Accurate rebar recognition is a key step of automatic online rebar counting based on image processing. Current popular rebar recognition methods based on front view image usually lead to missing detection for some special rebar such as obscured rebar, oxidation dark rebar and bent rebar. So a novel rebar recognition method based on multi-view images was proposed in this paper. Firstly, rebar images were captured simultaneously from front view and top view using two cameras. Then, the centers of rebar were located respectively in top and front view image. Finally, in the image fusion step, the top recognition result was used to compensate and validate the front recognition result for reducing and eliminating above missing detection. Experiments showed that this method can efficiently recognize above missed rebar and improve rebar recognition rate.
In this paper we proposed an improved watershed algorithm for the quasi-circle overlapping images of the bars end face. According to the classical watershed algorithm, which often causes over-segmentation, the improved algorithm does a series of pretreatment with the original image, such as sobel filter. With the gradient operator and mathematical morphology method, we firstly obtain the smooth image of the forced local maximum marks. Then, on the basis of the quasi-circle characteristic of the target image, we proceed to maximize the erosion with circular structure in order to prevent under-segmentation. Finally, we use the watershed algorithm to segment the gray image based on distance transform. So we can separate the target from each other to achieve the accurate counting purpose. By using the proposed algorithm in the article, we obtain satisfactory segmentation results of the quasi-circle overlapping image of the bars end face image.
Waste ceramic tiles are broken into sandy gravel to configure reclaimed sand instead of part of normal sand. Bending strength tests and compressive strength tests of mortar samples were carried out under different substitution rate of ceramic tile wastes sand in different curing time, which the ratio of cement and the consistency of mixture were fixed. The results indicate that strength grade of masonry mortar recycled by ceramic tile wastes under different substitution rates can be improved 1 or 2 grades, and the strength linearly increases as the curing time increases under the same substitution rate, and the growth rates of bending strength and compressive strength are inconsistent.
BoVW (Bag of Visual Words) Model has attracted much attention for many computer vision applications in which an image is represented by a histogram of visual words. Two of its critical steps are to construct a visual dictionary and to quantize each local feature to its nearest visual word in the dictionary. In this paper, we present the framework of a generalized BoVW (GBoVW) Model in which feature quantization can be replaced by sparse coding based feature encoding. We also propose to use spectral clustering to construct a visual dictionary to overcome the shortcomings of K-Means based clustering algorithms. Image retrieval experiments on ZuBud database indicate that GBoVW Model improves BoVW Model and the visual dictionary generated by spectral clustering achieves better performance than that by K-Means based clustering methods.
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