The core of our solution is to infer the temporal regularity from the original unprocessed video , and use it as a temporal consistency guide to stabilize the processed sequence . We formally characterize the frequency properties of our technique , and demonstrate , in practice , its ability to stabilize a wide range of popular image processing techniques including enhancement and stylization of color and tone , intrinsic images , and depth estimation . The method can be generalized to arbitrary scenes , rather than focusing on a particular class of object . The synthesized light field can be used for various applications , such as depth estimation and refocusing . All these factors contribute to difficulty in accurate depth estimation . In this paper , we review five papers that attempt to solve the depth estimation problem with various techniques including supervised , weakly-supervised , and unsupervised learning techniques . We then compare these papers and understand the improvements made over one another . Our approach combines the Fourier decomposition of the Photography operator with a generalization of the discrete Radon transform and provides a fast and exact computation of the focal stack . We also generalize our formulation to compute the modified laplacian stack that can be used to solve the depth estimation problem . Finally we provide some computational results that show the validity of the approach . As these items are of great interest in home robotics and assistive technologies , we have investigated methods to reduce and sometimes even eliminate these effects without any modification of the hardware . In particular , we complement the depth estimate within the Kinect by a cross-modal stereo path that we obtain from disparity matching between the included IR and RGB sensor of the Kinect . We investigate how the RGB channels can be combined optimally in order to mimic the image response of the IR sensor by an early fusion scheme of weighted channels as well as a late fusion scheme that computes stereo matches between the different channels independently . While many stereo vision algorithms can quickly and robustly estimate sparse geometry from sets of photos , dense reconstruction , where depth estimate is required at per-pixel or sub-pixel level , remains a time-consuming and memory-intensive process . In this work , we propose a fast hierarchical upsampling method for dense image-based depth estimation . The paper aims at characterizing the role of camera capture process in depth quality estimation by stereo-matching methods . An evaluation software application has been developed which integrates a modular image processing pipeline ( IPP ) in to the depth estimation process . The application allows for simulating the presence of camera-specific processing artifacts and their influence on the captured stereo imagery in typical imaging scenarios . The high-dimensional spatial resolution is to preserve more detailed local information , while the large number of channels ensures more complex feature representation . For the current Light-field depth estimation algorithm , the designers usually choose to maintain a high spatial dimension in the network to improve the accuracy of the depth estimation , which results in a situation where the model has a large size and will take up huge amount of computing resources in the depth estimation process . In this paper , we introduce an effective pooling method : spectral pooling , to improve the problems of the original Light-field depth estimation network mentioned above . This paper presents a novel iterative feedback framework for simultaneous estimation of depth map and All-In-Focus ( AIF ) image , which benefits each other in each stage to obtain final convergence : For the recovery of AIF image , sparse prior of natural image is incorporated to ensure high quality defocus removal even under inaccurate depth estimation . In depth estimation step , we feed back the constraints from the high quality AIF image and adopt a numerical solution which is robust to the inaccuracy of AIF recovery to further raise the performance of DFD algorithm . Compared with traditional DFD methods , another advantage offered by this iterative framework is that by introducing AIF , which follows the prior knowledge of natural images to regularize the depth map estimation , DFD is much more robust to camera parameter changes . Disclosed are a highlight image restoration method and device based on an optical field . The method comprises a depth estimation step , a highlight detection step and a highlight removing step ; in the depth estimation step , optical-field image depth estimation is performed through combination of defocusing and stereo viewpoint matching ; in the highlight detection step , highlight detection is performed based on diagonal multi-viewpoint color space projection differences ; and , in the highlight removing step , depth values corresponding to detected highlight points are obtained from a depth estimation result , macro pixels corresponding to the highlight points are found through refocusing , pixels in the macro pixels cluster into two types according to brightness , equation sets are built for the two types by utilizing double-color reflection models , specular reflection components of the highlight points are obtained after the equation sets are solved , the specular reflection components are removed , and then highlight removing is achieved . The device comprises a depth estimation module , a highlight detection module and a highlight removing module which achieve the steps . In order to perform an effective and reliable automatic microassembly , depth information estimation is the first task , a blur parameter model of defocus image based on Markov random field has been present . It converts problem of depth estimation into optimization problem . The proposed method comprises two stages . First , a generative adversarial network generates a depth image estimation from a single RGB image . Then , the 3D point cloud is calculated from the depth image . section : Introduction Low - level computer vision problems such as semantic image segmentation or depth estimation often involve assigning a label to each pixel in an image . While the feature representation used to classify individual pixels plays an important role in this task , it is similarly important to consider factors such as image edges , appearance consistency and spatial consistency while assigning labels in order to obtain accurate and precise results . In contrast to the current depth estimators , our new system can obtain the whole GNSS information of pedestrians rather than the depth information only . Our method produces competitive results for monocular depth estimation on the KITTI driving dataset . Furthermore , on the dataset collected by ourselves , the system outperforms the current depth estimators especially for estimating long distances . Inspired by the success of adversarial learning , we propose a new end-to-end unsupervised deep learning framework for monocular depth estimation consisting of two Generative Adversarial Networks ( GAN ) , deeply coupled with a structured Conditional Random Field ( CRF ) model . The two GANs aim at generating distinct and complementary disparity maps and at improving the generation quality via exploiting the adversarial learning strategy . Recent work has demonstrated that it is possible to learn deep neural networks for monocular depth and ego-motion estimation from unlabelled video sequences , an interesting theoretical development with numerous advantages in applications . In this paper , we propose a number of improvements to these approaches . The mechanism for discrimination described by A. S. Householder is applied to the problem of monocular depth perception and the Weber ratio is determined . The ratio obtained varies as a small positive power of the distance for nearby objects , while for distant objects it is indicated that discrimination is relatively impossible . Our research on indoor scene understanding provides a comprehensive scene interpretation at various perspectives and scales . For monocular image depth estimation , previous approaches are limited in that they only reason about depth locally on a single scale , and do not utilize the important information of geometric scene structures . Here , we developed a novel graphical model , which reasons about detailed depth while leveraging geometric scene structures at multiple scales . This idea is inspired by the experimental results of the paper that the depth estimation accuracy of a stereo pair as input is better than that of a monocular view as input in the same self-supervised network model . Therefore , we decompose the monocular depth estimation problem into two sub-problems , a right view synthesized process followed by a semi-supervised stereo matching process . In order to improve the accuracy of the synthetic right view , we innovate beyond the existing view synthesis method Deep3D by adding a left-right consistency constraint and a smoothness constraint . The resulting model outperforms all the previous monocular depth estimation methods as well as the stereo block matching method in the challenging KITTI dataset by only using a small number of real training data . The model also generalizes well to other monocular depth estimation benchmarks . We also discuss the implications and the advantages of solving monocular depth estimation using stereo methods . Relevant datasets and 13 state-of-the-art deep learning-based approaches for monocular depth estimation are reviewed , evaluated and discussed . We conclude this paper with a perspective towards future research work requiring further investigation in monocular depth estimation challenges . Room layout estimation is a challenge of segmenting a cluttered room image into floor , walls and ceiling . We apply a Double Refinement Network ( DRN ) which has been successfully used to the monocular depth map estimation . Our method is the first not using encoder-decoder architecture for the room layout estimation . We present a generalised self-supervised learning approach for monocular estimation of the real depth across scenes with diverse depth ranges from 1 - -100s of meters . Existing supervised methods for monocular depth estimation require accurate depth measurements for training . Event-based cameras are a new passive sensing modality with a number of benefits over traditional cameras , including extremely low latency , asynchronous data acquisition , high dynamic range , and very low power consumption . There has been a lot of recent interest and development in applying algorithms to use the events to perform a variety of three-dimensional perception tasks , such as feature tracking , visual odometry , and stereo depth estimation . However , there currently lacks the wealth of labeled data that exists for traditional cameras to be used for both testing and development . Despite the notable progress in stereo disparity estimation , algorithms are still prone to errors in challenging conditions . Thus , heuristic disparity refinement techniques are usually deployed to improve accuracy . Among the existing techniques , stereo matching remains one of the most widely used in the literature due to its strong connection to the human binocular system . Traditionally , stereo-based depth estimation has been addressed through matching hand-crafted features across multiple images . Despite the extensive amount of research , these traditional techniques still suffer in the presence of highly textured areas , large uniform regions , and occlusions . Motivated by their growing success in solving various 2D and 3D vision problems , deep learning for stereo-based depth estimation has attracted growing interest from the community , with more than 150 papers published in this area between 2014 and 2019 . This new generation of methods has demonstrated a significant leap in performance , enabling applications such as autonomous driving and augmented reality . Real-time active 3D range cameras based on time-of-flight technology using the Photonic Mixer Device ( PMD ) can be considered as a complementary technique for stereo-vision based depth estimation . Since those systems directly yield 3D measurements , they can also be used for initializing vision based approaches , especially in highly dynamic environments . Image analysis and enhancement tasks such as tone mapping , colorization , stereo depth , and photomontage , often require computing a solution ( e.g. , for exposure , chromaticity , disparity , labels ) over the pixel grid . Computational and memory costs often require that a smaller solution be run over a downsampled image . Scene flow describes 3D motion in a 3D scene . It can either be modeled as a single task , or it can be reconstructed from the auxiliary tasks of stereo depth and optical flow estimation . While the second method can achieve real-time performance by using real-time auxiliary methods , it will typically produce non-dense results . Such approaches can handle lack of texture , but tend to force nonplanar objects onto planes . We propose a compromise approach that prefers stereo depth estimates but can replace estimates in textureless regions with planes in a principled manner at near real-time rates . Our approach segments the image via a novel real-time color segmentation algorithm ; we subsequently fit planes to textureless segments and refine them using consistency constraints . Our assumption is that , entropy of any point on an image will be significantly higher than the entropy of its corresponding point on the image 's depth map . In this paper , we propose a confidence measure , Entropy-Difference ( ED ) for stereo depth estimates and a binary classification method to identify incorrect depths . Experiments on the Middlebury dataset show the effectiveness of our method . The design of a hardware co-processor for stereo depth detection , based on a parallel implementation of the sum of absolute differences algorithm , is presented . Model-based designs are followed , and a parameterisable open source VHDL library component appropriate for integration within a system-on-a- programmable chip is created . In autonomous driving , stereo vision-based depth estimation technology can help to estimate the distance of obstacles accurately , which is crucial for correctly planning the path of the vehicle . Recent work has formulated the stereo depth estimation problem into a deep learning model with convolutional neural networks . However , these methods need a lot of post-processing and do not have strong adaptive capabilities to ill-posed regions or new scenes . For each test , power consumed for common fusion workloads has been measured at a range of frame rates and resolutions . Detailed analyses from our power efficiency comparison for workloads specific to stereo depth mapping and sensor fusion are summarized . Preliminary mission feasibility results from testing with off-the-shelf long-wave infrared and visible cameras in Alaska and Arizona are also summarized to demonstrate the value of the SDMSI for applications such as ice tracking , ocean color , soil moisture , animal and marine vessel detection and tracking . Experiments demonstrate that the approach obviously improves the performance for depth completion and outperforms all the previous published methods . The authors believe their works also have the guidance significance for stereo images depth estimation fused with sparse LiDAR depth data . In recent years the field of computer vision has seen an explosion of success stories involving convolutional neural networks ( convnets ) . Such architectures currently provide top results for general object classification , general object detection , feature matching , stereo matching , scene recognition , pose estimation , action recognition and many other tasks . Pedestrian detection is a canonical case of object detection with relevant applications in car safety , surveillance , and robotics . Other features include bag - of - words over colour , HOG , or LBP features ; learning sparse dictionary encoders ; and training features via a convolutional neural network . Additional features specific for stereo depth or optical flow have been proposed , however we consider these beyond the focus of this paper . For our flow experiments we will use difference of frames from weakly stabilized videos ( SDt ) . This paper presents a new technique for depth and motion estimation from image sequence , aiming at the model-based pose estimation problem . The key of the proposed technique is a novel depth estimation approach . This can be achieved by either moving the sensor linearly along the optical axis of the main lens or changing the focal length of the main lens while the shutter is open . A consistently sharp image can then be obtained by the application of a fixed inverse filter kernel , thus preventing the need for depth and motion estimation . Moreover , high frequencies are better preserved during the acquisition , such that the results achieved by inverse filtering are superior to those achievable with conventional shutter/sensor operations . We find that by connecting stochastic digital components according to simple mathematical rules , one can build massively parallel , low precision circuits that solve Bayesian inference problems and are compatible with the Poisson firing statistics of cortical neurons . We evaluate circuits for depth and motion perception , perceptual learning and causal reasoning , each performing inference over 10,000 + latent variables in real time - a 1,000x speed advantage over commodity microprocessors . These results suggest a new role for randomness in the engineering and reverse-engineering of intelligent computation . A lowtech approach for clinical low luminance testing is the SKILL acuity card [ Haegerstrom-Portnoy97 ] that approximates low luminance , low contrast conditions at normal illumination levels with black letters on a gray background , however it has not been verified that performance under these conditions is the same as under actual low luminance conditions . Outside the laboratory , low luminance testing has largely been limited to measures of acuity and contrast sensitivity , so tests of other basic functions such as color discrimination , depth and motion perception , and visual search , and direct tests of performance on real-world visual tasks would be valuable . High contrast and glare Visual assessment under high dynamic range conditions typically falls under the rubric of glare testing [ Elliot93 ] . The effectiveness of the proposed method is demonstrated for our integrated monocular space-variant vision system . This system uses SVAV for real-time fixation control , depth-from motion estimation and SVDNF for the control of visual attention . The purpose of depth-from-motion estimation is to determine the depth function of a scene using the image sequence acquired by a mobile sensor whose motion is known . An elementary depth estimation can be deduced from the apparent motion in the image plane for each pair of consecutive images . Automatic motion estimation of multiple depth cameras has remained a challenging topic in computer vision due to its reliance on the image correspondence problem . In this paper , spherical objects are employed to estimate motion parameters between multiple depth cameras . The continuous perception of motion-through-depth is critical for both navigation and interacting with objects in a dynamic three-dimensional ( 3D ) world . Here we used 3D tracking to simultaneously assess the perception of motion in all directions , facilitating comparisons of responses to motion-through-depth to frontoparallel motion . The algorithm extracts body parts even when the target person approaches other objects . This is a common problem for depth-based camera systems . Real-time visual extraction of a subject 's head and arms is performed during preprocessing in order to determine their current action and presents two key innovations . The neurones exhibit ambituity to single-dot motions , line motions as well as to complex motions characterizing looming including radial and oblique motions in depth . The implications of the findings for the general problem of depth motion perception are discussed . Abstract In computer vision , occlusions are almost always seen as undesirable singularities that pose difficult challenges to image motion analysis problems , such as optic flow computation , motion segmentation , disparity estimation , or egomotion estimation . However , it is well known that occlusions are extremely powerful cues for depth or motion perception , and could be used to improve those methods . In this paper , we propose to recover camera motion information based uniquely on occlusions , by observing two specially useful properties : occlusions are independent of the camera rotation , and reveal direct information about the camera translation . Model MST cells that encode the winning direction feed back to model MT cells , where they boost directionally consistent cell activities and suppress inconsistent activities over the spatial region to which they project . This feedback accomplishes directional and depthful motion capture within that region . Model simulations include the barberpole illusion , motion capture , the spotted barberpole , the triple barberpole , the occluded translating square illusion , motion transparency and the chopsticks illusion . These decision signals arise from network properties dependent on feedforward and feedback inputs ; however , the relative contributions of these inputs are poorly understood . We determined the role of feedforward pathways to decision signals in MT by recording neuronal activity while monkeys performed motion and depth tasks . During each session , we reversibly inactivated V2 and V3 , which provide feedforward input to MT that conveys more information about depth than motion .
The core of our solution is to infer the temporal regularity from the original unprocessed video , and use it as a temporal consistency guide to stabilize the processed sequence . We formally characterize the frequency properties of our technique , and demonstrate , in practice , its ability to stabilize a wide range of popular image processing techniques including enhancement and stylization of color and tone , intrinsic images , and depth estimation .
The method can be generalized to arbitrary scenes , rather than focusing on a particular class of object . The synthesized light field can be used for various applications , such as depth estimation and refocusing .
All these factors contribute to difficulty in accurate depth estimation . In this paper , we review five papers that attempt to solve the depth estimation problem with various techniques including supervised , weakly-supervised , and unsupervised learning techniques . We then compare these papers and understand the improvements made over one another .
Our approach combines the Fourier decomposition of the Photography operator with a generalization of the discrete Radon transform and provides a fast and exact computation of the focal stack . We also generalize our formulation to compute the modified laplacian stack that can be used to solve the depth estimation problem . Finally we provide some computational results that show the validity of the approach .
As these items are of great interest in home robotics and assistive technologies , we have investigated methods to reduce and sometimes even eliminate these effects without any modification of the hardware . In particular , we complement the depth estimate within the Kinect by a cross-modal stereo path that we obtain from disparity matching between the included IR and RGB sensor of the Kinect . We investigate how the RGB channels can be combined optimally in order to mimic the image response of the IR sensor by an early fusion scheme of weighted channels as well as a late fusion scheme that computes stereo matches between the different channels independently .
While many stereo vision algorithms can quickly and robustly estimate sparse geometry from sets of photos , dense reconstruction , where depth estimate is required at per-pixel or sub-pixel level , remains a time-consuming and memory-intensive process . In this work , we propose a fast hierarchical upsampling method for dense image-based depth estimation .
The paper aims at characterizing the role of camera capture process in depth quality estimation by stereo-matching methods . An evaluation software application has been developed which integrates a modular image processing pipeline ( IPP ) in to the depth estimation process . The application allows for simulating the presence of camera-specific processing artifacts and their influence on the captured stereo imagery in typical imaging scenarios .
The high-dimensional spatial resolution is to preserve more detailed local information , while the large number of channels ensures more complex feature representation . For the current Light-field depth estimation algorithm , the designers usually choose to maintain a high spatial dimension in the network to improve the accuracy of the depth estimation , which results in a situation where the model has a large size and will take up huge amount of computing resources in the depth estimation process . In this paper , we introduce an effective pooling method : spectral pooling , to improve the problems of the original Light-field depth estimation network mentioned above .
This paper presents a novel iterative feedback framework for simultaneous estimation of depth map and All-In-Focus ( AIF ) image , which benefits each other in each stage to obtain final convergence : For the recovery of AIF image , sparse prior of natural image is incorporated to ensure high quality defocus removal even under inaccurate depth estimation . In depth estimation step , we feed back the constraints from the high quality AIF image and adopt a numerical solution which is robust to the inaccuracy of AIF recovery to further raise the performance of DFD algorithm . Compared with traditional DFD methods , another advantage offered by this iterative framework is that by introducing AIF , which follows the prior knowledge of natural images to regularize the depth map estimation , DFD is much more robust to camera parameter changes .
Disclosed are a highlight image restoration method and device based on an optical field . The method comprises a depth estimation step , a highlight detection step and a highlight removing step ; in the depth estimation step , optical-field image depth estimation is performed through combination of defocusing and stereo viewpoint matching ; in the highlight detection step , highlight detection is performed based on diagonal multi-viewpoint color space projection differences ; and , in the highlight removing step , depth values corresponding to detected highlight points are obtained from a depth estimation result , macro pixels corresponding to the highlight points are found through refocusing , pixels in the macro pixels cluster into two types according to brightness , equation sets are built for the two types by utilizing double-color reflection models , specular reflection components of the highlight points are obtained after the equation sets are solved , the specular reflection components are removed , and then highlight removing is achieved . The device comprises a depth estimation module , a highlight detection module and a highlight removing module which achieve the steps .
In order to perform an effective and reliable automatic microassembly , depth information estimation is the first task , a blur parameter model of defocus image based on Markov random field has been present . It converts problem of depth estimation into optimization problem .
The proposed method comprises two stages . First , a generative adversarial network generates a depth image estimation from a single RGB image . Then , the 3D point cloud is calculated from the depth image .
section : Introduction Low - level computer vision problems such as semantic image segmentation or depth estimation often involve assigning a label to each pixel in an image . While the feature representation used to classify individual pixels plays an important role in this task , it is similarly important to consider factors such as image edges , appearance consistency and spatial consistency while assigning labels in order to obtain accurate and precise results .
In contrast to the current depth estimators , our new system can obtain the whole GNSS information of pedestrians rather than the depth information only . Our method produces competitive results for monocular depth estimation on the KITTI driving dataset . Furthermore , on the dataset collected by ourselves , the system outperforms the current depth estimators especially for estimating long distances .
Inspired by the success of adversarial learning , we propose a new end-to-end unsupervised deep learning framework for monocular depth estimation consisting of two Generative Adversarial Networks ( GAN ) , deeply coupled with a structured Conditional Random Field ( CRF ) model . The two GANs aim at generating distinct and complementary disparity maps and at improving the generation quality via exploiting the adversarial learning strategy .
Recent work has demonstrated that it is possible to learn deep neural networks for monocular depth and ego-motion estimation from unlabelled video sequences , an interesting theoretical development with numerous advantages in applications . In this paper , we propose a number of improvements to these approaches .
The mechanism for discrimination described by A. S. Householder is applied to the problem of monocular depth perception and the Weber ratio is determined . The ratio obtained varies as a small positive power of the distance for nearby objects , while for distant objects it is indicated that discrimination is relatively impossible .
Our research on indoor scene understanding provides a comprehensive scene interpretation at various perspectives and scales . For monocular image depth estimation , previous approaches are limited in that they only reason about depth locally on a single scale , and do not utilize the important information of geometric scene structures . Here , we developed a novel graphical model , which reasons about detailed depth while leveraging geometric scene structures at multiple scales .
This idea is inspired by the experimental results of the paper that the depth estimation accuracy of a stereo pair as input is better than that of a monocular view as input in the same self-supervised network model . Therefore , we decompose the monocular depth estimation problem into two sub-problems , a right view synthesized process followed by a semi-supervised stereo matching process . In order to improve the accuracy of the synthetic right view , we innovate beyond the existing view synthesis method Deep3D by adding a left-right consistency constraint and a smoothness constraint .
The resulting model outperforms all the previous monocular depth estimation methods as well as the stereo block matching method in the challenging KITTI dataset by only using a small number of real training data . The model also generalizes well to other monocular depth estimation benchmarks . We also discuss the implications and the advantages of solving monocular depth estimation using stereo methods .
Relevant datasets and 13 state-of-the-art deep learning-based approaches for monocular depth estimation are reviewed , evaluated and discussed . We conclude this paper with a perspective towards future research work requiring further investigation in monocular depth estimation challenges .
Room layout estimation is a challenge of segmenting a cluttered room image into floor , walls and ceiling . We apply a Double Refinement Network ( DRN ) which has been successfully used to the monocular depth map estimation . Our method is the first not using encoder-decoder architecture for the room layout estimation .
We present a generalised self-supervised learning approach for monocular estimation of the real depth across scenes with diverse depth ranges from 1 - -100s of meters . Existing supervised methods for monocular depth estimation require accurate depth measurements for training .
Event-based cameras are a new passive sensing modality with a number of benefits over traditional cameras , including extremely low latency , asynchronous data acquisition , high dynamic range , and very low power consumption . There has been a lot of recent interest and development in applying algorithms to use the events to perform a variety of three-dimensional perception tasks , such as feature tracking , visual odometry , and stereo depth estimation . However , there currently lacks the wealth of labeled data that exists for traditional cameras to be used for both testing and development .
Despite the notable progress in stereo disparity estimation , algorithms are still prone to errors in challenging conditions . Thus , heuristic disparity refinement techniques are usually deployed to improve accuracy .
Among the existing techniques , stereo matching remains one of the most widely used in the literature due to its strong connection to the human binocular system . Traditionally , stereo-based depth estimation has been addressed through matching hand-crafted features across multiple images . Despite the extensive amount of research , these traditional techniques still suffer in the presence of highly textured areas , large uniform regions , and occlusions . Motivated by their growing success in solving various 2D and 3D vision problems , deep learning for stereo-based depth estimation has attracted growing interest from the community , with more than 150 papers published in this area between 2014 and 2019 . This new generation of methods has demonstrated a significant leap in performance , enabling applications such as autonomous driving and augmented reality .
Real-time active 3D range cameras based on time-of-flight technology using the Photonic Mixer Device ( PMD ) can be considered as a complementary technique for stereo-vision based depth estimation . Since those systems directly yield 3D measurements , they can also be used for initializing vision based approaches , especially in highly dynamic environments .
Image analysis and enhancement tasks such as tone mapping , colorization , stereo depth , and photomontage , often require computing a solution ( e.g. , for exposure , chromaticity , disparity , labels ) over the pixel grid . Computational and memory costs often require that a smaller solution be run over a downsampled image .
Scene flow describes 3D motion in a 3D scene . It can either be modeled as a single task , or it can be reconstructed from the auxiliary tasks of stereo depth and optical flow estimation . While the second method can achieve real-time performance by using real-time auxiliary methods , it will typically produce non-dense results .
Such approaches can handle lack of texture , but tend to force nonplanar objects onto planes . We propose a compromise approach that prefers stereo depth estimates but can replace estimates in textureless regions with planes in a principled manner at near real-time rates . Our approach segments the image via a novel real-time color segmentation algorithm ; we subsequently fit planes to textureless segments and refine them using consistency constraints .
Our assumption is that , entropy of any point on an image will be significantly higher than the entropy of its corresponding point on the image 's depth map . In this paper , we propose a confidence measure , Entropy-Difference ( ED ) for stereo depth estimates and a binary classification method to identify incorrect depths . Experiments on the Middlebury dataset show the effectiveness of our method .
The design of a hardware co-processor for stereo depth detection , based on a parallel implementation of the sum of absolute differences algorithm , is presented . Model-based designs are followed , and a parameterisable open source VHDL library component appropriate for integration within a system-on-a- programmable chip is created .
In autonomous driving , stereo vision-based depth estimation technology can help to estimate the distance of obstacles accurately , which is crucial for correctly planning the path of the vehicle . Recent work has formulated the stereo depth estimation problem into a deep learning model with convolutional neural networks . However , these methods need a lot of post-processing and do not have strong adaptive capabilities to ill-posed regions or new scenes .
For each test , power consumed for common fusion workloads has been measured at a range of frame rates and resolutions . Detailed analyses from our power efficiency comparison for workloads specific to stereo depth mapping and sensor fusion are summarized . Preliminary mission feasibility results from testing with off-the-shelf long-wave infrared and visible cameras in Alaska and Arizona are also summarized to demonstrate the value of the SDMSI for applications such as ice tracking , ocean color , soil moisture , animal and marine vessel detection and tracking .
Experiments demonstrate that the approach obviously improves the performance for depth completion and outperforms all the previous published methods . The authors believe their works also have the guidance significance for stereo images depth estimation fused with sparse LiDAR depth data .
In recent years the field of computer vision has seen an explosion of success stories involving convolutional neural networks ( convnets ) . Such architectures currently provide top results for general object classification , general object detection , feature matching , stereo matching , scene recognition , pose estimation , action recognition and many other tasks . Pedestrian detection is a canonical case of object detection with relevant applications in car safety , surveillance , and robotics .
Other features include bag - of - words over colour , HOG , or LBP features ; learning sparse dictionary encoders ; and training features via a convolutional neural network . Additional features specific for stereo depth or optical flow have been proposed , however we consider these beyond the focus of this paper . For our flow experiments we will use difference of frames from weakly stabilized videos ( SDt ) .
This paper presents a new technique for depth and motion estimation from image sequence , aiming at the model-based pose estimation problem . The key of the proposed technique is a novel depth estimation approach .
This can be achieved by either moving the sensor linearly along the optical axis of the main lens or changing the focal length of the main lens while the shutter is open . A consistently sharp image can then be obtained by the application of a fixed inverse filter kernel , thus preventing the need for depth and motion estimation . Moreover , high frequencies are better preserved during the acquisition , such that the results achieved by inverse filtering are superior to those achievable with conventional shutter/sensor operations .
We find that by connecting stochastic digital components according to simple mathematical rules , one can build massively parallel , low precision circuits that solve Bayesian inference problems and are compatible with the Poisson firing statistics of cortical neurons . We evaluate circuits for depth and motion perception , perceptual learning and causal reasoning , each performing inference over 10,000 + latent variables in real time - a 1,000x speed advantage over commodity microprocessors . These results suggest a new role for randomness in the engineering and reverse-engineering of intelligent computation .
A lowtech approach for clinical low luminance testing is the SKILL acuity card [ Haegerstrom-Portnoy97 ] that approximates low luminance , low contrast conditions at normal illumination levels with black letters on a gray background , however it has not been verified that performance under these conditions is the same as under actual low luminance conditions . Outside the laboratory , low luminance testing has largely been limited to measures of acuity and contrast sensitivity , so tests of other basic functions such as color discrimination , depth and motion perception , and visual search , and direct tests of performance on real-world visual tasks would be valuable . High contrast and glare Visual assessment under high dynamic range conditions typically falls under the rubric of glare testing [ Elliot93 ] .
The effectiveness of the proposed method is demonstrated for our integrated monocular space-variant vision system . This system uses SVAV for real-time fixation control , depth-from motion estimation and SVDNF for the control of visual attention .
The purpose of depth-from-motion estimation is to determine the depth function of a scene using the image sequence acquired by a mobile sensor whose motion is known . An elementary depth estimation can be deduced from the apparent motion in the image plane for each pair of consecutive images .
Automatic motion estimation of multiple depth cameras has remained a challenging topic in computer vision due to its reliance on the image correspondence problem . In this paper , spherical objects are employed to estimate motion parameters between multiple depth cameras .
The continuous perception of motion-through-depth is critical for both navigation and interacting with objects in a dynamic three-dimensional ( 3D ) world . Here we used 3D tracking to simultaneously assess the perception of motion in all directions , facilitating comparisons of responses to motion-through-depth to frontoparallel motion .
The algorithm extracts body parts even when the target person approaches other objects . This is a common problem for depth-based camera systems . Real-time visual extraction of a subject 's head and arms is performed during preprocessing in order to determine their current action and presents two key innovations .
The neurones exhibit ambituity to single-dot motions , line motions as well as to complex motions characterizing looming including radial and oblique motions in depth . The implications of the findings for the general problem of depth motion perception are discussed .
Abstract In computer vision , occlusions are almost always seen as undesirable singularities that pose difficult challenges to image motion analysis problems , such as optic flow computation , motion segmentation , disparity estimation , or egomotion estimation . However , it is well known that occlusions are extremely powerful cues for depth or motion perception , and could be used to improve those methods . In this paper , we propose to recover camera motion information based uniquely on occlusions , by observing two specially useful properties : occlusions are independent of the camera rotation , and reveal direct information about the camera translation .
Model MST cells that encode the winning direction feed back to model MT cells , where they boost directionally consistent cell activities and suppress inconsistent activities over the spatial region to which they project . This feedback accomplishes directional and depthful motion capture within that region . Model simulations include the barberpole illusion , motion capture , the spotted barberpole , the triple barberpole , the occluded translating square illusion , motion transparency and the chopsticks illusion .
These decision signals arise from network properties dependent on feedforward and feedback inputs ; however , the relative contributions of these inputs are poorly understood . We determined the role of feedforward pathways to decision signals in MT by recording neuronal activity while monkeys performed motion and depth tasks . During each session , we reversibly inactivated V2 and V3 , which provide feedforward input to MT that conveys more information about depth than motion .
Blind video temporal consistency Synthesizing a 4D Spatio-Angular Consistent Light Field from a Single Image Monocular Depth Estimation: A Survey The Discrete Focal Stack Transform Improving the Kinect by Cross-Modal Stereo Hierarchical upsampling for fast image-based depth estimation Influence Of camera imaging pipeline on stereo-matching quality: An experimental study Spectral Pooling Based CNN Model Compression for Light Field Depth-Estimation Iterative Feedback Estimation of Depth and Radiance from Defocused Images Highlight image restoration method and device based on optical field Automatic Micro-manipulation Based on Visual Servoing Generative Adversarial Network-Based Method for Transforming Single RGB Image Into 3D Point Cloud Conditional Random Fields as Recurrent Neural Networks A Novel Pedestrian Positioning System Using Monocular in-Vehicle Cameras Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation Supervising the New with the Old: Learning SFM from SFM A neural mechanism for discrimination IV: Monocular depth perception 2D+3D Indoor Scene Understanding from a Single Monocular Image A Semi-Supervised Monocular Stereo Matching Method Single View Stereo Matching Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review Double Refinement Network for Room Layout Estimation RealMonoDepth: Self-Supervised Monocular Depth Estimation for General Scenes The Multivehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception Leveraging Confident Points for Accurate Depth Refinement on Embedded Systems A Survey on Deep Learning Techniques for Stereo-based Depth Estimation A Combined Approach for Estimating Patchlets from PMD Depth Images and Stereo Intensity Images Joint bilateral upsampling Dense Scene Flow from Stereo Disparity and Optical Flow Near Real-time Stereo for Weakly-Textured Scenes Entropy-difference based stereo error detection Hardware implementation of a stereo co-processor in a medium-scale field programmable gate array Depth estimation for advancing intelligent transport systems based on self-improving pyramid stereo network Software defined multi-spectral imaging for Arctic sensor networks Multi-scale features fusion from sparse LiDAR data and single image for depth completion Taking a deeper look at pedestrians Filtered channel features for pedestrian detection Effective pose estimation from point pairs Motion invariant imaging by means of focal sweep Building fast Bayesian computing machines out of intentionally stochastic, digital parts High Dynamic Range Displays and Low Vision Space-variant dynamic neural fields for visual attention Estimation of depth-from-motion combining iterative prediction scheme and regularization framework Motion estimation of multiple depth cameras using spheres Dynamic mechanisms of visually guided 3D motion tracking. Visual Tracking of Human Head and Arms Using Adaptive Multiple Importance Sampling on a Single Camera in Cluttered Environments Neural vector analysis and ambiguity in motion sensitive neurones in the brain of the blowfly Motion from occlusions Neural dynamics of motion integration and segmentation within and across apertures A Modality-Specific Feedforward Component of Choice-Related Activity in MT
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EUSIPCO BMVC SIGGRAPH Posters ISCAS IGTA ACCV (4) ICIRA (1) 2015 IEEE International Conference on Computer Vision (ICCV) ECCV (10) ACPR (1) CVPR Workshops DAGM-Symposium BMVC IVMSP 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Color Imaging Conference CVPR ICPR ICIP
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