Search Results (51)

Understanding fish habitats is essential for fisheries management, habitat restoration, and species protection. Automated fish detection is a key tool in these applications, which enables real-time monitoring and quantitative analysis. Recent advancements in high-resolution cameras and machine learning technologies have facilitated image analysis automation, promoting remote fish tracking. However, many of these detection methods require large volumes of annotated data, which involve considerable effort and time. Additionally, their practical implementation remains challenging in environments with limited data. Hence, this study proposes an anomaly-based fish detection approach by integrating Patch Distribution Modeling with data augmentation techniques, including Affine Transformations, Neural Filters, and SinGAN. Field experiments were conducted in Lake Izunuma-Uchinuma, Japan, using an electrofishing boat to acquire data. Evaluation metrics, such as AUROC and F1-score, assessed detection performance. The results indicate that, compared to the original dataset (AUROC: 0.836, F1-score: 0.483), Neural Filters (AUROC: 0.940, F1-score: 0.879) and Affine Transformations (AUROC: 0.942, F1-score: 0.766) improve anomaly detection. However, SinGAN exhibited no measurable enhancement, indicating the necessity for further optimization. This shows the potential of the proposed approach to enhance automated fish detection in limited-data environments, supporting aquatic ecosystem sustainability. Full article

Maintaining stable image feature extraction under viewpoint changes is challenging, particularly when the angle between the camera’s reverse direction and the object’s surface normal exceeds 40 degrees. Such conditions can result in unreliable feature detection. Consequently, this hinders the performance of vision-based systems. To address this, we propose a feature point extraction method named Large Viewpoint Feature Extraction (LV-FeatEx). Firstly, the method uses a dual-threshold approach based on image grayscale histograms and Kapur’s maximum entropy to constrain the AGAST (Adaptive and Generic Accelerated Segment Test) feature detector. Combined with the FREAK (Fast Retina Keypoint) descriptor, the method enables more effective estimation of camera motion parameters. Next, we design a longitude sampling strategy to create a sparser affine simulation model. Meanwhile, images undergo perspective transformation based on the camera motion parameters. This improves operational efficiency and aligns perspective distortions between two images, enhancing feature point extraction accuracy under large viewpoints. Finally, we verify the stability of the extracted feature points through feature point matching. Comprehensive experimental results show that, under large viewpoint changes, our method outperforms popular classical and deep learning feature extraction methods. The correct rate of feature point matching improves by an average of 40.1 percent, and speed increases by an average of 6.67 times simultaneously. Full article

Primary open-angle glaucoma (POAG) is the most common form of glaucoma. This condition leads to optic nerve degeneration and eventually to blindness. Tobacco smoking, alcohol consumption, fast-food diets, obesity, heavy weight lifting, high-intensity physical exercises, and many other bad habits are lifestyle-related risk factors for POAG. By contrast, moderate-intensity aerobic exercise and the Mediterranean diet can alleviate POAG. In this work, we for the first time estimated the phylostratigraphic age indices (PAIs) of all 153 POAG-related human genes in the NCBI Gene Database. This allowed us to separate them into two groups: POAG-related genes that appeared before and after the phylum Chordata, that is, ophthalmologically speaking, before and after the camera-type eye evolved. Next, in the POAG-related genes’ promoters, we in silico predicted all 3835 candidate SNP markers that significantly change the TATA-binding protein (TBP) affinity for these promoters and, through this molecular mechanism, the expression levels of these genes. Finally, we verified our results against five independent web services—PANTHER, DAVID, STRING, MetaScape, and GeneMANIA—as well as the ClinVar database. It was concluded that POAG is likely to be a symptom of the human self-domestication syndrome, a downside of being civilized. Full article

Perception systems for assisted driving and autonomy enable the identification and classification of objects through a concentration of sensors installed in vehicles, including Radio Detection and Ranging (RADAR), camera, Light Detection and Ranging (LIDAR), ultrasound, and HD maps. These sensors ensure a reliable and robust navigation system. Radar, in particular, operates with electromagnetic waves and remains effective under a variety of weather conditions. It uses point cloud technology to map the objects in front of you, making it easy to group these points to associate them with real-world objects. Numerous clustering algorithms have been developed and can be integrated into radar systems to identify, investigate, and track objects. In this study, we evaluate several clustering algorithms to determine their suitability for application in automotive radar systems. Our analysis covered a variety of current methods, the mathematical process of these methods, and presented a comparison table between these algorithms, including Hierarchical Clustering, Affinity Propagation Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Mini-Batch K-Means, K-Means Mean Shift, OPTICS, Spectral Clustering, and Gaussian Mixture. We have found that K-Means, Mean Shift, and DBSCAN are particularly suitable for these applications, based on performance indicators that assess suitability and efficiency. However, DBSCAN shows better performance compared to others. Furthermore, our findings highlight that the choice of radar significantly impacts the effectiveness of these object recognition methods. Full article

The study involved measurements of surface tension of liquid binary copper-titanium alloys with respect to their chemical composition and temperature as well as investigations of the liquid alloy–refractory material-gaseous phase system wettability using usual refractory materials, i.e., graphite, aluminum oxide and magnesium oxide. The experiments were performed with the use of the sessile drop method and a high-temperature microscope coupled with a camera and a computer. The aim of this study was to determine the influence of titanium content in the Cu-Ti alloy on the surface tension and contact angle at the interface between the liquid alloy and the refractory material. The influence of temperature on these parameters was also examined. The tests were carried out for copper-titanium alloys with a maximum content of 1.5% wt. Ti, in the temperature range of 1373 to 1573 K. The test results indicate that as the titanium content in the alloy increases, its surface tension increases slightly. However, an increase in temperature causes a decrease in the surface tension of the alloys. In the case of an alloy containing 1.5% wt. Ti, surface tension at a temperature of 1373 K reaches 1351 mN∙m⁻¹, and at a temperature of 1573 K, it decreases to 1315 mN∙m⁻¹. As the temperature and titanium content in the alloy increase, a decrease in the contact angle is observed. The highest values of contact angles were recorded in the case of contact of the liquid alloy with graphite. For an alloy containing 0.1% wt. Ti at a temperature of 1373 K, the contact angle reaches 132°, while at a temperature of 1573 K, it decreases to 128°. For an alloy containing 1.5% wt. Ti, the values of contact angles are 100° and 96°, respectively. However, the contact angles have the lowest values for magnesium oxide. In the case of a temperature of 1573 K and an alloy containing 1.5% wt. Ti, the contact angle reaches 49°. Such a significant impact of titanium content on the contact angles may be due to its high affinity for oxygen (contact with a substrate made of Al₂O₃ and MgO and its reactivity with carbon (contact with graphite). Full article

In this paper, we present a noise-robust approach for the 3D pose estimation of multiple people using appearance similarity. The common methods identify the cross-view correspondences between the detected keypoints and determine their association with a specific person by measuring the distances between the epipolar lines and the joint locations of the 2D keypoints across all the views. Although existing methods achieve remarkable accuracy, they are still sensitive to camera calibration, making them unsuitable for noisy environments where any of the cameras slightly change angle or position. To address these limitations and fix camera calibration error in real-time, we propose a framework for 3D pose estimation which uses appearance similarity. In the proposed framework, we detect the 2D keypoints and extract the appearance feature and transfer it to the central server. The central server uses geometrical affinity and appearance similarity to match the detected 2D human poses to each person. Then, it compares these two groups to identify calibration errors. If a camera with the wrong calibration is identified, the central server fixes the calibration error, ensuring accuracy in the 3D reconstruction of skeletons. In the experimental environment, we verified that the proposed algorithm is robust against false geometrical errors. It achieves around 11.5% and 8% improvement in the accuracy of 3D pose estimation on the Campus and Shelf datasets, respectively. Full article

Due to its small size and high affinity binding, the engineered scaffold protein ADAPT6 is a promising targeting probe for radionuclide imaging of human epidermal growth factor receptor type 2 (HER2). In a Phase I clinical trial, [^99mTc]Tc-ADAPT6 demonstrated safety, tolerability and capacity to visualize HER2 expression in primary breast cancer. In this study, we aimed to select the optimal parameters for distinguishing between breast cancers with high and low expression of HER2 using [^99mTc]Tc-ADAPT6 in a planned Phase II study. HER2 expression was evaluated in primary tumours and metastatic axillary lymph nodes (mALNs). SPECT/CT imaging of twenty treatment-naive breast cancer patients was performed 2 h after injection of [^99mTc]Tc-ADAPT6. The imaging data were compared with the data concerning HER2 expression obtained by immunohistochemical evaluation of samples obtained by core biopsy. Maximum Standard Uptake Values (SUV_max) afforded the best performance for both primary tumours and mALNs (areas under the receiver operating characteristic curve (ROC AUC) of 1.0 and 0.97, respectively). Lesion-to-spleen ratios provided somewhat lower performance. However, the ROC AUCs were still over 0.90 for both primary tumours and mALNs. Thus, lesion-to-spleen ratios should be further evaluated to find if these could be applied to imaging using stand-alone SPECT cameras that do not permit SUV calculations. Full article

Convolutional neural networks (CNNs) have become instrumental in advancing multi-frame image super-resolution (SR), a technique that merges multiple low-resolution images of the same scene into a high-resolution image. In this paper, a novel deep learning multi-frame SR algorithm is introduced. The proposed CNN model, named Exponential Fusion of Interpolated Frames Network (EFIF-Net), seamlessly integrates fusion and restoration within an end-to-end network. Key features of the new EFIF-Net include a custom exponentially weighted fusion (EWF) layer for image fusion and a modification of the Residual Channel Attention Network for restoration to deblur the fused image. Input frames are registered with subpixel accuracy using an affine motion model to capture the camera platform motion. The frames are externally upsampled using single-image interpolation. The interpolated frames are then fused with the custom EWF layer, employing subpixel registration information to give more weight to pixels with less interpolation error. Realistic image acquisition conditions are simulated to generate training and testing datasets with corresponding ground truths. The observation model captures optical degradation from diffraction and detector integration from the sensor. The experimental results demonstrate the efficacy of EFIF-Net using both simulated and real camera data. The real camera results use authentic, unaltered camera data without artificial downsampling or degradation. Full article

Traditional non-coplanar calibration methods, represented by Tsai’s method, are difficult to apply in multi-camera-based stereo vision measurements because of insufficient calibration accuracy, inconvenient operation, etc. Based on projective theory and matrix transformation theory, a novel mathematical model is established to characterize the transformation from targets’ 3D affine coordinates to cameras’ image coordinates. Then, novel non-coplanar calibration methods for both monocular and binocular camera systems are proposed in this paper. To further improve the stability and accuracy of calibration methods, a novel circular feature points extraction method based on region Otsu algorithm and radial section scanning method is proposed to precisely extract the circular feature points. Experiments verify that our novel calibration methods are easy to operate, and have better accuracy than several classical methods, including Tsai’s and Zhang’s methods. Intrinsic and extrinsic parameters of multi-camera-systems can be calibrated simultaneously by our methods. Our novel circular feature points extraction algorithm is stable, and with high precision can effectively improve calibration accuracy for coplanar and non-coplanar methods. Real stereo measurement experiments demonstrate that the proposed calibration method and feature extraction method have high accuracy and stability, and can further serve for complicated shape and deformation measurements, for instance, stereo-DIC measurements, etc. Full article

Mechanically separated meat (MSM) is a by-product of the poultry industry that requires routine quality assessment. Calcium content is an indirect indicator of bone debris in MSM but is difficult to determine by EDTA titration due to the poor solubility of calcium phosphate. Therefore, 1,2-diaminocyclohexane-N,N,N’,N’-tetraacetic acid was used instead, which has two orders of magnitude higher affinity for calcium ions. In addition, the auxiliary complexing agents triethanolamine and Arsenazo III, an indicator that is sensitive to low calcium concentrations, were used. Automatic titration endpoint detection was performed using an immersion probe at 660 nm. It has been shown that the color change in Arsenazo III can also be read with an RGB camera. The CDTA titration procedure has been tested on commercial Bologna-type sausages and the results were in line with AAS and ICP reference data. The content of calcium in sausages turned out to be very diverse and weakly correlated with the content of MSM. The tested MSM samples had a wide range of calcium content: from 62 to 2833 ppm. Calcium-rich poultry by-products include fat and skin (115 to 412 ppm), articular cartilage (1069 to 1704 ppm), and tendons (532 to 34,539 ppm). The CDTA titration procedure is fully suitable for small meat processing plants due to its simplicity of use and low cost. Full article

Thermal imaging cameras and infrared (IR) temperature measurement devices act as state-of-the-art techniques for non-contact temperature determination of the skin surface. The former is cost-intensive in many cases for widespread application, and the latter requires manual alignment to the measuring point. Due to this background, this paper proposes a new method for automated, non-contact, and area-specific temperature measurement of the facial skin surface. It is based on the combined use of a low-cost thermopile sensor matrix and a 2D image sensor. The temperature values as well as the 2D image data are fused using a parametric affine transformation. Based on face recognition, this allows temperature values to be assigned to selected facial regions and used specifically to determine the skin surface temperature. The advantages of the proposed method are described. It is demonstrated by means of a participant study that the temperature absolute values, which are achieved without manual alignment in an automated manner, are comparable to a commercially available IR-based forehead thermometer. Full article

The development of choroidal neovascularization (CNV) is a crucial factor in the pathophysiology and prognosis of exudative age-related macular degeneration (AMD). Therefore, the detection of CNV is essential for establishing an appropriate diagnosis and treatment plan. Current ophthalmic imaging techniques, such as fundus fluorescent angiography and optical coherence tomography, have limitations in accurately visualizing CNV lesions and expressing CNV activity, owing to issues such as excessive dye leakage with pooling and the inability to provide functional information. Here, using the arginine−glycine−aspartic acid (RGD) peptide’s affinity for integrin α_vβ₃, which is expressed in the neovascular endothelial cells in ocular tissues, we propose the use of fluorescein isothiocyanate (FITC)-labeled RGD peptide as a novel dye for effective molecular imaging of CNV. FITC-labeled RGD peptides (FITC-RGD₂), prepared by bioconjugation of one FITC molecule with two RGD peptides, demonstrated better visualization and precise localization of CNV lesions than conventional fluorescein dyes in laser-induced CNV rodent models, as assessed using various imaging techniques, including a commercially available clinical fundus camera (Optos). These results suggest that FITC-RGD₂ can serve as an effective novel dye for the diagnosis of neovascular retinal diseases, including AMD, by enabling early detection and treatment of disease occurrence and recurrence after treatment. Full article

Many unmanned aerial vehicles (UAV) host rolling shutter (RS) cameras, i.e., cameras where image rows are exposed at slightly different times. As the camera moves in the meantime, this causes inconsistencies in homologous ray intersections in the bundle adjustment, so correction models have been proposed to deal with the problem. This paper presents a series of test flights and simulations performed with different UAV platforms at varying speeds over terrain of various morphologies with the objective of investigating and possibly optimising how RS correction models perform under different conditions, in particular as far as block control is concerned. To this aim, three RS correction models have been applied in various combinations, decreasing the number of fixed ground control points (GCP) or exploiting GNSS-determined camera stations. From the experimental tests as well as from the simulations, four conclusions can be drawn: (a) RS affects primarily horizontal coordinates and varies notably from platform to platform; (b) if the ground control is dense enough, all correction models lead practically to the same mean error on checkpoints; however, some models may cause large errors in elevation if too few GCP are used; (c) in most cases, a specific correction model is not necessary since the affine deformation caused by RS can be adequately modelled by just applying the extended Fraser camera calibration model; (d) using GNSS-assisted block orientation, the number of necessary GCP is strongly reduced. Full article

The detection of moving objects in images is a crucial research objective; however, several challenges, such as low accuracy, background fixing or moving, ‘ghost’ issues, and warping, exist in its execution. The majority of approaches operate with a fixed camera. This study proposes a robust feature threshold moving object identification and segmentation method with enhanced optical flow estimation to overcome these challenges. Unlike most optical flow Otsu segmentation for fixed cameras, a background feature threshold segmentation technique based on a combination of the Horn–Schunck (HS) and Lucas–Kanade (LK) optical flow methods is presented in this paper. This approach aims to obtain the segmentation of moving objects. First, the HS and LK optical flows with the image pyramid are integrated to establish the high-precision and anti-interference optical flow estimation equation. Next, the Delaunay triangulation is used to solve the motion occlusion problem. Finally, the proposed robust feature threshold segmentation method is applied to the optical flow field to attract the moving object, which is the. extracted from the Harris feature and the image background affine transformation model. The technique uses morphological image processing to create the final moving target foreground area. Experimental results verified that this method successfully detected and segmented objects with high accuracy when the camera was either fixed or moving. Full article

Aiming at the approach and landing of an aircraft under low visibility, this paper studies the use of an infrared heat-transfer imaging camera and visible-light camera to obtain dynamic hyperspectral images of flight approach scenes from the perspective of enhancing pilot vision. Aiming at the problems of affine deformation, difficulty in extracting similar geometric features, thermal shadows, light shadows, and other issues in heterogenous infrared and visible-light image registration, a multi-modal image registration method based on RoI driving in a virtual scene, RoI feature extraction, and virtual-reality-fusion-based contour angle orientation is proposed, and this could reduce the area to be registered, reduces the amount of computation, and improves the real-time registration accuracy. Aiming at the differences in multi-modal image fusion in terms of resolution, contrast, color channel, color information strength, and other aspects, the contour angle orientation maintains the geometric deformation of multi-source images well, and the virtual reality fusion technology effectively deletes incorrectly matched point pairs. By integrating redundant information and complementary information from multi-modal images, the visual perception abilities of pilots during the approach process are enhanced as a whole. Full article