This work proposes a method for an effective and quick monitoring of video contents produced by TV Broadcasters by means of a fully automatic system. The proposed system performs acquisition, recognition and classification of logos labeling video contents hosted by video-sharing platforms. This challenge is addressed in the Laguerre-Gauss wavelet domain; as soon as a logo is located, in any area of the video screen, a detection strategy, based on the analysis of local Fisher information of the selected logo region, is applied. A distance metric on the LG saliency maps, based nearest neighbor algorithm, is defined, to classify the logo in the relevant video portion. A preliminary test on a dataset of 300 heterogeneous videos, produced by several European Broadcasters, was performed, to verify the effectiveness of the proposed method. The experimental results proved the robustness of the implemented logo recognition and classification method, also for video content labeled with different logo sizes and shapes and for video content corrupted by geometric transformations and/or coding degradations.
This paper aims at investigating the ancient Chinese textile in order to facilitate the growing trend of an interdisciplinary study between art history, industrial design and imaging science. This is an early attempt to study how decorative patterns of the textiles were created with various weaving techniques with the help of digital technology. Since the captured fabric image only reveals the floating yarn, the combination of the underneath yarns are unknown. From the mathematical point of view, the weaving technique can be regarded as a research problem of combinatorics that contains how the yarns of weft and warp intersect with each other. Hence, the analysis of the weaving pattern contains two layers: (a) detection of the floating yarn and (b) estimation of the combination of the underneath yarns. Previously, the regular bands (RB) method is a tool for regularity analysis that has been successfully applied to patterned fabric inspection. This paper achieves the first layer goal, which applies computer vision technique in the imaging science through the RB method to achieve the detection of the floating yarns of images of some ancient Chinese textiles. Ancient textile samples from Ming dynasty, China (ca. 1368-1644 CE) are utilized for the experiments in the paper.