Environmental Effects on Measurement Uncertainties of Time-of-Flight Cameras

Guomundsson, S.A.; Aanæs, Henrik; Larsen, Rasmus

doi:10.1109/isscs.2007.4292664

Cited by 63 publications

(47 citation statements)

References 3 publications

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“…4). We have not considered environmental effects or measurement uncertainties related to the ToF camera as in [5,9] (e.g. the reflection issue in a cluttered environment and the influence of intensity on depth), which are beyond the scope of this paper.…”

Section: Discussionmentioning

confidence: 99%

Combining Stereo and Time-of-Flight Images with Application to Automatic Plant Phenotyping

et al. 2011

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Abstract. This paper shows how stereo and Time-of-Flight (ToF) images can be combined to estimate dense depth maps in order to automate plant phenotyping. We focus on some challenging plant images captured in a glasshouse environment, and show that even the state-of-the-art stereo methods produce unsatisfactory results. By developing a geometric approach which transforms depth information in a ToF image to a localised search range for dense stereo, a global optimisation strategy is adopted for producing smooth and discontinuity-preserving results. Since pixel-by-pixel depth data are unavailable for our images and many other applications, a quantitative method accounting for the surface smoothness and the edge sharpness to evaluate estimation results is proposed. We compare our method with and without ToF against other state-ofthe-art stereo methods, and demonstrate that combining stereo and ToF images gives superior results.

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Section: Discussionmentioning

confidence: 99%

Combining Stereo and Time-of-Flight Images with Application to Automatic Plant Phenotyping

et al. 2011

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“…The probability of the number of object parts present is computed according to (5). In case of the particle filter, the probability is approximated by P (R k = r|z 1:k ) ≈ N δ(R (n) k , r)/N , where N is the number of particles.…”

Section: Methodsmentioning

confidence: 99%

“…Further, range cameras provide an instant single image of a scene at a high frame rate though with a lower image quality in general [2], [3]. The 3D information acquired with a range camera is strongly affected by noise, outliers and distortions, because of its particular measurement principle using a CMOS/CCD imager [4], [5], [6]. This makes it difficult to apply range image algorithms developed in the past.…”

Section: Introductionmentioning

confidence: 99%

Incremental object part detection toward object classification in a sequence of noisy range images

Gachter¹,

Harati²,

Siegwart³

2008

2008 IEEE International Conference on Robotics and Automation

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Abstract-This paper presents an incremental object part detection algorithm using a particle filter. The method infers object parts from 3D data acquired with a range camera. The range information is uniquely quantized and enhanced by local structure information to partially cope with considerable measurement noise and distortion. The augmented voxel representation allows the adaptation of known track-before-detect algorithms to infer multiple object parts in a range image sequence. The appropriateness of the method is successfully demonstrated by an experiment.

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“…Another parameter, the amplitude threshold, defines the minimum amplitude that needs to be exceeded to accept a measurement. This is used to suppress noisy values where the pixels have low amplitude (reflectance), as well as around the edges of the sensor [15]. In our experiments, we used integration times of 6.3-8.3ms leading to frame rates of 23.4-19.4fps respectively.…”

Section: System Setupmentioning

confidence: 99%

Using Time-of-Flight Measurements for Privacy-Preserving Tracking in a Smart Room

Jia

Radke

2014

IEEE Trans. Ind. Inf.

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Abstract-We present a method for real-time person tracking and coarse pose recognition in a smart room using timeof-flight measurements. The time-of-flight images are severely downsampled to preserve the privacy of the occupants and simulate future applications that use single-pixel sensors in "smart" ceiling panels. The tracking algorithms use grayscale morphological image reconstruction to avoid false detections, and are designed not to mistakenly detect pieces of furniture as people. A maximum likelihood estimation method using a simple Markov model was implemented for robust pose classification. We show that the algorithms work effectively even when the sensors are spaced apart by 25cm, using both real-world experiments and environmental simulation.

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Environmental Effects on Measurement Uncertainties of Time-of-Flight Cameras

Cited by 63 publications

References 3 publications

Combining Stereo and Time-of-Flight Images with Application to Automatic Plant Phenotyping

Combining Stereo and Time-of-Flight Images with Application to Automatic Plant Phenotyping

Incremental object part detection toward object classification in a sequence of noisy range images

Using Time-of-Flight Measurements for Privacy-Preserving Tracking in a Smart Room

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