Three-Dimensional Registration for Handheld Profiling Systems Based on Multiple Shot Structured Light

Ayaz, Shirazi Muhammad; Khan, Danish; Kim, Min Young

doi:10.3390/s18041146

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…It's used to scan objects with complex shapes or surfaces that are difficult to capture with other techniques. There are various types of such scanners: from huge setups to handheld apparatus [3]. And this is the best thing about it because the ambulance is not big enough to hold a number of big-sized pieces of equipment.…”

Section: D Scanningmentioning

confidence: 99%

3D bioprinting on moving surfaces using computer vision

Zakharova,

Czekanski

2023

Progress in Canadian Mechanical Engineering. Volume 6

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3D bioprinting is a rapidly developing field with many potential applications in medicine, bioengineering, and, possibly, even other areas. However, one of the main challenges in bioprinting is maintaining accuracy and precision during the printing process, especially when printing on a moving surface. The moving surface terminology means a not properly fixed or unstable surface for printing. For example, a human body, healing harmed tissues directly in an ambulance on the way to the hospital or any other emergency. This paper proposes a novel method for 3D bioprinting on moving surfaces using computer vision and machine learning. The approach involves the use of video processing techniques to analyze the bioprinting process in real-time, enabling the identification of potential issues and the adjustment of the printing process to improve the quality of the printed tissue. This is an important step toward the development of scalable and reproducible bioprinting technologies for tissue engineering applications.

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Section: D Scanningmentioning

confidence: 99%

3D bioprinting on moving surfaces using computer vision

Zakharova,

Czekanski

2023

Progress in Canadian Mechanical Engineering. Volume 6

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“…The geometric registration model from the spot image to the footprint image determines the corresponding position of the spot center on the footprint image. The projective mapping between the two images or planes is known as the homograph matrix [19,20]. The conversion from the image coordinates of the spot image to the image coordinates of the footprint image can be expressed as Equation (1).…”

Section: Geometric Registration Modelmentioning

confidence: 99%

Laser Spot Center Location Method for Chinese Spaceborne GF-7 Footprint Camera

Ren

Xie

Xiong³

et al. 2020

Sensors

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The Gaofen-7 (GF-7) satellite is equipped with two area array sensor footprint cameras to capture the laser altimeter spot. In order to establish a direct correspondence between the laser data and the stereo image data, a new method is proposed to fit the center of the spot using the brightness difference between the spot image and the footprint image. First, the geometric registration between the spot image and the footprint image is completed based on feature matching or template matching. Then, the brightness values between the two images are extracted from the corresponding image position to form a measurement, and the least squares adjustment method is used to calculate the parameters of the brightness conversion model between the spot image and the footprint image. Finally, according to the registration relationship, the center of the identified spots is respectively positioned in the footprint images, so that the laser spots are accurately identified in the along-track stereo footprint images. The experimental results show that the spot error of this method is less than 0.7 pixel, which has higher reliability and stability, and can be used for a GF-7 satellite footprint camera.

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“…3D reconstruction using projected light tries to find a compromise between the previous two methods by projecting structured light onto the items and capturing 2D images, such as in [ 12 , 13 , 14 ]. The projection of structured light solves the problem of calibrating the image as well as the differences in textures in the different food items being captured.…”

Section: Introductionmentioning

confidence: 99%

DietSensor: Automatic Dietary Intake Measurement Using Mobile 3D Scanning Sensor for Diabetic Patients

Makhsous

Bharadwaj

Atkinson

et al. 2020

Sensors

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Diabetes is a global epidemic that impacts millions of people every year. Enhanced dietary assessment techniques are critical for maintaining a healthy life for a diabetic patient. Moreover, hospitals must monitor their diabetic patients’ food intake to prescribe a certain amount of insulin. Malnutrition significantly increases patient mortality, the duration of the hospital stay, and, ultimately, medical costs. Currently, hospitals are not fully equipped to measure and track a patient’s nutritional intake, and the existing solutions require an extensive user input, which introduces a lot of human errors causing endocrinologists to overlook the measurement. This paper presents DietSensor, a wearable three-dimensional (3D) measurement system, which uses an over the counter 3D camera to assist the hospital personnel with measuring a patient’s nutritional intake. The structured environment of the hospital provides the opportunity to have access to the total nutritional data of any meal prepared in the kitchen as a cloud database. DietSensor uses the 3D scans and correlates them with the hospital kitchen database to calculate the exact consumed nutrition by the patient. The system was tested on twelve volunteers with no prior background or familiarity with the system. The overall calculated nutrition from the DietSensor phone application was compared with the outputs from the 24-h dietary recall (24HR) web application and MyFitnessPal phone application. The average absolute error on the collected data was 73%, 51%, and 33% for the 24HR, MyFitnessPal, and DietSensor systems, respectively.

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Three-Dimensional Registration for Handheld Profiling Systems Based on Multiple Shot Structured Light

Cited by 5 publications

References 28 publications

3D bioprinting on moving surfaces using computer vision

3D bioprinting on moving surfaces using computer vision

Laser Spot Center Location Method for Chinese Spaceborne GF-7 Footprint Camera

DietSensor: Automatic Dietary Intake Measurement Using Mobile 3D Scanning Sensor for Diabetic Patients

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