From text to image:challenges in integrating vision into ChatGPT for medical image interpretation

Large language models(LLMs),such as ChatGPT developed by OpenAI,represent a significant advancement in artificial intelligence(AI),designed to understand,generate,and interpret human language by analyzing extensive text data.Their potential integration into clinical settings offers a promising avenue that could transform clinical diagnosis and decision-making processes in the future(Thirunavukarasu et al.,2023).This article aims to provide an in-depth analysis of LLMs’current and potential impact on clinical practices.Their ability to generate differential diagnosis lists underscores their potential as invaluable tools in medical practice and education(Hirosawa et al.,2023;Koga et al.,2023).

Improving the Transmission Security of Vein Images Using a Bezier Curve and Long Short-Term Memory

The act of transmitting photos via the Internet has become a routine and significant activity.Enhancing the security measures to safeguard these images from counterfeiting and modifications is a critical domain that can still be further enhanced.This study presents a system that employs a range of approaches and algorithms to ensure the security of transmitted venous images.The main goal of this work is to create a very effective system for compressing individual biometrics in order to improve the overall accuracy and security of digital photographs by means of image compression.This paper introduces a content-based image authentication mechanism that is suitable for usage across an untrusted network and resistant to data loss during transmission.By employing scale attributes and a key-dependent parametric Long Short-Term Memory(LSTM),it is feasible to improve the resilience of digital signatures against image deterioration and strengthen their security against malicious actions.Furthermore,the successful implementation of transmitting biometric data in a compressed format over a wireless network has been accomplished.For applications involving the transmission and sharing of images across a network.The suggested technique utilizes the scalability of a structural digital signature to attain a satisfactory equilibrium between security and picture transfer.An effective adaptive compression strategy was created to lengthen the overall lifetime of the network by sharing the processing of responsibilities.This scheme ensures a large reduction in computational and energy requirements while minimizing image quality loss.This approach employs multi-scale characteristics to improve the resistance of signatures against image deterioration.The proposed system attained a Gaussian noise value of 98%and a rotation accuracy surpassing 99%.

Image Retrieval with Text Manipulation by Local Feature Modification

The demand for image retrieval with text manipulation exists in many fields, such as e-commerce and Internet search. Deep metric learning methods are used by most researchers to calculate the similarity between the query and the candidate image by fusing the global feature of the query image and the text feature. However, the text usually corresponds to the local feature of the query image rather than the global feature. Therefore, in this paper, we propose a framework of image retrieval with text manipulation by local feature modification(LFM-IR) which can focus on the related image regions and attributes and perform modification. A spatial attention module and a channel attention module are designed to realize the semantic mapping between image and text. We achieve excellent performance on three benchmark datasets, namely Color-Shape-Size(CSS), Massachusetts Institute of Technology(MIT) States and Fashion200K(+8.3%, +0.7% and +4.6% in R@1).

An Efficient Text Recognition System from Complex Color Image for Helping the Visually Impaired Persons

The challenge faced by the visually impaired persons in their day-today lives is to interpret text from documents.In this context,to help these people,the objective of this work is to develop an efficient text recognition system that allows the isolation,the extraction,and the recognition of text in the case of documents having a textured background,a degraded aspect of colors,and of poor quality,and to synthesize it into speech.This system basically consists of three algorithms:a text localization and detection algorithm based on mathematical morphology method(MMM);a text extraction algorithm based on the gamma correction method(GCM);and an optical character recognition(OCR)algorithm for text recognition.A detailed complexity study of the different blocks of this text recognition system has been realized.Following this study,an acceleration of the GCM algorithm(AGCM)is proposed.The AGCM algorithm has reduced the complexity in the text recognition system by 70%and kept the same quality of text recognition as that of the original method.To assist visually impaired persons,a graphical interface of the entire text recognition chain has been developed,allowing the capture of images from a camera,rapid and intuitive visualization of the recognized text from this image,and text-to-speech synthesis.Our text recognition system provides an improvement of 6.8%for the recognition rate and 7.6%for the F-measure relative to GCM and AGCM algorithms.

浅谈如何使用SQL中的image和text数据

SQL中的image和text类型的数据带给用户很多便利。但具体使用时常常会遇到许多问题，那幺该如何解决呢，我们可以用两个命令提示待下的命令bcp和textcopy来解决。

SQL Server中text/image类型数据的使用

使用MS SQL Server进行数据库软件开发时,对于text和image类型的数据在进行存取操作时,有别与其他数据类型.结合应用实例介绍了通过Transact-SQL语句、API函数、sp_tableoption存储过程、bcp命令、textcopy命令等几种常用的方法使用text/image类型数据.

Integral imaging-based tabletop light field 3D display with large viewing angle

Light field 3D display technology is considered a revolutionary technology to address the critical visual fatigue issues in the existing 3D displays.Tabletop light field 3D display provides a brand-new display form that satisfies multi-user shared viewing and collaborative works,and it is poised to become a potential alternative to the traditional wall and portable display forms.However,a large radial viewing angle and correct radial perspective and parallax are still out of reach for most current tabletop light field 3D displays due to the limited amount of spatial information.To address the viewing angle and perspective issues,a novel integral imaging-based tabletop light field 3D display with a simple flat-panel structure is proposed and developed by applying a compound lens array,two spliced 8K liquid crystal display panels,and a light shaping diffuser screen.The compound lens array is designed to be composed of multiple three-piece compound lens units by employing a reverse design scheme,which greatly extends the radial viewing angle in the case of a limited amount of spatial information and balances other important 3D display parameters.The proposed display has a radial viewing angle of 68.7°in a large display size of 43.5 inches,which is larger than the conventional tabletop light field 3D displays.The radial perspective and parallax are correct,and high-resolution 3D images can be reproduced in large radial viewing positions.We envision that this proposed display opens up possibility for redefining the display forms of consumer electronics.

Criticism of Modern Western Travel Texts on China and Image Crisis of China

From the 13th century to the middle of the 18th century, the travel texts to China depicted a beautiful Chinese image of a country of wealth, morality, civilization, wisdom and belief to the west. The author analyzed the western missionaries’ criticism of China from the 18th to the 19th century, the professional navigators’ criticism of China, and the researchers’ criticism of China’s decline, decay and stagnation, so as to project their historical pursuit of change and self transcendence. During this period, more and more national landscape images of decline, decay and stagnation appeared in the travel texts, and the idealized image of China began to walk into the tomb of history.

An Automatic Text Region Positioning Method for the Low-Contrast Image

Text extraction is the key step in the character recognition;its accuracy highly relies on the location of the text region. In this paper, we propose a new method which can find the text location automatically to solve some regional problems such as incomplete, false position or orientation deviation occurred in the low-contrast image text extraction. Firstly, we make some pre-processing for the original image, including color space transform, contrast-limited adaptive histogram equalization, Sobel edge detector, morphological method and eight neighborhood processing method (ENPM) etc., to provide some results to compare the different methods. Secondly, we use the connected component analysis (CCA) method to get several connected parts and non-connected parts, then use the morphology method and CCA again for the non-connected part to erode some noises, obtain another connected and non-connected parts. Thirdly, we compute the edge feature for all connected areas, combine Support Vector Machine (SVM) to classify the real text region, obtain the text location coordinates. Finally, we use the text region coordinate to extract the block including the text, then binarize, cluster and recognize all text information. At last, we calculate the precision rate and recall rate to evaluate the method for more than 200 images. The experiments show that the method we proposed is robust for low-contrast text images with the variations in font size and font color, different language, gloomy environment, etc.

Text Extraction in Complex Color Document Images for Enhanced Readability

Often we encounter documents with text printed on complex color background. Readability of textual contents in such documents is very poor due to complexity of the background and mix up of color(s) of foreground text with colors of background. Automatic segmentation of foreground text in such document images is very much essential for smooth reading of the document contents either by human or by machine. In this paper we propose a novel approach to extract the foreground text in color document images having complex background. The proposed approach is a hybrid approach which combines connected component and texture feature analysis of potential text regions. The proposed approach utilizes Canny edge detector to detect all possible text edge pixels. Connected component analysis is performed on these edge pixels to identify candidate text regions. Because of background complexity it is also possible that a non-text region may be identified as a text region. This problem is overcome by analyzing the texture features of potential text region corresponding to each connected component. An unsupervised local thresholding is devised to perform foreground segmentation in detected text regions. Finally the text regions which are noisy are identified and reprocessed to further enhance the quality of retrieved foreground. The proposed approach can handle document images with varying background of multiple colors and texture;and foreground text in any color, font, size and orientation. Experimental results show that the proposed algorithm detects on an average 97.12% of text regions in the source document. Readability of the extracted foreground text is illustrated through Optical character recognition (OCR) in case the text is in English. The proposed approach is compared with some existing methods of foreground separation in document images. Experimental results show that our approach performs better.

Tourism Image Perception Research of Mount Tai Scenic Area: Text Analysis Based on Ctrip Tourist Comments

Tourism destination image research has always been a hot issue in tourism research, which is related to the development and sustainability of destination tourism. Mount Tai Scenic Area, a world double-heritage site is taken as the research object. By collecting comments about Mount Tai Scenic Area on Ctrip, and using content analysis, cognitive image, emotional image and overall image of Mount Tai Scenic Area are analyzed with the help of “cognitive-emotional” model. The results show that visitors' perception of the cognitive image of Mount Tai Scenic Area is multi-dimensional;tourists' perception of the emotional image of Mount Tai Scenic Area is positive;the tourists' perception of the overall image of Mount Tai Scenic Area shows a four-layer “core-edge” structure of “core-sub core-sub edge-edge”. On this basis, the paper puts forward some suggestions for improving the tourism development of Mount Tai Scenic Area.

Clustering based segmentation of text in complex color images

We propose a novel scheme based on clustering analysis in color space to solve text segmentation in complex color images. Text segmentation includes automatic clustering of color space and foreground image generation. Two methods are also proposed for automatic clustering: The first one is to determine the optimal number of clusters and the second one is the fuzzy competitively clustering method based on competitively learning techniques. Essential foreground images obtained from any of the color clusters are combined into foreground images. Further performance analysis reveals the advantages of the proposed methods.

Understanding and Detecting Inefficient Image Displaying Issues in Android Apps

Mobile applications(apps for short)often need to display images.However,inefficient image displaying(IID)issues are pervasive in mobile apps,and can severely impact app performance and user experience.This paper first establishes a descriptive framework for the image displaying procedures of IID issues.Based on the descriptive framework,we conduct an empirical study of 216 real-world IID issues collected from 243 popular open-source Android apps to validate the presence and severity of IID issues,and then shed light on these issues’characteristics to support research on effective issue detection.With the findings of this study,we propose a static IID issue detection tool TAPIR and evaluate it with 243 real-world Android apps.Encouragingly,49 and 64 previously-unknown IID issues in two different versions of 16 apps reported by TAPIR are manually confirmed as true positives,respectively,and 16 previously-unknown IID issues reported by TAPIR have been confirmed by developers and 13 have been fixed.Then,we further evaluate the performance impact of these detected IID issues and the performance improvement if they are fixed.The results demonstrate that the IID issues detected by TAPIR indeed cause significant performance degradation,which further show the effectiveness and efficiency of TAPIR.

PC-BASED SURFACE RECONSTRUCTION OF MEDICAL CT IMAGES

It is an active research area to reconstruct 3-D object and display its visible surfacesfrom cross-sectional images. In this paper, the methods of reconstructing 3-D object from medicalCT images and displaying the visible surfaces are discussed. A polygon approximation methodthat forms polygon with the same number of segment points and a fast interpolation method forcross-sectional contours are presented at first. Then the voxel set of a human liver is reconstructed.And then the liver voxel set is displayed using depth and gradient shading methods. The softwareis written in C programming language at a microcomputer image processing system with a PC/ATcomputer as the host and a PC-VISION board as the image processing unit. The result of theprocessing is satisfying.

A Visible Light Imaging System for the Estimation of Plasma Vertical Displacement in J-TEXT

A wide-viewing-angle visible light imaging system （VLIS） was mounted on the Joint Texas Experimental Tokamak （J-TEXT） to monitor the discharge process. It is proposed that by using the film data recorded the plasma vertical displacement can be estimated. In this paper installation and operation of the VLIS are presented in detailed. The estimated result is further compared with that measured by using an array of magnetic pickup coils. Their consistency verifies that the estimation of the plasma vertical displacement in J-TEXT by using the imaging data is promising.

The Flipping-Free Full-Parallax Tabletop Integral Imaging with Enhanced Viewing Angle Based on Space-Multiplexed Voxel Screen and Compound Lens Array

Tabletop integral imaging display with a more realistic and immersive experience has always been a hot spot in three-dimensional imaging technology,widely used in biomedical imaging and visualization to enhance medical diagnosis.However,the traditional structural characteristics of integral imaging display inevitably introduce the flipping effect outside the effective viewing angle.Here,a full-parallax tabletop integral imaging display without the flipping effect based on space-multiplexed voxel screen and compound lens array is demonstrated,and two holographic functional screens with different parameters are optically designed and fabricated.To eliminate the flipping effect in the reconstruction process,the space-multiplexed voxel screen consisting of a projector array and the holographic functional screen is presented to constrain light beams passing through the corresponding lens.To greatly promote imaging quality within the viewing area,the aspherical structure of the compound lens is optimized to balance the aberrations.It cooperates with the holographic functional screen to modulate the light field spatial distribution.Compared with the simulation results,the distortion rate of the imaging display is reduced to less than 9%from more than 30%.In the experiment,the floating high-quality reconstructed three-dimensional image without the flipping effect can be observed with the correct 3D perception at 96°×96°viewing angle,where 44,100 viewpoints are employed.