Mitsuhiko Meguro

Skin-color extraction in images with complex background and varying illumination

A skin-color extraction algorithm is proposed to detect human faces in color images with varying illumination condition and presence of complex background. The approach is based on both a Gaussian mixture model of human skin-color distribution and image segmentation using all automatic and adaptive multi-thresholding technique. Detected regions are then refined by morphological operations. Experimental results on images presenting a wide range of variations in lighting condition, face orientation, scale, pose, facial expression and background, demonstrate the efficiency of our skin-segmentation algorithm. Using additional information about facial features, our method becomes an efficient step in localizing candidate faces for a face detection system.

Data-dependent weighted median filtering with robust motion information for image sequence restoration

In this study, we consider a filtering method for image sequence degraded by additive Gaussian noise and/or impulse noise (i.e., mined noise). In general, for the image sequence filtering, motion compensation (MC) method is required in order to obtain good filtering performance both in the still and moving regions of an image sequence. Nevertheless a heavy computation load is imposed on MC method and MC tends to get mistaken motion vector owing to additive noise. To overcome above drawbacks of MC, we have proposed a Video-Data Dependent Weighted Average (Video-DDWA) filter for image sequence restoration degraded by additive Gaussian noise. The Video-DDWA filter whose weights are controlled by some local information contain a motion information as a motion detector is shown that the motion information method is more effective tool than MC method for image sequence restoration. However Video-DDWA filter is not proper for removing the mixed noise. Therefore, we replace weighted average filters and a motion information of the Video-DDWA with weighted median filters and a mixed noise robust motion information, respectively. We propose this filter as a Video-Data Dependent Weighted Median (Video-DDWM) filter for removing mixed noise from image sequence. Through some simulations, the Video-DDWM filter is proven to be more effective both the restoration results and computation time than the 3D-DDWM filter with impulse robust MC for removing mixed noise from image sequence.

Simple color conversion method to perceptible images for color vision deficiencies

In this paper, we propose a color conversion method for realizing barrier free systems for color-defective vision. Human beings are perceiving colors by a ratio of reaction values by three kinds of cones on the retina. The three cones have different sensitivity to a wavelength of light. Nevertheless, dichromats, who are lacking of one of the three cones, tends to be diffcult for discriminating colors of a certain combination. The proposed techniques make new images by converting color for creating perceptible combination of color. The proposed method has three parts of processes. Firstly, we do image segmentation based on the color space L*a*b*. Secondly, we judge whether mean colors of divided regions of the segmented image tend to be confusion or not by using confusion color loci and color vision models of the persons with color-defective vision. Finally, the proposed technique realizes the perceptible images for dichromats by changing the confusion color in several regions of images. We show how effectiveness of the method by some application results.

Adaptive weighted median filters by using fuzzy techniques

In this paper, we propose adaptive weighted median (AWM) filters based on local statistics. We show two ways of realizing the AWM filters. One is a simple type of AWM filter, whose weights are given by a simple non-linear function of three local characteristics. The other is the AWM filter which is constructed by fuzzy rules (fuzzy weighted median: FWM filters). By using the rule-based fuzzy techniques, the better weights are easily derived. Experimental results show AWM filters can suppress nonimpulsive and impulsive noise, while preserving signal details.

Adaptive weighted median filter utilizing impulsive noise detection

The removal of noise in image is one of the current important issues. It is also useful as a preprocessing for edge detection, motion estimation and so on. In this paper, an adaptive weighted median filter utilizing impulsive noise detection is proposed for the removal of impulsive noise in digital images. The aim of our proposed method is to eliminate impulsive noise effectively preserving original fine detail in images. This aim is same for another median-type nonlinear filters try to realized. In our method, we use weighted median filter whose weights should be determined by balancing between the signal preserving ability and noise reduction performance. The trade off between these two inconsistent properties is realized using the noise detection mechanism and optimized adaptation process. In the previous work, threshold value between the signal and the output of the median filter have to be decided for the noise detection. Adaptive algorithm for optimizing WM filters uses the teacher image for training process. In our method, following two new approaches are introduced in the filtering. (1) The noise detection process uses the discriminant method to the histogram distribution of the derivation from median filter output. (2) Filter weights which have been learned by uncorrupted pixels and their neighborhood without the original image are used for the restoration filtering for noise corrupted pixels. The validity of the proposed method is shown through some experimental results.

Sharpening a noisy image by using fuzzy rules

Unsharp Masking (UM) is well known one of the most classical techniques in image enhancement. Due to the presence of the highpass filter, the UM operators very sensitive to noise. In order to conquer this defect, Ramponi has proposed the cubic UM operator which used not only highpass filtering but also edge sensor. By introducing edge sensor, edge enhancement is realized without noise amplification, to a certain extent. It is clear that the combination of edge sensors and highpass filter is effective for sharpening images corrupted by noise. In this paper, we introduce fuzzy rules in the UM operation. Fuzzy rules link the outputs of edge sensor and highpass filter to conclusions about sharpening component of processed image. We show how effectiveness of proposed method by some application results.

New design method of general weighted median filters admitting negative weights for enhancement of images degraded by additive noise

In this paper, we propose a new design method of general weighted median filters admitting negative weights for enhancement of images degraded by additive impulsive noise. The general weighted median (GWM) filters are already proposed as frequency selective nonlinear filters. Nevertheless, no one considers how to apply the GWM filters for enhancing degraded images. To enhance images degraded by additive noise, preferable frequency response is varied greatly with positions of window in images. Therefore, GWM filters with fixed weights are not preferable in image processing. Proposed method consists with three steps. At first, we divide block in sliding windows of filters into some number of classes according to difference of spectral characteristics. Second, we optimize some number of GWM filters to have proper frequency response in each class of block. At last, the GWM filters switched in each class are used for enhancement of images. To prepare the GWM filters in each class, the proposed filtering method is better than the GWM filters with fixed weights. Through some simulations, we show the above efficiency of the proposed filters comparing to the original WM filters and linear filters. The proposed method has the robustness for impulsive noise contamination and the frequency selective filtering property.

Forming Shared Attention between Robot and Users in Conversational Situations

The ability to interact with multiple users as well as to recognize the ways in which people are interacting is essential for a robot participating in society. Forming shared attention is regarded as fundamental in improving the flexibility of human-robot interaction. This paper proposes an algorithm that enables shared attention between a robot and speakers in alternative conversational situations. We firstly present a microphone array technique and a method to combine auditory and visual information for estimating the physical location of the sound source. Secondly, the speaker is detected according to the linear combination of the result of sound source localization and the likelihood map representing the distribution of pixels having skin color. Finally, in the conversational situations, the speakers focus of attention is alternatively detected and shared by the robot for upcoming communication based on the proposed algorithm. Several experimental results are presented that demonstrate the effectiveness of the proposed method.

Burst noise reduction of image by decimation and adaptive weighted median filter

The removal of noise in image is one of the important issues, and useful as a preprocessing for edge detection, motion estimation and so on. Recently, many studies on the nonlinear digital filter for impulsive noise reduction have been reported. The median filter, the representative of the nonlinear filters, is very effective for removing impulsive noise and preserving sharp edge. In some cases, burst (i.e., successive) impulsive noise is added to image, and this type of noise is difficult to remove by using the median filter. In this paper, we propose an Adaptive Weighted Median (AWM) filter with Decimation (AWM-D filter) for burst noise reduction. This method can also be applied to recover large destructive regions, such as blotch and scratch. The proposed filter is an extension of the Decimated Median (DM) filter, which is useful for reducing successive impulsive noise. The DM filter can split long impulsive noise sequences into short ones, and remove burst noise in spite of the short filter window. Nevertheless, the DM filter also has two disadvantages. One is that the signals without added noise is unnecessary filtered. The other is that the position information in the window is not considered in the weight determinative process, as common in the median type filter. To improve detail-preserving property of the DM filter, we use the noise detection procedure and the AWM-D filter, which can be tuned by Least Mean Absolute (LMA) algorithm. The AWM-D filter preserves details more precisely than the median-type filter, because the AWM-D filter has the weights that can control the filter output. Through some simulations, the higher performance of the proposed filter is shown compared with the simple median, the WM filter, and the DM filter.

Data-dependent weighted median filtering with motion information for image sequence restoration

In this study, we consider a filtering method for image sequence degraded by additive Gaussian noise and/or impulse noise. In general, for the image sequence filtering, motion compensation (MC) method is required in order to obtain good filtering performance both in the still and moving regions of an image sequence. Nevertheless, a heavy computation load is imposed on MC method and MC tends to get mistaken motion vector owing to additive noise. To overcome above drawbacks of MC, we propose a Video-DDWM filter. The Video-DDWM filter is derived by the following 2 steps. In the first step, 2D-data- dependent weighted median (DDWM) filter, whose all weights are decided by local information is extend to 3D-DDWM filter. In the second step, a motion information as the motion detector with robustness for eliminating impulse noise is taken into the 3D-DDWM filter. In addition to less computational load than the 3D-DDWM filtering with MC, Video-DDWM filtering gives better image sequence restoration results.