A New Algorithm for Two-Dimensional Line Clipping via Geometric Transformation

Line segment clipping is a basic operation of the visualization process in computer graphics. So far there exist four computational models for clipping a line segment against a window, (1) the encoding, (2) the parametric, (3) the geometric transformation, and (4) the parallel cutting. This paper presents an algorithm that is based on the third method. By making use of symmetric properties of a window and transformation operations, both endpoints of a line segment are transformed, so that the basic cases are reduced into two that can be easily handled, thus the problems in NLN and AS where there are too many sulyprocedure calls and basic cases that are difficult to deal with are tackled. Both analytical and experimental results from random input data show that the algorithm is better than other developed ones, in view of the speed and the number of operations.

A Geometrical Transformations Resistant Digital Watermarking Based on Quantization

A geometrical transformations resistant digital image watermarking based on quantization is described. Taking advantage of the rotation, scale and translation invariants of discrete Fourier transform (DFT), each watermark bit is embedded into each homocentric circles around the zero frequency term in DFT domain by quantizing the magnitude vector of Fourier spectrum. The embedded sequence can be extracted by “majority principles” without restoring to the original unmarked image. The experimental results show that the watermark is invisible and robust to any combination of geometrical transformations or common image processing techniques.

Enhancing Ulcerative Colitis Diagnosis:A Multi-Level Classification Approach with Deep Learning

The evaluation of disease severity through endoscopy is pivotal in managing patients with ulcerative colitis,a condition with significant clinical implications.However,endoscopic assessment is susceptible to inherent variations,both within and between observers,compromising the reliability of individual evaluations.This study addresses this challenge by harnessing deep learning to develop a robust model capable of discerning discrete levels of endoscopic disease severity.To initiate this endeavor,a multi-faceted approach is embarked upon.The dataset is meticulously preprocessed,enhancing the quality and discriminative features of the images through contrast limited adaptive histogram equalization(CLAHE).A diverse array of data augmentation techniques,encompassing various geometric transformations,is leveraged to fortify the dataset’s diversity and facilitate effective feature extraction.A fundamental aspect of the approach involves the strategic incorporation of transfer learning principles,harnessing a modified ResNet-50 architecture.This augmentation,informed by domain expertise,contributed significantly to enhancing the model’s classification performance.The outcome of this research endeavor yielded a highly promising model,demonstrating an accuracy rate of 86.85%,coupled with a recall rate of 82.11%and a precision rate of 89.23%.

Two-Fold and Symmetric Repeatability Rates for Comparing Keypoint Detectors

The repeatability rate is an important measure for evaluating and comparing the performance of keypoint detectors.Several repeatability rate measurementswere used in the literature to assess the effectiveness of keypoint detectors.While these repeatability rates are calculated for pairs of images,the general assumption is that the reference image is often known and unchanging compared to other images in the same dataset.So,these rates are asymmetrical as they require calculations in only one direction.In addition,the image domain in which these computations take place substantially affects their values.The presented scatter diagram plots illustrate how these directional repeatability rates vary in relation to the size of the neighboring region in each pair of images.Therefore,both directional repeatability rates for the same image pair must be included when comparing different keypoint detectors.This paper,firstly,examines several commonly utilized repeatability rate measures for keypoint detector evaluations.The researcher then suggests computing a two-fold repeatability rate to assess keypoint detector performance on similar scene images.Next,the symmetric mean repeatability rate metric is computed using the given two-fold repeatability rates.Finally,these measurements are validated using well-known keypoint detectors on different image groups with various geometric and photometric attributes.

Сomputational Experience Optimization of Colors in Complex Fractal Images in Carpet Design Using the Simplex Method

This article proposes a new approach based on linear programming optimization to solve the problem of determining the color of a complex fractal carpet pattern.The principle is aimed at finding suitable dyes for mixing and their exact concentrations,which,when applied correctly,gives the desired color.The objective function and all constraints of the model are expressed linearly according to the solution variables.Carpet design has become an emerging technological field known for its creativity,science and technology.Many carpet design concepts have been analyzed in terms of color,contrast,brightness,as well as other mathematical concepts such as geometric changes and formulas.These concepts represent a common process in the carpet industry.This article discusses the use of complex fractal images in carpet design and simplex optimization in color selection.

A Blind Watermarking Algorithm Resisting to Geometric Transforms Based on SVD

A new watermarking algorithm resisting to geometric transformation based on singular value decomposition （SVD） in logarithm polar coordinate is proposed. The log-polar mapping （LPM） is used to resist rotation and scaling attacks, and the odd-even quantization algorithm is used to embed watermark so it can be extracted without the original host image. The experiments show that the proposed algorithm not only resists various geomet- ric attacks but also is robust enough to the common signal processing.

Geometrically robust video water-marking based on wavelet transform

Geometrical attacks can destroy most watermarking systems at present. So how to efficiently resist such kind of attacks remains a challenging direction in watermarking research. In this paper, a novel sequence watermarking scheme, which exploits a geometrical invariant, i.e. average AC energy （AAE） to combat arbitrary geometrical attacks, is presented. The scheme also uses some other measures, such as synchronization and optimal whitening filter to resist other attacks and improve detection performance. The experimental results show that the scheme can efficiently improve the visual quality of the watermarked video and achieve good robustness against random geometrical attacks. The scheme also has good robustness against other attacks, such as low-pass filtering along time axis and frame removal.

Multiresolution Free Form Object Modeling with PointSampled Geometry

In this paper an efficient framework for the creation of 3D digital contentwith point sampled ge-ometry is proposed. A new hierarchy of shape representations with three levelsis adopted in this framework. Based on this new hierarchical shape representation, the proposedframework offers concise integration of various volumetric- and surface-based modeling techniques,such as Boolean operation, offset, blending, free-form defor-mation, parameterization and texturemapping, and thus simplifies the complete modeling process. Previously to achieve the same goal,several separated algorithms had to be used independently with inconsistent volumetric and surfacerepresentations of the free-form object. Both graphics and industrial applications are presented todemonstrate the effectiveness and efficiency of the proposed framework.