Artificial Intelligence for Computer Vision

Computer Vision for automatic appraisal computation in leasing company 

ABSTRACT

This work is a study of how to develop an automated system based on artificial vision for the detection of claims and the prediction of costs associated with them for a vehicle leasing company. The main objective is to have an analytical model based on Deep Learning for the prediction of the costs of accidents caused by vehicles using an infrastructure tunnel with image acquisition hardware.

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Computer Vision for wood classification  

ABSTRACT

The technology for inspecting wood is essential in many facets of contemporary industry. Among other issues, the number of rings in a stave has a direct relationship with the wood quality. The appearance of sawn wood has many natural variations and distinct appearance that a human inspector can easily compensate when determining the type of each stave or board. However, for automatic wood inspection systems, these variations are a major source of complication. Several approaches to an automatic detection of tree-ring boundaries exist; however, they use basic image processing techniques. As a result, their accuracy is limited, and their application is mainly restricted to wood where the tree-ring boundaries are clearly defined. There also exists some works based on segmentation deep learning techniques but again, the wood processed has ring boundaries easily detectable. The aim of this paper is to deal with the problem of wood classification when there is noticeable heterogeneity in the texture of the samples. To the authors’ best knowledge, this is the first approach to grain classification in such heterogeneous images. The solution uses a hybrid approach combining classic computer vision for preprocessing and deep learning-based algorithms in order to classify wood into three quality categories. Cropping data was used in order to augment the original dataset, to avoid intra-class problem that appears in single staves and to improve the performance and accuracy of the final voting system.