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Metallic pieces are typically machined by continuous tool passes, which usually causes regular patterns in the form of straight edges in the surface of the pieces. An irregular pattern on the surface of the piece implies a decrease of the quality of the machined piece. In this paper, we propose an acquisition system and a machine-vision based method to describe the texture of the inner and outer surfaces of machined pieces with cylindrical holes. In order to capture images of the hole surface, we used a microscope camera connected to a rigid industrial boroscope. Considering the extracted texture descriptors, a significant correlation is shown. Consequently, the feature vector is reduced and then classified by several algorithms using an exhaustive grid search strategy with 10-fold cross validation. Best results are achieved with the Extremely Randomized Trees classifier with a mean test score on the hold out set of 92.98%, what improves previous research and meets the requirements of the field.

Micro milling is a versatile machining process owing to its capability to machine, by material removal, micro-sized components with complex geometrical features. However, micro milling tools wear quickly, being a key issue to determine tool wear condition in order to prevent excessive tool wear or a sudden tool breakage while machining, which would waste the workpiece. Due to the small size of micro milling tools, direct measurement of the worn tool is not possible. In order to overcome this drawback, this paper presents a new method based on digital image processing where image captures of the micro tool and subsequent analysis provides a valuable information to determine the progression of tool wear. Tool wear is measured in flank wear, crater wear and tool radius reduction. Different approaches are compared so as to determine the best option for every set of images. These methods are based on the use of morphological operations, k-means clustering and Otsu Multilevel algorithm. Results show a good performance with differences of 5% between predicted and actual worn area, which satisfies the industrial requirements. This procedure can be transferred to industrial environments and implemented in collaborative robots, increasing the level of automation and facilitating the decision-making process.

Recognizing infrequent or emerging named entities in a user-generated text is a challenging task, especially when informal or slang text is used. Some recent works propose to use a gazetteer to solve this problem, but this solution is not general because the gazetteer is task-specific and its maintenance is costly. In this paper, we overcome this drawback by presenting Local Distance Neighbor (LDN), a novel feature that substitutes the gazetteer and makes that the model obtains state-of-the-art results. LDN captures an initial guess for each input token based on the categories of its neighboring tokens within an embedding space. We evaluated the proposed network on the W-NUT-2017 dataset, and we obtained the state-of-the-art F1 score for the Group, Person, and Product categories. We employed our new feature together with the model proposed by Aguilar et al. to recognize named entities in the Tor Darknet related to suspicious activities associated with weapons and drug selling. After increasing the samples of the W-NUT-2017 dataset with 851 manually annotated entries, we repeated our evaluation in this extended version of the dataset, achieving entity and surface F1 scores of 52.96% and 50.57%, respectively. Furthermore, we demonstrate that our proposal can be useful for Law Enforcement Agencies in mining the textual information in the Tor hidden services, being especially adequate for the Group, Person, and Product categories.

The combination of SIFT descriptors with other features usually improves image classification, like Edge-SIFT, which extracts keypoints from an edge image obtained after applying the compass operator to a colour image. We evaluate for the first time, how the use of different radii in the compass operator affects the classification performance. We demonstrate that the value proposed in the literature, radius=4.00, is not the optimum from an image classification point of view. We also put in evidence that in ideal conditions, choosing an appropriate radius for each image yields accuracy values even higher than 95%. Finally, we propose a new method to estimate the best radius for the compass operator in each dataset. Using a training subset selected on the basis of a minimum dispersion criterion of edges density, we construct a richer dictionary for each dataset in our Bag of Words pipeline. From that dictionary it is selected a radius for the whole dataset that yields higher accuracy than using the value proposed in the literature. Using this method, we obtained improvements in the accuracy up to 24.4% in Soccer, 6.77% in COIL-RWTH-2, 4.46% in Birds, 3.82% in ImageNet_Dogs, 2.75% in ImageNet_Birds, 2.02% in Flowers and 1.75% in Caltech101 datasets. It was demonstrated that compass radius in Edge-SIFT affects to classification.The classification performance of different radii was evaluated on eight datasets.It is shown that selecting a radius for each image results in better classification.A method to automatically estimate a better radius for each dataset is proposed.The estimated radius guarantees better results than the state-of-the-art.