Hand-eye calibration using a single image and robotic picking up using images lacking in contrast
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This article proposes a hand-eye calibration using a new and easy method suitable for a camera mounted on the end-effector of an industrial robot using only a single image. The hand-eye calibration information could be used in robotic picking up of cubes using a monocular camera. Images captured from a particular pose of the camera have been segmented using a fusion of multiple methods such that the object information is obtained even in cases when there is less contrast between the object and the background, or in the presence of variation in lighting. The edge information, and subsequently the pose of the object was estimated using minimum number of images. In some of the cases a single image was sufficient but in case only a single edge edge is obtained, an additional image is grabbed after aligning the camera with the detected edge. An additional edge is estimated using a directional thresholding operation. The edge information in 3-D obtained using the calibration information was then used to calculate the pose of the object to facilitate robotic pick up. To ensure safety; a verification of the estimate was done using projection of the computed coordinates, and final pick up was done while monitoring the force to avoid damage due to collisions. The proposed approaches were physically implemented and experimentally validated.Keywords:
Monocular
In the current commercial automotive market, the need for intelligent headlight control systems has increased more and more. Camera-based night-time vehicle detection has become a crucial issue in determining the performance of such control systems. The purpose of this paper is to offer an answer to the question, 'Which thresholding method is suitable in terms of detection performance for a night-time vehicle candidate selection process?' For such purposes, two local adaptive thresholding methods are introduced and tested. One is local maximum-based thresholding, and the other is local mean-based thresholding. Efficient implementation methodologies are also introduced for real-time processing. Through the simulations tested on road image sequences with different exposure times, we prove that local adaptive thresholding methods have better performance than other well-known global thresholding methods. In particular, the simulations show that the proposed mean-based thresholding method has better performance on both long- and short-exposure sequences.
Balanced histogram thresholding
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Balanced histogram thresholding
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In this work an extension of an iterative technique for the thresholding of a given image using a single-level threshold to multi-level thresholding is presented. The technique is applied on images of machine parts and the features are extracted by border-following. The results of the multi-level thresholding and a comparison with results obtained by single-level thresholding are also presented.
Balanced histogram thresholding
Feature (linguistics)
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This work aimed to find a robust thresholding technique to image binarization for the gray level images. Thresholding is a simple method that plays a vital role in image segmentation. This comparative study provides to select the robust thresholding technique for general images and MRI head scans. This paper analyses the five thresholding techniques such as Sauvola thresholding, Niblack thresholding, Ridler and Calvard thresholding, Kittler and Illingworth thresholding and Otsu Thresholding for general gray images, normal and abnormal MRI head scans. The performance analysis was carried out by using the region non-uniformity parameter. Experiments were done using the mixture of gray images chosen form popularly available image databases.
Balanced histogram thresholding
Otsu's method
Gray (unit)
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A localized thresholding method based on boundary detection is proposed. The performance of global thresholding is likely to degrade when the gray level of the background in an image fluctuates and non-background regions with relatively large area exists. The proposed approach, localized thresholding based on boundary (LOTBOB), estimates the regions which are likely to contain the interesting objects by presuming that the appearance of boundaries indicates the existence of objects with a high possibility. Thresholding is then limited in these regions to avoid global thresholding. Experiments on real-world images show that LOTBOB is effective with relatively small and clearly-edged objects.
Balanced histogram thresholding
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Edge detection is one of the fundamental operations in computer vision. In the literature there are numerous algorithms for edge detection. This paper presents a new approach to edge detection based on thresholding of a gradient magnitude map. Standard thresholding methods use a single threshold in order to isolate edge points. The introduced technique is based on thresholding with hysteresis. It is obtained by modifying the procedure originally proposed by Canny. The effectiveness of the introduced methods are experimentally verified on standard test images.
Canny edge detector
Deriche edge detector
Image gradient
Balanced histogram thresholding
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We present a variation on classic beam thresholding techniques that is up to an order of magnitude faster than the traditional method, at the same performance level. We also present a new thresholding technique, global thresholding, which, combined with the new beam thresholding, gives an additional factor of two improvement, and a novel technique, multiple pass parsing, that can be combined with the others to yield yet another 50% improvement. We use a new search algorithm to simultaneously optimize the thresholding parameters of the various algorithms.
Balanced histogram thresholding
Factor (programming language)
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Edge detection is forefront of image processing algorithms. There are problems associated with different edge detectors. Thresholding is critical problem in edge detection. The paper represents three different methods of fuzzy based thresholding and resulted adaptive threshold is used in edge detection algorithm. Simulation results shows fuzzy thresholding based edge detection gives better results than conventional methods.
Balanced histogram thresholding
Deriche edge detector
Canny edge detector
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In this paper, the problem of edge detection is addressed using the first order statistics and automatic thresholding technique. The general idea of edge detection using the simple edge detectors such as gradient operators or second derivative operators is extended to the statistic domain. The statistical features are used to describe the relationship between the current pixel and its neighboring, then, the thresholding technique is employed to determine the edge of gray level image. The proposed method improves the accuracy of the edge detection and suppresses the impact of the noise on the results, while the edge has a good consistency. The proposed method is validated by performing a comparative study with respect to other existing techniques. The experimental segmentation results, on standard and textured images, highlight the effectiveness of the proposed method.
Key words: Thresholding, statistical features, first order statistics, noise, segmentation, edge detection, defect detection.
Statistic
Morphological gradient
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