This paper proposes an image classification method based on fuzzy bag-of-words (FBoW) model and the fuzzy system with positive and negative rules. Firstly, the Gaussian membership function was adopted to construct multiple fuzzy membership histograms for image description, based on the distance between image features and multiple codebooks. Next, the fuzzy system with positive and negative rules was introduced to fuze the image description and image classification into a unified learning framework. After that, the precedent and antecedent parameters of the fuzzy system were learned by particle swarm optimization (PSO) and recursive least squares (RLS) algorithm, such that the parameters can be adjusted constantly in the learning process and that image description can fit in with the image classifier. Finally, the FBoW model was verified through experiment on the standard image dataset PASCAL Visual Object Classes Challenge 2007 (VOC2007). The results show that our method outperformed the classic FBoW model in image classification.
The traditional manual crack detection method for parts is inefficient, subjective, and has low accuracy. Laser and radar equipment is accurate but costly. Thus, image processing methods based on machine vision are widely used owing to the rapid detection speed and low cost. However, these methods are inappropriate to a low-contrast, high-noise, and poorresolution environment. To obtain accurate detection results in complex environments, this study proposed a new automatic crack identification model. Image quality was enhanced using the multi-scale Retinex algorithm modified by a three-sided filter. The algorithm for spectral clustering was employed to segment the cracks, thereby potentially preserving the relevant information between the samples. Pixel interference regions were removed on the bases of shape feature and contour information. Target feature vectors were extracted according to the characteristics of the crack, and five features of the crack image were constructed. The SVDD(support vector data description) classifier was constructed by using machine learning methods to automatically distinguish between crack and non-crack structures. The crack direction was identified on the basis of the projections in the vertical and horizontal directions. The accuracy of the model was verified by experiments. Results indicate that the crack images with a size of 300 × 400 have good segmentation effect and low complexity through the clustering algorithm, while the cluster number is equal to 2. Noise interference can be further reduced by setting the crack area between 2% to 28%. Moreover, the detected crack width can be accurate to 1 pixel. The crack and non-crack areas can be classified using the SVDD classifier without large samples. The proposed method provides good reference for inspection platform construction and state assessment.
This paper investigates experimentally the edge chipping of a rock to assess the cutting performance of a conical and a pyramidal tip. It was found that the conical tip generates many radiated cracks and results in a larger amount of fine rock grains due to crashing. The size of chips produced by the pyramidal tip is bigger. It was concluded that the critical chipping energy has approximately a linear relation with h9/4 of which h is the depth of cut of the tip.
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