For detecting prohibited items inside packages, visual image doesn’t work well as items overlapped or occluded. Thus, Penetrable X-ray image was employed to solve the occlusion problem by coloring items in different material, yet the overlapping problem still remains unresolved. Therefore, this paper proposes an X-ray image segmentation and metal prohibited items detection following a coarse-to-fine scheme. Firstly, the overlapping metal objects are clipped according to the value in the H channel and R channel. Secondly, individual metal items are extracted using regional growing. Thirdly, metal prohibited items are detected by matching its shape with those predefined in Database. Our experiment show that the proposed method can detect 94.74% metal prohibited items inside packages.
Calligraphy is the art of writing and each writer has his own features, where calligraphy strokes carry the most important features. In order to identify the stroke and the writer, this paper proposes an approach of stroke extraction and presentation. Firstly, skeleton segments of the character are extracted according to the writing rules. Secondly, neighbouring common segment strokes are connected to build a complete stroke, followed by special short stroke backtrack. Thirdly, individual outline strokes are isolated by using curves to build the closed contour for the stroke. Finally, generate and save outline strokes for future stroke feature extraction. Our approach can extract strokes from parts of regular script and official script.
A large collection of reproductions of calligraphy on paper was scanned into images to enable web access for both the academic community and the public. Calligraphic paper digitization technology is mature, but technology for segmentation, character coding, style classification, and identification of calligraphy are lacking. Therefore, computational tools for classification and quantification of calligraphic style are proposed and demonstrated on a statistically characterized corpus. A subset of 259 historical page images is segmented into 8719 individual character images. Calligraphic style is revealed and quantified by visual attributes (i.e., appearance features) of character images sampled from historical works. A style space is defined with the features of five main classical styles as basis vectors. Cross-validated error rates of 10% to 40% are reported on conventional and conservative sampling into training/test sets and on same-work voting with a range of voter participation. Beyond its immediate applicability to education and scholarship, this research lays the foundation for style-based calligraphic forgery detection and for discovery of latent calligraphic groups induced by mentor-student relationships.
Calligraphic style is considered, for this research, visual attributes of images of calligraphic characters sampled randomly
from a "work" created by a single artist. It is independent of page layout or textual content. An experimental design is
developed to investigate to what extent the source of a single, or of a few pairs, of character images can be assigned to
the either same work or to two different works. The experiments are conducted on the 13,571 segmented and labeled
600-dpi character images of the CADAL database. The classifier is not trained on the works tested, only on other works.
Even when only a few samples of same-class pairs are available, the difference-vector of a few simple features extracted
from each image of a pair yields over 80% classification accuracy for a same-work vs. different-work dichotomy. When
many pairs of different classes are available for each pair, the accuracy, using the same features, is almost the same.
These style-verification experiments are part of our larger goal of style identification and forgery detection.
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