Design of Content-Based Retrieval System in Remote Sensing Image Database

To retrieve the object region efficaciously from massive remote sensing image database, a model for content-based retrieval of remote sensing image is given according to the characters of remote sensing image application firstly, and then the algorithm adopted for feature extraction and multidimensional indexing, and relevance feedback by this model are analyzed in detail. Finally, the contents intending to be researched about this model are proposed.

CMA:an efficient index algorithmof clustering supporting fast retrieval oflarge image databases

To realize content-hased retrieval of large image databases, it is required to develop an efficient index and retrieval scheme. This paper proposes an index algorithm of clustering called CMA, which supports fast retrieval of large image databases. CMA takes advantages of k-means and self-adaptive algorithms. It is simple and works without any user interactions. There are two main stages in this algorithm. In the first stage, it classifies images in a database into several clusters, and automatically gets the necessary parameters for the next stage-k-means iteration. The CMA algorithm is tested on a large database of more than ten thousand images and compare it with k-means algorithm. Experimental results show that this algorithm is effective in both precision and retrieval time.

CASIA-Iris-Africa:A Large-scale African Iris Image Database

Iris biometrics is a phenotypic biometric trait that has proven to be agnostic to human natural physiological changes.Research on iris biometrics has progressed tremendously,partly due to publicly available iris databases.Various databases have been available to researchers that address pressing iris biometric challenges such as constraint,mobile,multispectral,synthetics,long-distance,contact lenses,liveness detection,etc.However,these databases mostly contain subjects of Caucasian and Asian docents with very few Africans.Despite many investigative studies on racial bias in face biometrics,very few studies on iris biometrics have been published,mainly due to the lack of racially diverse large-scale databases containing sufficient iris samples of Africans in the public domain.Furthermore,most of these databases contain a relatively small number of subjects and labelled images.This paper proposes a large-scale African database named Chinese Academy of Sciences Institute of Automation(CASIA)-Iris-Africa that can be used as a complementary database for the iris recognition community to mediate the effect of racial biases on Africans.The database contains 28717 images of 1023 African subjects(2046 iris classes)with age,gender,and ethnicity attributes that can be useful in demographically sensitive studies of Africans.Sets of specific application protocols are incorporated with the database to ensure the database’s variability and scalability.Performance results of some open-source state-of-the-art(SOTA)algorithms on the database are presented,which will serve as baseline performances.The relatively poor performances of the baseline algorithms on the proposed database despite better performance on other databases prove that racial biases exist in these iris recognition algorithms.

Research on Image Preprocessing Algorithm for Rail Surface Recognition

The rail surface status image is affected by the noise in the shooting environment and contains a large amount of interference information, which increases the difficulty of rail surface status identification. In order to solve this problem, a preprocessing method for the rail surface state image is proposed. The preprocessing process mainly includes image graying, image denoising, image geometric correction, image extraction, data amplification, and finally building the rail surface image database. The experimental results show that this method can efficiently complete image processing, facilitate feature extraction of rail surface status images, and improve rail surface status recognition accuracy.

Construction of an artificial intelligence system in dermatology: effectiveness and consideration of Chinese Skin Image Database (CSID)

After more than 60 years of development,artificial intelligence(AI)has been widely used in various fields.Especially in recent years,with the development of deep learning,AI has made many remarkable achievements in the medical field.Dermatology,as a clinical discipline with morphology as its main feature,is particularly suitable for the development of AI.The rapid development of skin imaging technology has helped dermatologists to assist in the diagnosis of diseases and has greatly improved the accuracy of diagnosis.Skin imaging data have natural big data attributes,which is important for AI research.The establishment of the Chinese Skin Image Database(CSID)has solved many problems such as isolated data islands and inconsistent data quality.Based on the CSID,many pioneering achievements have been made in the research and development of AI-assisted decision-making software,the establishment of expert organizations,personnel training,scientific research,and so on.At present,there are still many problems with AI in the field of dermatology,such as clinical validation,medical device licensing,interdisciplinary,and standard formulation,which urgently need to be solved by joint efforts of all parties.

Digitization of Astronomical Photographic Plates of China and Astrometric Measurement of Single-exposure Plates

From the mid-19th century to the end of the 20th century, photographic plates served as the primary detectors for astronomical observations. Astronomical photographic observations in China began in 1901, and over a century, a total of approximately 30,000 astronomical photographic plates were captured. These historical plates play an irreplaceable role in conducting long-term, time-domain astronomical research. To preserve and explore these valuable original astronomical observational data, Shanghai Astronomical Observatory has organized the transportation of plates, taken during nighttime observations from various stations across the country, to the Sheshan Plate Archive for centralized preservation. For the first time, plate information statistics were calculated. On this basis, the plates were cleaned and digitally scanned, and finally digitized images were acquired for 29,314 plates. In this study, using Gaia DR2 as the reference star catalog, astrometric processing was carried out successfully on 15,696 single-exposure plates, including object extraction, stellar identification,and plate model computation. As a result, for long focal length telescopes, such as the 40 cm double-tube refractor telescope, the 1.56 m reflector telescope at Shanghai Astronomical Observatory, and the 1m reflecting telescope at Yunnan Astronomical Observatory, the astrometric accuracy obtained for their plates is approximately 0."1–0."3. The distribution of astrometric accuracy for medium and short focal length telescopes ranges from 0."3 to 1."0. The relevant data of this batch of plates, including digitized images and a stellar catalog of the plates, are archived and released by the National Astronomical Data Center. Users can access and download plate data based on keywords such as station, telescope, observation year, and observed celestial coordinates.

Distribution of Large Volume Image Data Using the Mixed Mode of Java Servlet and COM on Web

This paper reports some researches on distribution of large volume image data using techniques of the Mixed Mode of Java Servlet and COM on Web. The architecture and key technologies are discussed in detail. The web distribution system of image is implemented and the system is tested by the application instances. At last, the advantages and disadvantages for this web image distribution mode are analyzed.

Intelligent Detection Model Based on a Fully Convolutional Neural Network for Pavement Cracks

The crack is a common pavement failure problem.A lack of periodic maintenance will result in extending the cracks and damage the pavement,which will affect the normal use of the road.Therefore,it is significant to establish an efficient intelligent identification model for pavement cracks.The neural network is a method of simulating animal nervous systems using gradient descent to predict results by learning a weight matrix.It has been widely used in geotechnical engineering,computer vision,medicine,and other fields.However,there are three major problems in the application of neural networks to crack identification.There are too few layers,extracted crack features are not complete,and the method lacks the efficiency to calculate the whole picture.In this study,a fully convolutional neural network based on ResNet-101 is used to establish an intelligent identification model of pavement crack regions.This method,using a convolutional layer instead of a fully connected layer,realizes full convolution and accelerates calculation.The region proposals come from the feature map at the end of the base network,which avoids multiple computations of the same picture.Online hard example mining and data-augmentation techniques are adopted to improve the model’s recognition accuracy.We trained and tested Concrete Crack Images for Classification(CCIC),which is a public dataset collected using smartphones,and the Crack Image Database(CIDB),which was automatically collected using vehicle-mounted charge-coupled device cameras,with identification accuracy reaching 91.4%and 86.4%,respectively.The proposed model has a higher recognition accuracy and recall rate than Faster RCNN and different depth models,and can extract more complete and accurate crack features in CIDB.We also analyzed translation processing,fuzzy,scaling,and distorted images.The proposed model shows a strong robustness and stability,and can automatically identify image cracks of different forms.It has broad application prospects in practical engineering problems.

BGIDB:A fundus ground truth building tool with automatic DDLS classification for glaucoma research

Taking advantage of the new standard HTML5,we designed an online tool called a browser/server-based glaucoma image database builder(BGIDB)for the demarcation of the optic disk and cup’s ellipse-like boundaries.The B-spline interpolation algorithm is used,and a specially designed algorithm is proposed for classifying the disease grade according to the disc damage likelihood scale criterion,which is correlated strongly with the glaucoma process by quantity.This tool exhibits the best performance with a low overlapping error of 4.34%for the optic disk demarcation and 8.31%for the optic cup demarcation.It also has preferable time-consuming as compared to other tools and is a cross-platform system.This tool has already been utilized in building the ophthalmic image database in the cooperation of Center for Ophthalmic Imaging Research and The Second Xiangya Hospital.

Visual Ontology Construction for Digitized Art Image Retrieval

Current investigations on visual information retrieval are generally content-based methods. The significant difference between similarity in low-level features and similarity in high-level semantic meanings is still a major challenge in the area of image retrieval. In this work, a scheme for constructing visual ontology to retrieve art images is proposed. The proposed ontology describes images in various aspects, including type ＆ style, objects and global perceptual effects. Concepts in the ontology could be automatically derived. Various art image classification methods are employed based on low-level image features. Non-objective semantics are introduced, and how to express these semantics is given. The proposed ontology scheme could make users more naturally find visual information and thus narrows the “semantic gap”. Experimental implementation demonstrates its good potential for retrieving art images in a human-centered manner.

Morphomics via next-generation electron microscopy

The living body is composed of innumerable fine and complex structures.Although these structures have been studied in the past,a vast amount of information pertaining to them still remains unknown.When attempting to observe these ultra-structures,the use of electron microscopy(EM)has become indispensable.However,conventional EM settings are limited to a narrow tissue area,which can bias observations.Recently,new trends in EM research have emerged,enabling coverage of far broader,nano-scale fields of view for two-dimensional wide areas and three-dimensional large volumes.Moreover,cutting-edge bioimage informatics conducted via deep learning has accelerated the quantification of complex morphological bioimages.Taken together,these technological and analytical advances have led to the comprehensive acquisition and quantification of cellular morphology,which now arises as a new omics science termed‘morphomics’.

Ellipse-based shape description and retrieval method

Using a group of ellipses to approach the shape contour, a new shape retrieval method is presented in this paper. In order to keep shape-based retrieval invariant to its position, orientation and size, the shape normalization method is presented. From our research, any closed shape contour can be uniquely decomposed into a group of ellipses, and the original shape contour can be re-constructed using the decomposed ellipses. The ellipse-based shape description and similar retrieval method is introduced in this paper. Based on ellipse's contribution to shape contour, the decomposed ellipses are parted into low-order ellipses and high-order ellipses. The low-order ellipses measure the macroscopic feature of a shape contour, and the high-order ellipses measure the microscopic feature. The two-phase shape matching method is given. Through the experiment test, our method has better shape retrieval effect.