This paper presents a literature survey on existing disparity map algorithms. It focuses on four main stages of processing as proposed by Scharstein and Szeliski in a taxonomy and evaluation of dense two-frame stereo correspondence algorithms performed in 2002. To assist future researchers in developing their own stereo matching algorithms, a summary of the existing algorithms developed for every stage of processing is also provided. The survey also notes the implementation of previous software-based and hardware-based algorithms. Generally, the main processing module for a software-based implementation uses only a central processing unit. By contrast, a hardware-based implementation requires one or more additional processors for its processing module, such as graphical processing unit or a field programmable gate array. This literature survey also presents a method of qualitative measurement that is widely used by researchers in the area of stereo vision disparity mappings.

/pdf/literature-survey-on-stereo-vision-disparity-map-algorithms-3mtr9e2zyc.pdf

Literature Survey on Stereo Vision Disparity Map Algorithms

Despite the remarkable progresses made in deep learning based depth map super-resolution (DSR), how to tackle real-world degradation in low-resolution (LR) depth maps remains a major challenge. Existing DSR model is generally trained and tested on synthetic dataset, which is very different from what would get from a real depth sensor. In this paper, we argue that DSR models trained under this setting are restrictive and not effective in dealing with realworld DSR tasks. We make two contributions in tackling real-world degradation of different depth sensors. First, we propose to classify the generation of LR depth maps into two types: non-linear downsampling with noise and interval downsampling, for which DSR models are learned correspondingly. Second, we propose a new framework for real-world DSR, which consists of four modules : 1) An iterative residual learning module with deep supervision to learn effective high-frequency components of depth maps in a coarse-to-fine manner; 2) A channel attention strategy to enhance channels with abundant high-frequency components; 3) A multi-stage fusion module to effectively reexploit the results in the coarse-to-fine process; and 4) A depth refinement module to improve the depth map by TGV regularization and input loss. Extensive experiments on benchmarking datasets demonstrate the superiority of our method over current state-of-the-art DSR methods.

/pdf/channel-attention-based-iterative-residual-learning-for-1gc19szc4m.pdf

Channel Attention Based Iterative Residual Learning for Depth Map Super-Resolution

Stereo matching algorithm based on per pixel difference adjustment, iterative guided filter and graph segmentation

Semantic segmentation is one of the most important tasks in the field of computer vision. It is the main step towards scene understanding. With the advent of RGB-Depth sensors, such as Microsoft Kinect, nowadays RGB-Depth images are easily available. This has changed the landscape of some tasks such as semantic segmentation. As the depth images are independent of illumination, the combination of depth and RGB images can improve the quality of semantic labeling. The related research has been divided into two main categories, based on the usage of hand-crafted features and deep learning. Although the state-of-the-art results are mainly achieved by deep learning methods, traditional methods have also been at the center of attention for some years and lots of valuable work have been done in that category. As the field of semantic segmentation is very broad, in this survey, a comprehensive analysis has been carried out on RGB-Depth semantic segmentation methods, their challenges and contributions, available RGB-Depth datasets, metrics of evaluation, state-of-the-art results, and promising directions of the field.

A survey on indoor RGB-D semantic segmentation: from hand-crafted features to deep convolutional neural networks

Video frame interpolation is an important area in the computer vision research activities for video post-processing, surveillance, and video restoration tasks. It aims toward increasing the frame rate of a video sequence by calculating intermittent frames between consecutive input frames. This ensures extra smooth, clear motion in order to make animation fluid enough and reduce display motion blur. Advanced deep learning algorithms have the potential to discover knowledge from large-scale diverse video data. These algorithms gain insights about intermediate motion and provide new opportunities to further improve video interpolation technologies. This survey demonstrates a comprehensive overview of about a good number of contributions over past decade pertinent to the latest developments in this domain. The survey paper highlights common challenges in the area of video frame interpolation based on three key aspects: high visual quality, low complexity, and high efficiency of interpolated output from regular videos with the standard frame rate. We scrutinize the architectures, workflows, performance, advantages, and disadvantages and generate a broad categorization along with an overview of experimental results of various state-of-the-art methods executed on benchmark datasets. This survey discusses applications of diverse interpolation frameworks. It provides a backbone reference that inspires future researchers to optimize current techniques on academic and industrial grounds.

A comprehensive survey on video frame interpolation techniques

A physical unclonable function (PUF) is a foundation of anti-counterfeiting processes due to its inherent uniqueness. However, the self-limitation of conventional graphical/spectral PUFs in materials often makes it difficult to have both high code flexibility and high environmental stability in practice. In this study, we propose a universal, fractal-guided film annealing strategy to realize the random Au network-based PUFs that can be designed on demand in complexity, enabling the tags' intrinsic uniqueness and stability. A dynamic deep learning-based authentication system with an expandable database is built to identify and trace the PUFs, achieving an efficient and reliable authentication with 0% "false positives". Based on the roughening-enabled plasmonic network platform, Raman-based chemical encoding is conceptionally demonstrated, showing the potential for improvements in security. The configurable tags in mass production can serve as competitive PUF carriers for high-level anti-counterfeiting and data encryption. 

/pdf/random-fractal-enabled-physical-unclonable-functions-with-2asfv5er.pdf

Random fractal-enabled physical unclonable functions with dynamic AI authentication

All depth data captured by Kinect devices are noisy, and sometimes even lost or shifted, especially around the edges of the depth. In this paper, we propose an approach to generate a per-pixel confidence measurement for each depth map captured by Kinect devices in indoor environments through supervised learning. Several distinguishing features from both the color images and depth maps are selected to train depth map estimators using Random Forest regressor. Using this estimator, we can predict a confidence map of any depth map captured by Kinect devices. Usage of other devices, such as an industrial laser scanner, is unnecessary, making the implementation more convenient. The experiments demonstrate precise confidence prediction of the depth.

Estimation of Kinect depth confidence through self-training

Current low-cost depth sensing techniques, such as Microsoft Kinect, still can achieve only limited precision. The resultant depth maps are often found to be noisy, misaligned with the color images, and even contain many large holes. These limitations make it difficult to be adopted by many graphics applications. In this paper, we propose a computational approach to address the problem. By fusing raw depth values with image color, edges and smooth priors in a Markov random field optimization framework, both misalignment and large holes can be eliminated effectively, our method thus can produce high-quality depth maps that are consistent with the color image. To achieve this, a confidence map is estimated for adaptive weighting of different cues, an image inpainting technique is introduced to handle large holes, and contrasts in the color image are also considered for an accurate alignment. Experimental results demonstrate the effectiveness of our method.

Depth map enhancement based on color and depth consistency

The invention discloses a visual tele-existence device based on real-time calibration of a camera and a working method thereof. The visual tele-existence device comprises two computers, a helmet display and a binocular camera which is controlled to rotate by a cradle head. The basic working method of the visual tele-existence device comprises the steps of: carrying out calibration on the camera of the helmet display, and calculating the orientation and the moving track of the head of an operator; by calibration of the camera of the binocular camera, calculating the accurate position and the orientation of the cradle head, combining the accurate position and the orientation to generate a rotation control instruction of the cradle head, leading the cradle head and the head of the operator to have the same moving track, sending back left-eye and right-eye images acquired by the binocular camera to the local computers in real time, displaying the images on a helmet in real time and realizing that a remote three-dimensional scene which conforms to the moving posture of the head of a human body is seen locally. In the visual tele-existence device, the remote scene can be really displayed in a three-dimensional manner, and the invention also can be used for other applications such as virtual reality and the like.

Visual tele-existence device based on real-time calibration of camera and working method thereof

Touch response of zebrafish larvae refers to the behaviors after the mechanical stimuli that varies under different conditions (different drugs), and statistical analysis of the behaviors in the organism level is becoming more attractive in drug screening. Collecting repeatable and reliable experimental data on the alive animals in a high-throughput manner is of importance and challenging. Thus, we for the first time introduce a multi-larvae experimentation platform for the fully automated data acquisition to screen the touch-response behaviors of zebrafish in the high-throughput manner. A multi-larvae touching strategy is designed for this platform, which provides a new alternative method to generate touch-response experimental data in a higher efficiency than in the single-larva case (in average 3.58 videos collected per well instead of only one, with 33.00 s duration on each larva instead of 55.95 s). As well, for the proof of the concept, the unknown parameters used for the strategy are decided by experiments. This platform will help in the high-throughput screening of the drugs in the whole animal (organism) level.

High-Throughput Data Acquisition Platform for Multi-Larvae Touch-Response Behavior Screening of Zebrafish*

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