A real-time stereo vision system for distance measurement and underwater image restoration

Sánchez-Ferreira, Camilo; Mori, Jones Y.; Farias, Mylène C. Q.; Llanos, Carlos H.

doi:10.1007/s40430-016-0596-5

Cited by 22 publications

(12 citation statements)

References 29 publications

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“…Hence the absolute value is taken. It is also observed that our depth estimation technique is outperforming the MyleneC.Q.Farias [22] stereo vision system based distance measurement in single camera system by giving a good quality of curve fit with RMSE of 0.041356 on Nikon model data set acquisition and 0.056232 on canon model. The details are furnished in the Table 15.…”

Section: Comparison Of Our Work With Existing Workmentioning

confidence: 74%

“…We are able to achieve an optimum 'k'value= 0.45 for Nikon model with f stop =2.8 mm and k = 1.44 for cannon model with f stop = 7.1 mm. When compared with the Mylene C.Q.Farias [22], Yujiao Chen [21] stereo vision set-up and Said Pertuz et al [17] Palmieri et al [23] light field imaging experimental set up, the proposed techniques used single In our current study we are confined to finding the depth estimate only when object is on camera axial line. But in future the work is likely to be extended in such a way that depth of person is estimated when person is in slanting position.…”

Section: Discussionmentioning

confidence: 99%

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Depth perception in single rgb camera system using lens aperture and object size: a geometrical approach for depth estimation

Alphonse

Sriharsha

2021

SN Appl. Sci.

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In recent years, with increase in concern about public safety and security, human movements or action sequences are highly valued when dealing with suspicious and criminal activities. In order to estimate the position and orientation related to human movements, depth information is needed. This is obtained by fusing data obtained from multiple cameras at different viewpoints. In practice, whenever occlusion occurs in a surveillance environment, there may be a pixel-to-pixel correspondence between two images captured from two cameras and, as a result, depth information may not be accurate. Moreover use of more than one camera exclusively adds burden to the surveillance infrastructure. In this study, we present a mathematical model for acquiring object depth information using single camera by capturing the in focused portion of an object from a single image. When camera is in-focus, with the reference to camera lens center, for a fixed focal length for each aperture setting, the object distance is varied. For each aperture reading, for the corresponding distance, the object distance (or depth) is estimated by relating the three parameters namely lens aperture radius, object distance and object size in image plane. The results show that the distance computed from the relationship approximates actual with a standard error estimate of 2.39 to 2.54, when tested on Nikon and Cannon versions with an accuracy of 98.1% at 95% confidence level.

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Section: Comparison Of Our Work With Existing Workmentioning

confidence: 74%

Section: Discussionmentioning

confidence: 99%

Depth perception in single rgb camera system using lens aperture and object size: a geometrical approach for depth estimation

Alphonse

Sriharsha

2021

SN Appl. Sci.

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“…It is found from Table 7 that our model performs equally well with the Chen [14] depth estimation technique by demonstrating elevated consistency between the expected (estimated) and reference (real) depth values (r) with a Pearson's correlation coefficient of r=0.95. Unlike the microlens and primary lens arrangement, as mentioned in the works [13,14,17,23], for 2D images, we used single DSLR nikkor 18-55 mm zoom lens. Unlike the disparity and distance between the image planes and the micro lens, we used body dimension and centroid as characteristics for true height forecast.…”

Section: Model Accuracy With Reference To the Existing Workmentioning

confidence: 99%

Depth Perception in a Single RGB Camera Using Body Dimensions and Centroid Property

Alphonse¹,

Sriharsha²

2020

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Infrastructure Supervision is a compelling need for buildings and open areas. It is facilitated through the joint use of stereo vision cameras, techniques and algorithms. This Stereoscopic assessment helps monitoring systems to reconstruct people's visible surface and also provides a robust estimation of the position and posture of the person that allows 3D scene activities and interactions. In practice, in occluded fields, the correspondence between pixels and pixels interferes with the flow of data in surveillance. Structured light ToF imaging and Light Field imaging sensors came into being considering the restriction. These techniques, however failed in addressing the inaccuracies and noise introduced in the phase of profound capture. Based on the Human Anthropometric research, we suggested a technique for estimating the depth of an individual from a single RGB camera. As we deal with moving objects in a scene, also consideration is given to centroid ownership. The system is trained by feeding stature, body width and centroid as inputs to estimate a person's actual height using gradient boosting model. And a person's further anticipated height and actual height are used to predict distance. After taking actual depth (camera to person distance) and real height as ground truth, the suggested model is validated and it is inferred that the camera to person distance anticipated (Preddist) from estimated Real height is 95% correlated with actual Camera to Person distance (or depth) at a confidence level of 99.9% with RMSE of 0.092.

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“…For field applications, performance is an important consideration, because the identification algorithm has to provide information to operators during construction and assembly processes. A possible strategy in embedded systems is the use of FPGAs when custom hardware is made for the application [27]. Another approach used to accelerate computation is to take advantage of high parallelism and low cost of graphic processing units (GPUs) [4,9].…”

Section: Related Workmentioning

confidence: 99%

A robust methodology for outdoor optical mark recognition

Andrade

Trabasso

Eguti

et al. 2017

J Braz. Soc. Mech. Sci. Eng.

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Outdoor optical mark recognition is an extremely useful tool for recognition of large industrial equipment and application of computer vision-based systems for tracking and positioning. However, current algorithms rely on thresholding and corner detection to identify checkerboard-like patterns, which is not appropriate for non-uniform lighting conditions. This paper presents a robust methodology to identify optical markers in outdoor environments. A GPU-based region filling algorithm automatically detects all contiguous color regions without computing seed points. Post-processing steps extract highlevel information from these regions. Analysis of identified contiguous color region allows simultaneous identification of all checkerboard and targets (concentric regions) in the scene. Analysis of variance demonstrates that the proposed methodology is robust to lighting, environment, perspective, and occlusion. Tests indicate that precision and recall for checkerboard and target identification in outdoor conditions are expected to be above 97%. The parallel algorithm implementation using OpenCL yields better results and is two times faster than previous region filling algorithms, taking about 0.6 s to process a full-HD picture using modern hardware Keywords Computer vision Á Optical mark recognition Á OpenCL Á Parallel processing Á Design of experiments Á Heterogeneous computing

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A real-time stereo vision system for distance measurement and underwater image restoration

Abstract: an output of 26.56 frames per second for images of 800 × 480 pixels. The synthesis results and the measurement precision show that the developed system is suitable for real-time tasks.

Cited by 22 publications

References 29 publications

Depth perception in single rgb camera system using lens aperture and object size: a geometrical approach for depth estimation

Depth perception in single rgb camera system using lens aperture and object size: a geometrical approach for depth estimation

Depth Perception in a Single RGB Camera Using Body Dimensions and Centroid Property

A robust methodology for outdoor optical mark recognition

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