Kinds Of Images Research Articles

A REVIEW ON SENTIMENT ANALYSIS

Sentiment analysis is an technique which identifies text from different kind of image and dataset. It also characterized the data from Twitter, Facebook and YouTube. Sentiment analysis is a part of machine learning technique which find the trends of data and predict future trend. This paper shows different kind of machine learning and lexicon analysis methodology in the current trend and also we compare different kind of algorithms in sentiment analysis in different dataset.

MIND AS A CAUSAL SYSTEM IN AL FARABI’S PHILOSOPHY OF MUSIC

Music elaborates through with a more powerful cognition system in which melodies shares some aspects with mathematics and language in general. This research reveals the basic aspects of analysis, music as a self-referential structure and transformative system, and focuses on the concept and definition of perception. There is still not many treatises in the philosophy of music about the functioning of musical emotion in the brain. Al Farabi’s Great Music Book is the first and still unique in this regard. One of the most important reasons that makes this work valuable is that Fârâbî’s work named Kitab al Musiqa brings new information to his music studies. This review details the evolutionary development of music and its cultural and psychological effects on the human mind. For centuries, philosophers queried the interaction and fundamental principles and causality of music in human life. The concept of musical perception, which is formed as a result of the relationship between perception and music, has been examined. A musical perception process was created by using general perception processes. Al Farabi illustrates the clarity of these questions from a neuroscience perspective in his Kitab al Musiqa al Kabir review. We examine with innovative results in the conditions of an intense history of intellectual research established by cognitive systems that insist on various aspects of human musical behavior. The main purpose of this article is to remember Al Farabi’s authority in music philosophy. This article claims that Farabi’s philosophy is valid in the conceptual framework of this century. Methodologically, we chose to translate from the original Arabic manuscript of Kitab al Musiqa al Kabir. The proposition in Al Farabi’s Philosophy that music is a kind of image is impressive.

Focused and TSOM Images Two-Input Deep-Learning Method for Through-Focus Scanning Measuring

Through-focus scanning optical microscopy (TSOM) is one of the recommended measurement methods in semiconductor manufacturing industry in recent years because of its rapid and nondestructive properties. As a computational imaging method, TSOM takes full advantage of the information from defocused images rather than only concentrating on focused images. In order to improve the accuracy of TSOM in nanoscale dimensional measurement, this paper proposes a two-input deep-learning TSOM method based on Convolutional Neural Network (CNN). The TSOM image and the focused image are taken as the two inputs of the network. The TSOM image is processed by three columns convolutional channels and the focused image is processed by a single convolution channel for feature extraction. Then, the features extracted from the two kinds of images are merged and mapped to the measuring parameters for output. Our method makes effective use of the image information collected by TSOM system, for which the measurement process is fast and convenient with high accuracy. The MSE of the method can reach 5.18 nm2 in the measurement of gold lines with a linewidth range of 247–1010 nm and the measuring accuracy is much higher than other deep-learning TSOM methods.

Intelligent Image Diagnosis of Pneumoconiosis Based on Wavelet Transform-Derived Texture Features.

Objective Early diagnosis and treatment of occupational pneumoconiosis can delay the development of the disease. This study is aimed at investigating the intelligent diagnosis of occupational pneumoconiosis by wavelet transform-derived entropy. Method From June 2013 to June 2020, the high KV digital radiographs (DR) and computed tomography (CT) images from a total of 60 patients with occupational pneumoconiosis in our department were selected. The wavelet transform-derived texture features were extracted from all images, and the decision tree was used for feature selection. The support vector machines (SVM) with three kernel functions were selected to classify the two kinds of images, and their diagnostic efficiency was compared. Result After eight times of wavelet decomposition, eight wavelet entropy texture features (feature set) were extracted, and six were selected to form the feature subset. The classification effect of linear kernel function SVM is better than those of other functions, with an accuracy of 84.2%. The diagnostic values of DR and CT for occupational pneumoconiosis were the same (kappa = 0.737, P < 0.001). The detection rate of CT for stage I of occupational pneumoconiosis was significantly higher than that of DR (P = 0.031). Conclusion It is helpful to improve the early diagnosis level of pneumoconiosis by using SVM to make an intelligent diagnosis based on the wavelet entropy.

A Novel Deep Learning Method to Predict Lung Cancer Long-Term Survival With Biological Knowledge Incorporated Gene Expression Images and Clinical Data.

Lung cancer is the leading cause of the cancer deaths. Therefore, predicting the survival status of lung cancer patients is of great value. However, the existing methods mainly depend on statistical machine learning (ML) algorithms. Moreover, they are not appropriate for high-dimensionality genomics data, and deep learning (DL), with strong high-dimensional data learning capability, can be used to predict lung cancer survival using genomics data. The Cancer Genome Atlas (TCGA) is a great database that contains many kinds of genomics data for 33 cancer types. With this enormous amount of data, researchers can analyze key factors related to cancer therapy. This paper proposes a novel method to predict lung cancer long-term survival using gene expression data from TCGA. Firstly, we select the most relevant genes to the target problem by the supervised feature selection method called mutual information selector. Secondly, we propose a method to convert gene expression data into two kinds of images with KEGG BRITE and KEGG Pathway data incorporated, so that we could make good use of the convolutional neural network (CNN) model to learn high-level features. Afterwards, we design a CNN-based DL model and added two kinds of clinical data to improve the performance, so that we finally got a multimodal DL model. The generalized experiments results indicated that our method performed much better than the ML models and unimodal DL models. Furthermore, we conduct survival analysis and observe that our model could better divide the samples into high-risk and low-risk groups.

Feminist Hermeneutic Tools: Visual Metaphors of Christine de Pizan's Literary Labor

This essay treats two images that represent Christine de Pizan's literary work as male-associated physical labor. The images, made by the Maître de la Cité des Dames, are found in two manuscripts supervised by the author: BL MS Harley 4431, in Le Livre de la cité des dames, and BnF MS fr. 603, in Le Livre des fais d'armes et de chevalerie. These images show Christine using workers' tools: in the former, a trowel and mortar pan; in the latter, a tree pruner. Many scholars have studied the author's various self-representations in text and image: the most typical images of her writing at a desk are literal and metatextual representations of her authorship. This essay contributes to discussions of her authorial self-representation by considering a different kind of image, in which literary labor is still metatextual—we still see the author represented inside her own text in the act of creation—but is metaphorical instead of literal: the images show Christine's authorial persona doing hard, physical work. By putting laborers' tools in the Christine-figure's hands, these images emphasize the fact that Christine accomplished authorial work traditionally restricted to men. The images recall the Christine-persona's first actions—picking up hammer, nails, and mortar—in the famous Mutacion de fortune passage about her transformation into a "true man." These two images, furthermore, accurately metaphorize particular aspects of her writing alongside the texts they illustrate. Offering another, more imaginative, even playful representational mode, these images enlarge our understanding of Christine's authorial self-fashioning.

Accurate Matching of Invariant Features Derived from Irregular Curves

High-quality feature matching is a critical prerequisite in a wide range of applications. Most contemporary methods concentrate on detecting keypoints or line features for matching, which have achieved adequate results. However, in some low-texture environments where these features are both lacking, previously used approaches may result in an insufficient number of matches. Besides, in repeated-texture environments, feature matching is also a challenging task. As a matter of fact, there exist numerous irregular curves that can be detected in all kinds of images, including low-texture and repeated-texture scenes, which inspires us to move a step further and dig into the research of curves. In this paper, we propose an accurate method to match invariant features from irregular curves. Our method consists of two stages, the first of which is to match the curves as accurately as possible by an elaborate three-step matching strategy. The second is to extract the matching features with the presented self-adaptive curve fitting approach. Experiments have shown that the matching performances of our features in ordinary scenes are comparable to previous keypoints. Particularly, our features can outperform the keypoints in low-texture and repeated-texture scenes.

Learning to Reason on Tree Structures for Knowledge-Based Visual Question Answering.

Collaborative reasoning for knowledge-based visual question answering is challenging but vital and efficient in understanding the features of the images and questions. While previous methods jointly fuse all kinds of features by attention mechanism or use handcrafted rules to generate a layout for performing compositional reasoning, which lacks the process of visual reasoning and introduces a large number of parameters for predicting the correct answer. For conducting visual reasoning on all kinds of image–question pairs, in this paper, we propose a novel reasoning model of a question-guided tree structure with a knowledge base (QGTSKB) for addressing these problems. In addition, our model consists of four neural module networks: the attention model that locates attended regions based on the image features and question embeddings by attention mechanism, the gated reasoning model that forgets and updates the fused features, the fusion reasoning model that mines high-level semantics of the attended visual features and knowledge base and knowledge-based fact model that makes up for the lack of visual and textual information with external knowledge. Therefore, our model performs visual analysis and reasoning based on tree structures, knowledge base and four neural module networks. Experimental results show that our model achieves superior performance over existing methods on the VQA v2.0 and CLVER dataset, and visual reasoning experiments prove the interpretability of the model.

Reliability of cone beam CT for morphometry of nasolabial soft tissue in patients with skeletal class III malocclusion: A qualitative and quantitative analysis.

To assess reliability of cone-beam CT (CBCT) for nasolabial soft tissue measurements in patients with skeletal class III malocclusion based on 3-dimensional (3D) facial scanner results. CBCT and 3D facial scan images of 20 orthognathic patients are used in this study. Eleven soft tissue landmarks and 15 linear and angular measurements are identified and performed. For qualitative evaluation, Shapiro-Wilk test and Bland-Altman plots are applied to analyze the equivalence of the measurements derived from these two kinds of images. To quantify specific deviation of CBCT measurements from facial scanner, the latter is set as a benchmark, and mean absolute difference (MAD) and relative error magnitude (REM) for each variable are also calculated. Statistically significant differences are observed in regions of nasal base and lower lip vermilion between two methods. MAD value for all length measurements are less than 2 mm and for angular variables < 8°. The average MAD and REM for length measurements are 0.94 mm and 5.64%, and for angular measurements are 2.27° and 3.78%, respectively. The soft tissue results measured by CBCT show relatively good reliability and can be used for 3D measurement of soft tissue in the nasolabial region clinically.

Subcutaneous Low-Density Foreign Bodies Detection via Grating-Based Multimodal X-ray Imaging

Detecting low-density foreign bodies within soft tissues still stands for a serious challenge. Grating-based multimodal X-ray imaging typically has low hardware requirements while simultaneously providing three kinds of imaging information, i.e., absorption, phase-contrast, and dark-field. We aimed to explore the capacity of grating-based multimodal X-ray imaging technology for detecting common foreign bodies within subcutaneous tissues, and to assess the advantages as well as disadvantages of the three kinds of images obtained via grating-based X-ray multimodal technology in relation to diverse kinds of foreign bodies within different tissues. In this study, metal, glass, wood, plastic, graphite, and ceramic foreign bodies were injected into chunks of the pig adipose tissue and chicken thigh muscles. Next, a grating-based multimodal X-ray imaging device developed in our laboratory was used to detect the above foreign bodies within the adipose and muscle tissues. Our results show that grating-based multimodal X-ray imaging clearly revealed the subcutaneous foreign bodies within the adipose and muscle tissues by acquiring complementary absorption, phase-contrast, and dark-field imaging data in a single shot. Grating-based multimodal X-ray imaging has an exciting potential to detect foreign bodies underneath the epidermis.

Adaptive Registration for Optical and SAR Images With a Scale-Constrained Matching Method

Images registration for optical and synthetic aperture radar (SAR) is a key of multi-sensor images analysis. And parameters selection affects the final result of the registration algorithms for optical and SAR images. For images obtained by different sensors, how to choose an appropriate parameter for accurate registration is a key problem. In this letter, a parameter adaptive registration algorithm between optical and SAR images based on SIFT is proposed. Because of the different imaging mechanisms of these two kinds of images, the algorithm uses the multiscale Sobel operator to calculate the gradient for the optical image, while for the SAR image, a new adaptive operator based on the neighborhood pixel value is proposed to calculate the gradient. In the feature extraction, the adaptive value estimated by constant false alarm rate (CFAR) detection is used instead of the fixed threshold. Finally, a matching method constrained by image size scaling (Scale-Constrained Fast Sample Consensus) is proposed. The evaluation was designed in two aspects: feature extraction and image registration. The algorithm we proposed shows excellent performances in the aspects of repeatability, correct matching rate and root mean square error. The experimental results show that our method maintains the performance of the original algorithm and has some optimization and breakthrough.

Cross-Modality Image Matching Network With Modality-Invariant Feature Representation for Airborne-Ground Thermal Infrared and Visible Datasets

Thermal infrared (TIR) remote-sensing imagery can allow objects to be imaged clearly at night through the long-wave infrared, so that the fusion of thermal infrared and visible (VIS) imagery is a way to improve the remote-sensing interpretation ability. However, due to the large radiation difference between the two kinds of images, it is very difficult to match them. One of the most important issues is the lack of comprehensive consideration of the modality-specific information and modality-shared information, which makes it difficult for the existing methods to obtain a modality-invariant feature representation. In this article, a cross-modality image matching network, which we refer to as CMM-Net, is proposed to realize thermal infrared and visible image matching by learning a modality-invariant feature representation. First, in order to extract the modality-specific features of the imagery, the framework constructs a shallow two-branch network to make full use of the modality-specific information, without sharing parameters. Second, in order to extract the high-level semantic information between the different modalities, modality-shared layers are embedded into the deep layers of the network. In addition, three novel loss functions are designed and combined to learn the modality-invariant feature representation, that is, the discriminative loss of the non-corresponding features in the same modality, the cross-modality loss of the corresponding features between different modalities, and the cross-modality triplet (CMT) loss. The multimodal matching experiments conducted with ground- and airborne-based thermal infrared images and visible images showed that the proposed method outperforms the existing image matching methods by about 2% and 6% for the ground and airborne images, respectively.

Carotid Artery Segmentation Using Convolutional Neural Network in Ultrasound Images

Cardiovascular disease (CVD) is one of the leading causes of death in urban areas. Carotid artery segmentation is the initial step in the automated diagnosis of carotid artery disease. The segmentation of carotid wall and lumen region boundaries are used as an essential part in assessing plaque morphology. In this paper, two types of Convolutional Neural Network (CNN) architectures are used for segmentation: U-Net and SegNet. The models used in this paper are applied on 257 ultrasound images containing a transverse section of the vessel acquired by ultrasound. Ultrasound imaging is noninvasive, completely unharming for the patient and a low-cost imaging method, but the main challenge when working with this kind of images is a very low signal to noise ratio and the process of imaging is highly dependent on the device operator. Different models are tested for various ranges of hyperparameter values and compared using different metrics. The model presented in this paper achieved over 94% Dice Coefficient for wall and lumen segmentation when trained during 100 epochs.

L’image de Santa Maria citra Montes à Saint-Nizier de Lyon : le rôle de la composition de l’espace ecclésial dans l’organisation d’un culte

According to a bull of Innocent IV, present in Lyons for the Ecumenical Council of Lyons, the image of «Our Lady on this side of the Alps» was brought to Lugdunum by Saint Pothin, the first bishop of the city. What kind of image? There is still a late statue, hidden during the religious wars, then found near the crypt in the 19th century, and a keystone (during the 15thcentury) probably representing this image. Minutes of the visit of the 14th-15th century clearly describe the organisation of the space of the shrine whose archaeology has shown that it had not been modified since the High Middle Ages: crypt at the crossroads, altar on the crypt (in the Paleo-Christian way), the main altar, the tombs of the bishops of the sixth century and, at the bottom of the apse, the image of the Virgin. This image was both an object of worship and the object of a pilgrimage of healing. After the destruction of the religious wars, the statue was moved to the south arm of the transept and became the cult image of the Brotherhood of Our Lady of Grace. The upheaval of the spatial organisation is heavy with meaning: loss of relationship with the history of the early Church of Lyon and refocusing on the life of the brotherhoods, but with a primacy given to the image which is not enclosed in an autonomous chapel, but remains directly related to the sanctuary space

Electrical Fault Detection Equipment Based on Infrared Image Fusion

Maintaining the safe and stable operation of power equipment is the first task of power system operation and maintenance. The equipment in normal operation may have various faults, so it needs to be located, detected and repaired. In order to improve the on-line detection efficiency of electrical equipment, diagnose the operation state of equipment in time and locate faults, this project proposes a multi focus infrared and visible image fusion algorithm based on NSST and adaptive PCNN. Taking the power equipment as the research object, the infrared image of the power equipment is compared with the visible image, and the feature points are selected, so as to realize the image registration. After image registration, the registered visible and infrared images are fused and reconstructed, and the effective features of the two kinds of images are integrated into one image. The target image constructed by fusion not only has thermal energy information, but also has object texture, highlights the target, and can detect the fault location without power failure. It has certain practical significance for quickly removing the fault point of power equipment.

Feature Extraction of Plant Leaf Using Deep Learning

Half a million species of plants could be existing in the world. Classification of plants based on leaf features is a critical job as feature extraction (includes shape, margin, and texture) from binary images of leaves may result in duplicate identification. However, leaves are an effective means of differentiating plant species because of their unique characteristics like area, diameter, perimeter, circularity, aspect ratio, solidity, eccentricity, and narrow factor. This paper presents the extraction of plant leaf gas alongside other features from the camera images or a dataset of images by applying a convolutional neural network (CNN). The extraction of leaf gas enables identification of the actual level of chlorophyll (Ch) and nitrogen (N) which may help to interpret future predictions. Our contribution includes the study of texture and geometric features, analyzing ratio of Ch and N in both healthy and dead leaves, and the study of color‐based methods via CNN. Several steps are included to obtain the results: image preprocessing, testing, training, enhancement, segmentation, feature extraction, and aggregation of results. A vital contrast of the results can be seen by considering the kind of image, whether a healthy or dead leaf.

Deep learning based cervical screening by the cross-modal integration of colposcopy, cytology, and HPV test

PurposeTo develop and evaluate the colposcopy based deep learning model using all kinds of cervical images for cervical screening, and investigate the synergetic benefits of the colposcopy, the cytology test, and the HPV test for improving cervical screening performance. MethodsThis study consisted of 2160 women who underwent cervical screening, there were 442 cases with the histopathological confirmed high-grade squamous intraepithelial lesion (HSIL) or cancer, and the remained 1718 women were controls. Three kinds of cervical images were acquired from colposcopy including the saline image of cervix after saline irrigation, the acetic acid image of cervix after applying acetic acid solution, and the iodine image of cervix after applying Lugol’s iodine solution. Each kind of image was used to build a single-image based deep learning model by the VGG-16 convolutional neural network, respectively. A multiple-images based deep learning model was built using multivariable logistic regression (MLR) by combining the single-image based models. The performance of the visual inspection was also obtained. The results of the cytology test and HPV test were used to build a Cytology-HPV joint diagnostic model by MLR. Finally, a cross-modal integrated model was built using MLR by combining the multiple-images based deep learning model, the cytology test results, and the HPV test results. The performances of models were tested in an independent test set using the area under the receiver operating characteristic curve (AUC). ResultsThe saline image, acetic acid image, and iodine image based deep learning models had AUC of 0.760, 0.791, and 0.840. The multiple-images based deep learning model achieved an improved AUC of 0.845. The AUC of the visual inspection was 0.751. The Cytology-HPV joint diagnostic model had an AUC of 0.837, which was higher than the cytology test (AUC = 0.749) and the HPV test (AUC = 0.742). The cross-modal integrated model achieved the best performance with AUC of 0.921. ConclusionsCombining all kinds of cervical images were benefit for improving the performance of the colposcopy based deep learning model, and more accurate cervical screening could be achieved by incorporating the colposcopy based deep learning model, the cytology test results, and the HPV test results.

Soft Compression for Lossless Image Coding Based on Shape Recognition.

Soft compression is a lossless image compression method that is committed to eliminating coding redundancy and spatial redundancy simultaneously. To do so, it adopts shapes to encode an image. In this paper, we propose a compressible indicator function with regard to images, which gives a threshold of the average number of bits required to represent a location and can be used for illustrating the working principle. We investigate and analyze soft compression for binary image, gray image and multi-component image with specific algorithms and compressible indicator value. In terms of compression ratio, the soft compression algorithm outperforms the popular classical standards PNG and JPEG2000 in lossless image compression. It is expected that the bandwidth and storage space needed when transmitting and storing the same kind of images (such as medical images) can be greatly reduced with applying soft compression.

Removal of Multiplicative Gamma Noise from Images via SRAD Model Amelioration

In this paper, an improved Speckle Reducing Anisotropic Diffusion (SRAD), destined to remove multiplicative gamma noise applied to different images is proposed. The basic idea is to divide the image into several riddled areas and then calculate the Equivalent Number of Look (ENL) of each region. The largest value of the ENL is the best optimal homogeneous region of the image. This optimal choice allows us to solve the major problem of the SRAD algorithm articulated around a visual choice of the homogeneous region which is not satisfactory and causes non-uniformity in this area. To give more validity to the proposed method, several experimentations were conducted using different kinds of images and were approved by some quantitative metrics like PSNR, SNR, VSNR, and SSIM. The computer simulation results confirm the efficiency of the proposed method which outperformances the classical SRAD method.

Disguise of Steganography Behaviour: Steganography Using Image Processing with Generative Adversarial Network

The deep learning based image steganalysis is becoming a serious threat to modification-based image steganography in recent years. Generation-based steganography directly produces stego images with secret data and can resist the advanced steganalysis algorithms. This paper proposes a novel generation-based steganography method by disguising the stego images into the kinds of images processed by normal operations (e.g., histogram equalization and sharpening). Firstly, an image processing model is trained using DCGAN and WGAN-GP, which is used to generate the images processed by normal operations. Then, the noise mapped by secret data is inputted into the trained model, and the obtained stego image is indistinguishable from the processed image. In this way, the steganographic process can be covered by the process of image processing, leaving little embedding trace in the process of steganography. As a result, the security of steganography is guaranteed. Experimental results show that the proposed scheme has better security performance than the existing steganographic methods when checked by state-of-the-art steganalytic tools, and the superiority and applicability of the proposed work are shown.