Set Of Images Research Articles

Sighted People Recognizing Emotions in Facial Expression Images of People With Visual Disabilities Via Cyberspace

As technology advances today, there are many opportunities for people to communicate in cyberspace (e.g., videoconferencing) regardless of visual ability/disability. People share various information including facial expressions that are considered essential for social interaction. Despite the importance of facial expressions in conveying emotions, there is limited knowledge on how sighted people perceive the emotions of people with visual disabilities from their facial expressions online. To address the knowledge gap, this study showed sighted participants facial expression images of people with visual disabilities via Zoom and asked them to report the emotion they perceived for each image. Afterward, participants were educated about the facial modeling from a previous study and shown another set of facial expression images of people with visual disabilities. Participants were asked to report the emotion they perceived in each image. This study found many cases of incongruently perceived emotions, in which participants perceived emotions differently from the facially expressed emotions of people with visual disabilities. Yet, after education, participants were more likely to congruently perceive emotions. The research findings would be beneficial to many stakeholders in the domain of inclusive communication for everyone in cyberspace.

Physics-based thermal noise effect reduction in sonic IR crack length estimation

ABSTRACT Using heat diffusion models in sonic infrared (IR) inspection technology that captures arbitrary frictional heat generation function along a crack lends itself to estimation of crack length using thermal images alone. This study introduces novel physics-based approach that relies on the governing heat diffusion model linearity and superposition of point heat sources along crack to reduce thermal noise effect in crack length estimation. It utilises thermal images and their timestamps in sonic IR inspection to deliver spatiotemporal average representation of the aforementioned point heat sources to reduce thermal noise effect in crack length estimation. The proposed methodology is tested against two-dimensional and three-dimensional finite element simulations with an arbitrary frictional heat generation function along crack. Virtual random thermal noise levels ranging between 50 and 200 mK, which are higher than IR cameras Noise Equivalent Temperature Difference values, are added to simulated results to reflect realistic and drastic thermal noise levels. Spatiotemporal averaging using combined sets of different thermal images captured at various times in single sonic IR inspection delivered about 16% total uncertainty in the estimation of a crack length at 95% confidence level based on 60 experiments. Similar uncertainty level is obtained using independently reported sonic IR experimental data.

Automated filtering of façade defect images using a similarity method for enhanced inspection documentation

ABSTRACT Unmanned Aerial Vehicles (UAVs) are routinely utilized to capture images of building façades for structural health inspections. These images are often taken at regular time intervals, capturing overlapping views to ensure complete coverage of the façade. However, this practice generates numerous highly similar images, leading to inefficiencies in documentation and potentially biased analysis due to redundant data. Hence, this study proposes an automated image data filtering method based on defect similarity. Given façade images where defects are detected, this method applies key point detection and descriptor matching to measure image similarity concerning defect presence. Subsequently, a selection strategy is proposed to produce a minimal set of representative images covering all defects. This method was validated using a dataset of UAV-captured façade images across three façades, successfully deriving the set to six representative images. Overall, the proposed method offers a practical solution to reduce the quantity of images required for documentation, thereby enhancing the efficiency of UAV deployments for façade inspection.

Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study

BackgroundCalcification is a common phenomenon in both benign and malignant thyroid nodules. However, the clinical significance of calcification remains unclear. Therefore, we explored a more objective method for distinguishing between benign and malignant thyroid calcified nodules.MethodsThis retrospective study, conducted at two centers, involved a total of 631 thyroid nodules, all of which were pathologically confirmed. Ultrasound image sets were employed for analysis. The primary evaluation index was the area under the receiver-operator characteristic curve (AUROC). We compared the diagnostic performance of deep learning (DL) methods with that of radiologists and determined whether DL could enhance the diagnostic capabilities of radiologists.ResultsThe Xception classification model exhibited the highest performance, achieving an AUROC of up to 0.970, followed by the DenseNet169 model, which attained an AUROC of up to 0.959. Notably, both DL models outperformed radiologists (P < 0.05). The success of the Xception model can be attributed to its incorporation of deep separable convolution, which effectively reduces the model’s parameter count. This feature enables the model to capture features more effectively during the feature extraction process, resulting in superior performance, particularly when dealing with limited data.ConclusionsThis study conclusively demonstrated that DL outperformed radiologists in differentiating between benign and malignant calcified thyroid nodules. Additionally, the diagnostic capabilities of radiologists could be enhanced with the aid of DL.

Semi-Supervised Semantic Segmentation-Based Remote Sensing Identification Method for Winter Wheat Planting Area Extraction

To address the cost issue associated with pixel-level image annotation in fully supervised semantic segmentation, a method based on semi-supervised semantic segmentation is proposed for extracting winter wheat planting areas. This approach utilizes self-training with pseudo-labels to learn from a small set of images with pixel-level annotations and a large set of unlabeled images, thereby achieving the extraction. In the constructed initial dataset, a random sampling strategy is employed to select 1/16, 1/8, 1/4, and 1/2 proportions of labeled data. Furthermore, in conjunction with the concept of consistency regularization, strong data augmentation techniques are applied to the unlabeled images, surpassing classical methods such as cropping and rotation to construct a semi-supervised model. This effectively alleviates overfitting caused by noisy labels. By comparing the prediction results of different proportions of labeled data using SegNet, DeepLabv3+, and U-Net, it is determined that the U-Net network model yields the best extraction performance. Moreover, the evaluation metrics MPA and MIoU demonstrate varying degrees of improvement for semi-supervised semantic segmentation compared to fully supervised semantic segmentation. Notably, the U-Net model trained with 1/16 labeled data outperforms the models trained with 1/8, 1/4, and 1/2 labeled data, achieving MPA and MIoU scores of 81.63%, 73.31%, 82.50%, and 76.01%, respectively. This method provides valuable insights for extracting winter wheat planting areas in scenarios with limited labeled data.

Deep learning model for automatic differentiation of EMAP from AMD in macular atrophy

To create a deep learning (DL) classifier pre-trained on fundus autofluorescence (FAF) images that can assist the clinician in distinguishing age-related geographic atrophy from extensive macular atrophy and pseudodrusen-like appearance (EMAP). Patients with complete outer retinal and retinal pigment epithelium atrophy secondary to either EMAP (EMAP Group) or to dry age related macular degeneration (AMD group) were retrospectively selected. Fovea-centered posterior pole (30° × 30°) and 55° × 55° degree-field-of-view FAF images of sufficiently high quality were collected and used to train two different deep learning (DL) classifiers based on ResNet-101 design. Testing was performed on a set of images coming from a different center. A total of 300 patients were recruited, 135 belonging to EMAP group and 165 belonging to AMD group. The 30° × 30° FAF based DL classifier showed a sensitivity of 84.6% and a specificity of 85.3% for the diagnosis of EMAP. The 55° × 55° FAF based DL classifier showed a sensitivity of 90% and a specificity of 84.6%, a performance that was significantly higher than that of the 30° × 30° classifer (p = 0.037). Artificial intelligence can accurately distinguish between atrophy caused by AMD or by EMAP on FAF images. Its performance are improved using wide field acquisitions.

Cost-Efficient Deep Learning Model for Colorectal Cancer Diagnosis: Leveraging Unsupervised Learning Composite Network

Despite this, the mortality rate associated with CRC is among the highest of all malignancies. Even while prompt and accurate diagnosis can significantly improve patient outcomes, traditional diagnostic procedures are usually expensive, invasive, and time-consuming. This is despite the fact that rapid diagnosis can significantly improve patient outcomes. Deep learning has emerged as a potential avenue for the diagnosis of cancer, despite the prohibitive nature of the costs associated with developing robust models in terms of both computational and financial resources. In this article, the authors offer a deep learning model that is optimized for the diagnosis of colorectal cancer at a low cost. The model was trained using an unsupervised learning composite network (ULCN). Unsupervised learning is utilized by the ULCN in order to pre-process enormous amounts of unlabeled data in order to cut down on the necessary volume of labelled data as well as the associated cost. Our model was able to capitalize on the underlying structures and patterns in the data, which allowed it to perform far better than other methods available at the time on a test set of histopathological images. The unsupervised feature extraction phase significantly reduced the amount of time required for training, hence showcasing the efficiency of the model. The findings indicate that by combining unsupervised learning into deep learning systems, it may be possible to create a diagnostic tool that is more easily accessible and scalable in clinical settings.

Image processing methods based on physical models

In order to achieve better image processing results, the author proposes research on image processing methods based on physical models. The author first developed a simple and user-friendly GUIDE interactive interface based on Matlab software and image processing technology. Calculate and process the interference fringe patterns captured in static and dynamic experimental measurements in optical experiments separately to achieve experimental measurement objectives. Secondly, the image processing program was analyzed to highlight important information using methods such as Gaussian blur and binarization, and algorithms were used to construct surface 3D images or detect stripe motion features, establish 3D models, and accurately calculate corresponding experimental results. The final experimental results indicate that the phase transition value of piezoelectric devices increases linearly with the increase of voltage, which is consistent with the working characteristics of piezoelectric devices in the linear region. This program can analyze the image sets of horizontal, vertical, and oblique stripes, and calculate the piezoelectric moduli of piezoelectric devices to be 3.355 nm/V, 3.120 nm/V, and 3.557 nm/V, respectively, with relative errors of E1 = 4.9 %, E2 = 2.9 %, and E3 = 12 %. This method is suitable for various complex experimental measurement scenarios and provides an intuitive method for innovative design of optical interference experimental measurement teaching.

Falconer type functions in three variables

Let f∈R[x,y,z] be a quadratic polynomial that depends on each variable and that does not have the form g(h(x)+k(y)+l(z)). Let A,B,C be compact sets in R. Suppose that dimH⁡(A)+dimH⁡(B)+dimH⁡(C)>2, then we prove that the image set f(A,B,C) is of positive Lebesgue measure. This dimensional condition is best possible in general. Our proof is based on a result due to Eswarathasan, Iosevich, and Taylor (Advances in Mathematics, 2011), and a combinatorial argument from the finite field model. As a consequence, we improve a result of Iosevich and Liu (2016) on the Falconer distance problem for Cartesian product sets in three dimensions.

On global solvability of a class of possibly nonconvex QCQP problems in Hilbert spaces

We show that KKT-type conditions are necessary and sufficient for global optimality for some quadratically constrained (possibly nonconvex) quadratic programming QCQP problems in Hilbert space. The key property is the convexity of an image-type set, called generalized image set, related to the functions appearing in the formulation of the problem. The proof of the main result relies on a generalized version of the (Jakubovich) S-Lemma in Hilbert spaces. As an application, we consider the class of QCQP problems with a special form of the quadratic terms of the constraints.

Convolutional neural network-based kidney volume estimation from low-dose unenhanced computed tomography scans

PurposeKidney volume is important in the management of renal diseases. Unfortunately, the currently available, semi-automated kidney volume determination is time-consuming and prone to errors. Recent advances in its automation are promising but mostly require contrast-enhanced computed tomography (CT) scans. This study aimed at establishing an automated estimation of kidney volume in non-contrast, low-dose CT scans of patients with suspected urolithiasis.MethodsThe kidney segmentation process was automated with 2D Convolutional Neural Network (CNN) models trained on manually segmented 2D transverse images extracted from low-dose, unenhanced CT scans of 210 patients. The models’ segmentation accuracy was assessed using Dice Similarity Coefficient (DSC), for the overlap with manually-generated masks on a set of images not used in the training. Next, the models were applied to 22 previously unseen cases to segment kidney regions. The volume of each kidney was calculated from the product of voxel number and their volume in each segmented mask. Kidney volume results were then validated against results semi-automatically obtained by radiologists.ResultsThe CNN-enabled kidney volume estimation took a mean of 32 s for both kidneys in a CT scan with an average of 1026 slices. The DSC was 0.91 and 0.86 and for left and right kidneys, respectively. Inter-rater variability had consistencies of ICC = 0.89 (right), 0.92 (left), and absolute agreements of ICC = 0.89 (right), 0.93 (left) between the CNN-enabled and semi-automated volume estimations.ConclusionIn our work, we demonstrated that CNN-enabled kidney volume estimation is feasible and highly reproducible in low-dose, non-enhanced CT scans. Automatic segmentation can thereby quantitatively enhance radiological reports.

EMPLOYER BRANDING AS A TOOL FOR EMPLOYEE LOYALTY: THE CASE OF MOROCCAN COMPANIES LISTED ON CASABLANCA STOCK EXCHANGE

To attract qualified profiles, known as talents, and to retain them, many enterprises are actively leveraging on employer branding. The objective of this research is to shed light on the role played by the concept of employer branding, defined as the set of brand image assets with respect to employees and their impact on human resources loyalty. Our hypothetico-deductive research method will be completed by conducting an empirical study based on a series of semi-structured interviews with 10 large companies listed on Casablanca stock exchange market. We suggest a model to articulate the employer brand around three key dimensions: low propensity to seek a new job, affective engagement and task performance. The collected and analyzed feedback led us to a clear and obvious conclusion. The intention to leave the company, the affective commitment as well as the performance in the task are directly impacted by the attributes of the employer brand. Therefore, organizational loyalty is influenced by the employer brand. The latter is a powerful loyalty tool that allows the company to retain talent. Further research should be conducted to explore the potential of employer branding as a powerful tool for talent retention.

A Method of Extracting the SWH Based on a Constituted Wave Slope Feature Vector (WSFV) from X-Band Marine Radar Images

The shadow statistical method (SSM) used for extracting the significant wave height (SWH) from X-band marine radar images was further investigated because of its advantage of not requiring an external reference for calibration. Currently, a fixed shadow segmentation threshold is utilized to extract the SWH from a radar image based on the SSM. However, the retrieval accuracy of the SWH is not ideal for low wind speeds since the echo intensity of sea waves rapidly decays over distance. In order to solve this problem, an adaptive shadow threshold, which varies with echo intensity over distance and can accurately divide the radar image into shadow and nonshadow areas, is adopted to calculate the wave slope (WS) based on the texture feature of the edge image. Instead of using the averaged WS, the wave slope feature vector (WSFV) is constructed for retrieving the SWH since the illumination ratio and the calculated WS in the azimuth are different for shore-based radar images. In this paper, the SWH is calculated based on the constructed WSFV and classical support vector regression (SVR) technology. The collected 222 sets of X-band marine radar images with an SWH range of 1.0∼3.5 m and an average wind speed range of 5∼10 m/s were utilized to verify the performance of the proposed approach. The buoy record, which was deployed during the experiment, was used as the ground truth. For the proposed approach, the mean bias (BIAS) and the mean absolute error (MAE) were 0.03 m and 0.14 m when the ratio of the training set to the test set was 1:1. Compared to the traditional SSM, the correlation coefficient (CC) of the proposed approach increased by 0.27, and the root mean square error (RMSE) decreased by 0.28 m.

Investigating the role of imaging factors in the variability of CT-based texture analysis metrics.

This study assesses the robustness of first-order radiomic texture features namely interquartile range (IQR), coefficient of variation (CV) and standard deviation (SD) derived from computed tomography (CT) images by varying dose, reconstruction algorithms and slice thickness using scans of a uniform water phantom, a commercial anthropomorphic liver phantom, and a human liver in-vivo. Scans were acquired on a 16cm detector GE Revolution Apex Edition CT scanner with variations across three different nominal slice thicknesses: 0.625, 1.25, and 2.5mm, three different dose levels: CTDIvol of 13.86 mGy for the standard dose, 40% reduced dose and 60% reduced dose and two different reconstruction algorithms: a deep learning image reconstruction (DLIR-high) algorithm and a hybrid iterative reconstruction (IR) algorithm ASiR-V50% (AV50) were explored, varying one at a time. To assess the effect of non-linear modifications of images by AV50 and DLIR-high, images of the water phantom were also reconstructed using filtered back projection (FBP). Quantitative measures of IQR, CV and SD were extracted from twelve pre-selected, circular (1cm diameter) regions of interest (ROIs) capturing different texture patterns across all scans. Across all scans, imaging, and reconstruction settings, CV, IQR and SD were observed to increase with reduction in dose and slice thickness. An exception to this observation was found when using FBP reconstruction. Lower values of CV, IQR and SD were observed in DLIR-high reconstructions compared to AV50 and FBP. The Poisson statistics were more stringently noted in FBP than DLIR-high and AV50, due to the non-linear nature of the latter two algorithms. Variation in image noise due to dose reduction algorithms, tube current, and slice thickness show a consistent trend across phantom and patient scans. Prospective evaluation across multiple centers, scanners and imaging protocols is needed for establishing quality assurance standards of radiomics.

Fusion of electronic health records and radiographic images for a multimodal deep learning prediction model of atypical femur fractures

Atypical femur fractures (AFF) represent a very rare type of fracture that can be difficult to discriminate radiologically from normal femur fractures (NFF). AFFs are associated with drugs that are administered to prevent osteoporosis-related fragility fractures, which are highly prevalent in the elderly population. Given that these fractures are rare and the radiologic changes are subtle currently only 7% of AFFs are correctly identified, which hinders adequate treatment for most patients with AFF. Deep learning models could be trained to classify automatically a fracture as AFF or NFF, thereby assisting radiologists in detecting these rare fractures. Historically, for this classification task, only imaging data have been used, using convolutional neural networks (CNN) or vision transformers applied to radiographs. However, to mimic situations in which all available data are used to arrive at a diagnosis, we adopted an approach of deep learning that is based on the integration of image data and tabular data (from electronic health records) for 159 patients with AFF and 914 patients with NFF. We hypothesized that the combinatorial data, compiled from all the radiology departments of 72 hospitals in Sweden and the Swedish National Patient Register, would improve classification accuracy, as compared to using only one modality. At the patient level, the area under the ROC curve (AUC) increased from 0.966 to 0.987 when using the integrated set of imaging data and seven pre-selected variables, as compared to only using imaging data. More importantly, the sensitivity increased from 0.796 to 0.903. We found a greater impact of data fusion when only a randomly selected subset of available images was used to make the image and tabular data more balanced for each patient. The AUC then increased from 0.949 to 0.984, and the sensitivity increased from 0.727 to 0.849.These AUC improvements are not large, mainly because of the already excellent performance of the CNN (AUC of 0.966) when only images are used. However, the improvement is clinically highly relevant considering the importance of accuracy in medical diagnostics. We expect an even greater effect when imaging data from a clinical workflow, comprising a more diverse set of diagnostic images, are used.

A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA

The sustainable management of fisheries and aquaculture requires an understanding of how these activities interact with natural fish populations. GoPro cameras were used to collect an underwater video data set on and around shellfish aquaculture farms in an estuary in the NE Pacific from June to August 2017 and June to August 2018 to better understand habitat use by the local fish and crab communities. Images extracted from these videos were labeled to produce a data set that is suitable for use in training computer vision models. The labeled data set contains 77,739 images sampled from the collected video; 67,990 objects (fishes and crustaceans) have been annotated in 30,384 images (the remainder have been annotated as “empty”). The metadata of the data set also indicates whether a physical magenta filter was used during video collection to counteract reduced visibility. These data have the potential to help researchers address system-level and in-depth regional shellfish aquaculture questions related to ecosystem services and shellfish aquaculture interactions.

Development of a New Numerical Conjugate Gradient Technique for Image Processing

We present a new iterative conjugate gradient technique for image processing. The technique is based on a new derivation of the conjugacy coefficient and develops a variant of the classical Fletcher-Reeves conjugate gradient method. The derivation exploits a quadratic function model. The new method is intended to minimize the presence of noise by utilizing the adaptive median filter (AMF) to reduce salt-and-pepper noise, while the adaptive center-weighted median filter (ACWMF) is used to reduce random-valued noise. The theoretical convergence properties of the method are proven and then tested on a basic set of images using MATLAB. The results show that the proposed algorithm is more efficient than the classical Fletcher-Reeves (FR) method, as measured by the signal-to-noise ratio (PSNR). The number of iterations and the number of function evaluations are also lower for the proposed method. The favorable performance of the new algorithm provides promise for deriving similar techniques that enhance the speed and efficiency of image-processing libraries.

Analysis of Training Data Augmentation for Diabetic Foot Ulcer Semantic Segmentation

Deep learning model training and achieved performance relies on available data. Diabetic foot ulcers and other image processing applications in the medical domain add another layer of complexity to training data collection. Data collection is troublesome and data annotation requires medical expertise. This problem is usually solved by employing training data augmentation. Although in previous research augmentation was facilitated in various ways, it is rarely evaluated or reported how much it contributes to achieved performance. The current research seeks to answer this question by applying individual photometric and geometric augmentation techniques and comparing the model performance achieved for semantic segmentation of diabetic foot ulcers. It was found that geometric augmentation techniques help achieve a better model performance when compared with photometric techniques. The model trained using an augmented dataset and applying a shear technique was found to improve segmentation results the most; the benchmark dice score was increased by 6%. An additional improvement over the benchmark was observed (a total of 6.9%) when the model was trained using data combining image sets generated by the three best-performing augmentation techniques. The highest test dice score achieved was 91%.

HFRAS: design of a high-density feature representation model for effective augmentation of satellite images

AbstractEfficiently extracting features from satellite images is crucial for classification and post-processing activities. Many feature representation models have been created for this purpose. However, most of them either increase computational complexity or decrease classification efficiency. The proposed model in this paper initially collects a set of available satellite images and represents them via a hybrid of long short-term memory (LSTM) and gated recurrent unit (GRU) features. These features are processed via an iterative genetic algorithm, identifying optimal augmentation methods for the extracted feature sets. To analyse the efficiency of this optimization process, we model an iterative fitness function that assists in incrementally improving the classification process. The fitness function uses an accuracy &amp; precision-based feedback mechanism, which helps in tuning the hyperparameters of the proposed LSTM &amp; GRU feature extraction process. The suggested model used 100 k images, 60% allocated for training and 20% each designated for validation and testing purposes. The proposed model can increase classification precision by 16.1% and accuracy by 17.1% compared to conventional augmentation strategies. The model also showcased incremental accuracy enhancements for an increasing number of training image sets.

VisActive: Visual-concept-based Active Learning for Image Classification under Class Imbalance

Active learning methods recommend the most informative images from a large unlabeled dataset for manual labeling. These methods improve the performance of an image classifier while minimizing manual labeling efforts. We propose VisActive, a visual-concept-based active learning method for image classification under class imbalance. VisActive learns a visual concept, a generalized representation that holds the most important image characteristics for class prediction, and then recommends for each class four sets of unlabeled images with different visual concepts to increase the diversity and enlarge the training dataset. Experimental results on four datasets show that VisActive outperforms the state-of-the-art deep active learning methods.