Hideo Kitajima

A genetic algorithm for determining multiple routes and its applications

This paper presents a genetic algorithm approach for routing applications. The genetic algorithm produces many possible solutions in its search process. By utilizing this feature, our method determines both the shortest route and multiple semi-shortest routes in one search. Existing methods, such as Dijkstras algorithm, search only for the shortest route, and cannot determine any other short routes. Therefore the proposed algorithm is useful for this kind of routing. Furthermore, the method can determine the routes which are short and not similar depending on geographical conditions since the genotype structure of our method has a weighting factor which controls route length.

A soccer field tracking method with wire frame model from TV images

This paper proposes a tracking method of soccer field area in a soccer video captured from TV. The camera taking the soccer game video is controlled by three parameters: its mount position, the angle, and the magnification. In order to estimate these three parameters, the proposed method designs a wire frame model, which represents the official layout of the soccer field lines; and by the wire frame model matching with the field area in the video, the above three parameters can be estimated, even if the soccer video includes the camera panning, zooming, etc. By using the estimated parameters, we can accurately obtain where the field area corresponds in the actual soccer field. Some experiments in tracking the field area in actual soccer videos are performed and their results verify the high performance of the proposed method.

An ARMA order selection method with fuzzy reasoning

A fuzzy reasoning based approach for ARMA order selection is discussed in this communication. The proposed method attempts to select the optimal ARMA order of a time-varying ARMA model. This method improves model validity-criterion based order selection, such as the Akaikes information criterion and the minimum description length, by applying a fuzzy recursive reasoning method and a fuzzy c-means clustering method.

Fractal interpolation for natural images

This paper proposes a fractal interpolation for natural images. Generally, linear interpolation and spline interpolation are used for image interpolation. However, an image interpolated by the above conventional methods loses some high-frequency components of an original image. The loss of components lowers fidelity of the interpolated images. Since the proposed method reduces the loss, an interpolated image generated by the proposed method has higher fidelity than the one generated by the conventional method. The reduction of the loss is realized by using the fractional Brownian motion (FBM) in a process of the interpolation. The proposed method uses a characteristic that the fractal dimension is strongly correlated with a sense of roughness.

An accurate noise detector for image restoration

This paper proposes a new noise-detection method for restoration of images corrupted by impulsive noise. The proposed method consists of two stages. In the first stage, the pixels classified according to a new flag image are processed by different noise detectors. They are realized by using two median filters with different sizes of windows. In the second stage, each pixel once detected as an impulse in the first stage is verified by using a new system. According to the above stages, the proposed method can accurately detect the location of the impulsive noise and be effectively used as a preprocessor for noise reduction filtering. Experiments show that the proposed method can effectively detect impulsive noises in noisy images even when they are very highly corrupted.

Fast line extraction from digital images using line segments

This paper presents a fast line extraction method using the line segments found in digital images. A digital line can be decomposed into line segments, which consist of continuous edge pixels, in four directions. The directions of the line segments are varied and limited by the relationship between the line segments and the slopes of analog lines. The proposed method attains high speed and accuracy by tracking each line segment in the same direction which comes from the relationship. Experimental results are included to show that the proposed method can achieve high accuracy with a large reduction in the computation time and has robustness in the presence of noise.

Audio signal segmentation and classification for scene-cut detection

A scene is regarded as a basic unit of audiovisual material, and thereby the boundaries between two adjacent scenes, which are called scene-cuts, must be detected in advance for audiovisual indexing. This paper proposes a scene-cut detection method. Since scene-cuts are associated with a simultaneous change of visual and audio characteristics, both audio and visual analyses are required for the scene-cut detection. For the audio signal analysis, the proposed method utilizes an audio signal segmentation and classification method using fuzzy c-means clustering, which has been proposed by the authors. For the visual signal analysis, the proposed method utilizes some visual segmentation methods. By using these methods simultaneously, the proposed method can accurately detect the scene-cuts, and thereby it is highly valuable for the preprocessing for audiovisual indexing. Experimental results performed by applying the proposed method to real audiovisual material are shown to verify its high performance.

A genetic-algorithm based quantization method for fractal image coding

This paper proposes a high-accuracy quantization method for IFS parameters in fractal image coding by using a genetic algorithm (GA). The development of IFS-parameter quantization techniques is significant for the image coding because its errors make more serious problems in the iteration procedures than the other quantization topics. Even if the errors are small, high-quality reconstructed images are not necessarily obtained. Therefore, the high-accuracy quantization methods are required for the parameters. The proposed method provides higher quality reconstructed images than a conventional method which merely minimizes the errors.

Restoration method of missing areas in still images using GMRF model

This paper proposes a GMRF (Gaussian Markov random field)-model based restoration method of missing areas in still images. The GMRF model used in the proposed method is realized by a new assumption that reasonably holds for an image source. This model can express important image features such as edges because of the use of the new assumption. Therefore, the proposed method restores the missing areas using the modified GMRF model and can correctly reconstruct the missing edges. Consequently, the proposed method achieves more accurate restoration than those of the traditional methods on both objective and subjective measures. Extensive experimental results demonstrate the improvement of the proposed method over previous methods.

Accurate audio-segment classification using feature extraction matrix

This paper proposes an accurate audio signal classification method using a feature extraction matrix. The proposed method classifies the segments of the audio signal into the following five audio classes: silence, speech, music, speech with music background, and speech with noise background. In this classification, a diagonal matrix, which is called the feature extraction matrix, is utilized in order to extract the effective audio features for the classification. By using this feature extraction matrix, the five audio classes are clearly separated each other in the feature space, and thereby highly precise classification can be attained. Experimental results performed by applying the proposed method to real audio signals are shown to verify its high performance.