Quality Classified Image Analysis with Application to Face Detection and Recognition

Motion blur, out of focus, insufficient spatial resolution, lossy compression and many other factors can all cause an image to have poor quality. However, image quality is a largely ignored issue in traditional pattern recognition literature. In this paper, we use face detection and recognition as case studies to show that image quality is an essential factor which will affect the performances of traditional algorithms. We demonstrated that it is not the image quality itself that is the most important, but rather the quality of the images in the training set should have similar quality as those in the testing set. To handle real-world application scenarios where images with different kinds and severities of degradation can be presented to the system, we have developed a quality classified image analysis framework to deal with images of mixed qualities adaptively. We use deep neural networks first to classify images based on their quality classes and then design a separate face detector and recognizer for images in each quality class. We will present experimental results to show that our quality classified framework can accurately classify images based on the type and severity of image degradations and can significantly boost the performances of state-of-the-art face detector and recognizer in dealing with image datasets containing mixed quality images.Comment: 6 page

Blind Multimodal Quality Assessment of Low-light Images

Blind image quality assessment (BIQA) aims at automatically and accurately forecasting objective scores for visual signals, which has been widely used to monitor product and service quality in low-light applications, covering smartphone photography, video surveillance, autonomous driving, etc. Recent developments in this field are dominated by unimodal solutions inconsistent with human subjective rating patterns, where human visual perception is simultaneously reflected by multiple sensory information. In this article, we present a unique blind multimodal quality assessment (BMQA) of low-light images from subjective evaluation to objective score. To investigate the multimodal mechanism, we first establish a multimodal low-light image quality (MLIQ) database with authentic low-light distortions, containing image-text modality pairs. Further, we specially design the key modules of BMQA, considering multimodal quality representation, latent feature alignment and fusion, and hybrid self-supervised and supervised learning. Extensive experiments show that our BMQA yields state-of-the-art accuracy on the proposed MLIQ benchmark database. In particular, we also build an independent single-image modality Dark-4K database, which is used to verify its applicability and generalization performance in mainstream unimodal applications. Qualitative and quantitative results on Dark-4K show that BMQA achieves superior performance to existing BIQA approaches as long as a pre-trained model is provided to generate text description. The proposed framework and two databases as well as the collected BIQA methods and evaluation metrics are made publicly available on here.Comment: 15 page

Effects of sintering temperatures on the microstructure and mechanical properties of S390 powder metallurgy high-speed steel

High-performance complex gear cutters and high-temperature bearings are just some of the applications where high-speed steels (HSSs) shine as a preferred material choice owing to their high hardness and outstanding wear resistance. In this work, the effects of sintering temperature on the microstructure and mechanical properties of S390 HSS prepared via spark plasma sintering (SPS) were investigated with a range of sintering temperatures from 930°C to 1,090°C, a uniaxial pressure of 50 MPa, and a holding time of 5 min. The results demonstrated that the improvements in density, hardness, red hardness, and three-point bending strength were confirmed as the sintering temperature increased from 930°C to 1,090°C. Temperature-induced microstructure evolutions were assessed for their contribution to property enhancement, such as powders with varying dimensions and carbides with diverse morphology and diameter. The specimen with the best comprehensive mechanical properties (67.1 HRC and 1,196.67 MPa) was prepared at 1,050°C via SPS. The wear coefficients decreased as the sintering temperature increased, and the observation results of worn surfaces of test pins confirmed that abrasive wear and oxidation wear dominated the wear experiments. Furthermore, the wear mechanism of dense and porous SPS HSS was illustrated and analyzed in terms of the debris and trapped carbides

Radiomics nomogram for prediction of glypican-3 positive hepatocellular carcinoma based on hepatobiliary phase imaging

IntroductionThe hepatobiliary-specific phase can help in early detection of changes in lesion tissue density, internal structure, and microcirculatory perfusion at the microscopic level and has important clinical value in hepatocellular carcinoma (HCC). Therefore, this study aimed to construct a preoperative nomogram for predicting the positive expression of glypican-3 (GPC3) based on gadoxetic acid-enhanced (Gd-EOB-DTPA) MRI hepatobiliary phase (HBP) radiomics, imaging and clinical feature.MethodsWe retrospectively included 137 patients with HCC who underwent Gd-EOB-DTPA-enhanced MRI and subsequent liver resection or puncture biopsy at our hospital from January 2017 to December 2021 as training cohort. Subsequently collected from January 2022 to June 2023 as a validation cohort of 49 patients, Radiomic features were extracted from the entire tumor region during the HBP using 3D Slicer software and screened using a t-test and least absolute shrinkage selection operator algorithm (LASSO). Then, these features were used to construct a radiomics score (Radscore) for each patient, which was combined with clinical factors and imaging features of the HBP to construct a logistic regression model and subsequent nomogram model. The clinicoradiologic, radiomics and nomogram models performance was assessed by the area under the curve (AUC), calibration, and decision curve analysis (DCA). In the validation cohort,the nomogram performance was assessed by the area under the curve (AUC).ResultsIn the training cohort, a total of 1688 radiomics features were extracted from each patient. Next, radiomics with ICCs<0.75 were excluded, 1587 features were judged as stable using intra- and inter-class correlation coefficients (ICCs), 26 features were subsequently screened using the t-test, and 11 radiomics features were finally screened using LASSO. The nomogram combining Radscore, age, serum alpha-fetoprotein (AFP) >400ng/mL, and non-smooth tumor margin (AUC=0.888, sensitivity 77.7%, specificity 91.2%) was superior to the radiomics (AUC=0.822, sensitivity 81.6%, specificity 70.6%) and clinicoradiologic (AUC=0.746, sensitivity 76.7%, specificity 64.7%) models, with good consistency in calibration curves. DCA also showed that the nomogram had the highest net clinical benefit for predicting GPC3 expression.In the validation cohort, the ROC curve results showed predicted GPC3-positive expression nomogram model AUC, sensitivity, and specificity of 0.800, 58.5%, and 100.0%, respectively.ConclusionHBP radiomics features are closely associated with GPC3-positive expression, and combined clinicoradiologic factors and radiomics features nomogram may provide an effective way to non-invasively and individually screen patients with GPC3-positive HCC

Study on microstructures and work hardening behavior of ferrite-martensite dual-phase steels with high-content martensite

A kind of medium-carbon low-alloy dual-phase steels with high-content martensite produced by intercritical annealing at 785-830 ºC for 10-50 minutes were studied in aspect of microstructures and work hardening behavior using SEM and tensile testing machine. The experimental results showed that the work hardening of the studied steels obeyed the two-stage work hardening mechanism, whose work hardening exponent of the first stage was higher than that of the second stage. The work hardening exponent increased with increasing the intercritical annealing temperature and time. For series A steel intercritically annealed at 785 ºC with starting microstructure of ferrite plus pearlite, austenite nucleated at the pearlite colonies, so the holding time of only 50 minutes can increase the work hardening exponent obviously. For series B steel with starting microstructure of martensite, austenite nucleated at lath interfaces, lath colony boundaries of primary martensite and carbides, accelerating the formation of austenite, so holding time for 30 minutes made the work hardening exponent increase obviously. High work hardening rate during initial plastic deformation (<0.5% strain) was observed

Gambogic Acid Inhibits Gastric Cancer Cell Proliferation through Necroptosis

Gambogic acid (GA) is a natural xanthonoid secreted by Garcinia hanburyi tree. It possesses anti-cancer activity in various types of cancers. In gastric cancer, it inhibits cell proliferation through increasing apoptosis. However, whether necroptosis is involved in the GA-induced proliferation inhibited in gastric cancer is unknown. In the present study, we found that RIPK1 specific inhibitor necrostatin-1 (Nec-1) attenuated GA-induced proliferation inhibition. GA treatment increased the phosphorylation of necroptosis-related proteins, RIPK1, RIPK3, and MLKL, and their interactions to form the necrosome complex. The effector protein Drp-1 was dephosphorylated by GA treatment. Inhibition of necroptosis by different inhibitors and PGAM5 knockdown attenuated GA-induced cell death in gastric cancer cell lines, thereby attenuating GA-caused cell proliferation inhibition. All the data supported the conclusion that GA could inhibit gastric cancer cell proliferation by inducing necroptosis

Just Noticeable Visual Redundancy Forecasting: A Deep Multimodal-Driven Approach

Just noticeable difference (JND) refers to the maximum visual change that human eyes cannot perceive, and it has a wide range of applications in multimedia systems. However, most existing JND approaches only focus on a single modality, and rarely consider the complementary effects of multimodal information. In this article, we investigate the JND modeling from an end-to-end homologous multimodal perspective, namely hmJND-Net. Specifically, we explore three important visually sensitive modalities, including saliency, depth, and segmentation. To better utilize homologous multimodal information, we establish an effective fusion method via summation enhancement and subtractive offset, and align homologous multimodal features based on a self-attention driven encoder-decoder paradigm. Extensive experimental results on eight different benchmark datasets validate the superiority of our hmJND-Net over eight representative methods

The Effect of MC-Type Carbides on the Microstructure and Wear Behavior of S390 High-Speed Steel Produced via Spark Plasma Sintering

The microstructure and wear behavior of S390 high-speed steel (HSS) reinforced with different volume fractions of MC-type carbides produced via spark plasma sintering were investigated using scanning electron microscopy (SEM) and transmission electron microscopy (TEM) in this study. SEM and TEM results show that V-W-rich carbides are formed around the added MC-type carbides, and these carbides have a similar composition to the M(C, N) carbides precipitated at high temperatures according to thermodynamic calculations. Both macrohardness and three-point bending results show that the carbide type is the dominant factor increasing the hardness, and the volume fraction of the carbide is the dominant factor leading to a decrease in the three-point bending strength. The wear mechanism of HSS metal matrix composites (MMCs) is confirmed as abrasive wear and oxidative wear via wear tracks and oxidation films. Compared with the sample without reinforcement (85 HRA, wear coefficient of 1.50 &times; 10&minus;15 m2/N), the best MT-3 sample exhibits a hardness increase of 1.8 HRA and a three-fold increase in wear resistance

The Effect of MC-Type Carbides on the Microstructure and Wear Behavior of S390 High-Speed Steel Produced via Spark Plasma Sintering

The microstructure and wear behavior of S390 high-speed steel (HSS) reinforced with different volume fractions of MC-type carbides produced via spark plasma sintering were investigated using scanning electron microscopy (SEM) and transmission electron microscopy (TEM) in this study. SEM and TEM results show that V-W-rich carbides are formed around the added MC-type carbides, and these carbides have a similar composition to the M(C, N) carbides precipitated at high temperatures according to thermodynamic calculations. Both macrohardness and three-point bending results show that the carbide type is the dominant factor increasing the hardness, and the volume fraction of the carbide is the dominant factor leading to a decrease in the three-point bending strength. The wear mechanism of HSS metal matrix composites (MMCs) is confirmed as abrasive wear and oxidative wear via wear tracks and oxidation films. Compared with the sample without reinforcement (85 HRA, wear coefficient of 1.50 × 10−15 m2/N), the best MT-3 sample exhibits a hardness increase of 1.8 HRA and a three-fold increase in wear resistance