In this article, a new analogic CNN algorithm to extract features of postage stamps in gray-scale images Is introduced. The Gradient Controlled Diffusion method plays an important role in the approach. In our algorithm, it is used for smoothing and separating Arabic figures drawn with a color which is similar to the background color. We extract Arabic figures in postage stamps by combining Gradient Controlled Diffusion with nearest neighbor linear CNN template and logic operations. Applying the feature extraction algorithm to different test images it has been verified that it is also effective in complex segmentation problems

This paper develops an efficient mechanism for extracting primary information requests from 'Seek-Object' type query messages. The mechanism consists of three steps. The first step extracts sentences which signal that the query is 'Seek-Object' type by recognizing distinctive surface expressions. The second step, biased by the expression patterns, analyzes their internal structures. The third step integrates these fragments by a partial discourse processing and represents writers' goal-directed information request; as these sentences often include referential expressions and the referred expressions are in background goal descriptions. We claim the mechanism can extract information requests fairly accurately, by showing evaluation results.

An image obtained by ultrasonic medical equipment is poor in quality because of speckle noise, that is caused by the quality of ultrasonic beam and so on. Thus, it is very difficult to detect internal organs or the diseased tissues from a medical ultrasonic image by the processing, which is used only gray-scale of the image. To analyze the ultrasonic image, it is necessary to use not only gray-scale but also appropriate statistical character. In this paper, we suggest a new method to extract regions of internal organs from an ultrasonic image by the discrimination function. The discrimination function is based on gray-scale and statistical characters of the image. This function is determined by using parameters of the multi-dimensional autoregressive model.

This paper describes COCE (COordinative Character Extractor), a method for extracting printed Japanese characters and their character strings from all sorts of document images. COCE is based on a multi-agent system where each agent tries to find a character string and extracts the characters in it. For the adaptability, the agents are allowed to look after arbitrary parts of documents and extract the characters using only the knowledge independent of the layouts. Moreover, the agents check and correct their results sometimes with the help of the other agents. From experimental results, we have verified the effectiveness of our approach.

This paper describes advances in the study of handwritten Kanji character recognition mainly performed in Japan. The research focus has shifted from the investigation of the possibility of recognition by the stroke structure analysis method to the study of the feasibility of recognition by the feature matching methods. A great number of features and their extraction methods have been proposed according to this approach. On the other hand, studies on pattern matching methods of recognizing Kanji characters using the character pattern itself have been made. The research efforts based on these two approaches have led to the empirical fact that handwritten Kanji character recognition would become more effective by paying greater attention to the feature of directionality. Furthermore, in an effort to achieve recognition with higher precision, active research work has been carried out on pre-processing techniques, such as the forced reshaping of input pattern, the development of more effective features, and nonlinear flexible matching algorithms. In spite of these efforts, the current character recognition techniques represent only a skill of guessing characters" and are still on an insufficient technical level. Subsequent studies on character recognition must address the question of how to understand characters".

This paper compares some popular character recognition techniques which have been proposed until today. 17 feature extraction methods and 4 neural network based recognition processes were used in handwritten numerals (postal codes) recognition. It was found that Weighted Direction Index Histogram, Peripheral Direction Contributivity Function and Expansion Cell feature extractions gave good results. As for the neural network recognition process, CombNET- and multi layer neural network showed good performances.

Through comparing the results of two successive IPTP Character Recognition Competitions which focused on 3-digit handprinted postal codes, we herein analyze the methodologies of the submitted algorithms along with the substituted or rejected patterns of these algorithms. Regarding their methodologies, lesser diversity was apparent specifically concerning the contour-chain code based on local stroke directions and statistical discriminant functions for feature extraction and discrimination. Analysis of the patterns demonstrated that the misrecognized patterns being most often improved were categorized as a decrease in peculiarly shaped handwritten characters or heavy-handed and disconnected strokes. However, most of the remaining misrecognitions were still classed as peculiarly shaped handwriting as commonly shared between the best three algorithms. From these analyses, we could delineate a direction to be taken for developing more effective methodologies and clarify the remaining problems to be overcome by the subsequent intensive research. Furthermore, we evaluate in this article our multi-expert recognition system for achieving higher recognition performances by means of combining complementary recognition algorithms. We performed a subsequent investigation of the Candidate Appearance Likelihood Method using novel experimental conditions and a new examination of the application of the neural network as the combining method for accumulating the broader candidate appearances. The results obtained confirm that combining through the neural network constitutes one of the most effective ways of making the multi-expert recognition system a reality.

This paper consists of two parts. The first part is devoted to comparative study on handwritten ZIP code numeral recognition using seventeen typical feature vectors and seven statistical classifiers. This part is the counterpart of the sister paper Handwritten Postal Code Recognition by Neural Network - A Comparative Study" in this special issue. In the second part, a procedure for feature synthesis from the original feature vectors is studied. In order to reduce the dimensionality of the synthesized feature vector, the effect of the dimension reduction on classification accuracy is examined. The best synthesized feature vector of size 400 achieves remarkably higher recognition accuracy than any of the original feature vectors in recognition experiment using a large number of numeral samples collected from real postal ZIP codes.

Mathematical morphology is inheriently suitable for range image processing because it can deal with the shape of a function in a natural and intuitive way. In this paper, a new approach to the extraction of the corner-edge-surface structure from 3D range images is proposed. Morphological operations are utilized for segmenting range images into smooth surface regions and high-variation surface regions, where the high-variation surface regions are further segmented into regions of edge type and regions of corner type. A new 3D feature, HV-skeleton, can be extracted for each high-variation surface region. The HV-skeletons can be thought of as the skeletons of high-variation surface regions and are useful for feature matching. The 3D features extracted by our approach are invariant to 3D translations and rotations, and can be utilized for higher-level vision tasks such as registration and recognition. Experimental results show that the new 3D feature extraction method works well for both simple geometric objects and complex shaped objects such as human faces.

On a topographical map of civil engineering, there are various enclosed areas, for instance, rice fields, meadows, buildings, roads, walls, regional boundaries and so on. Before a software system such as road planer is run, it is necessary to extract these various regions and features, and to transform them to 3D data. The automatic extraction and classification of all of them on a screen are difficult and very time-consuming. It is better to combine the automatic recognition with interactive operation. It is obvious that interaction is easily done when vector date of maps is applied. On the other hand, the vector data is much less than the raster data of the same map. This paper proposes a practical solution for understanding of vector maps, including two major methoeds; ditching (DIrected Track stretCHING) method for open regions (e.g., roads, slopes and walls etc.) and inward tracing method for bounded regions (e.g., various fields).

This paper proposes a robust method for detecting step and ramp edges. In this method, an edge is defined not as a point where there is a large change in intensity, but as a region boundary based on the separability of image features which can be calculated by linear discriminant analysis. Based on this definition of an edge, its intensity can be obtained from the separability, which depends only on the shape of an edge. This characteristic enables easy selection of the optimum threshold value for the extraction of an edge, and this method can be applied to color and texture edge extraction. Experimental results have demonstrated that this proposed method is robust to noise and dulled edges, and, in addition, allows easy selection of the optimum threshold value.

In this paper we describa a system for Off-line Recognition of Arabic characters and Numerals. This is based on expressing the machine printed Arabic alpha-numerical text in terms of strokes obtained by MCR (Minimum Covering Run) expression. The strokes are rendered meaningful by a labeling process. They are used to detect the baseline and to provide necessary features for recognition. The features selected proved to be effective to the extent that with simple right to left analysis we could achieve interesting results. The recognition is achieved by matching to reference prototypes designed for the 28 Arabic characters and 10 numerals. The recognition rate is 97%.

An automatic transistor-level performance fault tracing method is proposed which is applicable to the case where only CAD layout data is available in the CAD-linked electron beam test system. The technique uses an integrated algorithm that combines a previously proposed transistor-level fault tracing algorithm and a successive circuit extraction from CAD layout data. An expansion of the algorithm to the fault tracing in a combined focused ion beam and electron beam test system which enables us to measure signals on the interconnections in the lower layers is also described. An application of the technique to a CMOS model layout with about 100 transistors shows its validity.

Active net is a deformable model which utilizes the network analogy of a physical region. In the model, the region of a target is detected by minimizing the energy defined for the sample points of the model. The region of the target is extracted using fixed network topology in the orginally proposed algorithm. In this paper, we introduce the network reconfiguration mechanisms such as tearing and division to realize multiple objects detection and complex object detecion. The introduced algorithm dynamically unlinks the arcs of the network when their strain value exceeds predefined threshold level. In the method, we propose a new image energy which improves the position sensitivity of edges without increasing computation cost. Experimental results for images taken by video camera show the validity of our approach.

This paper describes a parameter extraction system that can easily accommodate many MOSFET models. The model-adaptability is contributed by tow factors; a model-adaptable initial value estimation technique and an environment which stores and reuses extraction procedures. A designer can easily develop an extraction procedure for a new MOSFET model by modifying a procedure for another MOSFET model developed previously. We have verified that the system is suitable for major SPICE models.

This paper discusses an all-optical tank circuit that uses the comb-shaped gain spectrum generated by a Brillouin amplifier. The theory of timing clock extraction is shown for two cases: with two gains and with three gains. In both cases, the waveform of the extracted timing clock is simulated. According to the simulation, unlike an ordinary tank circuit, the amplitude of the extracted clock is not constant even though the quality factor (Q) is infinite. The extracted clock is clearly influenced by the pattern of the original data stream if the Brillouin gain is finite. The ratio of the maximum extracted clock amplitude to the minimum extracted amplitude is calculated as a function of Brillouin gain. The detuning of the pump light frequency is also discussed. It induces not only changes in the Brillouin gain, but also phase shift in the amplified light. The relation between the frequency drift of the pump lights and the jitter of the extracted timing clock is shown, in both cases: two pump lights are used and three pump lights are used. It is numerically shown that when the all pump lights have the same frequency drift, i.e., their frequency separation is constant, the phase of the extracted clock is not influenced by the frequency drift of the pump lights. The operation principle is demonstrated at 5Gbit/s, 2.5Gbit/s, and 2Gbit/s using two pumping techniques. The parameters of quality factor and the suppression ratio in the baseband domain are measured. Q and the suppression ratio are found to be 160 and 28dB, respectively.

This letter presents a new method to efficiently extract closed loops as primitive symbols in line drawings. Our method uses a graph search technique for efficiency and exhaustibility, and also incorporates feasibility criteria of symbols. Experiments clearly demonstrated the method's effectiveness.

This paper reviews very high-speed optical signal processing technology based on the instantaneous characteristic of optical nonlinearities. Focus is placed on 100-Gbit/s optical time-division multiplexing (TDM) transmission systems. The key technologies including ultrashort optical pulse generation, all-optical multiplexing/demultiplexing and optical timing extraction techniques are alse described together with their major issues and future prospects.

In this paper, we present a method for extracting the Japanese printed characters from unformatted document images. This research takes into account the multiple general features specific to the Japanese printed characters. In our method, these features are thought of as the constraints for the regions to be extracted within the constraint satisfaction approach. This is achieved by minimizing a constraint function estimating quantitative satisfaction of the features. Our method is applicable to all kinds of the Japanese documents because it is no need of a priori knowledge about the document layout. We have favorable experimental results for the effectiveness of this method.

In a hybrid coder which employs motion compensation and discrete cosine transform (MC-DCT coder), up to 90% of bits are used to represent the quantized DCT blocks. So it is most important to represent them with as few bits as possible. In this paper, we propose an efficient method for encoding the quantized DCT blocks of motion compensated prediction (MCP) errors, which adaptively selects one of a few scanning patterns. The scanning pattern selection of an MCP error block is based on the motion compensated images which are always available at the decoder as well as at the encoder. No overhead information for the scanning patterns needs to be transmitted. Simulation results show that the average bit rate reduction amounts to 5%.