Distortion Of Object Research Articles

A multi-scale assessment of desertification severity in the Eastern Mediterranean region based on albedo-MSAVI feature space.

Resampling the same satellite image to conduct a multi-scale assessment of desertification can be accompanied by distortion of terrestrial objects and spectral information, which can lead to uncertainty in the generated information. To address this, this study assesses desertification severity in an area of arid and semi-arid climate in the Eastern Mediterranean (Jordan)that is characterised by cloudless scenesusingmulti-sensor data of the same scene at the same time. To this end, Sentinel-2 at 10 m and 60 m, Landsat-8 at 30 m and MODIS at 250 m and 500 m were collected to extract albedo and modified soil adjusted vegetation index (MSAVI), and subsequently to construct albedo-MSAVI feature space. Using the negative correlation between albedo and MSAVI, desertification degree index (DDI) was generated. The resulting multi-scale DDI maps bear a relative resemblance in terms of spatial distribution, patterns, and proportions. The DDI maps indicate that extremely serious and serious desertification are widespread, accounting for 50% of the study area, primarily in the eastern portions. However, finer DDI maps (10 m, 30 m and 60 m) are essential for detecting small-scale desertification characteristics due to their ability to capture local spatial variabilities, while coarser ones (250 m and 500 m) are better suited for capturing broad-scale desertification patterns driven by climatic factors, in which MODIS data exhibit a relatively higher positive correlation with seasonal average precipitation. Although finer DDI maps show higher accuracy compared to coarser ones, the accuracy of DDI maps of MODIS has shown an increase within a homogeneous landscape. Accordingly, synchronised multi-scale assessment of desertification severity is not only influenced by the spatial resolution but also by the landscape heterogeneity and the type of satellite sensor utilised. The multi-scale approach applied in this study can provide insights on scale-dependent desertificationthat help in devising overarching mitigation strategies.

AI-empowered Search Drone: Advanced AI-based Tiny Object Search Drone for Broad Areas

Abstract. Detecting the garbage distribution and position in broad terrains(beaches, grasslands, squares, etc) is extremely important for environmental protection and urban image. Manually screen these terrains is time-consuming and ineicient. Autonomous robot may find the wastes automatically, but they may hinder and even endanger the pedestrians. Unmanned aerial systems(UAS) are ideal tools for monitoring broad areas and detecting garbage. In this project we create an AI-empowered Search Drone for extensive garbage search in broad regions. The camera on our drone and the external image transmission module on the computer receive and process top-view image from the sky. The images from high altitude lead to the reduction in target size and object distortion. We innovately modified the network structure of the oicial YOLOv5 model by adding layers specifically for small object detection, enhancing its adaptability to small targets captured by drones.To address camera image distortion, we leveraged the OpenCV (cv2) library to obtain and apply a distortion correction matrix. Furthermore, we devised an algorithm that calculates the real-world location of targets based on their pixel positions, the drones height, and the ratio between pixel and real-world dimensions.To strengthen the models garbage search capabilities, we simulated various garbage search scenarios in open environments and collected image datasets using drones for specialized training. Finally, we utilized the PyQt library to develop a simplistic front-end interface for data analysis and comparison of the detection performance between the original and optimized models. Experimental results demonstrate that the optimized model outperforms the original model, effectively enhancing garbage search eiciency in extensive areas. This project represents a significant step forward in leveraging technology to combat environmental pollution.

Experimental Assessment of Magnetic Resonance Imaging Distortion for Radiation Therapy Planning

MRI is widely used in radiation therapy planning, particularly in stereotactic radiosurgery for brain metastases. The article addresses the geometric distortion of MRI images, which can lead to errors in radiation therapy planning. A series of experiments with a simple phantom were conducted on two MRI scanners with 0.5 T and 1.5 T magnetic fields. Deviations in the positions of the phantom objects from their actual locations were observed. The detected deviations can reach up to 5 mm. A theoretical dependence of the magnetic field gradient on the distance to the center of homogeneity was calculated, which agrees well with the approximation of experimental data. An assessment was made of the dependence of the image area distortion of objects located at the center of the field homogeneity on their actual sizes. It was found that the area deviation can reach up to 20%.

Multisensory perceptual distortion including auditory distortions in Alice in Wonderland syndrome: a case report

ABSTRACT Alice in Wonderland syndrome (AIWS) is a rare perceptual disorder characterized mainly by perceptual distortions of visual objects and one’s own body. While there are many case reports of visual and somatosensory distortions associated with AIWS, little is known about auditory distortion. Therefore, we present the case of a 22-year-old right-handed woman who described having auditory as well as visual and somatosensory distortion experiences and a family history of AIWS. The subject reported experiencing multisensory perceptual distortions, where she sees other people’s faces as larger and hears their voices as louder at the same time. This particular case suggests that auditory distortion – which contributes to constructing the perception of the surrounding space and the body – may also be characterized as a perceptual symptom of AIWS.

A Method of Image Restoration for Distortion of Object in Water-Air Cross-Media

Uneven water-air media distribution or irregular liquid flow can cause changes in light propagation, leading to blurring and distortion of the extracted image, which presents a challenge for object recognition accuracy. To address these issues, this paper proposes a repair network to correct object image distortion in water-air cross-media. Firstly, convolutional combination performs feature extraction on water-air cross-media images, which retains the same features at the same scale and marks feature points with large differences. Then, an attention correction module for geometric lines is proposed to correct geometric lines in water-air cross-media images by comparing and sensing the marked feature points with large differences and utilizing the line similarity in positive and negative samples. Finally, the blurring artifact elimination module eliminates artifacts caused by image blurring and geometric line correction by using multiscale fusion of individual U-Net information streams. This completes the image restoration of object distortion under water-air cross-media. The proposed method is feasible and effective for restoring aberrated objects in water-air cross-media environments, with numerous experiments conducted on water-air cross-media image datasets.

RICE-YOLO: In-Field Rice Spike Detection Based on Improved YOLOv5 and Drone Images

The rice spike, a crucial part of rice plants, plays a vital role in yield estimation, pest detection, and growth stage management in rice cultivation. When using drones to capture photos of rice fields, the high shooting angle and wide coverage area can cause rice spikes to appear small in the captured images and can cause angular distortion of objects at the edges of images, resulting in significant occlusions and dense arrangements of rice spikes. These factors are unique challenges during drone image acquisition that may affect the accuracy of rice spike detection. This study proposes a rice spike detection method that combines deep learning algorithms with drone perspectives. Initially, based on an enhanced version of YOLOv5, the EMA (efficient multiscale attention) attention mechanism is introduced, a novel neck network structure is designed, and SIoU (SCYLLA intersection over union) is integrated. Experimental results demonstrate that RICE-YOLO achieves a mAP@0.5 of 94.8% and a recall of 87.6% on the rice spike dataset. During different growth stages, it attains an AP@0.5 of 96.1% and a recall rate of 93.1% during the heading stage, and a AP@0.5 of 86.2% with a recall rate of 82.6% during the filling stage. Overall, the results indicate that the proposed method enables real-time, efficient, and accurate detection and counting of rice spikes in field environments, offering a theoretical foundation and technical support for real-time and efficient spike detection in the management of rice growth processes.

Novel person detection and suspicious activity recognition using enhanced YOLOv5 and motion feature map

Person and suspicious activity detection is a major challenge for image-based surveillance systems. However, the accuracy of person detection is affected by several factors, such as the presence of the person, his trajectory, posture, complex background, and object distortion. In this work, we developed a person-focused dataset that includes various behaviors of students in an educational institution, such as cheating, theft of lab equipment, fights, and threatening situations. This dataset ensures consistent and standardized identification annotations for individuals, making it suitable for detection, tracking, and behavioral analysis of individuals. In addition, we have increased the detection accuracy through an improved architecture called YOLOv5 and introduced an efficient method for detecting global and local anomalous behaviors. This method extracts motion features that accurately describe the person’s movement, speed, and direction. To evaluate the effectiveness of our proposed approach, we validated it against our proposed database and publicly available benchmark datasets. Our method achieves state-of-the-art detection accuracy, namely 96.12%, with an error rate of 6.68% compared to existing methods. The empirical results show a significant improvement in anomalous activity detection. Our paper concludes with a summary and a discussion of possible future research directions.

The occurrence of age-related macular degeneration in the presence of a transferred, confirmed COVID-19

BACKGROUND: COVID-19 can affect the eyes and both the anterior and posterior segments of the eye. Retinal complications associated with COVID-19 have been reported.
 CLINICAL CASE DESCRIPTION: We present a clinical case of age-related macular degeneration that developed during the acute period of coronavirus infection. A patient with COVID-19 and pneumonia (CT-1) experienced sudden decrease in visual acuity within 1 month. A dark spot appeared in front of the left eye, which did not disappear despite eye movements, and distortion of objects was observed. The disease was manifested acutely. A complete ophthalmological examination revealed a sharp decrease in visual acuity and a positive Amsler test (distortion of lines at a distance of 30 cm). In the field of view, scotomas were noted in the center. Ophthalmoscopy revealed light and dark foci in the central macular zone of the retina. Cptical coherence tomography with an angiographic mode detected severe changes at the level of the neuroepithelium and retinal pigment epithelium with the formation of gray tissue. Aflibercept (approved for use in the Russian Federation, No. LP-003544) was intravitreally prescribed according to the clinical guidelines for treating age-related macular degeneration. After treatment with positive dynamics, visual acuity increased to 1.0, and the thickness of the retina decreased by 102 microns.
 CONCLUSION: The key objective in making a correct diagnosis was the use of optical coherence tomography with an angiographic mode. This method allows the visualization of the retinal layers without invasion The angiography mode allows the assessment of the state of the foveolar avascular zone and the density and perfusion of blood vessels, which enable the diagnosis of age-related macular degeneration and choroidal neovascularization. Timely antiangiogenic treatment, that is, a single intraocular injection, completely restored the visual acuity and visual field and normalized the state of the retina according to objective data of optical coherence tomography with angiography mode.

On deformable object handling: multi-tool end-effector for robotized manipulation and layup of fabrics and composites

Over the past decades, robotic automation has expanded in numerous industrial sectors; however, manufacturing operations involving the manipulation of non-rigid products are mostly preserved manual. The dynamic distortion of flexible objects underlines limitations in robot cognition and dexterity. Inspired by the gaps in composites industry automation, this paper presents a novel multifunctional robot end-effector for the robotic automation of composites layup. Aiming a holistic confrontation of layup challenges, the proposed end-effector incorporates tools for (a) the manipulation of sheet materials, like composite fabrics of variable dimensions; (b) the grasping of core materials, like foam blocks; (c) the handling of peripheral tools; (d) the exertion of fitting forces; and (e) the application of resin in diverse mold geometry cavities. The meticulous design of the end-effector’s configuration allows for consistent and collision-free tool usage with no excess robot tool changes, granting even higher productivity and efficiency. The enhancement in robot dexterity and proficiency for layup operations is validated within an automotive case, where semi-automation is intended for the improvement in overall workstation’s efficiency, quality, ergonomics, and well-being.

Adaptation of the Game Transfer Phenomena Scale into Spanish: Sensory-Perceptual Changes, Intrusive Thoughts, and Involuntary Behaviors in Mexican Video Game Players

Introduction: research on game transfer phenomena (GTP) is focused on understanding the impact of video game features and experiences on sensory perception, thoughts, and behaviors rather than on excessive/uncontrollable playing regardless of negative consequences. However, studies have shown a relationship between GTP and gaming disorder, and there is a resemblance between some forms of GTP and the perceptual disturbances that result as side effects of hallucinogens. Video game players have reported experiencing sensory perception changes (e.g., perceptual distortions of objects or environments and visual, auditory, or tactile hallucinations from games), intrusive thoughts, involuntary actions, and behaviors (e.g., the movement of fingers when interacting with game elements or a keyboard) . Objective: first, to adapt and validate the Game Transfer Phenomena Scale (GTPS) in Spanish with a Mexican sample. Second, to examine the adequacy of the frequency rating scale used in the GTPS. Method: cross-sectional survey with 607 players; most were males between 18 and 27 years old. Results: the higher-order factor model showed acceptable results. The prevalence of GTP was 96%. The number of game genres played and session length was correlated with all GTPS subscales, confirming the criterion validity. The examination of the frequency rating scale via the item response theory showed that selecting the second level of the scale “once” should be treated with caution. Discussion and conclusions: confirmatory factor analysis showed that the GTPS adapted into Spanish and tested in the Mexican sample was valid and reliable. Experiencing GTP more than once may be more meaningful for establishing the prevalence of GTP.

Equirectangular Image Data Detection, Segmentation and Classification of Varying Sized Traffic Signs: A Comparison of Deep Learning Methods.

There are known limitations in mobile omnidirectional camera systems with an equirectangular projection in the wild, such as momentum-caused object distortion within images, partial occlusion and the effects of environmental settings. The localization, instance segmentation and classification of traffic signs from image data is of significant importance to applications such as Traffic Sign Detection and Recognition (TSDR) and Advanced Driver Assistance Systems (ADAS). Works show the efficacy of using state-of-the-art deep pixel-wise methods for this task yet rely on the input of classical landscape image data, automatic camera focus and collection in ideal weather settings, which does not accurately represent the application of technologies in the wild. We present a new processing pipeline for extracting objects within omnidirectional images in the wild, with included demonstration in a Traffic Sign Detection and Recognition (TDSR) system. We compare Mask RCNN, Cascade RCNN, and Hybrid Task Cascade (HTC) methods, while testing RsNeXt 101, Swin-S and HRNetV2p backbones, with transfer learning for localization and instance segmentation. The results from our multinomial classification experiment show that using our proposed pipeline, given that a traffic sign is detected, there is above a 95% chance that it is classified correctly between 12 classes despite the limitations mentioned. Our results on the projected images should provide a path to use omnidirectional images with image processing to enable the full surrounding awareness from one image source.

Small Object Detection Methods in Complex Background: An Overview

Small object detection has been a research hotspot in the field of computer vision. Especially in complex backgrounds (CBs), SOD faces various challenges, including inconspicuous small object features, object distortion due to CBs interference, and inaccurate object localization due to various noises. So far, many methods have been proposed to improve the SOD content in CBs. In this paper, based on an extensive study of related literature, we first outline the current challenges and some cutting-edge solutions for SOD, and then introduce the complex background interference types present in small object images and the imaging characteristics of different types of images, as well as the characteristics of small objects. Next, the image pre-processing methods are summarized. Based on this, machine learning-based SOD methods and traditional SOD methods are focused on. Finally, the future development direction is given.

Multi-Generator Discriminator Network Using Texture-Edge Information

In the proposed paper, a parallel structure type Generative Adversarial Network (GAN) using edge and texture information is proposed. In the existing GAN-based model, many learning iterations had to be given to obtaining an output that was somewhat close to the original data, and noise and distortion occurred in the output image even when learning was performed. To solve this problem, the proposed model consists of two generators and three discriminators to propose a network in the form of a parallel structure. In the network, each edge information and texture information were received as inputs, learning was performed, and each character was combined and outputted through the Combine Discriminator. Through this, edge information and distortion of the output image were improved even with fewer iterations than DCGAN, which is the existing GAN-based model. As a result of learning on the network of the proposed model, a clear image with improved contour and distortion of objects in the image was output from about 50,000 iterations.

Лечение хориоидальной неоваскуляризации, ассоциированной с друзами диска зрительного нерва

Purpose. To describe a rare clinical case of bilateral choroidal neovascularization (CNV) associated with optic nerve head (ONH) drusen and to report a positive functional and clinical outcome of intravitreal injections of vascular endothelial growth factor inhibitors (anti-VEGF) in this patient. Material and methods. A 39-year-old man with ONH drusen complained of decreased visual acuity and distortion of objects. During the examination, the bilateral CNV was detected in peripapillary area. The patient received two consecutive intravitreal injections of anti-VEGF in the right eye and one injection in the left eye. Results. After the treatment, reabsorption of subretinal fluid and an increase of visual acuity were observed. There were no complications associated with injections. The best-corrected visual acuity remains stabilized at 20/20 at three years of followup. According to the optical coherence tomography parameters, there was no negative dynamics of CNV. Conclusion. ONH drusen may cause several complications, including the development of CNV and significant decrease in visual acuity, due to the CNV progression. Follow-up of patients with ONH drusen is necessary for early diagnosis and treatment of complications. The differential sign of CNV associated with ONH drusen is a localization in the peripapillary area nasal to the fovea. A search of the literature revealed only a few publications on the use of anti-VEGF in patients with CNV associated with ONH drusen. This method was effective and demonstrated positive functional and clinical outcome in the presented clinical case. Key word: subretinal neovascularization; choroidal neovascularization; optic nerve head drusen; optical coherence tomography; antivascular endothelial growth factor therapy

Хирургическое лечение дегенеративного ламеллярного макулярного разрыва с применением богатой тромбоцитами плазмы крови

Introduction. Surgical treatment of patients with degenerative lamellar macular hole (LMH) does not always improve visual acuity. One of the risk factors for such results is pronounced thinning of the retina in the area of the hole. In such cases, it was suggested to supplement the standard surgical technique with the application of autologous platelet-rich blood plasma (PRP) to the area of the hole. Purpose. To present a clinical case of surgical treatment of degenerative LMH with pronounced retinal thinning using PRP. Material and methods. Patient C., 68 years old, complained of distortion of objects contours and decreased vision in the right eye. His corrected visual acuity was 0.4. Optical coherence tomography revealed a degenerative LMH (Govetto A. et al. classification, 2016) of the right eye with significant retinal tissue defect, with ellipsoidal photoreceptor zone defect and minimal retinal thickness of 59 μm. A three-port subtotal 27G vitrectomy was performed with removal of the epiretinal membranes and internal limiting membrane, tamponade of the vitreal cavity with air, and application of autologous PRP to the hole area. Results. The dynamics of retinal recovery in terms of up to 9 months after surgery is described, the minimum retinal thickness increased from 59 to 238 μm; the maximum corrected visual acuity increased from 0.4 to 1.0. The formation of a hyperreflective zone similar in optical density to plexiform layers was noted in the area where the hole had previously occurred. The size of the hyperreflective zone decreased during observation. By 9 months after surgery, the ellipsoidal zone defect was virtually undetectable. Conclusion. The use of PRP resulted in not only closure of the hole but also normal retinal thickness in the foveal area which contributed to the restoration of the ellipsoidal zone of the photoreceptors and a significant increase of visual acuity. Presumably, PRP contributes to the speedy closure of the hole due to the formation of a glial scar of minimal size. Key words: degenerative lamellar macular hole, platelet-rich blood plasma, vitrectomy, hyperreflective zone, glial scar

Unbiased IoU for Spherical Image Object Detection

As one of the fundamental components of object detection, intersection-over-union (IoU) calculations between two bounding boxes play an important role in samples selection, NMS operation and evaluation of object detection algorithms. This procedure is well-defined and solved for planar images, while it is challenging for spherical ones. Some existing methods utilize planar bounding boxes to represent spherical objects. However, they are biased due to the distortions of spherical objects. Others use spherical rectangles as unbiased representations, but they adopt excessive approximate algorithms when computing the IoU. In this paper, we propose an unbiased IoU as a novel evaluation criterion for spherical image object detection, which is based on the unbiased representations and utilize unbiased analytical method for IoU calculation. This is the first time that the absolutely accurate IoU calculation is applied to the evaluation criterion, thus object detection algorithms can be correctly evaluated for spherical images. With the unbiased representation and calculation, we also present Spherical CenterNet, an anchor free object detection algorithm for spherical images. The experiments show that our unbiased IoU gives accurate results and the proposed Spherical CenterNet achieves better performance on one real-world and two synthetic spherical object detection datasets than existing methods.

Wide-Angle Image Rectification: A Survey

Wide field-of-view (FOV) cameras, which capture a larger scene area than narrow FOV cameras, are used in many applications including 3D reconstruction, autonomous driving, and video surveillance. However, wide-angle images contain distortions that violate the assumptions underlying pinhole camera models, resulting in object distortion, difficulties in estimating scene distance, area, and direction, and preventing the use of off-the-shelf deep models trained on undistorted images for downstream computer vision tasks. Image rectification, which aims to correct these distortions, can solve these problems. In this paper, we comprehensively survey progress in wide-angle image rectification from transformation models to rectification methods. Specifically, we first present a detailed description and discussion of the camera models used in different approaches. Then, we summarize several distortion models including radial distortion and projection distortion. Next, we review both traditional geometry-based image rectification methods and deep learning-based methods, where the former formulates distortion parameter estimation as an optimization problem and the latter treats it as a regression problem by leveraging the power of deep neural networks. We evaluate the performance of state-of-the-art methods on public datasets and show that although both kinds of methods can achieve good results, these methods only work well for specific camera models and distortion types. We also provide a strong baseline model and carry out an empirical study of different distortion models on synthetic datasets and real-world wide-angle images. Finally, we discuss several potential research directions that are expected to further advance this area in the future.

Motion parallax for object localization in electric fields

Parallax, as a visual effect, is used for depth perception of objects. But is there also the effect of parallax in the context of electric field imagery? In this work, the example of weakly electric fish is used to investigate how the self-generated electric field that these fish utilize for orientation and communication alike, may be used as a template to define electric parallax. The skin of the electric fish possesses a vast amount of electroreceptors that detect the self-emitted dipole-like electric field. In this work, the weakly electric fish is abstracted as an electric dipole with a sensor line in between the two emitters. With an analytical description of the object distortion for a uniform electric field, the distortion in a dipole-like field is simplified and simulated. On the basis of this simulation, the parallax effect could be demonstrated in electric field images i.e. by closer inspection of voltage profiles on the sensor line. Therefore, electric parallax can be defined as the relative movement of a signal feature of the voltage profile (here, the maximum or peak of the voltage profile) that travels along the sensor line peak trace (PT). The PT width correlates with the object’s vertical distance to the sensor line, as close objects create a large PT and distant objects a small PT, comparable with the effect of visual motion parallax.

Фотограмметрия в кадастре недвижимости

The geodesic method of the characteristic points’ coordinates measuring is the main method for urban cadastral works (including complex ones). Implementing digital aerial photography cameras, unmanned aerial vehicles and improving hardware and software systems for image processing enable achieving the necessary accuracy (10 cm in plan coordinates) when using the photogrammetric method. Stereo models and orthomosaics are the output products of the mentioned technology using for measurements. Due to the fact that at creating an orthomosaic, additional image conversion processes are required and they may cause the loss of accuracy and the presence of perspective distortions of high-altitude objects, orthomosaics cannot be used to determine the coordinates of characteristic points. It is proposed to use a stereo model, i.e. a three-dimensional high-precision image of the terrain, as a product for measuring characteristic points in cadastral works. The experiments’ results and the experience of production work proved that the accuracy of geodesic and stereophotogrammetric methods in the real estate cadaster are equal. At the same time, the mentioned method has some advantages

The effect of magnification technique on the optical density and size of object on mammography

To visualize the existence of micro-calcifications in mammography there are a number of ways carried out including by the magnification technique. This study evaluates the effect of magnification technique on optical density (OD) and size of the objects of the image produced on mammography. The objects used were three circular coins. Source to image distance (SID) was 65 cm, with a focal spot size of 0.1 cm, a combination of targets/filters was Mo/Mo with a filter’s thickness of 30 µm. The tube loading was 100 mAs, the tube voltage was 27 kVp, and the magnification was in the range of 1.00 - 2.00 times with an increase of 0.25. Measurement of OD and size of the objects was using an ImageJ software. The results of this study revealed that the OD value decreases proportional to the magnification. The OD values calculated on the 2x magnification were 13.74, 14.26, and 13.44% lower than 1.0 magnification (without magnification), while the resulting image will be oval in proportion to the position of the coin from the chest wall and the magnification. An implementation of the magnification method in the mammography must be carried out with caution. In addition to a benefit to larger appear of micro-calcification, it also causes a decrease in OD and distortion of objects in the image.