The Estimation of Image Jacobian Matrix with Time-Delay Compensation

Time delay exists in image-based visual servo system, which will have a certain impact on the system control. To solve the impact of time delay, the time delay compensation of the object feature point image and the image Jacobian matrix is discussed in this paper. Some work is done in this paper: The estimation of the object feature point image under time delay is based on a proposed robust decorrelation Kalman filtering model, for the measurement vectors which cannot be obtained during time delay in the robust Kalman filtering model, a polynomial fitting method is proposed in which the selection of the polynomial includes the position, velocity and acceleration of the object feature point which impact the feature point trajectory, then the more accurate object feature point image can be obtained. From the estimated object feature point image under time delay, the more accurate image Jacobian matrix under time delay can be obtained. Simulation and experimental results verify the feasibility and superiority of this paper method.

On the statistics of anomalous clumps in random point images

New algorithms for calculating exact analytical formulas describing two related probabilities are proposed, substantiated and software implemented: 1) the probability of the formation of anomalously large local groups in a random point image; 2) the probability of the absence of significant local groupings in a random point image.

Clinical Applications of Liquid Biopsy in Gastric Cancer

Liquid biopsy represents an exciting new area in the field of cancer diagnosis and management, offering a less invasive and more convenient approach to obtain a time-point image of the tumor burden and its genomic profile. Samples collected from several body fluids, mostly blood, can be used to gain access to circulating tumor cells and DNA, non-coding RNAs, microRNAs, and exosomes, at any moment, offering a dynamic picture of the tumor. For patients with GC, the use of blood-based biopsies may be particularly beneficial since tissue biopsies are difficult to obtain and cause real distress to the patient. With advantages such as repeatability and minimal invasion, it is no wonder that the field of liquid biopsy has received tremendous attention. However, the abundance of studies, involving a wide range of assays with different principles, prevented for the moment the reproducibility of the results and therefore the translation into the clinic of liquid biopsy. In this review, we present the latest technical development and data on circulating biomarkers available through liquid biopsy in gastric cancer with an emphasis on their clinical utility in areas such as cancer screening, prognostic stratification, and therapeutic management.

Pengolahan Citra untuk Pengenalan Wajah (Face Recognition) Menggunakan OpenCV

Wajah merupakan objek yang umum dalam materi penelitian teknologi computer vision dan image processing, penerapan pengolahan citra dan computer vision mempunyai tugas utama yaitu untuk membuat suatu keputusan tentang objek fisik nyata yang di dapat dari perangkat atau sensor. Untuk membedakan ID wajah yang satu dengan yang lainya butuh beberapa point untuk memilih data pengolahan citra,pengenalan wajah, pendeteksi wajah, penyelarasan wajah dan penyimpanan fitur wajah, algoritma pengenalan wajah menggunakan Eigenface dan diimplementasikan dalam OpenCv. Data berdasarkan sutau contoh citra wajah di cocokkan dengan citra wajah yang tersimpan dalam database yang tersedia dengan mengukur tingkat persamaan macam-macam point, image processing, face recognition, pendeteksi wajah, penyelarasan wajah, ekstraksi wajah, penyimpanan fitur wajah dan pencocokan wajah. Tujuan penelitian ini hanya untuk menerapakan pengenalan wajah (face recognition) pada library OpenCv yang di tulis menggunakan Bahasa pemrograman Python. Rata-rata wajah yang diuji sebanyak 5 citra wajah dapat dikenali dan 2 yang tidak tersimpan karena faktor pencahayaan yang lebih terang, posisi wajah dari jarak dekat dan jauh dari faktor-faktor ini menghasilkan nilai akurasi yang berbeda sesuai dengan dengan tingkat keberhasilan dalam mengenali wajah, dengan tingkat pengenalan rata-rata 85% setelah di proses perbandingan perbandingan hasil kedekatan sekitar 81% untuk kemiripan wajah menggunakan metode PCA Eigenface dapat mengenali seseorang yang terdapat pada database dan tidak dapat mengenali orang yang tidak terdapat dalam database.

Explicit Cartesian oval as a superconic surface for stigmatic imaging optical systems with real or virtual source or image

Cartesian ovals, also known as rigorously stigmatic surfaces, are the simplest optical systems capable of producing a perfect point image. Exist both implicit and explicit expressions to represent these surfaces, but they treat both refractive and reflective surfaces independently. Because of the complexity of explicit expressions, the ray-tracing techniques for these surfaces are implemented using third-party software. In this paper, we express Cartesian ovals as a degenerated superconic curve and get a new explicit formulation for Cartesian ovals capable of treating image formation using both object and image points, either real or virtual, and in this formulation can deal with both reflective and refractive rigorously stigmatic surfaces. Finally, using the resultant expressions and the vector Snell–Descartes Law, we propose a self-contained analytical ray-tracing technique for all these surfaces.

The choice of the optimal order of the neighborhood for separation a spatial point pattern into a cluster and noise component (by the example of analysis of location of antique settlements in the Kerch Peninsula)

When allocating spatial clusters of point objects, the problem of noise in the data often arises. This noise prevents clear boundaries of the clusters. One of the popular methods for separating the cluster and noise components of a point image is NNCR (Nearest Neighbor Clutter Removal), proposed in 1998 by Bayers and A.E. Raftery. The method is based on using the distance to the nearest neighbor in the calculations. The result of applying NNCR is highly dependent on the user selected neighborhood order. This paper describes a method for selecting the optimal neighborhood order for NNCR. This method focuses on the implementation of NNCR using the optional spatstat package of the programming language R. It is proposed to use the probability of the presence of a cluster component in the data as the main criterion for the optimal order of the neighborhood. With an optimal order of neighborhood, its value reaches its maximum value. In addition to this, it is proposed to analyze the probability of belonging to a cluster for all points assigned to the cluster component. For this, graphs of the dependence of the median and interquartile range of the probability of belonging on the order of the neighborhood are built. With an increase in the order of neighborhood, the median of the probability of belonging to the cluster component increases, tending to a value of 1.0. The interquartile range of the probability of belonging, on the contrary, decreases with an increase in the order of neighborhood, tending to a value of 0.0. The inflection in these graphs indicates the optimal order of the neighborhood. A user function is written in the programming language R, which makes it possible to automate the comparison of the NNCR results obtained in various orders of the neighborhood. It returns a matrix whose columns are the median of the probability of belonging, the interquartile range of the probability of belonging, and the probability of the presence of a cluster component in the data. The proposed method for choosing the optimal neighborhood order has been tested to analyze the point layer of ancient settlements of the Kerch Peninsula. For this data, the third order of neighborhood was optimal.

A Stellar Imaging Error Correction Method Based on an Ellipsoid Model: Taking Ziyuan 3-02 Satellite Data Analysis as an Example

Stellar point image coordinates are one of the important observations needed for high-precision space attitude measurement with a star sensor. High-coupling imaging errors occur under dynamic imaging conditions. Using the results of preliminary star point extraction from star sensor imaging data combined with a superimposed time series, we analyze the relative motion and trajectory based on the star point image, establish an image error ellipsoid fitting model based on the elliptical orbit of a satellite platform, and achieve geometric error correction of a star sensors’ image star point using multi-parameter screening of the ambiguous solutions of intersection of the elliptic equations. The simulation data showed that the accuracy of the correction error of this method reached 89.8%, and every star point coordinate required 0.259 s to calculate, on average. In addition, it was applied to real data from the satellite Ziyuan 3-02 to carry out the correction of the star points. The experiment shows that the mean of attitude quaternion errors for all its components was reduced by 52.3%. Our results show that the estimation parameters of dynamic imaging errors can effectively compensate for the star point image observation value and improve the accuracy of attitude calculation.