuzzy clustering with dynamic deep auto-encoder approach fared better than the current methods, achieving the best accuracy of 97.605%, the highest $\boldsymbol{F_{1}}$ score of 90.210%, better precision of 90.001%, and the highest recall of 95.149%.

The magnitude vector of images

The magnitude of a finite metric space has recently emerged as a novel invariant quantity, allowing to measure the effective size of a metric space. Despite encouraging first results demonstrating the descriptive abilities of the magnitude, such as being able to detect the boundary of a metric space, the potential use cases of magnitude remain under-explored. In this work, we investigate the properties of the magnitude on images, an important data modality in many machine learning applications. By endowing each individual image with its own metric space, we are able to define the concept of magnitude on images and analyse the individual contribution of each pixel with the magnitude vector. In particular, we theoretically show that the previously known properties of boundary detection translate to edge detection abilities in images. Furthermore, we demonstrate practical use cases of magnitude for machine learning applications and propose a novel magnitude model that consists of a computationally efficient magnitude computation and a learnable metric. By doing so, we address one computational hurdle that used to make magnitude impractical for many applications and open the way for the adoption of magnitude in machine learning research.

Sensitivity Study of Feature Vectors Formed on the Basis of Multiple Scale Transformations of Processed Images

Introduction. Under the conditions of globalization, transportation of oil products by tanker fleets becomes one of the causes of man-made disasters in the water areas of seas and oceans. In this context, environmental monitoring acquires particular significance as a tool for ensuring timely detection of negative consequences of man-made disasters. This task is facilitated by recognition of images obtained from unmanned aerial vehicles with selection of those depicting the traces of man-made accidents or their consequences.Aim. To develop approaches for carrying out automatic selection of input data obtained from unmanned aerial vehicles in the form of photo and video images at the preliminary stage of image recognition.Materials and methods. The theoretical part of the study employed a classification method based on pattern recognition theory. Mathematical processing and calculation were carried out in the MATLAB environment. Simulation was conducted using the MathCAD environment.Results. A series of experiments was conducted to select a basis for discrete wavelet transforms. Modeling was conducted using the study results of the sensitivity of the feature vectors of images formed on the basis of different types of wavelet transforms. A concept of image construction for the purposes of feature vector formation was developed.Conclusion. An approach to the formalization of images for the purposes of feature vector formation is proposed. A metric of their contrast estimation is substantiated. It was established that the sensitivity of a recognition system based on the representation of images in the form of discrete wavelet transform matrices depends not only on the type of the mother wavelet, but also on the value of the scale parameter.

Aligning the domains in cross domain model inversion attack

Model Inversion Attack reconstructs confidential training dataset from a target deep learning model. Most of the existing methods assume the adversary has an auxiliary dataset that has similar distribution with the private dataset. However, this assumption does not always hold in real-world scenarios. Since the private dataset is unknown, the domain divergence between the auxiliary dataset and the private dataset is inevitable. In this paper, we use Cross Domain Model Inversion Attack to represent the distribution divergence scenario in MIA. With the distribution divergence between the private images and auxiliary images, the distribution between the feature vectors of the private images and those of the auxiliary images is also different. Moreover, the outputted prediction vectors of the auxiliary images are also misclassified. The inversion attack is thus hard to be performed. We perform both the feature vector inversion task and prediction vector inversion task in this cross domain setting. For feature vector inversion, Domain Alignment MIA (DA-MIA) is proposed. While performing the reconstruction task, DA-MIA aligns the feature vectors of auxiliary images with the feature vectors of private images in an adversarial manner to mitigate the domain divergence between them. Thus, semantically meaningful images can be reconstructed. For prediction vector inversion, we further introduce an auxiliary classifier and propose Domain Alignment MIA with Auxiliary Classifier (DA-MIA-AC). The auxiliary classifier is pretrained by the auxiliary dataset and fine-tuned during the adversarial training stage. Thus, the misclassification problem caused by domain divergence can be solved, and the images can be reconstructed correctly. Various experiments are performed to show the advancement of our methods, the results show that DA-MIA can improve the SSIM score of the reconstructed images for up to 191%, DA-MIA-AC can increase the classification accuracy score of the reconstructed images from 9.18% to 81.32% in Cross Domain Model Inversion Attack.

A multimodal framework for extraction and fusion of satellite images and public health data

In low- and middle-income countries, the substantial costs associated with traditional data collection pose an obstacle to facilitating decision-making in the field of public health. Satellite imagery offers a potential solution, but the image extraction and analysis can be costly and requires specialized expertise. We introduce SatelliteBench, a scalable framework for satellite image extraction and vector embeddings generation. We also propose a novel multimodal fusion pipeline that utilizes a series of satellite imagery and metadata. The framework was evaluated generating a dataset with a collection of 12,636 images and embeddings accompanied by comprehensive metadata, from 81 municipalities in Colombia between 2016 and 2018. The dataset was then evaluated in 3 tasks: including dengue case prediction, poverty assessment, and access to education. The performance showcases the versatility and practicality of SatelliteBench, offering a reproducible, accessible and open tool to enhance decision-making in public health.

Show-through removal with sparsity-based blind deconvolution.

When scanning a document printed on both sides by using an electronic scanner, the printed material on the back (front) side may be transmitted to the front (back) side. This phenomenon is called show-through. The problem to remove the show-through from scanned images is called the show-through removal in the literature. In this paper, we propose a new method of show-through removal based on the following principle. The proposed method uses two scanned images with the front side and with the back side as input images. The proposed method is based on Ahmed's Blind Image Deconvolution method discovered in 2013, which succeeded in formulating Blind Image Deconvolution as a nuclear norm minimization. Since the structure of show-through removal resembles that of Blind Image Deconvolution, we discovered that the show-through removal can be reformulated into a nuclear norm minimization in the space of outer product matrix constructed from an image vector and a point spread function vector of blurring. Using this key idea, we constructed the proposed method as follows. First, our cost function consists of the following three terms. The first term is the data term and the second term is the nuclear norm derived from the above reformulation. The third term is a regularization term to overcome the underdetermined nature of show-through removal problem and the existence of noise in the measured images. The regularization term consists of Total Variation imposed on the images. The resulting nuclear norm minimization problem is solved by using Accelerated Proximal Gradient method and Singular Value Projection with some problem-specific modifications, which converges fast and requires a simple implementation. We show results of simulation studies as well as results of real image experiments to demonstrate the performances of the proposed method.

Improved vigenere using affine functions surrounded by two genetic crossovers for image encryption

This paper presents an improved method for encrypting color images, surpassing the effectiveness of genetic crossover and substitution operations. The technique incorporates dynamic random functions to enhance the integrity of the resulting vector, increasing temporal complexity to thwart potential attacks. The improvement involves integrating genetic crossover and utilizing two extensive pseudorandom replacement tables derived from established chaotic maps in cryptography. Following the controlled vectorization of the original image, our approach initiates with a first genetic crossover inspired by deoxyribonucleic acid (DNA) behavior at the pixel level. This genetic crossover is succeeded by a confusion-diffusion lap, reinforcing the connection between encrypted pixels and their neighboring counterparts. The confusion-diffusion process employs dynamic pseudorandom affine functions at the pixel level. Then a second genetic crossover operator is applied. Simulations conducted on a diverse set of images with varying sizes and formats showcase the robustness of our method against statistical, brute-force, and differential attacks.

Robust Design of 5th Generation District Heating and Cooling (5GDHC) Systems with Seasonal Thermal Energy Storage via GIS Assessment

This research addresses the need for a method to compare waste heat potential and recovery in 5th Generation District Heating and Cooling (5GDHC) systems. Prosumer buildings, known for enhancing flexibility, are analyzed using Geographic Information Systems (GIS) and image vectorization to evaluate 5GDHC systems. The study predicts the amount of waste heat available with an R2 of 0.96, utilizing Chaikin's algorithm to refine thermal images for automatic recognition of the sources of waste heat. Emphasis is placed on the absence of a method for asset design in 5GDHC, with considerations for annual energy calculations, radial network connections, and energy shares among building types. Recommendations for building connecting in Belgium are proposed, favoring 'B'/'C'-labeled buildings. The research explores the impact of building design on heating and suggests energy savings through regulation strategy changes. Dynamic models for heat pumps aim to reduce errors and emissions. The study connects energy indicators and GIS software, contributing to a top-down design approach in 5GDHC systems. The overall goal is to contribute to decarbonization and reduce CO2 emissions in the energy sector.

Text and image generation from intracranial electroencephalography using an embedding space for text and images

Objective. Invasive brain–computer interfaces (BCIs) are promising communication devices for severely paralyzed patients. Recent advances in intracranial electroencephalography (iEEG) coupled with natural language processing have enhanced communication speed and accuracy. It should be noted that such a speech BCI uses signals from the motor cortex. However, BCIs based on motor cortical activities may experience signal deterioration in users with motor cortical degenerative diseases such as amyotrophic lateral sclerosis. An alternative approach to using iEEG of the motor cortex is necessary to support patients with such conditions. Approach. In this study, a multimodal embedding of text and images was used to decode visual semantic information from iEEG signals of the visual cortex to generate text and images. We used contrastive language-image pretraining (CLIP) embedding to represent images presented to 17 patients implanted with electrodes in the occipital and temporal cortices. A CLIP image vector was inferred from the high-γ power of the iEEG signals recorded while viewing the images. Main results. Text was generated by CLIPCAP from the inferred CLIP vector with better-than-chance accuracy. Then, an image was created from the generated text using StableDiffusion with significant accuracy. Significance. The text and images generated from iEEG through the CLIP embedding vector can be used for improved communication.