By Masakazu Iwamura, Faisal Shafait
This booklet constitutes the completely refereed post-workshop court cases of the fifth overseas Workshop on Camera-Based record research and popularity, CBDAR 2013, held in Washington, DC, united states, in August 2013. The 14 revised complete papers offered have been rigorously chosen in the course of rounds of reviewing and development from various unique submissions. meant to offer a photograph of the state of the art study within the box of digital camera dependent record research and popularity, the papers are geared up in topical sections on textual content detection and popularity in scene photos and camera-based systems.
Read Online or Download Camera-Based Document Analysis and Recognition: 5th International Workshop, CBDAR 2013, Washington, DC, USA, August 23, 2013, Revised Selected Papers PDF
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Additional resources for Camera-Based Document Analysis and Recognition: 5th International Workshop, CBDAR 2013, Washington, DC, USA, August 23, 2013, Revised Selected Papers
2 Background According to the dichromatic reﬂection model , most of inhomogeneous objects have two kinds of reﬂections: diﬀuse and specular. In the dichromatic reﬂection model, the spectral factor is a weighted sum of the specular and diﬀuse reﬂectance functions. If we use a digital camera with three sensors (RGB) to 32 S. Banerjee et al. capture an image, the color I at each pixel x can be described as a linear combination of two components D and S as follows: I(x) = md (x)D(x) + ms (x)S(x), where I(x) = [Ir (x), Ig (x), Ib (x)]T is the RGB color at pixel x in the captured image.
Sys. Man Cybern. 9(1), 62–66 (1979) 21. : Hierarchical grouping to optimize an object function. J. Am. Stat. Assoc. in Abstract. Here, we present our recent study of a robust but simple approach to extraction of texts from camera-captured images. In the proposed approach, we ﬁrst identify pixels which are highly specular. Connected components of this set of specular pixels are obtained. Pixels belonging to each such component are separately binarized using the well-known Otsu’s approach. We next apply smoothing on the whole image before obtaining its Canny edge representation.
4 Integrated Discriminative Model Using the dictionary classiﬁer deﬁned in Sect. 2 and consensus feature extraction procedure in Sect. , |X|. ϕ (X, x ˜) denotes the component consensus feature extraction which includes the six dimensional features deﬁned by (2)–(7). x ˜ is a component being considered for inclusion into X. W T is a weight vector related to importance of each dimension of features and B is a threshold for the model when performing text classiﬁca˜ outputs a positive value it means that X ˜ is a text object; otherwise tion.