Computerized assessments for diagnosis or malignancy grading of cyto-histopathological specimens have drawn increased attention in the field of digital pathology. Automatic segmentation of cell nuclei is a fundamental step in such automated systems. Despite considerable research, nuclei segmentation is still a challenging task due noise, nonuniform illumination, and most importantly, in 2D projection images, overlapping and touching nuclei. In most published approaches, nuclei refinement is a post-processing step after segmentation, which usually refers to the task of detaching the aggregated nuclei or merging the over-segmented nuclei. In this work, we present a novel segmentation technique which effectively addresses the problem of individually segmenting touching or overlapping cell nuclei during the segmentation process. The proposed framework is a region-based segmentation method, which consists of three major modules: i) the image is passed through a color deconvolution step to extract the desired stains; ii) then the generalized fast radial symmetry transform is applied to the image followed by non-maxima suppression to specify the initial seed points for nuclei, and their corresponding GFRS ellipses which are interpreted as the initial nuclei borders for segmentation; iii) finally, these nuclei border initial curves are evolved through the use of a statistical level-set approach along with topology preserving criteria for segmentation and separation of nuclei at the same time. The proposed method is evaluated using Hematoxylin and Eosin, and fluorescent stained images, performing qualitative and quantitative analysis, showing that the method outperforms thresholding and watershed segmentation approaches.
Separation of keywords from non-keywords is the main problem in keyword spotting systems which has traditionally been approached by simplistic methods, such as thresholding of recognition scores. In this paper, we analyze this problem from a machine learning perspective, and we study several standard machine learning algorithms specifically in the context of non-keyword rejection. We propose a two-stage approach to keyword spotting and provide a theoretical analysis of the performance of the system which gives insights on how to design the classifier in order to maximize the overall performance in terms of F-measure.
Numeric strings such as identification numbers carry vital pieces of information in documents. In this paper, we present
a novel algorithm for automatic extraction of numeric strings in unconstrained handwritten document images. The
algorithm has two main phases: pruning and verification. In the pruning phase, the algorithm first performs a new
segment-merge procedure on each text line, and then using a new regularity measure, it prunes all sequences of
characters that are unlikely to be numeric strings. The segment-merge procedure is composed of two modules: a new
explicit character segmentation algorithm which is based on analysis of skeletal graphs and a merging algorithm which is
based on graph partitioning. All the candidate sequences that pass the pruning phase are sent to a recognition-based
verification phase for the final decision. The recognition is based on a coarse-to-fine approach using probabilistic RBF
networks. We developed our algorithm for the processing of real-world documents where letters and digits may be
connected or broken in a document. The effectiveness of the proposed approach is shown by extensive experiments done
on a real-world database of 607 documents which contains handwritten, machine-printed and mixed documents with
different types of layouts and levels of noise.
KEYWORDS: Signal to noise ratio, Wavelet transforms, Statistical analysis, Wavelets, Denoising, Image filtering, Image denoising, Global system for mobile communications, Continuous wavelet transforms, RGB color model
In this paper, two approaches for image denoising that take advantages of neighboring dependency in the wavelet domain are studied. The first approach is to take into account the higher order statistical coupling between neighboring wavelet coefficients and their corresponding coefficients in the parent level. The second is based on multivariate statistical modeling. The estimation of the clean coefficients is obtained by a general rule using Bayesian approach. Various estimation expressions can be obtained by <i>a priori</i> probability distribution, called multivariate generalized Gaussian distribution (MGGD). The experimental results show that both of our methods give comparatively higher peak signal to noise ratio (PSNR) as well as little visual artifact for monochrome images. In addition, we extend our approaches to a denoising algorithm for color image that has multiple color components. The proposed color denoising algorithm is a framework to consider the correlations between color components yet using the existing monochrome denoising method without modification. Denoising results in this framework give noticeable better improvement than in the case when the correlation between color components is not considered.
In this paper, we propose a new system for segmentation and recognition of unconstrained handwritten numeral strings. The system uses a combination of foreground and background features for segmentation of touching digits. The method introduces new algorithms for traversing the top/bottom-foreground-skeletons of the touched digits, and for finding feature points on these skeletons, and matching them to build all the segmentation paths. For the first time a genetic representation is used to show all the segmentation hypotheses. Our genetic algorithm tries to search and evolve the population of candidate segmentations and finds the one with the highest confidence for its segmentation and recognition. We have also used a new method for feature extraction which lowers the variations in the shapes of the digits, and then a MLP neural network is utilized to produce the labels and confidence values for those digits. The NIST SD19 and CENPARMI databases are used for evaluating the system. Our system can get a correct segmentation-recognition rate of 96.07% with rejection rate of 2.61% which compares favorably with those that exist in the literature.
We propose an invariant descriptor for recognizing complex patterns and objects composed of closed regions such as printed Chinese characters. The method transforms a 2D image into 1D line moments, performs wavelet transform on the moments, and then applies Fourier transform on each level of the wavelet coefficients and the average. The essential advantage of the descriptor is that a multiresolution querying strategy can be employed in the recognition process and that it is invariant to shift, rotation, and scaling of the original image. Experimental results show that the descriptor proposed in this paper is a reliable tool for recognizing Chinese characters.
A method for reconstruction of 3D object models from multiple views of range image is proposed. It is very important to use these partially redundant data effectively to get an integrated, complete and accurate object model. The object shape is unconstrained, curved surfaces are allowed. From each view of range image, surfaces are segmented and fitted into planar and quadratic patches by a robust residual analysis method (we address this method in another paper). Analyzing the errors of fitted surfaces from each view, the final expressions of the surfaces are merged from every view. A boundary representative model (B-rep) is used to express the final complete object. The method can be used to create 3D models for object recognition.
This paper presents a robust segmentation and fitting technique. The method randomly samples appropriate range image points and fits them into selected primitive type. From K samples we measure residual consensus to choose one set of sample points which determines an equation to have the best fit for a homogeneous patch in the current processing region. A method with compressed histogram is used to measure and compare residuals on various noise levels. The method segments range image into quadratic surfaces, and works very well even in smoothly connected regions.