Technique of Error Concealment for Block-Based Image Coding Using Genetic Algorithm

Since real world communication channels are not error free, the coded data transmitted on them may be corrupted, and block based image coding systems are vulnerable to transmission impairment. So the best neighborhood match method using genetic algorithm is used to conceal the error blocks. Experimental results show that the searching space can be greatly reduced by using genetic algorithm compared with exhaustive searching method, and good image quality is achieved. The peak signal noise ratios(PSNRs) of the restored images are increased greatly.

Color Image Segmentation Based on HSI Model

he objective of the research is to develop a fast procedure for segmenting typical videophone images. In this paper, a new approach to color image segmentation based on HSI(Hue, Saturation, Intensity) color model is reported. It is in contrast to the conventional approaches by using the three components of HSI color model in succession. This strategy makes the segmentation procedure much fast and effective. Experimental results with typical “headandshoulders” real images taken from videophone sequences show that the new appproach can fulfill the application requirements.

Novel region-based image compression method based on spiking cortical model

To get the high compression ratio as well as the high-quality reconstructed image, an effective image compression scheme named irregular segmentation region coding based on spiking cortical model（ISRCS） is presented. This scheme is region-based and mainly focuses on two issues. Firstly, an appropriate segmentation algorithm is developed to partition an image into some irregular regions and tidy contours, where the crucial regions corresponding to objects are retained and a lot of tiny parts are eliminated. The irregular regions and contours are coded using different methods respectively in the next step. The other issue is the coding method of contours where an efficient and novel chain code is employed. This scheme tries to find a compromise between the quality of reconstructed images and the compression ratio. Some principles and experiments are conducted and the results show its higher performance compared with other compression technologies, in terms of higher quality of reconstructed images, higher compression ratio and less time consuming.

Designing a High-Performance Deep Learning Theoretical Model for Biomedical Image Segmentation by Using Key Elements of the Latest U-Net-Based Architectures

Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.

MOTION-BASED REGION GROWING SEGMENTATION OF IMAGE SEQUENCES

This paper proposes a motion-based region growing segmentation scheme for the object-based video coding, which segments an image into homogeneous regions characterized by a coherent motion. It adopts a block matching algorithm to estimate motion vectors and uses morphological tools such as open-close by reconstruction and the region-growing version of the watershed algorithm for spatial segmentation to improve the temporal segmentation. In order to determine the reliable motion vectors, this paper also proposes a change detection algorithm and a multi-candidate pro- screening motion estimation method. Preliminary simulation results demonstrate that the proposed scheme is feasible. The main advantage of the scheme is its low computational load.

Recent Developments of Image Based Measurement Methods for Application to Transonic Flows in Industrial Wind Tunnels

The experimental investigation of unsteady complex flow fields in wind tunnels requires advanced measurement techniques. The most important of such image based measurement techniques are those for the measurement of planar flow velocity fields, planar pressure distribution, model location and deformation, model temperature and quantitative high speed flow visualization. The applications as carried out by DLR range from low speed flows to transonic flows, from high lift configurations to propellers and rotors, from wake vortex investigations in catapult facilities and water towing tanks to investigations of vortex break down phenomena on delta wings. The capability to use image based measurement techniques in transonic flows requires dedicated technical developments and experienced scientists due to the special environment of a transonic wind tunnel. In this paper an overview of the state-of-the art of the application of image based measurement techniques in transonic flows as performed by DLR＇s Institute of Aerodynamics and Flow Technology will be given.

Coding of Topological Entities in Feature-Based Parametric Modeling System

How to identify topological entities during rebuilding features is a critical problem in Feature-Based Parametric Modeling System (FBPMS). In the article, authors proposes a new coding approach to distinguish different entities. The coding mechanism is expatiated,and some typical examples are presented. At last, the algorithm of decoding is put forward based on set theory.

Color-texture segmentation using JSEG based on Gaussian mixture modeling

An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift （AMS） based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling （GMM） of image data for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust.

Measurement on soil deformation caused by expanded-base pile in transparent soil using particle image velocimetry (PIV)

A new small-scale geotechnical physical model in 1-g and unconfined condition, combining the transparent soil, close-range photogrammetry and particle image velocimetry(PIV), was employed, which provides a non-intrusively internal deformation measurement approach to monitor the internal deformation of soil caused by expanded-base pile jacking with casing. The transparent soil was made of fused quartz and its refractive index matched blended oil, adding reflective particles(glass beads). Closerange photogrammetry was employed to record the images of the process of casing jacking and extraction in transparent soil, allowing the use of Matlab-based Geo-PIV to figure out the displacement field converted from image space to object space. Analysis of test results indicates that the maximum displacement caused by casing jacking for expandedconical-base pile is decreased by 29% compared with that for expanded-flat-base pile. The main movement happens at the early stage of casing extraction. The maximum displacement caused by casing extraction for the conical base is about 43% of that for the flatbase, while the affected zone caused by casing extraction for the conical base accounts for about 1/3 of that for the flat base. The contraction for horizontal displacements tends to decrease with the depth increasing. By contrast, the contraction under pile base decreases with the increasing of displacement. The displacements generated by jacking a conventional pile having a diameter equal to the casing diameter of the expanded-base pile were comparable to the net displacement taking place due to expanded-base pile installation for the conical base pile.

DC2Net:An Asian Soybean Rust Detection Model Based on Hyperspectral Imaging and Deep Learning

Asian soybean rust(ASR)is one of the major diseases that causes serious yield loss worldwide,even up to 80%.Early and accurate detection of ASR is critical to reduce economic losses.Hyperspectral imaging,combined with deep learning,has already been proved as a powerful tool to detect crop diseases.However,current deep learning models are limited to extract both spatial and spectral features in hyperspectral images due to the use of fixed geometric structure of the convolutional kernels,leading to the fact that the detection accuracy of current models remains further improvement.

GLOBAL MEASURE ON IMAGE CONTENT

This paper investigated approaches to supporting effective and efficient retrieval of image based on principle component analysis. First, it extracted the image content, texture and color. Gabor wavelet transforms were used to extract texture feature of the image and the average color was used to extract the color features. The principle component of the feature vector of image can be constructed. Content based image retrieval was performed by comparing the feature vector of the query image with the projection feature vector of the image database on the principle component space of the query image. By this technique, it can reduce the dimensionality of feature vector, which in turn reduce the searching time.

Compression of MR Images Using DWT by Comparing RGB and YCbCr Color Spaces

This paper consists of a lossy image compression algorithm dedicated to the medical images doing comparison of RGB and YCbCr color space. Several lossy/lossless transform coding techniques are used for medical image compression. Discrete Wavelet Transform (DWT) is one such widely used technique. After a preprocessing step (remove the mean and RGB to YCbCr transformation), the DWT is applied and followed by the bisection method including thresholding, the quantization, dequantization, the Inverse Discrete Wavelet Transform (IDWT), YCbCr to RGB transform of mean recovering. To obtain the best compression ratio (CR), the next step encoding algorithm is used for compressing the input medical image into three matrices and forward to DWT block a corresponding containing the maximum possible of run of zeros at its end. The last step decoding algorithm is used to decompress the image using IDWT that is applied to get three matrices of medical image.

A polynomial hybrid reflection model and measurement of its parameters based on images of sample

Reflectance model is a basic concept in computer vision. Some existing models combining the classical diffuse reflectance model and those for surfaces containing specular components can approximately describe real reflectance. But the ratio of diffuse and specular reflection decided manually has no clear meaning. We propose a new polynomial hybrid reflectance model. The reflectance map equation with a known shape （for example cylinder） as a sample is used to estimate parameters of the proposed reflectance model by least square regression algorithm. Then the reflectance parameters for surfaces of the same class of materials can be determined. Experiments are performed for a metal surface. The synthesis images produced by the proposed method and existing ones are compared with the real acquired image, and the results show that the proposed reflectance model is suitable for describing real reflectance.

Particle-Image Velocimetry and Force Measurements of Leading-Edge Serrations on Owl-Based Wing Models

High-resolution Particle-Image Velocimetry （PIV） and time-resolved force measurements were performed to analyze the impact of the comb-like structure on the leading edge of barn owl wings on the flow field and overall aerodynamic performance. The Reynolds number was varied in the range of 40,000 to 120,000 and the range of angle of attack was 0° to 6° for the PIV and -15° to ＋20° for the force measurements to cover the full flight envelope of the owl. As a reference, a wind-tunnel model which possessed a geometry based on the shape of a typical barn owl wing without any owl-specific adaptations was built, and measurements were performed in the aforementioned Reynolds number and angle of attack： range. This clean wing model shows a separation bubble in the distal part of the wing at higher angles of attack. Two types of comb-like structures, i.e., artificial serrations, were manufactured to model the owl＇s leading edge with respect to its length, thickness, and material properties. The artificial structures were able to reduce the size of the separation region and additionally cause a more uniform size of the vortical structures shed by the separation bubble within the Reynolds number range investigated, resulting in stable gliding flight independent of the flight velocity. However, due to increased drag coefficients in conjunction with similar lift coefficients, the overall aerodynamic performance, i.e., lift-to-drag ratio is reduced for the serrated models. Nevertheless, especially at lower Reynolds numbers the stabilizing effect of the uniform vortex size outperforms the lower aerodynamic performance.

QR Code二维条形码的图像识别

在现代社会商品流通中,为了提高工作效率,条码识别技术得到了广泛的应用。一维条码受到容量的限制,仅能标识商品,而不能描述商品。二维条码解决了一维条码面临的容量问题,具有信息量大、可靠性高、保密防伪等优点。文中介绍了QR Code二维条形码的技术,给出了基于数字图像处理的QR Code二维条形码的识别方案。该方案综合运用了图像灰度化、二值化、去噪、边缘检测、图像旋转等多种图像处理方法实现条码图像的预处理、条码定位、条码分割和数据提取。实验表明:该方案大大提高了识读的灵活性和可靠性。

Multiview Visibility Estimation for Image-Based Modeling

In this paper, we investigate the problem of determining regions in 3D scene visible to some given viewpoints when obstacles are present in the scene. We assume that the obstacles are composed of some opaque objects with closed surfaces. The problem is formulated in an implicit framework where the obstacles are represented by a level set function. The visible and invisible regions of the given viewpoints are determined through an efficient implicit ray tracing technique. As an extension of our approach, we apply the multiview visibility estimation to an image-based modeling technique. The unknown scene geometry and multiview visibility information are incorporated into a variational energy functional. By minimizing the energy functional, the true scene geometry as well as the accurate visibility information of the multiple views can be recovered from a number of scene images. This makes it feasible to handle the visibility problem of multiple views by our approach when the true scene geometry is unknown.

石窟寺数据库建设中Image Based Modeling照片阵列建模的应用探索——以桂林石窟寺调查为例

石窟寺是文物的重要组成部分,本文以2021年全国石窟寺专项调查工作为基础,讨论了Image Based Modeling建模技术的相关发展以及应用,探寻其应用于石窟寺调查的可能性,及其应用于日后文物保护研究以及建设文物数据库的重要价值。

A fast algorithm for computing moments of gray images based on NAM and extended shading approach

Computing moments on images is very important in the fields of image processing and pattern recognition. The non-symmetry and anti-packing model （NAM） is a general pattern representation model that has been developed to help design some efficient image representation methods. In this paper, inspired by the idea of computing moments based on the S-Tree coding （STC） representation and by using the NAM and extended shading （NAMES） approach, we propose a fast algorithm for computing lower order moments based on the NAMES representation, which takes O（N） time where N is the number of NAM blocks. By taking three idiomatic standard gray images ＇Lena＇, ＇F16＇, and ＇Peppers＇ in tile field of image processing as typical test objects, and by comparing our proposed algorithm with the conventional algorithm and the popular STC representation algorithm for computing the lower order moments, the theoretical and experimental results presented in this paper show that the average execution time improvement ratios of the proposed NAMES approach over the STC approach, and also the conventional approach are 26.63%, and 82.57% respectively while maintaining the image quality.

Color space quantization-based clustering for image retrieval

Color descriptors of an image are the most widely used visual features in content-based image retrieval sys- tems. In this study, we present a novel color-based image retrieval framework by integrating color space quantization and feature coding. Although color features have advantages such as robustness and simple extraction, direct processing of the abundant amount of color information in an RGB image is a challenging task. To overcome this problem, a color space clustering quantization algorithm is proposed to obtain the clustering color space （CCS） by clustering the CIE1976L＊a＊b＊ space into 256 distinct colors, which ade- quately accommodate human visual perception. In addition, a new feature coding method called feature-to-character coding （FCC） is proposed to encode the block-based main color fea- tures into character codes. In this method, images are repre- sented by character codes that contribute to efficiently build- ing an inverted index by using color features and by utilizing text-based search engines. Benefiting from its high-efficiency computation, the proposed framework can also be applied to large-scale web image retrieval. The experimental results demonstrate that the proposed system can produce a signifi- cant augmentation in performance when compared to block- based main color image retrieval systems that utilize the tra- ditional HSV（Hue, Saturation, Value） quantization method.