Halftoning colour volume datasets based on subdivision

The algorithm for halftoning 2D gray scale images based on subdivision was extended to the processing of colour volume datasets. Two main improvements were made. The first is adding a procedure to deal with cases with large errors so as to reduce the total quantizing error. The second is randomizing the directions in which errors are propagated, assuring the even distribution of the halftoned binary voxels. In addition, a method used to process large volume datasets was also proposed. The new algorithm is simple in principle, but produces good halftoning results, especially in the boundary regions. It is especially applicable in the data preparation for the rapid forming of coloured models and heterogeneous objects.     

A combination model for orientation field of fingerprints

Orientation field is a global feature of fingerprints that is very important in automatic fingerprint identification systems (AFIS). Establishing an accurate and concise model for orientation fields will not only improve the performance of orientation estimation, but also make it feasible to apply orientation information in the matching process. In this paper, a novel model for the orientation field of fingerprints is proposed. We use a polynomial model to approximate the orientation field globally and a point-charge model at each singular point to improve the approximation locally. These two models are combined together by a weight function. Experimental results are provided to illustrate the fact that this combination model is more accurate and robust with respect to noise compared with the previous works. The application of the model is discussed at the end.     

Perceptual grouping of line features in 3-D space: a model-based framework

In this paper, we propose a novel model-based perceptual grouping algorithm for the line features of 3-D polyhedral objects. Given a 3-D polyhedral model, perceptual grouping is performed to extract a set of 3-D line segments which are geometrically consistent with the 3-D model. Unlike the conventional approaches, grouping is done in 3-D space in a model-based framework. In our unique approach, a decision tree classifier is employed for encoding and retrieving the geometric information of the 3-D model. A Gestalt graph is constructed by classifying input instances into proper Gestalt relations using the decision tree. The Gestalt graph is then decomposed into a few subgraphs, yielding appropriate groups of features. As an application, we suggest a 3-D object recognition system which can be accomplished by selecting a best-matched group. In order to evaluate the performance of the proposed algorithm, experiments are carried out on both synthetic and real scenes.     

Extraction of karyocytes and their components from microscopic bone marrow images based on regional color features

Extracting karyocytes and their components from microscopic bone marrow images is prerequisite for computer-aided early diagnosis of hemopathy. Most existing methods assume all pixels belonging to a karyon region or a cytoplasm region have similar colors. Practically, the color of neither a karyon nor a cytoplasm in a microscopic image is homogeneous in the pixel level. Therefore, the regional color features of a region are emphasized in this paper. Based on this observation, we propose a novel method to karyocyte extraction that first identifies a karyon by 4-connected block growing from a karyon feature block, then identifies feature blocks of its cytoplasm based on the extracted karyon, and finally extracts all cytoplasm regions by growing the cytoplasm feature blocks. Combining the karyon region and the corresponding cytoplasm regions can attain a complete karyocyte. Experimental results show that the proposed method is effective and robust.     

Automatic localization of craniofacial landmarks for assisted cephalometry

In this paper we propose a system for localization of cephalometric landmarks. The process of localization is carried out in two steps: deriving a smaller expectation window for each landmark using a trained neuro-fuzzy system (NFS) then applying a template-matching algorithm to pin point the exact location of the landmark. Four points are located on each image using edge detection. The four points are used to extract more features such as distances, shifts and rotation angles of the skull. Limited numbers of representative groups that will be used for training are selected based on k-means clustering. The most effective features are selected based on a Fisher discriminant for each feature set. Using fuzzy linguistics if-then rules, membership degree is assigned to each of the selected features and fed to the FNS. The FNS is trained, utilizing gradient descent, to learn the relation between the sizes, rotations and translations of landmarks and their locations. The data for training is obtained manually from one image from each cluster. Images whose features are located closer to the center of their cluster are used for extracting data for the training set. The expected locations on target images can then be predicted using the trained FNS. For each landmark a parametric template space is constructed from a set of templates extracted from several images based on the clarity of the landmark in that image. The template is matched to the search windows to find the exact location of the landmark. Decomposition of landmark shapes is used to desensitize the algorithm to size differences. The system is trained to locate 20 landmarks on a database of 565 images. Preliminary results show a recognition rate of more than 90%.     

Pattern codification strategies in structured light systems

Coded structured light is considered one of the most reliable techniques for recovering the surface of objects. This technique is based on projecting a light pattern and viewing the illuminated scene from one or more points of view. Since the pattern is coded, correspondences between image points and points of the projected pattern can be easily found. The decoded points can be triangulated and 3D information is obtained. We present an overview of the existing techniques, as well as a new and definitive classification of patterns for structured light sensors. We have implemented a set of representative techniques in this field and present some comparative results. The advantages and constraints of the different patterns are also discussed.     

Morphological hat-transform scale spaces and their use in pattern classification

In this paper we present a multi-scale method based on mathematical morphology which can successfully be used in pattern classification tasks. A connected operator similar to the morphological hat-transform is defined, and two scale-space representations are built. The most important features are extracted from the scale spaces by unsupervised cluster analysis, and the resulting pattern vectors provide the input of a decision tree classifier. We report classification results obtained using contour features, texture features, and a combination of these. The method has been tested on two large sets, a database of diatom images and a set of images from the Brodatz texture database. For the diatom images, the method is applied twice, once on the curvature of the outline (contour), and once on the grey-scale image itself.     

Multi-agent segmentation of IVUS images

A novel multi-agent image interpretation system has been developed which is markedly different from previous approaches in especially its elaborate high-level knowledge-based control over low-level image segmentation algorithms. Agents dynamically adapt segmentation algorithms based on knowledge about global constraints, contextual knowledge, local image information and personal beliefs. Generally agent control allows the underlying segmentation algorithms to be simpler and be applied to a wider range of problems with a higher reliability.The agent knowledge model is general and modular to support easy construction and addition of agents to any image processing task. Each agent in the system is further responsible for one type of high-level object and cooperates with other agents to come to a consistent overall image interpretation. Cooperation involves communicating hypotheses and resolving conflicts between the interpretations of individual agents.The system has been applied to IntraVascular UltraSound (IVUS) images which are segmented by five agents, specialized in lumen, vessel, calcified-plaque, shadow and sidebranch detection. IVUS image sequences from 7 patients were processed and vessel and lumen contours were detected fully automatically. These were compared with expert-corrected semiautomatically detected contours. Results show good correlations between agents and expert with r=0.84 for the lumen and r=0.92 for the vessel cross-sectional areas, respectively.     

Two realizations of a general feature extraction framework

A general feature extraction framework is proposed as an extension of conventional linear discriminant analysis. Two nonlinear feature extraction algorithms based on this framework are investigated. The first is a kernel function feature extraction (KFFE) algorithm. A disturbance term is introduced to regularize the algorithm. Moreover, it is revealed that some existing nonlinear feature extraction algorithms are the special cases of this KFFE algorithm. The second feature extraction algorithm, mean-STD¹-norm feature extraction algorithm, is also derived from the framework. Experiments based on both synthetic and real data are presented to demonstrate the performance of both feature extraction algorithms.     

Text information extraction in images and video: a survey

Text data present in images and video contain useful information for automatic annotation, indexing, and structuring of images. Extraction of this information involves detection, localization, tracking, extraction, enhancement, and recognition of the text from a given image. However, variations of text due to differences in size, style, orientation, and alignment, as well as low image contrast and complex background make the problem of automatic text extraction extremely challenging. While comprehensive surveys of related problems such as face detection, document analysis, and image & video indexing can be found, the problem of text information extraction is not well surveyed. A large number of techniques have been proposed to address this problem, and the purpose of this paper is to classify and review these algorithms, discuss benchmark data and performance evaluation, and to point out promising directions for future research.