Fuzzy logic-based approach to wavelet denoising of 3D images produced by time-of-flight cameras

Modeling, analysis, and optimization of random error in indirect time-of-flight camera

For indirect time-of-flight (iToF) cameras, we proposed a modeling approach focused on addressing random error. Our model characterizes random error comprehensively by detailing the propagation of error introduced by signal light, ambient light, and dark noise through phase calculation and system correction processes. This framework leverages correlations between incident light and tap responses to quantify noise impacts accurately. We then experimentally validated the theoretical model, confirming its predictive accuracy. Additionally, from a waveform design perspective, we recommend selecting an optimal duty cycle for the light waveform based on the relative intensities of ambient and signal light to effectively reduce random error.

Denoising in optical coherence tomography volumes for improved 3D visualization

Optical coherence tomography (OCT) has already become one of the most important diagnostic tools in different fields of medicine, as well as in various industrial applications. The most important characteristic of OCT is its high resolution, both in depth and the transverse direction. Together with the information on the tissue density, OCT offers highly precise information on tissue geometry. However, the detectability of small and low-intensity features in OCT scans is limited by the presence of speckle noise. In this paper we present a new volumetric method for noise removal in OCT volumes, which aims at improving the quality of rendered 3D volumes. In order to remove noise uniformly, while preserving important details, the proposed algorithm simultaneously observes the estimated amounts of noise and the sharpness measure, and iteratively enhances the volume until it reaches the required quality. We evaluate the proposed method using four quality measures as well as visually, by evaluating the visualization of OCT volumes on an auto-stereoscopic 3D screen. The results show that the proposed method outperforms reference methods both visually and in terms of objective measures.

Differential-geometry-based surface normal vector calculation method using a time-of-flight camera

A surface normal vector of an object is often needed to detect an orientation of the object. A simple calculation method of the surface normal vector by means of a time-of-flight (ToF) camera is thus proposed here, using a coordinate transformation of three-dimensional irregular points acquired by the ToF camera to regular grid representation. Each point of the regular grid representation has a depth (distance from the camera) defined on the regular grid. The surface normal vector on the regular grid can be derived based on differential geometry with partial derivatives of the depth, and can then be in the form of the discretized Fourier transformation to which the fast Fourier transformation algorithm is applicable. The method of the surface normal vector calculation is thus theoretically derived. Validation of the method is also experimentally performed.

Analysis of Camera Arrays Applicable to the Internet of Things

The Internet of Things is built based on various sensors and networks. Sensors for stereo capture are essential for acquiring information and have been applied in different fields. In this paper, we focus on the camera modeling and analysis, which is very important for stereo display and helps with viewing. We model two kinds of cameras, a parallel and a converged one, and analyze the difference between them in vertical and horizontal parallax. Even though different kinds of camera arrays are used in various applications and analyzed in the research work, there are few discussions on the comparison of them. Therefore, we make a detailed analysis about their performance over different shooting distances. From our analysis, we find that the threshold of shooting distance for converged cameras is 7 m. In addition, we design a camera array in our work that can be used as a parallel camera array, as well as a converged camera array and take some images and videos with it to identify the threshold.

Hybrid exposure for depth imaging of a time-of-flight depth sensor

A time-of-flight (ToF) depth sensor produces noisy range data due to scene properties such as surface materials and reflectivity. Sensor measurement frequently includes either a saturated or severely noisy depth and effective depth accuracy is far below its ideal specification. In this paper, we propose a hybrid exposure technique for depth imaging in a ToF sensor so to improve the depth quality. Our method automatically determines an optimal depth for each pixel using two exposure conditions. To show that our algorithm is effective, we compare the proposed algorithm with two conventional methods in qualitative and quantitative manners showing the superior performance of proposed algorithm.

Range clusters based time-of-flight 3D imaging obstacle detection in manifold space

A new obstacle detection method using time-of-flight 3D imaging sensor based on range clusters is proposed. To effectively reduce the influence of outlier and noise in range images, we utilize intensity images to estimate noise deviation of the range images and a weighted local linear smoothing is used to project the data into a new manifold surface. The proposed method divides the 3D imaging data into range clusters with different shapes and sizes according to the distance ambient relation between the pixels, and some regulation criterions are set to adjust the range clusters into optimal shape and size. Experiments on the SwissRanger sensor data show that, compared to the traditional obstacle detection methods based on regular data patches, the proposed method can give more precious detection results.

Supervised preserving projection for learning scene information based on time-of-flight imaging sensor

In this paper, we propose a new supervised manifold learning approach, supervised preserving projection (SPP), for the depth images of a 3D imaging sensor based on the time-of-flight (TOF) principle. We present a novel manifold sense to learn scene information produced by the TOF camera along with depth images. First, we use a local surface patch to approximate the underlying manifold structures represented by the scene information. The fundamental idea is that, because TOF data have nonstatic noise and distance ambiguity problems, the surface patches can more efficiently approximate the local neighborhood structures of the underlying manifold than TOF data points, and they are robust to the nonstatic noise of TOF data. Second, we propose SPP to preserve the pairwise similarity between the local neighboring patches in TOF depth images. Moreover, SPP accomplishes the low-dimensional embedding by adding the scene region class label information accompanying the training samples and obtains the predictive mapping by incorporating the local geometrical properties of the dataset. The proposed approach has advantages of both classical linear and nonlinear manifold learning, and real-time estimation results of the test samples are obtained by the low-dimensional embedding and the predictive mapping. Experiments show that our approach obtains information effectively from three scenes and is robust to the nonstatic noise of 3D imaging sensor data.