In this paper, we present an algorithm to determine the signature line in scanned documents through a fast and computationally efficient technique. To that effect, the algorithm consists of three modules, namely pre-processing, candidate line detection and classification. Preprocessing in this case consists of hard thresholding, which is essential to line detection in the next module. The Hough transform for a single angle (horizontal) is utilized in the line detection module. Connected component analysis is utilized to merge detected Hough lines belonging to the same line on the document. Finally, the classification module attempts to classify each line as a signature line or not. This is accomplished using Histogram of a Gradient (HoG) features and a Euclidean distance measure for comparison. Prior data for this similarity comparison consists of images of target text in different font sizes and styles. The proposed algorithm showed favorable results in terms of precision and accuracy.

In this paper, we consider a dual-mode process for the electrophotographic laser printer--a low-frequency halftoning for smooth regions and a high-frequency halftoning for detail regions. These regions are described by an object map that is extracted from the page description language version of the document. This manner of switching screens depending on the local content provides a stable halftone without artifacts in smooth areas and preserves the detail rendering in detail or texture areas. However, when switching between halftones with two different frequencies, jaggies may occur along the boundaries between areas halftoned with low- and high-frequency screens. To reduce the jaggies, our screens obey a harmonic relationship. In addition, we implement a blending process based on a transition region. We propose a nonlinear blending process in which at each pixel, we choose the maximum of the two weighted halftones, where the weights vary according to the position in the transition region. Moreover, we describe an online tone-mapping for the boundary blending process, based on an offline calibration procedure that effectively assures the desired tone values within the transition region.

Functional image classification is the assignment of different image types to separate classes to optimize their rendering for reading or other specific end task, and is an important area of research in the publishing and multi-Average industries. This paper presents recent research on optimizing the simultaneous classification of documents, photos and logos. Each of these is handled during printing with a class-specific pipeline of image transformation algorithms, and misclassification results in pejorative imaging effects. This paper reports on replacing an existing classifier with a Weka-based classifier that simultaneously improves accuracy (from 85.3% to 90.8%) and performance (from 1458 msec to 418 msec/image). Generic subsampling of the images further improved the performance (to 199 msec/image) with only a modest impact on accuracy (to 90.4%). A staggered subsampling approach, finally, improved both accuracy (to 96.4%) and performance (to 147 msec/image) for the Weka-base classifier. This approach did not appreciable benefit the HP classifier (85.4% accuracy, 497 msec/image). These data indicate staggered subsampling using the optimized Weka classifier substantially improves the classification accuracy and performance without resulting in additional “egregious” misclassifications (assigning photos or logos to the “document” class).

We propose an image segmentation methodology which exploits gradient information in a multiresolution framework. The proposed algorithm commences with a wavelet decomposition procedure to obtain a pyramidal representation of the input image, accompanied by an adaptive threshold generation scheme required for segregating regions of varying gradient densities. At low (coarse) resolution levels, progressive region growth, texture characterization, and region merging modules are integrated together to provide interim segmentations. These interim results are transferred from one resolution level to another as a-priori information, until the final result at the highest (original) resolution is achieved. Performance evaluation on several hundred images demonstrates that our algorithm computationally outperforms various published techniques, with superior segmentation quality.

Image segmentation is a fundamental task in many computer vision applications. In this paper, we propose a new unsupervised color image segmentation algorithm, which exploits the information obtained from detecting edges in color images in the CIE L *a *b * color space. To this effect, by using a color gradient detection technique, pixels without edges are clustered and labeled individually to identify some initial portion of the input image content. Elements that contain higher gradient densities are included by the dynamic generation of clusters as the algorithm progresses. Texture modeling is performed by color quantization and local entropy computation of the quantized image. The obtained texture and color information along with a region growth map consisting of all fully grown regions are used to perform a unique multiresolution merging procedure to blend regions with similar characteristics. Experimental results obtained in comparison to published segmentation techniques demonstrate the performance advantages of the proposed method.

Image segmentation is a fundamental task in many computer vision applications. In this paper, we present a novel unsupervised color image segmentation algorithm that utilizes color gradients, dynamic thresholding and texture modeling algorithms in a split and merge framework. To this effect, pixels without edges are clustered and labeled individually to identify the preliminary image content. Pixels that contain higher gradients are further classified by utilizing an iterative dynamic threshold generation technique and an appropriate entropy based texture model. The proposed algorithm was demonstrated successfully on an extensive database of images and benchmarked favorably against prior art.