Rear-view Images Research Articles

Image-Acceleration Multimodal Danger Detection Model on Mobile Phone for Phone Addicts.

With the popularity of smartphones, a large number of "phubbers" have emerged who are engrossed in their phones regardless of the situation. In response to the potential dangers that phubbers face while traveling, this paper proposes a multimodal danger perception network model and early warning system for phubbers, designed for mobile devices. This proposed model consists of surrounding environment feature extraction, user behavior feature extraction, and multimodal feature fusion and recognition modules. The environmental feature module utilizes MobileNet as the backbone network to extract environmental description features from the rear-view image of the mobile phone. The behavior feature module uses acceleration time series as observation data, maps the acceleration observation data to a two-dimensional image space through GADFs (Gramian Angular Difference Fields), and extracts behavior description features through MobileNet, while utilizing statistical feature vectors to enhance the representation capability of behavioral features. Finally, in the recognition module, the environmental and behavioral characteristics are fused to output the type of hazardous state. Experiments indicate that the accuracy of the proposed model surpasses existing methods, and it possesses the advantages of compact model size (28.36 Mb) and fast execution speed (0.08 s), making it more suitable for deployment on mobile devices. Moreover, the developed image-acceleration multimodal phubber hazard recognition network combines the behavior of mobile phone users with surrounding environmental information, effectively identifying potential hazards for phubbers.

Building Height Extraction Based on Satellite GF-7 High-Resolution Stereo Image

Abstract. High-resolution remote sensing images can distinguish objects of smaller size, so as to more clearly express the texture features and structural information of objects, and provide a data source for the development of large-scale mapping, high-precision stereo measurement and other fields. The purpose of this paper is to complete the height estimation of the buildings by analyzing the stereoscopic observation formed by the front and rear view images of the Gaofen-7 line array CCD. After determining the roof profile of the building on the rear-view image, assuming a series of object elevations of the building, that is, searching for elevations with a certain step distance within a certain elevation search range, adopt the object-based image matching VLL algorithm, Through the RFM imaging model of the Gaofen-7 sensor, the rear-view contour is projected to the front-view image, and then the PSNR is selected as the similarity measure of the window, and the similarity of the image block area corresponding to the front- and rear-view contour is calculated. Corresponds to the hypothetical object elevation as the estimated height of the building. Under the technical route of this paper, the height of buildings on high-resolution images can be estimated to a level within 3 meters of accuracy.

Detecting medial patellar luxation with ensemble deep convolutional neural network based on a single rear view image of the hindlimb

Medial patellar luxation (MPL) is a common orthopedic disease in dogs, which predisposes elderly and small-breed dogs. Unlike in humans, diagnosis in the early course of the disease is challenging because symptoms and joint-pain expression in canines are vague. Herein, we introduced a deep-learning system to diagnose MPL using a single rear-view hindlimb image. We believe that this is the first attempt to build a deep-learning system to diagnose MPL based on image analysis. Notably, 7689 images were collected from 2653 dogs in 30 private animal clinics between July 2021 and July 2022. Model performance was compared with ResNet50, VGG16, VGG19, Inception-V3, and veterinarian performance. For performance comparison, a professional veterinarian with > 10 years of experience selected images of 25 normal dogs and 25 dogs with MPL. The proposed model showed the highest performance, with 92.5% accuracy, whereas human experts showed an average accuracy of 55.2%. Therefore, our model can diagnose MPL using only a single rear-view hindlimb image. Furthermore, to solve the image uncertainty caused by the input image noise, we used a one-class SVM and ensemble learning methods to ensure model robustness. Our study will help diagnose MPL in clinical settings using a single rear-view hindlimb image.

Car make and model recognition system using rear-lamp features and convolutional neural networks

Recognizing cars based on their features is a difficult task. We propose a solution that uses a convolutional neural network (CNN) and image binarization method for car make and model classification. Unlike many previous works in this area, we use a feature extraction method combined with a binarization method. In the first stage of the pre-processing part we normalize and change the size of an image. The image is then used to recognize where the rear-lamps are placed on the image. We extract the region and use the image binarization method. The binarized image is used as input to the CNN network that finds the features of a specific car model. We have tested the combinations of three different neural network architectures and eight binarization methods. The convolutional neural network with parameters of the highest quality metrics value is used to find the characteristics of the rear lamps on the binary image. The convolutional network is tested with four different gradient algorithms. We have tested the method on two data sets which differ in the way the images were taken. Each data set consists of three subsets of the same car, but is scaled to different image dimensions. Compared to related works that are based on CNN, we use rear view images in different position and light exposure. The proposed method gives better results compared to most available methods. It is also less complex, and faster to train compared to other methods. The proposed approach achieves an average accuracy of 93,9% on the first data set and 84,5% on the second set.

A Study and Analysis on Pedestrian Detection and Tracking Through Rear-View Images

A Study and Analysis on Pedestrian Detection and Tracking Through Rear-View Images

Qualitative study of airflow structure across wooden louvred window panels for natural ventilation applications

Window ventilation usually used in energy-efficient buildings as an effective natural ventilation system to provide an adequate opening for fresh air to flow into the interior space. It could help to reduce the indoor air temperature and limit the contaminants in buildings. This study aimed to visualise the airflow structure across a wooden louvred window panel using the smoke flow visualisation technique at different airflow speeds of 0.5, 1.0, 2.0, 3.0 and 5.0 m/s in a closed-loop wind tunnel. Two Sony 1920 × 1080i cameras captured the airflow structure, which took the side view, rear view, front view and back view images. The wooden louvred window panel promotes optimum outdoor airflow and facilitates continuous air exchange to replace the indoor air. Results showed that smoke filled the space quickly at the highest airflow speed of 5 m/s. This study used an inclination angle of 75° for the wooden louvred window panel to avoid rain splatter. Besides, wood material could be used as a facade shading device. Therefore, installing the wooden louvred window panels could enhance natural ventilation, ensure indoor thermal comfort and reduce indoor air contaminants.

Fast and robust approaches for lane detection using multi‐camera fusion in complex scenes

Due to the limited sensing ability with the single-view camera and the real-time requirement for multi-view scenarios or deep learning-based methods in complex scenes, the output of lane detection is not applicable for the actual lane departure warning system. To tackle this challenge, the authors propose a fast and robust approach for lane detection based on well-designed multi-camera fusion, integrating vanishing point estimation, and specified feature fitting strategies. To meet real-time demand, several simple but effective image processing means are introduced and improved. Concretely, on account of statistical information, the authors’ method carries out an improved region of interest selection to speed up the detection. Afterwards, they used the B-spline fitting lane line on the strength of the RANdom SAmple consensus algorithm for the front view image detection and improved the Hough algorithm for the two rear-view images correspondingly. Using coordinate conversion and self-designed fusion strategy, they get the robust lane information based on symmetrical lane detection from the left/right sides of both front and side views. Experimental results in newly introduced multi-camera scenarios show that their multi-camera fusion framework contributes to significant improvement in accuracy and robustness in comparison with traditional methods.

충돌 및 균형상실 사고 방지를 위한 스마트폰 연동 전동 휠체어

This research has been conducted to develop a prevention module of collision and balance lost in electric wheelchairs. Ultrasonic sensors were used for detecting obstacles. Giro and acceleration sensors were used for measuring the tilt angle of wheel chair frame. Kalman filters were employed for filtering high-frequency noise of acceleration sensor and removing zero drift of the Giro sensor. The presence and location of obstacles were detected, and their information was sent to the user smartphone. When the tilt angle of the wheelchair frame exceeded a certain value, a warning signal was set to operate. Raspberry Pi camera module was used to transfer rear view image data to the smartphone to help the driver acknowledge obstacles in the rear side. A DSP processor with high computing performance was necessary to realize a real-time processing of the sensor and image data. Our research showed that a prevention module of collision and balance lost connected to a smartphone can be realized at a considerable low price.

Macular photostress and visual experience between microscope and intracameral illumination during cataract surgery

To evaluate macular photostress and visual experience between coaxial microscope illumination versus oblique intracameral illumination during cataract surgery. Gachon University Gil Hospital, Incheon, South Korea. Prospective case series. Consecutive patients who had cataract surgery using microscope illumination and intracameral illumination were included. The patients were asked to complete a questionnaire (seeing strong lights, feeling photophobia, feeling startled (fright) when seeing lights, seeing any colors, seeing any instruments or surgical procedures, and estimating intraoperative visual function) designed to describe their cataract surgery experience. The images projected on the retina of the model eye (rear view) with artificial opaque fragments in the anterior chamber during simulating cataract surgery were compared between the 2 illumination types. Sixty patients completed the questionnaire. Scores for strong lights, photophobia, fright, and color perception were significantly higher with microscope illumination than with intracameral illumination (all P<.001). More patients preferred the intracameral illumination (45 [75.0%]) to the microscope illumination (13 [21.7%]). In the rear-view images created in a model eye, only the bright microscope light in the center was seen without any lens image in the microscope illumination. However, in the intracameral illumination, the less bright light from the light pipein the periphery and the lens fragments were seen more clearly. In a view of the patients' visual experience, oblique intracameral illumination caused less subjective photostress and was preferred over coaxial microscope illumination. Objective findings from the model-eye experiment correlated to the result of visual experience.

Detection of approaching vehicle from motorcycle rearview image sequence

<b>Detection of approaching vehicle from motorcycle </b><b>rearview image sequence </b>

Expansion of the visual angle of a car rear-view image via an image mosaic algorithm

The rear-view image system is one of the active safety devices in cars and is widely applied in all types of vehicles and traffic safety areas. However, studies made by both domestic and foreign researchers were based on a single image capture device while reversing, so a blind area still remained to drivers. Even if multiple cameras were used to expand the visual angle of the car’s rear-view image in some studies, the blind area remained because different source images were not mosaicked together. To acquire an expanded visual angle of a car rear-view image, two charge-coupled device cameras with optical axes angled at 30 deg were mounted below the left and right fenders of a car in three light conditions—sunny outdoors, cloudy outdoors, and an underground garage—to capture rear-view heterologous images of the car. Then these rear-view heterologous images were rapidly registered through the scale invariant feature transform algorithm. Combined with the random sample consensus algorithm, the two heterologous images were finally mosaicked using the linear weighted gradated in-and-out fusion algorithm, and a seamless and visual-angle-expanded rear-view image was acquired. The four-index test results showed that the algorithms can mosaic rear-view images well in the underground garage condition, where the average rate of correct matching was the lowest among the three conditions. The rear-view image mosaic algorithm presented had the best information preservation, the shortest computation time and the most complete preservation of the image detail features compared to the mean value method (MVM) and segmental fusion method (SFM), and it was also able to perform better in real time and provided more comprehensive image details than MVM and SFM. In addition, it had the most complete image preservation from source images among the three algorithms. The method introduced by this paper provided the basis for researching the expansion of the visual angle of a car rear-view image in all-weather conditions.

Su2041 A Novel Dual-View Probe That Produces a Single Image and Improves Polyp Detection

Background: Colonoscopy is the procedure of choice for colon cancer screening, but is far from perfect, as lesions are missed due to lack of image contrast and due to their location behind haustral folds and at flexures. We have developed a novel dual-view optical probe that illuminates and images in the forward and omni-directional rear directions to provide a single registered video image. Objective: Evaluate the prototype dual-view probe in a standard colon model. Design: A colon model (Chamberlain Group, MA) was used for the study. Due to the stiffness of the model and the difficulty in advancing a colonoscope through it, the colon was cut into 3 sections (ascending, transverse, and descending colon) and the ends sealed to prevent leakage of light. A total of 31 red spheres and discs (to represent polyps and flat lesions) were glued to the inner surface of the model; approximately half were located in easily visible areas between folds, whereas the rest were placed in difficult to see locations proximal to folds. A research fellow advanced a standard colonoscope (Fujinon EC530-HL, Fujifilm, Wayne, NJ) with 140 degree forward view to the sealed end of each segment. The image was shown on a standard flat panel display. Each endoscopist (2 attendings, 2 fellows) examined the three colon segments with the room lights darkened. Polyps detected were called out by the endoscopist and recorded by research staff. The dual-view probe was subsequently attached to the tip of the colonoscope and the procedure repeated by each endoscopist 14 days later (so that location of polyps could not be recalled) using the dual-view probe's own light source and image display Results: Out of a total of 31 lesions a mean of 18.8 ± 2.2 lesions were visualized using standard forward-viewing colonoscope, whereas 29.3 ± 1.3 (an increase of 56%) were detected with the dual-view probe (p< 0.005, t-test). Limitations: Commercially available models made maneuverability of the colonoscope and probe difficult, necessitating the model to be cut into 3 segments. The size of this first proof of concept dual-view prototype is too large to be passed through the biopsy channel of a colonoscope and its attachment to the tip of the endoscope further limited flexion of the scope tip. Conclusions: The novel proof of concept dual-view probe is the first optical device to display both a forward and 360-degree rear-view image in the vertical plane in a single registered display. A single display allows both the forward and rear images to be viewed with ease. Imaging with the dual-view probe substantially improved polyp detection, particularly those located behind haustral folds. Ongoing work to reduce the size of the dual-view probe will allow it to be passed through a standard colonoscope for testing in patients.

Su2042 Corticotropin-Releasing Factor Changes the Phenotype and Function of Mouse Mesenteric Lymph Nodes Dendritic Cells (MLNDCs)

Background: Colonoscopy is the procedure of choice for colon cancer screening, but is far from perfect, as lesions are missed due to lack of image contrast and due to their location behind haustral folds and at flexures. We have developed a novel dual-view optical probe that illuminates and images in the forward and omni-directional rear directions to provide a single registered video image. Objective: Evaluate the prototype dual-view probe in a standard colon model. Design: A colon model (Chamberlain Group, MA) was used for the study. Due to the stiffness of the model and the difficulty in advancing a colonoscope through it, the colon was cut into 3 sections (ascending, transverse, and descending colon) and the ends sealed to prevent leakage of light. A total of 31 red spheres and discs (to represent polyps and flat lesions) were glued to the inner surface of the model; approximately half were located in easily visible areas between folds, whereas the rest were placed in difficult to see locations proximal to folds. A research fellow advanced a standard colonoscope (Fujinon EC530-HL, Fujifilm, Wayne, NJ) with 140 degree forward view to the sealed end of each segment. The image was shown on a standard flat panel display. Each endoscopist (2 attendings, 2 fellows) examined the three colon segments with the room lights darkened. Polyps detected were called out by the endoscopist and recorded by research staff. The dual-view probe was subsequently attached to the tip of the colonoscope and the procedure repeated by each endoscopist 14 days later (so that location of polyps could not be recalled) using the dual-view probe's own light source and image display Results: Out of a total of 31 lesions a mean of 18.8 ± 2.2 lesions were visualized using standard forward-viewing colonoscope, whereas 29.3 ± 1.3 (an increase of 56%) were detected with the dual-view probe (p< 0.005, t-test). Limitations: Commercially available models made maneuverability of the colonoscope and probe difficult, necessitating the model to be cut into 3 segments. The size of this first proof of concept dual-view prototype is too large to be passed through the biopsy channel of a colonoscope and its attachment to the tip of the endoscope further limited flexion of the scope tip. Conclusions: The novel proof of concept dual-view probe is the first optical device to display both a forward and 360-degree rear-view image in the vertical plane in a single registered display. A single display allows both the forward and rear images to be viewed with ease. Imaging with the dual-view probe substantially improved polyp detection, particularly those located behind haustral folds. Ongoing work to reduce the size of the dual-view probe will allow it to be passed through a standard colonoscope for testing in patients.

Semi‐automatic parking slot marking recognition for intelligent parking assist systems

This paper proposes a semi-automatic parking slot marking-based target position designation method for parking assist systems in cases where the parking slot markings are of a rectangular type, and its efficient implementation for real-time operation. After the driver observes a rearview image captured by a rearward camera installed at the rear of the vehicle through a touchscreen-based human machine interface, a target parking position is designated by touching the inside of a parking slot. To ensure the proposed method operates in real-time in an embedded environment, access of the bird's-eye view image is made efficient: image-wise batch transformation is replaced with pixel-wise instantaneous transformation. The proposed method showed a 95.5% recognition rate in 378 test cases with 63 test images. Additionally, experiments confirmed that the pixel-wise instantaneous transformation reduced execution time by 92%.

Investigation of Vibration Phenomena Induced by Air Flow Over Side View Mirror

This work develops an understanding of the flow mechanisms that induce vibrations on automotive side view mirrors. The unsteady nature of the flow over side view mirrors causes unsteady aerodynamic load distributions and flow-induced vibrations on the mirror assembly. These vibrations generate blurred rear-view images and higher noise levels, affecting the mirror functionality and passenger comfort. Certain geometrical design features of side view mirrors can exacerbate the flow-induced vibration levels of the mirror assembly significantly. This work quantifies the impact of these design features on the vibration amplitude, develops a methodology for testing mirror vibrations in a small, low-speed wind tunnel using only the mirror of interest, and delves into the interactions between the bluff body mirror geometry and its wake. Two similar side view mirror designs were investigated in this work by using laser-based vibrometry, flow visualization, particle image velocimetry, hot film anemometry, and surface stress sensitive film techniques. The magnitude of the vibrations was found to depend on the level of excursion in the dynamic location of flow separation, particularly when characteristic flow frequencies couple with the mirror housing natural frequency.

Trust in God

Photo number: 1 Photo location: Ghana photo description: a powerful rear-view image of a large blue truck carrying 40+ standing people and 10+ sitting people on the top rack. Across...

Optical recognition of motor vehicle license plates

A system for the recognition of car license plates is presented. The aim of the system is to read automatically the Italian license number of a car passing through a tollgate. A CCTV camera and a frame grabber card are used to acquire a rear-view image of the vehicle. The recognition process consists of three main phases. First, a segmentation phase locates the license plate within the image. Then, a procedure based upon feature projection estimates some image parameters needed to normalize the license plate characters. Finally, the character recognizer extracts some feature points and uses template matching operators to get a robust solution under multiple acquisition conditions. A test has been done on more than three thousand real images acquired under different weather and illumination conditions, thus obtaining a recognition rate close to 91%. >