Depth estimation from stereo images using sparsity

Quality and cloud flags of GOSAT/FTS: Fourier Transform Spectrometer onboard Greenhouse gasses Observation Satellite products taking into account cirrus clouds and thick aerosols are considered and proposed. Influence due to cirrus and thick aerosol on estimation of column CO2 and CH4 with GOSAT/FTS data is clarified. Relatively large estimation errors are observed in column CO2 and CH4 retrievals with FTS data in some atmospheric conditions. In order to find such cases, retrieval results and quality/cloud flags in the GOSAT/FTS data products are checked. Through the investigation, it is found that relatively large error is caused by convergence problem due to cirrus clouds and thick aerosols. In the proposed paper, some of the cases of which relatively large estimation error is occurred at the Saga TCCON (The Total Carbon Column Observing Network) site are investigated. Also, a comparative study is conducted between standard products provided by NASA/JPL and the Levenberg-Marquardt based least square method of column CO2 and CH4 retrieval. It is suggested that some improvements of estimation accuracy of column CO2 and CH4 retrieval with GOSAT/FTS data can be expected.

As location data are widely available to portable devices, trajectory tracking of moving objects has become an essential technology for most location-based services. To maintain such streaming data of location updates from mobile clients, conventional approaches such as time-based regular location updating and distance-based location updating have been used. However, these methods suffer from the large amount of data, redundant location updates, and large trajectory estimation errors due to the varying speed of moving objects. In this paper, we propose a simple but efficient online trajectory data reduction method for portable devices. To solve the problems of redundancy and large estimation errors, the proposed algorithm computes trajectory errors and finds a recent location update that should be sent to the server to satisfy the user requirements. We evaluate the proposed algorithm with real GPS trajectory data consisting of 17 201 trajectories. The intensive simulation results prove that the proposed algorithm always meets the given user requirements and exhibits a data reduction ratio of greater than 87 % when the acceptable trajectory error is greater than or equal to 10 meters.

Effective visual representation plays an important role in the scene classification systems. While many existing methods are focused on the generic descriptors extracted from the RGB color channels, we argue the importance of depth context, since scenes are composed with spatial variability and depth is an essential component in understanding the geometry. In this letter, we present a novel depth representation for RGB-D scene classification based on a specific designed convolutional neural network (CNN). Contrast to previous deep models that transfer from pretrained RGB CNN models, we harness model by using the multiviewpoint depth image augmentation to overcome the data scarcity problem. The proposed CNN framework contains the dilated convolutions to expand the receptive field and a subsequent spatial pooling to aggregate multiscale contextual information. The combination of contextual design and multiviewpoint depth images are important toward a more compact representation, compared to directly using original depth images or off-the-shelf networks. Through extensive experiments on SUN RGB-D dataset, we demonstrate that the representation outperforms recent state of the arts, and combining it with standard CNN-based RGB features can lead to further improvements.

We present a novel hand localization technique for 3D user interfaces. Our method is designed to overcome the difficulty of fitting anatomical models which fail to converge or converge with large errors in complex scenes or suboptimal imagery. We learn a discriminative model of the hand from depth images by using fast to compute features and a Random Forest classifier. The learned model is then combined with a spatial clustering algorithm to localize the hand position. We propose three formulations of low-level image features for use in model training. We evaluate the performance of our method by testing on low resolution depth maps of users two to three meters from the sensor in natural poses. Our method can detect an arbitrary number of hands per scene and preliminary results show that it is robust to suboptimal imagery.

In this paper, several VLSI architectures and implementations of Shape-Adaptive Discrete Wavelet Transform (SA-DWT) with odd symmetric biorthogonal filters are presented. The hardware implementation issues of SA-DWT algorithm are first addressed, and one lifting scheme together with some appropriate shape information processing units is introduced for the 1-D SA-DWT architectures. These architectures can efficiently perform 1-D length-adaptive DWT on continuous line segments and solve the corresponding boundary extension and subsampling problems without any overhead. In addition to 1-D architectures, two 2-D SA-DWT architectures are also proposed. One is the direct method, which uses the 1-D SA-DWT architectures as basis to extend to 2-D SA-DWT by direct row-column approach. The other is the line-based method, which is an efficient and feasible architecture that uses internal line buffers rather than external frame memory. These two 2-D SA-DWT architectures are both VLSI implemented by using cell-based chip design flow. The Daubechies (9,7) filter is used for the implementation by direct method, and the Daubechies (9,3) filter is used for the line-based implementation. The proposed architectures can be applied both in arbitrarily shaped visual objects coding such as MPEG-4 still texture coding and rectangular region image coding such as JPEG2000 still image coding.

Geoneutrino observations, first achieved by KamLAND in 2005 and followed by Borexino in 2010, have accumulated statistics and improved sensitivity for more than ten years. The uncertainty of the geoneutrino flux at the surface is now reduced to a level small enough to set useful constraints on U and Th abundances in the Bulk Silicate Earth (BSE). However, in order to make inferences on earth’s compositional model, the contributions from the local crust need to be understood within a similar uncertainty. Here we develop a new method to construct a stochastic crustal composition model utilizing Bayesian inference. While the methodology has general applicability, it incorporates all the local uniqueness in its probabilistic framework. Unlike common approaches for this type of problem, our method does not depend on crustal segmentation into upper, (middle) and lower, whose classification and boundaries are not always well defined. We also develop a new modeling method to infer rock composition distributions that conserve mass balance and therefore do not bias the results. Combined with a new vast collection of geochemical data for rock samples in the Japan arc, we apply this method to geoneutrino observation at Kamioka, Japan. Currently a difficulty remains in the handling of correlations in the flux integration; we conservatively assume maximum correlation, which leads to large flux estimation errors of 60–70%. Despite the large errors, this is the first local crustal model for geoneutrino flux prediction with probabilistic error estimation in a reproducible way.

This paper studies the application of augmented reality real-time depth image technology to 3D human motion recognition technology. The accuracy and real-time performance of sensor-based 3D human reconstruction are affected by visual characteristics and illumination changes. Features are not easily extracted and cannot be tracked, leading to failures in the 3D reconstruction of the human body. Based on this system, the sensor-based visual inertial initialization algorithm is studied, which is integrated in the two-frame image time interval to provide accurate initial values for vision-based motion estimation, improve the accuracy of the calculated posture, and finally improve the accuracy of the 3D reconstruction system. Based on the relationship between the depth image and the distance and reflectivity, a model for correcting the distance error and reflectivity error of the depth image is established to improve the accuracy of the depth image, and finally the accuracy of the three-dimensional reconstruction of the human body.

A multi-cue guidance network for depth completion

Binocular stereo vision is an important branch of the research area in computer vision. Stereo matching is the most important process in binocular vision. In this paper, a new stereo matching scheme using shape-based matching (SBM) is presented to improve the depth reconstruction method of binocular stereo vision systems. The method works in two steps. First, an operator registers the pattern including the key features of an object to be measured. Then during the operation stage, the stereo camera snaps stereo images and finds the patterns in right and left images separately by means of the SBM. The 3D positions of the object are calculated by using the corresponding points of the stereo images and the projection matrices of the stereo camera. Since we apply robust image processing algorithms, such as the SBM, the proposed method becomes more reliable than the conventional stereo vision systems.

The tiny wires are the great threat to the safety of the UAV flight. Because they have only several pixels isolated far from the background, while most of the existing stereo matching methods require a certain area of the support region to improve the robustness, or assume the depth dependence of the neighboring pixels to meet requirement of global or semi global optimization method. So there will be some false alarms even failures when images contains tiny wires. A new stereo matching algorithm is approved in the paper based on double components model. According to different texture types the input image is decomposed into two independent component images. One contains only sparse wire texture image and another contains all remaining parts. Different matching schemes are adopted for each component image pairs. Experiment proved that the algorithm can effectively calculate the depth image of complex scene of patrol UAV, which can detect tiny wires besides the large size objects. Compared with the current mainstream method it has obvious advantages.

We introduce a new algorithm, K-best-first search (KBFS), which is a generalization of the well known best-first search (BFS). In KBFS, each iteration simultaneously expands the K best nodes from the open-list (rather than just the best as in BFS). We claim that KBFS outperforms BFS in domains where the heuristic function has large errors in estimation of the real distance to the goal state or does not predict dead-ends in the search tree. We present empirical results that confirm this claim and show that KBFS outperforms BFS by a factor of 15 on random trees with dead-ends, and by a factor of 2 and 7 on the Fifteen and Twenty-Four tile puzzles, respectively. KBFS also finds better solutions than BFS and hill-climbing for the number partitioning problem. KBFS is only appropriate for finding approximate solutions with inadmissible heuristic functions.

A common goal of conventional fisheries management is to maintain fishing mortality at a rate that ensures an adequate level of lifetime egg production (LEP) for population sustainability. However, larvae from young spawners can experience higher mortality rates than larvae of older spawners, reducing the effect of egg production by young females (hereafter, maternal age effects). This reduction leads to an error in LEP that can be accounted for by reducing the fishing mortality rate, but raises the question of the magnitude of these errors if they are present but not accounted for. Calculations using parameters from a typical long-lived fish demonstrated that maternal age effects resulted in large errors in estimates of lifetime reproduction when there was a large contrast in the larval mortality rate extending over the reproductive life span. Errors were small when maternal age effects reduced the reproductive potential of only the very youngest spawners, at ages when a small fraction of females are mature. A specific example using the empirically derived maternal age effect for black rockfish (Sebastes melanops) indicated that errors in traditional management would be small for this species.

Upsurges and downsurges are calculated and plotted for a simple pump discharge line provided with an air chamber. Basic parameters such as pipeline constant, air chamber parameter, pipe wall friction, and orifice resistance are used. The results of this paper can be used to determine the necessary volume of the air chamber. Computer studies indicate that the assumption of the rigid water column and the concentration of pipe friction at the pump end of the pipeline yields reasonably good results at the pump end; however, because of these assumptions, large errors in estimation of both upsurges and downsurges occur at the midpoint and particularly at the quarter point of the pipeline. Pipe friction has a substantially different effect on surges than that of the orifice resistance; these two effects should therefore be considered separately. A differential orifice is recommended and considered; this orifice should have a low resistance to flow out of the chamber.

The analysis of nutrient budgets is a common method for assessing the biogeochemical function of estuaries including denitrification and nutrient retention rates. The Land Ocean Interactions in the Coastal Zone (LOICZ) guidelines for constructing such budgets concentrate on the simplest case where an estuary or embryment is treated as a single box which is well-mixed both vertically and horizontally and at steady-state. We show that these simplifying assumptions can lead to significant and sometimes very large errors in estimates of internal retention (or production) rates. If the load to an estuary varies significantly through the year, the use of time-averaged concentrations of nutrient and salinity to calculate retention rates is shown to cause errors of up to 30% depending on the circumstances. The second case considered examines the consequences of treating an estuary which has significant long-estuary salinity and nutrient gradients as a well-mixed box. In a simple case considered, the calculated internal production from a distributed source is underestimated by a factor of two. The errors are shown to depend on the estuary's mixing and geometrical characteristic as well as on the location of the nutrient sources. The third case considers the errors in the calculated internal retention/production rates of treating an estuary with a two-layer circulation as a single-box system. The potential errors are severe. A comparison between a one-box analysis and a two-box analysis which accounts for the true estuarine circulation shows that the two analyses can yield calculated retention rates of opposite sign. In this situation, one configuration for the estuary would appear to sequester nutrient, whereas the other would be a net exporter of nutrient.

This paper presents a new algorithm for the joint restoration of depth and intensity images constructed from the time-correlated single-photon counting (TCSPC) measurement in the limit of very few photon counts [1]. Under some justified approximations, the restoration problem (regularized likelihood) reduces to a convex formulation with respect to the parameters of interest. The first advantage of this formulation is that it only processes the corrupted depth and intensity images obtained from preliminary estimation, without the need for the use of full TCSPC waveforms. The second advantage is its flexibility in being able to use different convex regularization terms such as: total variation (TV); and sparsity of the discrete cosine transform (DCT) coefficients. The estimation problems are efficiently solved using the alternating direction method of multipliers (ADMM) that presents good convergence properties and thus a reduced computational cost. Results on single photon depth data from field trials show the benefit of the proposed strategy that improves the quality of the estimated depth and intensity images.