The coming information society will require images at the high end of the quality range. By using a new method which focuses on the assessment words of the high order sensation, we are investigating the important physical factors for the difficult reproduction of high level, high quality sensation in the electronic capture and display of images. We have found a key assessment word "image depth" that describes appropriately the high order subjective sensation that is indispensable for the display of extra high quality images. Related to the depth of images, we have discovered a new physical factor and the degree of precision required of already known physical factors for the display of extra high quality images. The cross modulation among R, G and B signals is the newly discovered important physical factor affecting the quality of an electronic display. In addition, we have found that very strict control of distortion in the gamma and the step response and the strict suppression of the halation in a CRT display are necessary. We note that aliasing of the displayed images also destroys the sensation of depth. This paper first outlines the overall objective of our work, second considers the important physical factors as important for extra high quality imaging, and then describes the specific effects of cross modulation distortion, gamma, step response, halation and aliasing as they relate to image depth. Finally, the relation of the discussed physical factors with the high order sensation are discussed broadly.

This paper addresses the problem of determining the 3-D pose of a curved rigid object from a single 2-D image. The surface of the object are assumed to be modeled with several patches, each of which be expressed by an implicit polynomial. Moreover, the sensed data are assumed to be the coordinates of those points that are on the image contours. Based on the idea of contour matching, the algorithm proposed computes the parameters defining the pose of the object, and achieves the segmentation of the sensed data and the recognition of the object.

We introduce an image contour clustering method based on a multiscale image representation and its application to image compression. Multiscale gradient planes are obtained from the mean squared sum of 2D wavelet transform of an image. The decay on the multiscale gradient planes across scales depends on the Lipshitz exponent. Since the Lipshitz exponent indicates the spatial differentiability of an image, the multiscale gradient planes represent smoothness or sharpness around edges on image contours. We apply vector quatization to the multiscale gradient planes at contours, and cluster the contours in terms of represntative vectors in VQ. Since the multiscale gradient planes indicate the Lipshitz exponents, the image contours are clustered according to its gradients and Lipshitz exponents. Moreover, we present an image recovery algorithm to the multiscale gradient planes, and we achieve the skech-based image compression by the vector quantization on the multiscale gradient planes.

When moving images are displayed on color PDPs, motional artifacts such as disturbances of gray scales and colors are often observed. Reduction of the disturbances is essential in achieving PDPs with acceptable picture quality for TV use. The moving picture quality has been improved by using a modified-binary-coded light-emission-period scheme and a 3dimensional (2D in space and 1D in time) scattering technique. In the 10-sub-field modified-binary-code scheme for 256 gray level expression, sub-field B (of period equivalent to 64) and C (128) of conventional 8-sub-field binary-coded scheme are added and then re-distributed into four sub-fields D (48). The modifiedbinary-coded scheme therefore has the light-emitting-period ratio 1:2:4:8:16:32:48:48:48:48. The maximum period, 128 of the conventional, is reduced to 48. By using the modified-binary-coded scheme, the motional artifacts are reduced significantly, but still perceptible because they appear in forms of continuous lines. In order to make the disturbance less conspicuous, a 3D scattering technique is introduced. The technique has been made possible because of the redundancies of the modified-binary-coded scheme: namely, (1) the position of sub-field-block A (63) can be placed at one of the five positions among four sub-fields D (48), (2) there are various choices when newly assigning one of the four sub-fields D, (3) one can arbitrarily choose whether or not to assign a new sub-field D between the gray levels 48-63, 96-111, 144-160, and 192-207. By randomly selecting one of these emission patterns, the disturbances change their forms from continuous lines to scattered dots. The randomization can be performed at each horizontal line of the display, at each vertical line, at each pixel, of at each TV field. An appreciable improvement of moving picture quality has been realized without influencing the still image.

Unlike the time-consuming contour tracking method of snakes [5] which requires a considerable number of iterated computations before contours are successfully tracked down, we present a faster and accurate model-based landmarks" tracking method where a single iteration of the dynamic programming is sufficient to obtain a local minimum to an integral measure of the elastic and the image energy functionals. The key lies in choosing a relatively small number of salient land-marks", or features of objects, rather than their contours as a target of tracking within the image structure. The landmarks comprising singular points along the model contours are tracked down within the image structure all inside restricted search areas of 41 41 pixels whose respective locations in image structure are dictated by their locations in the model. A Manhattan distance and a template corner detection function of Singh and Shneier [7] are used as elastic energy and image energy respectively in the algorithm. A first approximation to the image contour is obtained in our method by applying the thin-plate spline transformation of Bookstein [2] using these landmarks as fixed points of the transformation which is capable of preserving a global shape information of the model including the relative configuration of landmarks and consequently surrounding contours of the model in the image structure. The actual image contours are further tracked down by applying an active edge tracker using now simplified line search segments so that individual differences persisting between the mapped model contour are substantially eliminated. We have applied our method tentatively to portraits of a class album to demonstrate the effectiveness of the method. Our experiments convincingly show that using only about 11 feature points our method provides not only a much improved computational complexity requiring only 0.94sec. in CPU time by SGI's indigo2 but also more accurate shape representations than those obtained by the snakes methods. The method is powerful in a problem domain where the model-based approach is applicable, possibly allowing real time processing because a most time consuming algorithm of corner template evaluation can be easily implemented by parallel processing firmware.

A method for the recognition of handprinted Thai characters input using an image scanner is presented. We use methods of edge detection and boundary contour tracing algorithms to extract loop structures from input characters. The number of loops and their locations are detected and used as information for rough classification. For fine classification, local feature analysis of Thai characters is presented to discriminate an output character from a group of similar characters. In this paper, four parts of the recognition system are presented: Preprocessing, single-character segmentation, loop structure extraction and character identification. Preprocessing consists of pattern binarization, noise reduction and slant normalization based on geometrical transformation for the forward (backward) slanted word. The method of single-character segmentation is applied during the recognition phase. Each character from an input word including the character line level information is subjected to the processes of edge detection, contour tracing and thinning to detect loop structures and to extract topological properties of strokes. The decision trees are constructed based on the obtained information about loops, end points of strokes and some local characteristics of Thai characters. The proposed system is implemented on a personal computer, and a high recognition rate is obtained for 1000 samples of handprinted Thai words from 20 subjects.

This paper addresses the problem of estimating 3-D motion of a rigid object from a sequence of monocular 2-D images. The surface of object is assumed to be modeled with several patches, each of which is expressed by an implicit equation. The proposed method estimates the pose (i.e., the location and orientation) of object that corresponds to each image in the sequence: The sequence of the estimated poses gives the motion of the object. The estimation is done by solving a system of equations, each of which is typically an algebraic equation of low degree, that is derived from the expressions of the surface patches and image contours data: so the method does not require establishing the correspondence between successive two frames in the image sequence or computing optic flow. Allowing several-patch models for objects enables the proposed approach to deal with a great variety of objects. The paper includes a numerical example, where our aproach has been applied to a polyhedral object modeled with several patches.

A method of tone recognition has been developed for dissyllabic speech of Standard Chinese based on discrete hidden Markov modeling. As for the feature parameters of recognition, combination of macroscopic and microscopic parameters of fundamental frequency contours was shown to give a better result as compared to the isolated use of each parameter. Speaker normalization was realized by introducing an offset to the fundamental frequency. In order to avoid recognition errors due to syllable segmentation, a scheme of concatenated learning was adopted for training hidden Markov models. Based on the observations of fundamental frequency contours of dissyllables, a scheme was introduced to the method, where a contour was represented with a series of three syllabic tone models, two for the first and the second syllables and one for the transition part around the syllabic boundary. Corresponding to the voiceless consonant of the second syllable, fundamental frequency contour of a dissyllable may include a part without fundamental frequencies. This part was linearly interpolated in the current method. To prove the validity of the proposed method, it was compared with other methods, such as representing all of the dissyllabic contours as the concatenation of two models, assigning a special code to the voiceless part, and so on. Tone sandhi was also taken into account by introducing two additional models for the half-third tone and for the first 4th tone of the combination of two 4th tones. With the proposed method, average recognition rate of 96% was achieved for 5 male and 5 female speakers.

The Paper describes a comprehensive system for image recognition based on the technique of boundary spline matching. It can be used to accurately compare two objects and determine whether they are identical or not. The result is extremely satisfactory for comparing planar objects as revealed from the illustrative example presented in this paper. In real practice, images of the same scene object can easily be considered as belonging to different objects if the objects are viewed from different orientations and ranges. Thus, image recognition calls for choosing the proper geometric transformation functions to match images as the initial step so that recognition by template matching can be done as the second step. However, there are a large variety of transformation functions available and the subsequent evaluation of transformation parameters is a highly nonlinear optimisation procedure which is both time consuming and not solution guaranteed, making real-time estimation impossible. This paper describes a new method that represents the boundary of each of two image objects by B-splines and matches the B-splines of two image objects to determine whether they belong to the same scene object. The algorithm developed in this paper concentrates on solving linear simultaneous equations only when handling the geometric transformation functions, which takes almost negligible computational time by using the standard Gaussian Elimination. Representation of the image boundary by B-splines provides a flexible and continuous matching environment so that the level of accuracy can be freely adjusted subject to the requirement of the user. The non-linear optimisation involves only one parameter, i.e. the starting point of each boundary under B-spline simulation, thus guaranteeing a very high speed computational system. The real time operation is deemed possible even there is a wide choice of proper transformation functions.

An active contour model which is called Snakes was proposed to extract the border line of an object from an image. This method presents the minimization problem of the energy function defined on the contour curve. The authors obtained an excellent result by applying genetic algorithm to the contour extraction. In this paper, the biphased genetic algorithm, which is a new type of genetic algorithm, is proposed to minimize the energy function of Snakes. The parameters of the genetic algorithm are examined to tune up its local and global search abilities. The biphased genetic algorithm composed of two phases of genetic search is constructed to use both abilities of the exploration and the exploitation properties of the genetic algorithm. The processing results of the biphased genetic algorithm are compared with those of the previous methods, and the advantages of the proposed algorithm are shown by several experiments.

This paper describes a new feature extraction model (Active Model) which is extended from the active contour model (Snakes). Active Model can be applied to various energy minimizing models since it integrates most of the energy terms ever proposed into one model and also provides the new terms for multiple images such as motion and stereo images. The computational order of energy minimization process is estimated in comparison with a dynamic programming method and a greedy algorithm, and it is shown that the energy minimization process in Active Model is faster than the others. Some experimental results are also shown.

This paper proposes a new, high-speed method of filling in the contours of alpha-numeric characters to produce correct binary image patterns. We call this method the improved edge-fill method because it improves on a previously developed edge-fill method. Ambiguity of the conventional edge-fill method on binary images are eliminated by selecting fill pixels from combinations of Freeman's chain code, which expresses contour lines. Consequently, the areas inside the contour lines are filled in rapidly and correctly. With the new method, the processing time for character image generation is reduced by ten to tewnty percent over the conventional method. The effectiveness of the new method is examined in experiments using both Arabic numerals and letters from the Roman alphabet. Results show that this fill method is able to produce correct image patterns and that it can be applied to alpha-numeric-character contour filling.

This paper presents a new line extraction method to capture vectors based on contours and skeletons from line drawing raster images in which the lines are touched by characters or other lines. Conventionally, two line extraction methods have generally been used. One is a thinning method. The other is a medial line extraction method based on parallel pairs of contours. The thinning method tends to distort the extracted lines, especially at intersections and corners. On the other hand, the medial line extraction method has a poor capability as regards capturing correct lines at intersections. Contours are able to maintain edge shapes well, while skeletons preserve topological features; thus, a combination of these features effectively leads to the best fitting line. In the proposed method, the line which best fits the original image is selected from among various candidate lines. The candidates are created from several merged short skeleton fragments located between pairs of short contour fragments. The method is also extended to circular arc fitting. Experimental results show that the proposed line fitting method is effective.

Prosodic features of the spoken Japanese play an important role in the transmission of linguistic information concerning the lexical word accent, the sentence structure and the discourse structure. In order to construct prosodic rules for synthesizing high-quality speech, therefore, prosodic features of speech should be quantitatively analyzed with respect to the linguistic information. With a special focus on the fundamental frequency contour, we first define four prosodic units for the spoken Japanese, viz., prosodic word, prosodic phrase, prosodic clause and prosodic sentence, based on a decomposition of the fundamental frequency contour using a functional model for the generation process. Syntactic units are also introduced which have rough correspondence to these prosodic units. The relationships between the linguistic information and the characteristics of the components of the fundamental frequency contour are then described on the basis of results obtained by the analysis of two sets of speech material. Analysis of weathercast and newscast sentences showed that prosodic boundaries given by the manner of continuation/termination of phrase components fall into three categories, and are primarily related to the syntactic boundaries. On the other hand, analysis of noun phrases with various combinations of word accent types, syntactic structures, and focal conditions, indicated that the magnitude and the shape of the accent components, which of course reflect the information concerning the lexical accent types of constituent words, are largely influenced by the focal structure. The results also indicated that there are cases where prosody fails to meet all the requirements presented by word accent, syntax and discourse.

A text-to-speech conversion system for Japanese has been developed for the purpose of producing high-quality speech output. This system consists of four processing stages: 1) linguistic processing, 2) phonological processing, 3) control parameter generation, and 4) speech waveform generation. Although the processing at the first stage is restricted to the texts on general weather conditions, the other three stages can also cope with texts of news and narrations on other topics. Since the prosodic features of speech are largely related to the linguistic information, such as word accent, syntactic structure and discourse structure, linguistic processing of a wider range than ever, at least a sentence, is indispensable to obtain good quality speech with respect to the prosody. From this point of view, input text was restricted to the weather forecast sentences and a method for linguistic processing was developed to conduct morpheme, syntactic and semantic analyses simultaneously. A quantitative model for generating fundamental frequency contours was adopted to make a good reflection of the linguistic information on the prosody of synthetic speech. A set of prosodic rules was constructed to generate prosodic symbols representing prosodic structures of the text from the linguistic information obtained at the first stage. A new speech synthesizer based on the terminal analog method was also developed to improve the segmental quality of synthetic speech. It consists of four paths of cascade connection of pole/zero filters and three waveform generators. The four paths are respectively used for the synthesis of vowels and vowel-like sounds, nasal murmur and buzz bar, friction, and plosion, while the three generators produce voicing source waveform approximated by polynomials, white Gaussian noise source for fricatives and impulse source for plosives. The validity of the approach above has been confirmed by the listening tests using speech synthesized by the developed system. Improvements both in the quality of prosodic features and in the quality of segmental features were realized for the synthetic speech.

We describe an approach for modelling a person's face for model-based coding. The goal is to estimate the 3D shape by combining the contour analysis and shading analysis of the human face image in order to increase the quality of the estimated 3D shape. The motivation for combining contour and shading cues comes from the observation that the shading cue leads to severe errors near the occluding boundary, while the occluding contour cue provides incomplete surface information in regions away from contours. Towards this, we use the deformable model as the common level of integration such that a higher-quality measurement will dominate the depth estimate. The feasibility of our approach is demonstrated using a real facial image.