The Euler number of a binary image is an important topological property for pattern recognition, image analysis, and computer vision. A famous method for computing the Euler number of a binary image is by counting certain patterns of bit-quads in the image, which has been improved by scanning three rows once to process two bit-quads simultaneously. This paper studies the bit-quad-based Euler number computing problem. We show that for a bit-quad-based Euler number computing algorithm, with the increase of the number of bit-quads being processed simultaneously, on the one hand, the average number of pixels to be checked for processing a bit-quad will decrease in theory, and on the other hand, the length of the codes for implementing the algorithm will increase, which will make the algorithm less efficient in practice. Experimental results on various types of images demonstrated that scanning five rows once and processing four bit-quads simultaneously is the optimal tradeoff, and that the optimal bit-quad-based Euler number computing algorithm is more efficient than other Euler number computing algorithms.

This paper presents a new connected component labeling algorithm. The proposed algorithm scans image lines every three lines and processes pixels three by three. When processing the current three pixels, we also utilize the information obtained before to reduce the repeated work for checking pixels in the mask. Experimental results demonstrated that our method is more efficient than the fastest conventional labeling algorithm.

This paper proposes a graph-theory-based Euler number computing algorithm. According to the graph theory and the analysis of a mask's configuration, the Euler number of a binary image in our algorithm is calculated by counting four patterns of the mask. Unlike most conventional Euler number computing algorithms, we do not need to do any processing of the background pixels. Experimental results demonstrated that our algorithm is much more efficient than conventional Euler number computing algorithms.

We have developed a new computer-aided diagnostic system for differentiating oral borderline malignancies in hematoxylin-eosin stained microscopic images. Epithelial dysplasia and carcinoma in-situ (CIS) of oral mucosa are two different borderline grades similar to each other, and it is difficult to distinguish between them. A new image processing and analysis method has been applied to a variety of histopathological features and shows the possibility for differentiating the oral cancer borderline grades automatically. The method is based on comparing the drop-shape similarity level in a particular manually selected pair of neighboring rete ridges. It was found that the considered similarity level in dysplasia was higher than those in epithelial CIS, of which pathological diagnoses were conventionally made by pathologists. The developed image processing method showed a good promise for the computer-aided pathological assessment of oral borderline malignancy differentiation in clinical practice.

In this paper, we propose an image completion algorithm which takes advantage of the countless number of images available on Internet photo sharing sites to replace occlusions in an input image. The algorithm 1) automatically selects the most suitable images from a database of downloaded images and 2) seamlessly completes the input image using the selected images with minimal user intervention. Experimental results on input images captured at various locations and scene conditions demonstrate the effectiveness of the proposed technique in seamlessly reconstructing user-defined occlusions.

A projection onto convex sets (POCS)-based annotation method for semantic image retrieval is presented in this paper. Utilizing database images previously annotated by keywords, the proposed method estimates unknown semantic features of a query image from its known visual features based on a POCS algorithm, which includes two novel approaches. First, the proposed method semantically assigns database images some clusters and introduces a nonlinear eigenspace of visual and semantic features in each cluster into the constraint of the POCS algorithm. This approach accurately provides semantic features for each cluster by using its visual features in the least squares sense. Furthermore, the proposed method monitors the error converged by the POCS algorithm in order to select the optimal cluster including the query image. By introducing the above two approaches into the POCS algorithm, the unknown semantic features of the query image are successfully estimated from its known visual features. Consequently, similar images can be easily retrieved from the database based on the obtained semantic features. Experimental results verify the effectiveness of the proposed method for semantic image retrieval.

In order to reconstruct 3-D Euclidean shape by the Tomasi-Kanade factorization, one needs to specify an affine camera model such as orthographic, weak perspective, and paraperspective. We present a new method that does not require any such specific models. We show that a minimal requirement for an affine camera to mimic perspective projection leads to a unique camera model, called symmetric affine camera, which has two free functions. We determine their values from input images by linear computation and demonstrate by experiments that an appropriate camera model is automatically selected.

Gastric cancer is a great part of the cancer occurrence and the mortality from cancer in Korea, and the early detection of gastric cancer is very important in the treatment and convalescence. This paper, for the early detection of gastric cancer, proposes the analysis system of an endoscopic image of the stomach, which detects abnormal regions by using the change of color in the image and by providing the surface tissue information to the detector. While advanced inflammation or cancer may be easily detected, early inflammation or cancer is difficult to detect and requires more attention to be detected. This paper, at first, converts an endoscopic image to an image of the IHb (Index of Hemoglobin) model and removes noises incurred by illumination and, automatically detects the regions suspected as cancer and provides the related information to the detector, or provides the surface tissue information for the regions appointed by the detector. This paper does not intend to provide the final diagnosis of abnormal regions detected as gastric cancer, but it intends to provide a supplementary mean to reduce the load and mistaken diagnosis of the detector, by automatically detecting the abnormal regions not easily detected by the human eye and this provides additional information for diagnosis. The experiments using practical endoscopic images for performance evaluation showed that the proposed system is effective in the analysis of endoscopic images of the stomach.

A new method for logical structure analysis of document images is proposed in this paper as the basis for a document reader which can extract logical information from various printed documents. The proposed system consists of five basic modules: text line classification, object recognition, object segmentation, object grouping, and object modification. Emergent computation, which is a key concept of artificial life, is adopted for the cooperative interaction among modules in the system in order to achieve effective and flexible behavior of the whole system. It has three principal advantages over other methods: adaptive system configuration for various and complex logical structures, robust document analysis tolerant of erroneous feature detection, and feedback of high-level logical information to the low-level physical process for accurate analysis. Experimental results obtained for 150 documents show that the method is adaptable, robust, and effective for various document structures.

A new and simple image processing approach for the measurement of the length of pedestrian crossings with a view to develop a travel aid for the blind people is described. In a crossing, the usual black road surface is painted with constant width periodic white bands. The crossing length is estimated using vector geometry from the left- and the right-border lines, the first-, the second- and the end-edge lines of the crossing region. Image processing techniques are applied on the crossing image to find these lines. Experimental results using real road scenes with pedestrian crossing confirm the effectiveness of the proposed method.

In this paper, a new image synthesis model based on a set of wavelet bases is proposed. In the proposed model, images are approximated by the sum of synthesis functions that are translated to image edge positions. By applying the proposed model to sketch-based image coding, no iterative image recovery procedure is required for image decoding. In the design of the synthesis functions, we define the synthesis functions as a linear combination of wavelet bases. The coefficients for wavelet bases are obtained from an iterative procedure. The vector quantization is applied to the vectors of the coefficients to limit the number of the synthesis functions. We apply the proposed synthesis model to the sketch-based image coding. Image coding experiments by eight synthesis functions and a comparison with the orthogonal transform methods are also given.

Confocal laser scanning microscope (CLSM) is capable of delivering a high axial resolution, and with this instrument even thin layers of cells can be imaged in good quality. Therefore, intracellular uptake and distribution properties of photosensitizer zinc coproporphyrin III tetrasodium salt (Zn CP-III) in human lung small cell carcinoma (Ms-1) were examined by using CLSM. In particular, the uptake of Zn CP-III in cytoplasm, plasma membrane, and nucleus was individually evaluated for the first time from fluorescence images obtained by CLSM. The results show that the Zn CP-III content in three cellular areas correlates with extracellular Zn CP-III concentration and time of incubation with Zn CP-III. Furthermore, it was found that the cytoplasmic fluorescence was approximately two times higher than that in the nucleus under all uptake conditions. In addition, cellular accumulation of Zn CP-III was compared with photodynamic cytotoxicity. The photocytotoxicity was to a great extent dependent on the uptake of the photosensitizer. The damaged site of Ms-1 cells induced by photodynamic therapy was plasma membrane. However, the content of Zn CP-III accumulated in cytoplasm was the highest among the three areas, implying that, besides the direct damage on plasma membrane, an oxidative damage to cellular component arose from the cytoplasmic Zn CP-III may also play an important role in photocytotoxicity. The quantitative information obtained in this study will be useful for further investigation of the photocytotoxicity as well as the uptake mechanism of photosensitizer.

Functional magnetic resonance imaging (fMRI) allows to display functional activities of certain brain areas. In combination with a three dimensional anatomical dataset, acquired with a standard magnetic resonance (MR) scanner, it can be used to identify eloquent brain areas, resulting in so-called functional neuronavigation, supporting the neurosurgeon while planning and performing the operation. But during the operation brain shift leads to an increasing inaccuracy of the navigation system. Intraoperative MR imaging is used to update the neuronavigation system with a new anatomical dataset. To preserve the advantages of functional neuronavigation, it is necessary to save the functional information. Since fMRI cannot be repeated intraoperatively with the unconscious patient easily we tried to solve this problem by means of image processing and pattern recognition algorithms. In this paper we present an automatic approach for transfering preoperative markers into an intraoperative 3-D dataset. In the first step the brains are segmented in both image sets which are then registered and aligned. Next, corresponding points are determined. These points are then used to determine the position of the markers by estimating the local influence of brain shift.

Image classification is an important task in document image analysis and understanding, page segmentation-based document image compression, and image retrieval. In this paper, we present a new approach for distinguishing textual images from pictorial images using the Kolmogorov Complexity (KC) measure with randomly extracted blocks. In this approach, a number of blocks are extracted randomly from a binarized image and each block image is converted into a one-dimensional binary sequence using either horizontal or vertical scanning. The complexities of these blocks are then computed and the mean value and standard deviation of the block complexities are used to classify the image into textual or pictorial image based on two simple fuzzy rules. Experimental results on different textual and pictorial images show that the KC measure with randomly extracted blocks can efficiently classified 29 out 30 images. The performance of our approach, where an explicit training process is not needed, is comparable favorably to that of a neural network-based approach.

In this paper, we propose a new approach for describing image patterns. We integrate the concepts of multiscale image analysis, aura matrix (Gibbs random fields and cooccurrences related statistical model of texture analysis) to define image features, and to obtain the features having robustness with illumination variations and shading effects, we analyse images based on the Topographic Structure described by the Surface-Shape Operator, which describe gray-level image patterns in terms of 3D shapes instead of intensity values. Then, we illustrate usefulness of the proposed features with texture classifications. Results show that the proposed features extracted from multiscale images work much better than those from a single scale image, and confirm that the proposed features have robustness with illumination and shading variations. By comparisons with the MRSAR (Multiresolution Simultaneous Autoregressive) features using Mahalanobis distance and Euclidean distance, the proposed multiscale features give better performances for classifying the entire Brodatz textures: 112 categories, 2016 samples having various brightness in each category.

This paper proposes a design method of feature spaces in a two-stage image recognition method that improves the recognition accuracy and efficiency in statistical image recognition. The two stages are (1) image screening and (2) image recognition. Statistical image recognition methods require a lot of calculations for spatially matching between subimages and reference patterns of the specified objects to be detected in input images. Our image screening method is effective in lowering the calculation load and improving recognition accuracy. This method selects a candidate set of subimages similar to those in the object class by using a lower dimensional feature vector, while rejecting the rest. Since a set of selected subimages is recognized by using a higher dimensional feature vector, overall recognition efficiency is improved. The classifier for recognition is designed from the selected subimages and also improves recognition accuracy, since the selected subimages are less contaminated than the originals. Even when conventional recognition methods based on linear transformation algorithms, i. e. principal component analysis (PCA) and projection pursuit (PP), are applied to the recognition stage in our method, recognition accuracy and efficiency may be improved. A new criterion, called a screening criterion, for measuring overall efficiency and accuracy of image recognition is introduced to efficiently design the feature spaces of image screening and recognition. The feature space for image screening are empirically designed subject to taking the lower number of dimensions for the feature space referred to as LS and the larger value of the screening criterion. Then, the recognition feature space which number of dimensions is referred to as LR is designed under the condition LSLR. The two detection tasks were conducted in order to examine the performance of image screening. One task is to detect the eye- and-mouth-areas in a face image and the other is to detect the text-area in a document image. The experimental results demonstrate that image screening for these two tasks improves both recognition accuracy and throughput when compared to the conventional one-stage recognition method.

This paper presents a method for land cover classification using the SIR-C/X-SAR imagery based on the maximum likelihood method and the polarimetric filtering. The main feature is to use polarimetric enhanced image information in the pre-processing stage for the classification of SAR imagery. First, polarimetric filtered images are created where a specific target is enhanced versus another, then the image data are incorporated into the feature vector which is essential for the maximum likelihood classification. Specific target classes within the SAR image are categorized according to the maximum likelihood method using the wavelet transform. Addition of polarimetric enhanced image in the preprocessing stage contributes to the increase of classification accuracy. It is shown that the use of polarimetric enhanced images serves efficient classifications of land cover.

The pattern spectrum has been proposed to represent morphological size distribution of an image. However, the conventional pattern spectrum cannot extract approximate shape information from image objects spotted by noisy pixels since this is based only on opening. In this paper, a novel definition of the pattern spectrum, morphological multiresolution pattern spectrum (MPS), involving both opening and closing is proposed. MPS is capable of distinguishing details from approximate information of the image.

As a first step to develop a system to analyze or recognize patterns contained in mages, it is important to provide a good base representation that can facilitate efficiently the interpretation of such patterns. Since structural features of basic patterns in document images such as characters or tables are horizontal and vertical stroke components, we propose a new expression of document image based on the MCR expression that can express well such features of text and tabular components of an image.

This paper discusses the role of knowledge in document image understanding from the viewpoints of representation, utilization and acquisition. For the representation of knowledge, we propose two models, a layout model and a content model, which represent knowledge about the layout structure and content of a document, respectively. For the utilization of knowledge, we implement layout analysis and content analysis which utilize a layout model and a content model, respectively. The strategy of hypothesis generation and verification is introduced in order to integrate these two kinds of analysis. For the acquisition of knowledge, we propose a method of incremental acquisition of a layout model from a stream of example documents. From the experimental results of document image understanding and knowledge acquisition using 50 samples of visiting cards, we verified the effectiveness of the proposed method.