In the paper, a new method of blind estimation of noise variance in a single highly textured image is proposed. An input image is divided into 8x8 blocks and discrete cosine transform (DCT) is performed for each block. A part of 64 DCT coefficients with lowest energy calculated through all blocks is selected for further analysis. For the DCT coefficients, a robust estimate of noise variance is calculated. Corresponding to the obtained estimate, a part of blocks having very large values of local variance calculated only for the selected DCT coefficients are excluded from the further analysis. These two steps (estimation of noise variance and exclusion of blocks) are iteratively repeated three times. For the verification of the proposed method, a new noise-free test image database TAMPERE17 consisting of many highly textured images is designed. It is shown for this database and different values of noise variance from the set {25, 49, 100, 225}, that the proposed method provides approximately two times lower estimation root mean square error than other methods.
The main stage in the development of an algorithm for recognizing human actions is the construction of an informative and distinctive descriptor. As part of the development of a robot control system based on the recognition of human actions, this stage can be decisive. The use of technical vision elements in real conditions introduces a number of difficulties: an inhomogeneous background, uncontrolled working environment, irregular lighting, partial occlusion of the observed object, speed of actions, etc. In this paper, we propose an algorithm for recognizing human actions on complexly structured images based on a 3-D binary descriptor of micro-block difference. The developed algorithm is based on the fusion of multimodal information obtained by depth sensors and cameras of the visible range. The complementarity of information obtained in various ways allows minimizing the influence of external factors on the quality of video content: poor lighting, loss of information during data transmission, noise, etc. Combining data of both modalities ensures the complementary nature of the final video stream, which may contain information inaccessible when working with separate sources. In addition to the main descriptor, the paper proposes to use the analysis of the human skeleton. These data will reduce the recognition error and will focus the attention of the proposed method on smaller actions performed by a person's hands or wrist. The experimental results showed the effectiveness of the proposed algorithm on known data sets.
The accurate detection of cracks in paintings, which generally portray rich and varying content, is a challenging task. Traditional crack detection methods are often lacking on recent acquisitions of paintings as they are poorly adapted to high-resolutions and do not make use of the other imaging modalities often at hand. Furthermore, many paintings portray a complex or cluttered composition, significantly complicating a precise detection of cracks when using only photographic material. In this paper, we propose a fast crack detection algorithm based on deep convolutional neural networks (CNN) that is capable of combining several imaging modalities, such as regular photographs, infrared photography and X-Ray images. Moreover, we propose an efficient solution to improve the CNN-based localization of the actual crack boundaries and extend the CNN architecture such that areas where it makes little sense to run expensive learning models are ignored. This allows us to process large resolution scans of paintings more efficiently. The proposed on-line method is capable of continuously learning from newly acquired visual data, thus further improving classification results as more data becomes available. A case study on multimodal acquisitions of the Ghent Altarpiece, taken during the currently ongoing conservation-restoration treatment, shows improvements over the state-of-the-art in crack detection methods and demonstrates the potential of our proposed method in assisting art conservators.INDEX TERMS Digital painting analysis, crack detection, virtual restoration, machine learning, morphological filtering, convolutional neural networks, transfer learning, multimodal data, Ghent Altarpiece.
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