Hyoseok Hwang

Bio: Hyoseok Hwang is an academic researcher from Samsung. The author has contributed to research in topics: Stereo display & Feature (computer vision). The author has an hindex of 10, co-authored 41 publications receiving 1324 citations. Previous affiliations of Hyoseok Hwang include KAIST & Gachon University.

Adaptive median filters: new algorithms and results

Abstract: Based on two types of image models corrupted by impulse noise, we propose two new algorithms for adaptive median filters. They have variable window size for removal of impulses while preserving sharpness. The first one, called the ranked-order based adaptive median filter (RAMF), is based on a test for the presence of impulses in the center pixel itself followed by a test for the presence of residual impulses in the median filter output. The second one, called the impulse size based adaptive median filter (SAMF), is based on the detection of the size of the impulse noise. It is shown that the RAMF is superior to the nonlinear mean L/sub p/ filter in removing positive and negative impulses while simultaneously preserving sharpness; the SAMF is superior to Lin's (1988) adaptive scheme because it is simpler with better performance in removing the high density impulsive noise as well as nonimpulsive noise and in preserving the fine details. Simulations on standard images confirm that these algorithms are superior to standard median filters. >

Spatio-Temporal Representation of an Electoencephalogram for Emotion Recognition Using a Three-Dimensional Convolutional Neural Network

Abstract: Emotion recognition plays an important role in the field of human-computer interaction (HCI) An electroencephalogram (EEG) is widely used to estimate human emotion owing to its convenience and mobility Deep neural network (DNN) approaches using an EEG for emotion recognition have recently shown remarkable improvement in terms of their recognition accuracy However, most studies in this field still require a separate process for extracting handcrafted features despite the ability of a DNN to extract meaningful features by itself In this paper, we propose a novel method for recognizing an emotion based on the use of three-dimensional convolutional neural networks (3D CNNs), with an efficient representation of the spatio-temporal representations of EEG signals First, we spatially reconstruct raw EEG signals represented as stacks of one-dimensional (1D) time series data to two-dimensional (2D) EEG frames according to the original electrode position We then represent a 3D EEG stream by concatenating the 2D EEG frames to the time axis These 3D reconstructions of the raw EEG signals can be efficiently combined with 3D CNNs, which have shown a remarkable feature representation from spatio-temporal data Herein, we demonstrate the accuracy of the emotional classification of the proposed method through extensive experiments on the DEAP (a Dataset for Emotion Analysis using EEG, Physiological, and video signals) dataset Experimental results show that the proposed method achieves a classification accuracy of 9911%, 9974%, and 9973% in the binary classification of valence and arousal, and, in four-class classification, respectively We investigate the spatio-temporal effectiveness of the proposed method by comparing it to several types of input methods with 2D/3D CNN We then verify the best performing shape of both the kernel and input data experimentally We verify that an efficient representation of an EEG and a network that fully takes advantage of the data characteristics can outperform methods that apply handcrafted features

Surgical robots and control methods thereof

Abstract: A surgical robot may include: an image information acquisition unit configured to acquire image information of an intra-abdominal environment while the surgical robot performs a surgical operation; and/or a controller configured to recognize positions of an endoscope and a tool, mounted on the surgical robot, based on the acquired image information and kinematic information of links included in the endoscope and the tool. A surgical robot may include: an image information acquisition unit configured to acquire image information of an intra-abdominal environment while the surgical robot performs a surgical operation; an inertia measurement unit configured to acquire inertia measurement information of the surgical robot; and/or a controller configured to recognize positions of an endoscope and a tool, mounted on the surgical robot, based on the acquired image information and the inertia measurement information.

Flat Panel Light-Field 3-D Display: Concept, Design, Rendering, and Calibration

Abstract: Recent autostereoscopic 3-D (A3D) displays suffer from many limitations such as narrow viewing angle, low resolution, and shallow depth effects. As these limitations mainly originate from the insufficiency of pixel resources, it is not easy to obtain a feasible solution that can solve all the limitations simultaneously. In many cases, it will be better to find a good compromising design. Generally, the multiview display and the integral imaging display are the representative designs of A3D. However, as they are too canonical and lack flexibility in design, they tend to be a tradeoff. To address these design issues, we have analyzed the multiview display and the integral image display in a light-field coordinate and developed a 3-D display design framework in a light-field space. The developed framework does not use the “view” concept anymore. Instead, it considers the spatial distribution of rays of the 3-D display and provides more flexible and sophisticated design methods. In this paper, the developed design method is explained using a new pixel value assigning algorithm, called the light-field rendering, and vision-based parameter calibration methods for 3-D displays. We have also analyzed the blur effects caused by the depth and display characteristics. By implementing the proposed method, we have designed a 65-in 96-view display with a 4K panel. The developed prototype has showed almost seamless parallax with a high-resolution comparable to the conventional four to five views displays. This paper will be useful to readers interested in A3D displays, especially in the multiview and the integral imaging displays.

Method of Separating Object in Three Dimensional Point Cloud

Abstract: A method of separating an object in a three dimensional point cloud including acquiring a three dimensional point cloud image of an object using an image acquirer, eliminating an outlier from the three dimensional point cloud image using a controller, eliminating a plane surface area from the three dimensional point cloud image, of which the outlier has been eliminated using the controller, and clustering points of an individual object from the three dimensional point cloud image, of which the plane surface area has been eliminated using the controller.

Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization

Abstract: This paper proposes a two-phase scheme for removing salt-and-pepper impulse noise. In the first phase, an adaptive median filter is used to identify pixels which are likely to be contaminated by noise (noise candidates). In the second phase, the image is restored using a specialized regularization method that applies only to those selected noise candidates. In terms of edge preservation and noise suppression, our restored images show a significant improvement compared to those restored by using just nonlinear filters or regularization methods only. Our scheme can remove salt-and-pepper-noise with a noise level as high as 90%.

A switching median filter with boundary discriminative noise detection for extremely corrupted images

Abstract: A novel switching median filter incorporating with a powerful impulse noise detection method, called the boundary discriminative noise detection (BDND), is proposed in this paper for effectively denoising extremely corrupted images. To determine whether the current pixel is corrupted, the proposed BDND algorithm first classifies the pixels of a localized window, centering on the current pixel, into three groups-lower intensity impulse noise, uncorrupted pixels, and higher intensity impulse noise. The center pixel will then be considered as "uncorrupted," provided that it belongs to the "uncorrupted" pixel group, or "corrupted." For that, two boundaries that discriminate these three groups require to be accurately determined for yielding a very high noise detection accuracy-in our case, achieving zero miss-detection rate while maintaining a fairly low false-alarm rate, even up to 70% noise corruption. Four noise models are considered for performance evaluation. Extensive simulation results conducted on both monochrome and color images under a wide range (from 10% to 90%) of noise corruption clearly show that our proposed switching median filter substantially outperforms all existing median-based filters, in terms of suppressing impulse noise while preserving image details, and yet, the proposed BDND is algorithmically simple, suitable for real-time implementation and application.

Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median Filter

Abstract: A modified decision based unsymmetrical trimmed median filter algorithm for the restoration of gray scale, and color images that are highly corrupted by salt and pepper noise is proposed in this paper. The proposed algorithm replaces the noisy pixel by trimmed median value when other pixel values, 0's and 255's are present in the selected window and when all the pixel values are 0's and 255's then the noise pixel is replaced by mean value of all the elements present in the selected window. This proposed algorithm shows better results than the Standard Median Filter (MF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), and Progressive Switched Median Filter (PSMF). The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF).

Robust video denoising using low rank matrix completion

Abstract: Most existing video denoising algorithms assume a single statistical model of image noise, e.g. additive Gaussian white noise, which often is violated in practice. In this paper, we present a new patch-based video denoising algorithm capable of removing serious mixed noise from the video data. By grouping similar patches in both spatial and temporal domain, we formulate the problem of removing mixed noise as a low-rank matrix completion problem, which leads to a denoising scheme without strong assumptions on the statistical properties of noise. The resulting nuclear norm related minimization problem can be efficiently solved by many recently developed methods. The robustness and effectiveness of our proposed denoising algorithm on removing mixed noise, e.g. heavy Gaussian noise mixed with impulsive noise, is validated in the experiments and our proposed approach compares favorably against some existing video denoising algorithms.

A New Directional Weighted Median Filter for Removal of Random-Valued Impulse Noise

Abstract: The known median-based denoising methods tend to work well for restoring the images corrupted by random-valued impulse noise with low noise level but poorly for highly corrupted images. This letter proposes a new impulse detector, which is based on the differences between the current pixel and its neighbors aligned with four main directions. Then, we combine it with the weighted median filter to get a new directional weighted median (DWM) filter. Extensive simulations show that the proposed filter not only can provide better performance of suppressing impulse with high noise level but can preserve more detail features, even thin lines. As extended to restoring corrupted color images, this filter also performs very well