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Visual odometry (VO) is one of the essential techniques in mobile robots field; an accurate VO system is of great significance for mobile robot simultaneous localization and mapping. As for traditional monocular VO systems, they work by presuming the monocular scale is 1 (scale = 1), or relying on ground truth (GT) to estimate scale. As a result, the traditional monocular VO systems estimate the pose state with big drift or cannot work on the image sequence without GT. Although some classical monocular VO systems have been proposed, they still have imperfect performance or even unable to work in some extreme scene conditions, such as scene is monotony without obvious texture information or camera large-scale displacement motion. As for learning-based VO system, it is realized by training deep neural networks in supervised or self-supervised manner to end-to-end estimate the pose state; however, the accuracy of pose estimation entirely depends on the ability of networks. Although the ability of networks can be improved by increasing the number of training data sets and optimizing the network structure, it is inevitable to encounter problems such as insufficient generalization ability and insufficient accuracy on rotational pose estimation. In this article, a monocular VO system named DL_Hybrid is proposed, which takes full advantage of DL networks used in image processing and geometric localization theory based on hybrid pose estimation methods. The DL_Hybrid VO system can estimate a six-DoF pose one-frame-by-one-frame and recover camera trajectory, and it can extract accurate key points from per-frame even in extreme scene condition, and it has good performance even in the extreme moving condition, such as camera rotation-only action or static action, also it can work well in the condition of camera large-scale displacement motion. The real scale is also accurately estimated without depending on GT, and the pose estimation method is designed based on hybrid 2d–2d and 3d–2d localization theory to make the DL_Hyrid VO system to estimate translational and rotational information with accuracy and robustness. Experimental results show that the proposed DL_Hybrid VO system has a better performance than traditional and learning-based VO systems. [ABSTRACT FROM AUTHOR]

Real-time wild smoke detection utilizing machine based identification method is not produced proper accuracy, and it is not suitable for accurate prediction. However, various video smoke detection approaches involve minimum lighting, and it is required for the cameras to identify the existence of smoke particles in a scene. To overcome such challenges, our proposed work introduces a novel concept like deep VGG-Net Convolutional Neural Network (CNN) for the classification of smoke particles. This Deep Feature Synthesis algorithm automatically generated the characteristics for relational datasets. Also hybrid ABC optimization rectifies the problem related to the slow convergence since complexity is reduced. The proposed real-time algorithm uses some pre-processing for the image enhancement and next to the image enhancement processing; foreground and background regions are separated with Otsu thresholding. Here, to regulate the linear combination of foreground and background components alpha channel is applied to the image components. Here, Farneback optical flow evaluation technique diminishes the false finding rate and finally smoke particles are classified with the VGG-Net CNN classifier. In the end, the investigational outcome shows better statistical stability and performance regarding classification accuracy. The algorithm has better smoke detection performance among various video scenes. [ABSTRACT FROM AUTHOR]

Due to the rapid growth of technology and the dropping cost of cameras, multiview imaging applications have attracted many researchers in recent years. Free viewpoint and 3D Televisions are among these interesting applications. One of the problems that should be solved to realize such applications is rendering. In this paper, we propose an optical flow-assisted adaptive patch-based view synthesis algorithm. This patch-based scheme reduces the size and number of holes during reconstruction. The size of patch is determined in response to edge information for better reconstruction, especially near the boundaries. In the first stage of the algorithm, disparity is obtained using optical flow estimation. Then, a reconstructed version of the left and right views is generated using our adaptive patch-based algorithm. The mismatches between each view and its reconstructed version are obtained in the mismatch detection steps. This stage results in two masks as outputs, which help with the refinement of disparities and the selection of the best patches for final synthesis. Finally, the remaining holes are filled using our simple hole-filling scheme and the refined disparities. The objective and subjective performances of the proposed algorithm are compared with recent methods. The results show that the proposed algorithm achieves an improvement of 2.14 dB on average. [ABSTRACT FROM AUTHOR]

Abstract: We propose a novel real-time video stitching method using camera path estimation and homography refinement. The method can stably stitch multiple frames acquired from moving cameras in real time. In the proposed method, one initial between-camera (BC) homography and each camera path (CP) homography are used to estimate the BC homography at every frame. The BC homography is refined by using block matching to adjust the errors of estimated CPs (homography refinement). For fast processing, we extract features using the difference of intensities and use the optical flow to estimate camera motion (CM) homographies, which are multiplied with the previous CMs to calculate CPs (camera path estimations). In experiments, we demonstrated the performance of the CP estimation and homography refinement approach by comparing it with other methods. The experimental results show that the proposed method can stably stitch two image sequences at a rate exceeding 13 fps (frames per second). [ABSTRACT FROM AUTHOR]

By combining a polymer-clad optic fiber and a vitreous-clad optic fiber, we proposed and fabricated a novel optic fiber surface plasmon resonance (SPR) sensor to conduct two-channel sensing at the same detection area. The traditional optic fiber SPR sensor has many disadvantages; for example, removing the cladding requires corrosion, operating it is dangerous, adjusting the dynamic response range is hard, and producing different resonance wavelengths in the sensing area to realize a multi-channel measurement is difficult. Therefore, in this paper, we skillfully used bare fiber grinding technology and reverse symmetry welding technology to remove the cladding in a multi-mode fiber and expose the evanescent field. On the basis of investigating the effect of the grinding angle on the dynamic range change of the SPR resonance valley wavelength and sensitivity, we combined polymer-clad fiber and vitreous-clad fiber by a smart design structure to realize at a single point a two-channel measurement fiber SPR sensor. In this paper, we obtained a beautiful spectral curve from a multi-mode fiber two-channel SPR sensor. In the detection range of the refractive rate between 1.333 RIU and 1.385 RIU, the resonance valley wavelength of channel I shifted from 622 nm to 724 nm with a mean average sensitivity of 1961 nm/RIU and the resonance valley wavelength of channel II shifted from 741 nm to 976 nm with a mean average sensitivity of 4519 nm/RIU. [ABSTRACT FROM AUTHOR]

Recently, the stereo imaging-based image enhancement approach has attracted increasing attention in the field of video analysis. This paper presents a dual camera-based stereo image defogging algorithm. Optical flow is first estimated from the stereo foggy image pair, and the initial disparity map is generated from the estimated optical flow. Next, an initial transmission map is generated using the initial disparity map. Atmospheric light is then estimated using the color line theory. The defogged result is finally reconstructed using the estimated transmission map and atmospheric light. The proposed method can refine the transmission map iteratively. Experimental results show that the proposed method can successfully remove fog without color distortion. The proposed method can be used as a pre-processing step for an outdoor video analysis system and a high-end smartphone with a dual camera system. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method to directly estimate a camera's motion parameters by using normal flow vectors. In contrast to traditional methods, which tackle the problem by calculating optical flows or establishing motion correspondences, our proposed approach does not require conventional assumptions about the captured scene, such as consistent smoothness or distinct feature availability. In the proposed algorithm, the normal flows are classified into different groups, and each group will provide a possible solution regarding the camera's motion parameters. Then, the strategy of hypothesis and confirmation is adopted to eliminate the incorrect solutions. Finally, the optimal solution is obtained via the clustering algorithm. We have tested the proposed method on both synthetic image data and real image sequences. The experimental results illustrate the feasibility and reliability of the algorithm. [ABSTRACT FROM AUTHOR]

Abstract: In magnetic confinement fusion devices the use of cameras, both visible and infrared, has increased very significantly in the last years. The large amount of data (in the range of tens of Gbytes per shot) and the difficulty of the analysis tasks (ambiguity, ill posed problems, etc.), require new solutions. The technology of Cellular Nonlinear Networks (CNNs) has been successfully applied to various tasks, from the real time hot spot detection to the automatic identification of instabilities. The accuracy obtained is comparable to the one of more traditional serial algorithms but the CCNs guarantee deterministic computational times independently from the image contents. Moreover the latest developments have allowed obtaining these results also in the case of space variant image analysis, without compromising the computational speed (of the order of ten thousand frames per second). The method of the optical flow permits to derive information about the speed of the objects moving in the frames of a single camera. The results of previous applications have been so successful that the approach has been extended to videos in compressed format (MPEG) to reduce the computational time to less than 7ms per frame, preserving the accuracy of the results. Since in the next generation of devices, also the edge will emit Soft X-rays (SXR), new technologies are being developed to perform imaging over this region of the spectrum for a global view of the entire plasma column. [Copyright &y& Elsevier]

Classical methods for measuring image motion by computer have concentrated on the cases of optical flow in which the motion field is continuous, or layered motion in which the motion field is piecewise continuous. Here we introduce a third natural category which we call optical snow. Optical snow arises in many natural situations such as camera motion in a highly cluttered 3-D scene, or a passive observer watching a snowfall. Optical snow yields dense motion parallax with depth discontinuities occurring near all image points. As such, constraints on smoothness or even smoothness in layers do not apply. In the Fourier domain, optical snow yields a one-parameter family of planes which we call a bowtie. We present a method for measuring the parameters of the direction and range of speeds of the motion for the special case of parallel optical snow. We demonstrate the effectiveness of the method for both synthetic and real image sequences. [ABSTRACT FROM AUTHOR]