A Novel Architecture for Image Vectorization with Increasing Granularity

This paper analyses the structures of covariance functions of digital electroencephalography measurement vectors and digital vectors of two coronavirus images. For this research, we used the measurement results of 30-channel electroencephalography (E1–E30) and digital vectors of images of two SARS-CoV-2 variants (cor2 and cor4), where the magnitudes of intensity of the electroencephalography parameters and the parameters of the digital images of coronaviruses were encoded. The estimators of cross-covariance functions of the digital electroencephalography measurements’ vectors and the digital vectors of the coronavirus images and the estimators of auto-covariance functions of separate vectors were derived by applying random functions constructed according to the vectors’ parameter measurement data files. The estimators of covariance functions were derived by changing the values of the quantised interval k on the time and image pixel scales. The symmetric matrices of correlation coefficients were calculated to estimate the level of dependencies between the electroencephalography measurement results’ vectors and the digital vectors of the coronavirus images. The graphical images of the normalised cross-covariance functions for the electroencephalography measurement results’ vectors and the digital vectors of the coronavirus images within the period of all measurements are asymmetric. For all calculations, a computer program was developed by applying a package of Matlab procedures. A probabilistic interdependence between the results of the electroencephalography measurements and the parameters of the coronavirus vectors, as well as their variation on the time and image pixel scales, was established.

In this paper, we propose a novel face recognition method by using vector projection, which uses vector projection length to evaluate the similarity of two image vectors in face image vector space. The projection length of a test image vector on direction of a training image vector can measure the similarity of the two images. But the decision cannot be made by only a training image which is the most similar to the test one. The mean image vector of each class also contributes to the final classification. Thus, the decision of the proposed vector projection classification (VPC) approach is ruled in favor of the maximum combination projection length. The performance of the proposed VPC approach is evaluated using two standard face databases; a comparative study with the state-of-the-art approaches illustrates the efficacy of the proposed VPC approach.

Aligning the domains in cross domain model inversion attack

Face recognition has received significant attention in the past decades due to its potential applications in biometrics, information security, law enforcement, etc. Numerous methods have been suggested to address this problem [1]. Among appearance-based holistic approaches, principal component analysis (PCA) turns out to be very effective. As a classical unsupervised learning and data analysis technique, PCA was first used to represent images of human faces by Sirovich and Kirby in 1987 [2, 3]. Subsequently, Turk and Pentland [4, 5] applied PCA to face recognition and presented the well-known Eigenfaces method in 1991. Since then, PCA has been widely investigated and has become one of the most successful approaches to face recognition [6-15]. PCA-based image representation and analysis technique is based on image vectors. That is, before applying PCA, the given 2D image matrices must be mapped into 1D image vectors by stacking their columns (or rows). The resulting image vectors generally lead to a highdimensional image vector space. In such a space, calculating the eigenvectors of the covariance matrix is a critical problem deserving consideration. When the number of training samples is smaller than the dimension of images, the singular value decomposition (SVD) technique is useful for reducing the computational complexity [1-4]. However, when the training sample size becomes large, the SVD technique is helpless. To deal with this problem, an incremental principal component analysis (IPCA) technique has been proposed recently [16]. But, the efficiency of this algorithm still depends on the distribution of data. Over the last few years, two PCA-related methods, independent component analysis (ICA) [17] and kernel principal component analysis (KPCA) [18, 19] have been of wide concern. Bartlett [20], Yuen [21], Liu [22], and Draper [23] proposed using ICA for face representation and found that it was better than PCA when cosine was used as the similarity measure (however, the performance difference between ICA and PCA was not significant if the Euclidean distance is used [23]). Yang [24] and Liu [25] used KPCA for face feature extraction and recognition and showed that KPCA outperforms the classical PCA. Like PCA, ICA and KPCA both follow the matrix-to-vector mapping strategy when they are used for image analysis and, their algorithms are more complex than PCA. So, ICA and KPCA are considered to be computationally more expensive than PCA. The experimental results in 16

Previous efforts concerning lesion extraction in radiologic images indicated that autoassociative memory models can be a valuable tool in automated lesion detection. Preliminary results are expanded to resolve the technical problems of image registration and magnification. Instead of operating on selected portions of the MR images, each entire image matrix is operated upon as image vector comprising all stacked columns of the matrix. Spin density weighted images (TR = 3000 ms and TE = 40 ms) of 42 normal subjects were remapped and standardized with respect to location and magnification. All image vectors were orthonormalized to span a linear manifold. Standardized abnormal image vectors were then tested by the stored autoassociative memory and the abnormalities (novelties) were extracted by application of an autocorrelation matrix to the input vector. The autocorrelation matrix is computed using image vectors from normal subjects. The lesions (multiple sclerosis and tumors) are then identified as the orthogonal component to the linear manifold spanned by the basis vectors of the normal brain scans. Lesion extraction has been achieved with the intention of quantitating and staging diseased parenchyma after automated edge detection.

Image vectorization plays an important role in the digital image processing. Because the traditional linear vectorization methods have some shortcomings including processing data slowerly, being sensitive to noises and being easy to be distorted, this paper proposes an image vectorization method based on mathematical morphology. This method consists of the image edge detection method based on the four structural elements template, the morphological sequential homotypic skeleton abstraction method based on the eight structural elements template and the vectorization method based on the dynamic change of pace about Freeman's chain code. Examples have proved that this method can abstract high precision skeletons rapidly and attain high accuracy vector data measured by the arc segment. This method has many advantages, such as the faster processing rate, the higher accuracy and the less storage space. So it has certain feasibility and practicability in digital image vectorization.

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.

With smart environments, such as modern manufacturing facilities and smart homes, the demand for location-aware applications is swiftly increasing. Thus, wireless localization and tracking presently attracts high interest in research. Basically, radio frequency (RF)-based localization systems can be classified as active and passive, depending on the targeted entities, i.e. persons and/or objects, being equipped with an actively probing device or not [1]. In active the position of a target is determined based on an information exchange between a device and anchor nodes. In passive the position is estimated by a wireless sensor network, not requiring a target to carry any device. Therefore, passive systems are also referred to as device-free localization. The capability to infer a target's position attributes to the physical impact of a target on the received RF-signals in the network through absorption, reflection, diffraction, or scattering. A prominent example of passive localization was introduced in [2], as radio tomographic imaging (RTI). This device-free localization approach solely relies on variations of the received signal strength (RSS) between individual links of a wireless network. An RTI system employs K transceiving communication nodes spanning a dense network of bidirectional links in line-of-sight (LoS). In each link, the RSS is mainly influenced by transmit power, distance dependent path loss, fading loss, and a shadowing loss induced by obstacles within the link. In this regard, targets are considered as time-variant obstacles which attenuate the RSS of various links according to their position. In order to localize a target based on RSS-measurements, the network area is initially sectioned in N discrete regions with known location. On the basis of this partitioning, an image vector is introduced which describes the attenuation of each respective region. This allows to express the shadowing loss of any individual link as a weighted sum of the attenuation values. The differences in RSS are measured for every link resulting in a measurement vector, which depends on the position of the target. The measurement equations form a linear system with a weighting matrix and a noise vector incorporating fading and measurement noise. Thus, the image vector can be estimated, e.g., using a least-squares solution with Tikhonov regularization as proposed in [2]. Since the locations of the N regions corresponding to the image vector are known, the position of the attenuating obstacles can be directly inferred from the estimated image vector. As shown in [2], the performance of an RTI system is mainly influenced by the node density. Particularly, an increasing number of nodes leads to an increasing localization accuracy. This can be intuitively explained by the coverage of the network area with LoS-links. The lack of information in areas, where no link-related RSS measurements can be retrieved results in a poor localization performance. Motivated by the resource consuming deployment of communication infrastructure required in RTI the approach proposed in this document attempts to downsize the amount of nodes. In comparison to [2], only relying on RSS measurements between LoS-links, our approach bases on an additional exploitation of propagation characteristics. To gather this additional information, the measured channel impulse response (CIR) is processed at each node. Therewith, the proposed passive localization approach can be divided in two stages: 1. In an initial calibration phase, the additional information of the propagation channel is precisely estimated from all measured CIRs. 2. The second stage applies commonly known RTI processing, as e.g. described in [2], now including the additional channel characteristics, to determine the position of targets within the network. Due to the usage of additional information of the propagation channel, the proposed passive localization approach improves common RTI methods. Particularly, the approach addresses the problem of link coverage within an observation area. As link coverage correlates to localization quality, the supplemental integration of propagation characteristics results in either an increased localization performance for an unchanged amount of nodes, or allows to reduce the amount of required nodes to achieve a targeted localization performance. The algorithm is evaluated in an indoor scenario using five ultrawide-band (UWB) devices. Thereby, one UWB node is acting as transceiver, pinging four relay nodes in a round robin manner. The nodes are stationary mounted, spatially separated at different locations within the observation area. Thus, this setup spans a network of four direct links incorporating additional channel features, which can be used for the positioning of passive targets. For ground truth, the passive target is tracked with a Vicon high-precision motion tracking system. Using the proposed approach, it is shown for this setup, that the location of a moving person can be estimated and tracked, successfully. The positioning error is shown to be smaller than 1m.

Image Vectorization methodology now a day is based on multi-points representation. This technique requires intense digital processing mainly to better represent regions of the image where different surfaces or solids come together creating new boundaries as part of the representation process for a given complex image. When considering the market need for always more intense and dynamic sequence of 3D images the required processing time becomes a limitation to be overcome. The intense digital processing currently necessary will be greatly reduced under the new proposed Image Vectorization algorithm that is proposed by using the DNAx approach. The numerical process proposed for the Image Vectorization implemented using this nonlinear techniques here proposed allows a different approach that will enhance for instance the gaming experience among others. The processing time for equivalent images will be significantly reduced, and as a direct consequence the image quality will significantly increase.

Background subtraction is a useful and effective method for detecting moving objects in video images. Since this method assumes that image variations are caused only by moving objects (i.e., the background scene is assumed to be stationary), however, its applicability is limited. In this paper, we propose a background subtraction method that robustly handles various changes in the background. The method learns the chronological changes in the observed scene's background in terms of distributions of image vectors. The method operates the subtraction by evaluating the Mahalanobis distances between the averages of such image vectors and newly observed image vectors. The method we propose herein expresses actual changes in the background using a multi-dimensional image vector space. This enables the method to detect objects with the correct sensitivity. We also introduce an eigenspace to reduce the computational cost. We describe herein how approximate Mahalanobis distances are obtained in this eigenspace. In our experiments, we confirmed the proposed method's effectiveness for real world scenes.

Vectorization of objects from an image is necessary in many areas. The existing methods of vectorization of satellite images do not provide the necessary quality of automation. Therefore, manual labor is required in this area, but the volume of incoming information usually exceeds the processing speed. New approaches are needed to solve such problems. The method of vectorization of objects in images using image decomposition into topological features is proposed in the article. It splits the image into separate related structures and relies on them for further work. As a result, already at this stage, the image is divided into a tree-like structure. This method is unique in its way of working and is fundamentally different from traditional methods of vectorization of images. Most methods work using threshold binarization, and the main task for them is to select a threshold coefficient. The main problem is the situation when there are several objects in the image that require a different threshold. The method departs from direct work with the brightness characteristic in the direction of analyzing the topological structure of each object. The proposed method has a correct mathematical description based on algebraic topology. On the basis of the method a geoinformation technology has been developed for automatic vectorization of raster images in order to search for objects located on it. Testing was carried out on satellite images from different scales. The developed method was compared with a special tool for vectorization R2V and showed a higher average accuracy. The average percentage of automatic vectorization of the proposed method was 81%, and the semi-automatic vectorizing module R2V was 73%.

This article presents a Color Image Retrieval method based on Combined Color Descriptions and Features, which is called the CIRCCODF method hereafter. A main contribution of the article is that the method first devises a modified-color-feature-extraction algorithm, called Image Vector (IV). Then, another color features, Color Layout Descriptor (CLD), which is used in the MPEG-7, is selected. Subsequently, CLD and IV are effectively combined to represent each color image. Experimental results shows that the CIRCCODF method possesses better retrieval performance than that of the other existing schemes under considerations here.

Vector projection for face recognition

Raster image vectorization is increasingly important since vector-based graphical contents have been adopted in personal computers and on the Internet. In this paper, we introduce an effective vector-based representation and its associated vectorization algorithm for full-color raster images. There are two important characteristics of our representation. First, the image plane is decomposed into nonoverlapping parametric triangular patches with curved boundaries. Such a simplicial layout supports a flexible topology and facilitates adaptive patch distribution. Second, a subset of the curved patch boundaries are dedicated to faithfully representing curvilinear features. They are automatically aligned with the features. Because of this, patches are expected to have moderate internal variations that can be well approximated using smooth functions. We have developed effective techniques for patch boundary optimization and patch color fitting to accurately and compactly approximate raster images with both smooth variations and curvilinear features. A real-time GPU-accelerated parallel algorithm based on recursive patch subdivision has also been developed for rasterizing a vectorized image. Experiments and comparisons indicate our image vectorization algorithm achieves a more accurate and compact vector-based representation than existing ones do.

Image vectorization involves two major problems: how to extract proper geometric descriptors from the raster image and how to rasterize the vector representation for display. In this paper, we propose a novel image vectorization approach using diffusion curves as the geometric primitives. Our approach automatically extracts accurate diffusion curves from the input image without user interaction. We first segment the input image into a set of superpixels by a multi-layer algorithm. Then, boundary positions of these superpixels are explored to locate control points for diffusion curves, and color information is properly sampled to generate our double-boundary representation. To render the vector graphics, we formulate color diffusion as a random walk process. Experiments on different categories of photographs show that our approach successfully reveals detail contents in the reconstructed image, and that the rendering process can be performed nearly in realtime on a modern CPU.