Improving Shape from Focus Using Defocus Information

Pradeep, K; Rajagopalan, A. N.

doi:10.1109/icpr.2006.704

Cited by 4 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed method is independent of illumination condition and edge orientation. The camera parameters are known [6] in many algorithms but the proposed method calculates the camera parameters as explained in section V. Thus, our method uses a single camera, estimates camera parameters and computes depth from a single image with any type of edge orientation.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

“…After the sharp image has been obtained, the lens law can be used to compute the depth. DFF technique algorithms are simple but in order to extract depth information with high accuracy, many images are required per measurement [6]. Hence, the selection of the image sensor is a primary bottleneck and therefore high spatial and temporal resolution cannot be achieved.…”

Section: Related Workmentioning

confidence: 98%

See 1 more Smart Citation

Single image based depth estimation for robotic applications

Sabnis

Vachhani

2011

2011 IEEE Recent Advances in Intelligent Computational Systems

View full text Add to dashboard Cite

Goal of the robot vision is to exploit power of visual sensing to observe and perceive the environment and react it. Visual feedback is used to manipulate the robot among objects by estimating their depths. This paper presents a depth estimation technique based on the defocus blur associated with a camera setting. A sharp image of an object is obtained from a defocused image of the same object by applying sharpening filter. The defocused and sharp images of the object are used to calculate the spread parameter which is related to the object depth. The method calculates the constant camera parameters. The main advantage of this method is use of a single image by the robot to estimate depth. The method is independent of illumination condition and can be applied to the images with different edge orientations. Experiments on real scene images have demonstrated the feasibility of the proposed method for depth estimation. The results indicate that the depth estimation average errors are within two percent of true values. The proposed method is compared with the existing methods.Index Terms-Robot vision, depth estimation, defocus blur, sharpening filter, spread parameter.

show abstract

Section: Experimental Setup and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 98%

Single image based depth estimation for robotic applications

Sabnis

Vachhani

2011

2011 IEEE Recent Advances in Intelligent Computational Systems

View full text Add to dashboard Cite

show abstract

“…It worked well for laparoscopy due to the camera location outside the patient’s body, but there might have been a problem with the device size for endoscopy application. To improve SFF accuracy and stability, many software algorithms have addressed noise reduction [ 16 , 17 , 18 ] and point-spread-function optical modeling [ 19 ]. In addition, SFF hardware imagers have been proposed [ 20 , 21 ], but have not been introduced for endoscopy.…”

Section: Introductionmentioning

confidence: 99%

A Small and High-Speed Driving Mechanism for 3D Shape Measurement in Monocular Endoscopy

Nakajima

Tanigaki

Sugino

et al. 2021

Sensors

View full text Add to dashboard Cite

Three-dimensional (3D) shape acquisition has been widely introduced to enrich quantitative analysis with the combination of object shape and texture, for example, surface roughness evaluation in industry and gastrointestinal endoscopy in medicine. Shape from focus is a promising technique to measure substance surfaces in 3D space because no occlusion problem appears in principle, as does with stereo shape measurement, which is another commonly used option. We have been developing endoscopic shape measurement devices and shape reconstruction algorithms. In this paper, we propose a mechanism for driving an image sensor reciprocated for the shape from focus of 3D shape measurement in monocular endoscopy. It uses a stepping motor and a planar-end cam, which transforms the motor rotation to imaging sensor reciprocation, to implement the shape from focus of 3D shape measurement in endoscopy. We test and discuss the device in terms of its driving accuracy and application feasibility for endoscopic 3D shape measurement.

show abstract

“…In [4], a hybrid system that combines SFF and stereo has been proposed. In [5,6], the result of SFF is improved using the information of relative defocus.…”

Section: Introductionmentioning

confidence: 99%

Reliability measure for shape-from-focus

Pertuz

Puig

García

2013

Image and Vision Computing

View full text Add to dashboard Cite

This is the author’s version of a work that was accepted for publication in Journal Image and Vision Computing . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal Image and Vision Computing , 31, 10 (2013) DOI: 10.1016/j.imavis.2013.07.005Shape-from-focus (SFF) is a passive technique widely used in image processing for obtaining depth-maps. This technique is attractive since it only requires a single monocular camera with focus control, thus avoiding correspondence problems typically found in stereo, as well as more expensive capturing devices. However, one of its main drawbacks is its poor performance when the change in the focus level is difficult to detect. Most research in SFF has focused on improving the accuracy of the depth estimation. Less attention has been paid to the problem of providing quality measures in order to predict the performance of SFF without prior knowledge of the recovered scene. This paper proposes a reliability measure aimed at assessing the quality of the depth-map obtained using SFF. The proposed reliability measure (the R-measure) analyses the shape of the focus measure function and estimates the likelihood of obtaining an accurate depth estimation without any previous knowledge of the recovered scene. The proposed R-measure is then applied for determining the image regions where SFF will not perform correctly in order to discard them. Experiments with both synthetic and real scenes are presented

show abstract

Improving Shape from Focus Using Defocus Information

Cited by 4 publications

References 6 publications

Single image based depth estimation for robotic applications

Single image based depth estimation for robotic applications

A Small and High-Speed Driving Mechanism for 3D Shape Measurement in Monocular Endoscopy

Reliability measure for shape-from-focus

Contact Info

Product

Resources

About