RGB-D sensors are relatively inexpensive and are commercially available off-the-shelf. However, owing to their low complexity, there are several artifacts that one encounters in the depth map like holes, mis-alignment...
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ISBN:
(纸本)9781628414899
RGB-D sensors are relatively inexpensive and are commercially available off-the-shelf. However, owing to their low complexity, there are several artifacts that one encounters in the depth map like holes, mis-alignment between the depth and color image and lack of sharp object boundaries in the depth map. Depth map generated by Kinect cameras also contain a significant amount of missing pixels and strong noise, limiting their usability in many computer vision applications. In this paper, we present an efficient hole filling and damaged region restoration method that improves the quality of the depth maps obtained with the Microsoft Kinect device. The proposed approach is based on a modified exemplar-based inpainting and LPA-ICI filtering by exploiting the correlation between color and depth values in local image neighborhoods. As a result, edges of the objects are sharpened and aligned with the objects in the color image. Several examples considered in this paper show the effectiveness of the proposed approach for large holes removal as well as recovery of small regions on several test images of depth maps We perform a comparative study and show that statistically, the proposed algorithm delivers superior quality results compared to existing algorithms.
AprilTags and other passive fiducial markers require specialized algorithms to detect markers among other features in a natural scene. The vision processing steps generally dominate the computation time of a tag detec...
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ISBN:
(纸本)9781509037636
AprilTags and other passive fiducial markers require specialized algorithms to detect markers among other features in a natural scene. The vision processing steps generally dominate the computation time of a tag detection pipeline, so even small improvements in marker detection can translate to a faster tag detection system. We incorporated lessons learned from implementing and supporting the AprilTag system into this improved system. This work describes AprilTag 2, a completely redesigned tag detector that improves robustness and efficiency compared to the original AprilTag system. The tag coding scheme is unchanged, retaining the same robustness to false positives inherent to the coding system. The new detector improves performance with higher detection rates, fewer false positives, and lower computational time. Improved performance on small images allows the use of decimated input images, resulting in dramatic gains in detection speed.
This research is devoted to a study of features of the operation of the neural structures of the human brain responsible for "identification, friend-or-foe" patterns when different instructions are being car...
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This research is devoted to a study of features of the operation of the neural structures of the human brain responsible for "identification, friend-or-foe" patterns when different instructions are being carried out. Digital-imageprocessing methods are used to synthesize stimuli adequate for the task, consisting of images of optoclones of virtual people. Functional magnetic-resonance tomography (fMRT) is used to investigate the basic patterns of brain activity. The dynamics of blood flow in different phases of stimulation is estimated. The opposition principle of the interaction of the regions of the brain responsible for making decisions is detected. It is shown that, first, there is a complex system that jointly operates the zones of the brain, each of which makes its own specific contribution to the accomplishment of mental processes. Second, each of these zones of the brain can be involved in the implementation of various functions, depending on the instruction and the experimental conditions. Third, various structures of the brain interact on the opposition principle. Changing the instruction substantially affects the distribution over the brain of the BOLD signal, which reflects the functional architecture of a large-scale neural network. These results make a substantial contribution to the development of new algorithms for the operation of neuromorphic recognition systems and their practical application in control systems-for example, in analyzing masked mimetic facial expressions. (C) 2015 Optical Society of America.
作者：
Djamila DekkicheBastien vinckeAlain MerigotSATIE
Univ. Paris-Sud CNRS Universite Paris-Saclay 94235 Cachan France IRT SystemX 91120 Palaiseau France SATIE
Univ. Paris-Sud CNRS Universite Paris-Saclay 94235 Cachan France
The development of Advanced Driver Assistance systems (ADAS), such as pedestrian detection, requires real-time update rates at high image resolution. Hopefully, heterogeneous architectures with high computing performa...
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ISBN:
(纸本)9781509035502
The development of Advanced Driver Assistance systems (ADAS), such as pedestrian detection, requires real-time update rates at high image resolution. Hopefully, heterogeneous architectures with high computing performance have been developed for this purpose. To benefit from this hardware performance, different programming languages and acceleration frameworks have been developed. OpenvX framework provides a graph-based execution model to program imageprocessingalgorithms on heterogeneous platforms. In this work, we investigate OpenvX optimizations for computer vision applications. We examine how this framework responds to different data access patterns. We test three important optimizations of OpenvX: kernels merge, data tiling and parallelization via OpenMP. The contribution and the impact of each optimization on different data access pattern are explained.
The human detection and tracking in a video plays major roll in security systems. This paper proposes an approach to detect and track the persons in a video. This approach uses Gaussian Mixture Model to detect the per...
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ISBN:
(纸本)9781509006137
The human detection and tracking in a video plays major roll in security systems. This paper proposes an approach to detect and track the persons in a video. This approach uses Gaussian Mixture Model to detect the person and Kalman filter to track the detected person. The processing time to detect the person is reduced by performing the detection operation on down-sampled video. After detecting the person, the original size of the video is reconstructed using Papoulis-Gerchberg method. The performance analysis is carried out by comparing with the state-of-the-art-algorithms. The experimental results show that the proposed method is well suited for detecting and tracking the person in lower processing time.
This article presents a novel segmentation algorithm that allows the automatic segmentation of masonry blocks from a 3D point cloud acquired with LiDAR technology, for both stationary and mobile devices. The point clo...
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This article presents a novel segmentation algorithm that allows the automatic segmentation of masonry blocks from a 3D point cloud acquired with LiDAR technology, for both stationary and mobile devices. The point cloud segmentation algorithm is based on a 2.5D approach that creates images based on the intensity attribute of LiDAR systems. imageprocessingalgorithms based on an improvement of the marked-controlled watershed was successfully used to produce the automatic segmentation of the point cloud in the 3D space isolating each individual stone block. Finally, morphologic analysis in two case studies has been carried out. The morphologic analysis provides information about the assemblage of masonry pieces, which is valuable for the structural evaluation of masonry buildings.
Many existing person tracking systems are challenged by non-laboratory scenarios, including variable lighting conditions, rain, smoke, tracking distance, and tracking speed. We provide evidence that by using a 3D ther...
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ISBN:
(纸本)9781509039302
Many existing person tracking systems are challenged by non-laboratory scenarios, including variable lighting conditions, rain, smoke, tracking distance, and tracking speed. We provide evidence that by using a 3D thermal sensor, a person can be tracked in three dimensions with high success using very simple tracking methods, in many of the challenging lighting conditions and other weather conditions that confound other systems. In support of our claim, we present the PROWL (Perception for Robotic Operation over Widespread Lighting) sensor system, which uses thermal stereo imageprocessing and on-board sensor processing to perform person tracking and gesture recognition. PROWL, using only ICP-based point matching algorithms, obtains 100% person tracking success at 20 frames per second out to 13 meters and zero false-positive/false-negative gesture recognition within 7 meters in all tested scenarios, which includes a sunny outdoor environment, a nighttime outdoor environment, a blackout indoor environment, and a whiteout smoke-filled indoor environment.
This paper deals with automatic image colorization. This is a very difficult task, since it is an ill-posed problem that usually requires user intervention to achieve high quality. A fully automatic approach is propos...
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ISBN:
(纸本)9781509048489
This paper deals with automatic image colorization. This is a very difficult task, since it is an ill-posed problem that usually requires user intervention to achieve high quality. A fully automatic approach is proposed that is able to produce realistic colorization of an input grayscale image. Motivated by the recent success of deep learning techniques in imageprocessing, we propose a feed-forward, two-stage architecture based on Convolutional Neural Network that predicts the U and v color channels. Unlike most of the previous works, this paper presents a fully automatic colorization which is able to produce high-quality and realistic colorization even of complex scenes. Comprehensive experiments and qualitative and quantitative evaluations were conducted on the images of SUN database and on other images. We have found that Quaternion Structural Similarity (QSSIM) gives in some degree a good base for quantitative evaluation, that is why we chose QSSIM as an index-number for the quality of colorization.
Full reference image quality metrics are important tools for optimizing system design parameters associated with image acquisition, compression and transmission. While optimizing systems for perceptual quality is impo...
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Full reference image quality metrics are important tools for optimizing system design parameters associated with image acquisition, compression and transmission. While optimizing systems for perceptual quality is important, in the automotive environment Advanced Driver Assistance systems (ADAS) such as automated pedestrian detection are becoming a common feature of in-vehicular vision systems. As such, automotive image quality must also be tuned for optimal machine vision performance. In this paper the effects of transmission artifacts on the performance of a number of state-of-the-art pedestrian detection algorithms are evaluated. We demonstrate that the human visual system may not perceive distortions that adversely affect machine vision performance. As a result, existing full-reference image quality metrics are not necessarily accurate predictors of machine vision performance on transmitted video sequences. To address this problem, a novel, computationally inexpensive, full-reference objective quality metric based on histogram of oriented gradients is proposed. The proposed metric accurately predicts algorithm performance in the presence of transmission artifacts. The metric can be used at the system design stage in order to optimize image capture parameters for machine vision performance without the need for annotated test databases, which are both expensive and time consuming to produce. (C) 2014 Elsevier B.v. All rights reserved.
Inspired by the ability of -regularized algorithms and the close connection of total variation (Tv) to the norm, a th-power type Tv denoted as Tv is proposed for . The Tv-regularized problem for image denoising is non...
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Inspired by the ability of -regularized algorithms and the close connection of total variation (Tv) to the norm, a th-power type Tv denoted as Tv is proposed for . The Tv-regularized problem for image denoising is nonconvex thus difficult to tackle directly. Instead, we deal with the problem by proposing a weighted Tv (WTv) minimization where the weights are updated iteratively to locally approximate the Tv-regularized problem. The difficulty of WTv minimization is dealt with in a modified split Bregman framework. Numerical results are presented to demonstrate improved denoising performance of the new algorithm with relative to that obtained by the standard Tv minimization and several recent denoising methods from the literature on a variety of images.
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