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Visual inspection of rail fasteners is crucial to rail safety. However, the traditional method in which railway staffs manually inspect the conditions of fasteners is time-consuming and prone to human error. In this paper, we present a method to automatically detect missing rail fasteners from top-view images. Using a top-down approach, coarse bounding boxes of potential fastener areas are first located...
This research proposes a convolutional neural network based pupil center detection method from an eye image which is captured by a wearable eye camera. This paper investigates the detection accuracy by applying several preprocessing, such as edge detection, and binarization. We find the preprocess method in order to speed up the computation time. We collected approximately 8,000 eye images with six...
In recent years, monitoring of coral reef status and health are done with the assist from image processing technique. Since underwater images are always suffer from major drawbacks, research in this area is still active. In this paper, we propose to use edge based segmentation where we modify the original canny edge detector and then use the blob processing technique to extract dominant features from...
The paper presents a new method of vehicle speed estimation using image data processing. The presented method employs conversion of greyscale input images into binary form. Image conversion into binary form is based on small gradients in the input images. Contents of the obtained binary images correspond with traffic scenes presented in the input images. Vehicle speed is estimated on the basis of...
A nonparametric technique for the cleaning and enhancement of the chronic old age Modi document images is presented here. The technique is based on the dynamic thresholding and connected component analysis. The automatic fixation of the Niblack factor k is proposed as per the document degradation type. The dynamic thresholding algorithm is used to segment the noisy pixels from the text area and background...
This paper presents a novel background subtraction method that is flexible for various background scenarios. The method includes automated-directional masking (ADM) algorithm for adaptive background modeling and historical intensity pattern reference (HIPaR) algorithm for foreground segmentation. By selecting an appropriate mask in a set based on directional feature, ADM updates background smoothly...
Depth map estimation forms an integral part of many applications such as 2D-to-3D creation. There exists various methods in literature for depth map estimation using different cues and structure. Usually, depth information is decoded from these cues at the edges and matting is applied to spread it over neighboring regions. Defocus is one such cue due to its natural existence and does not require any...
In this study, a multiexposure image fusion approach for dynamic scenes is proposed, which includes four stages, namely, detail enhancement and reference image selection, ghost artifacts removal, weighting map estimation and refinement, and image fusion. Weighted least squares (WLS) optimization is used to enhance the details of input low dynamic range (LDR) images. To remove ghost artifacts, median...
An emerging trend in the field of image restoration is the removal of haze or fog from an image or video sequence to improve the quality of the image. Such image restoration techniques is widely used in applications like traffic monitoring and surveillance during hazy weather conditions, prediction and analysis of volcanic activities, etc. In this paper, we propose a novel algorithm based on a fusion...
Single-image blind deconvolution is one of the most challenging fields in image processing which restores a sharp image from its blurred version. Nowadays blind deconvolution algorithms have made significant progress. However, the restoration of blurred images with little scale edges and periodic textures is still a hard work. To solve this problem, this paper proposes a new normalized sparse regularization...
In this paper, we introduce robust and synergetic hand-crafted features and a simple but efficient deep feature from a convolutional neural network (CNN) architecture for defocus estimation. This paper systematically analyzes the effectiveness of different features, and shows how each feature can compensate for the weaknesses of other features when they are concatenated. For a full defocus map estimation,...
Camera motion introduces motion blur, affecting many computer vision tasks. Dark Channel Prior (DCP) helps the blind deblurring on scenes including natural, face, text, and low-illumination images. However, it has limitations and is less likely to support the kernel estimation while bright pixels dominate the input image. We observe that the bright pixels in the clear images are not likely to be bright...
This paper deals with deep neural networks for predicting accurate dense disparity map with Semi-global matching (SGM). SGM is a widely used regularization method for real scenes because of its high accuracy and fast computation speed. Even though SGM can obtain accurate results, tuning of SGMs penalty-parameters, which control a smoothness and discontinuity of a disparity map, is uneasy and empirical...
Structure-from-Motion (SfM) methods can be broadly categorized as incremental or global according to their ways to estimate initial camera poses. While incremental system has advanced in robustness and accuracy, the efficiency remains its key challenge. To solve this problem, global reconstruction system simultaneously estimates all camera poses from the epipolar geometry graph, but it is usually...
In this paper, we propose an alternative method to estimate room layouts of cluttered indoor scenes. This method enjoys the benefits of two novel techniques. The first one is semantic transfer (ST), which is: (1) a formulation to integrate the relationship between scene clutter and room layout into convolutional neural networks, (2) an architecture that can be end-to-end trained, (3) a practical strategy...
In this work, we investigate the relation between the edge profiles present in a motion blurred image and the underlying camera motion responsible for causing the motion blur. While related works on camera motion estimation (CME) rely on the strong assumption of space-invariant blur, we handle the challenging case of general camera motion. We first show how edge profiles alone can be harnessed to...
The detection of spatially-varying blur without having any information about the blur type is a challenging task. In this paper, we propose a novel effective approach to address this blur detection problem from a single image without requiring any knowledge about the blur type, level, or camera settings. Our approach computes blur detection maps based on a novel High-frequency multiscale Fusion and...
The article concerns the automatic calibration of a camera with radial distortion from a single image. It is known that, under the mild assumption of square pixels and zero skew, lines in the scene project into circles in the image, and three lines suffice to calibrate the camera up to an ambiguity between focal length and radial distortion. The calibration results highly depend on accurate circle...
In this paper, a fast algorithm is proposed to tackle the constrained total generalized variation (TGV) based image restoration problem. The proposed algorithm proceeds by splitting: the nonsmooth constrained TGV model is first decomposed into several subproblems easier to solve; then the linear gradient or proximity operators, including projections and shrinkages, of the subproblems are individually...
This paper presents a computational framework for accurately estimating the disparity map of plenoptic images. The proposed framework is based on the variational principle and provides intrinsic sub-pixel precision. The light-field motion tensor introduced in the framework allows us to combine advanced robust data terms as well as provides explicit treatments for different color channels. A warping...
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