ByungIn Yoo

Bit Rate Reduction Algorithm For A Digital VCR

The authors describe a bit rate reduction algorithm for use in digital VCRs, based on block scrambling and the two-dimensional adaptive discrete cosine transform. This method satisfies many of the demands unique to digital VCRs. Before the encoding process, the input image is divided into 8*8 pixel blocks and then scrambled on a block-by-block basis to achieve efficient bit distribution in a frame and to simplify buffer control strategy. In addition to block scrambling, a simple yet efficient variable length coding method is proposed. Simulation results are presented. >

3D user interface combining gaze and hand gestures for large-scale display

In this paper, we present a novel attentive and immersive user interface based on gaze and hand gestures for interactive large-scale displays. The combination of gaze and hand gestures provide more interesting and immersive ways to manipulate 3D information.

3D remote interface for smart displays

The paper presents a novel user interface combining bare hands and the line of sight (LoS) by using a depth camera from far distance without any handheld devices; as well as a 3D GUI providing both stereoscopy and motion parallax for smart displays. The proposed user interface provides a precise and convenient manipulation which is applicable to browsing thousands of channels andor media files. Especially, the combined interaction methods of the two modalities achieve 120(x) × 70(y) × 5(z) manipulation resolution. And then various user tasks were performed so as to assess the proposed user interface.

The seamless browser: enhancing the speed of web browsing by zooming and preview thumbnails

In this paper, we present a new web browsing system, Seamless Browser, for fast link traversal on a large screen like TV In navigating web, users mainly suffer from cognitive overhead of determining whether or not to follow links. This overhead can be reduced by providing preview information of the destination of links, and also by providing semantic cues on the nearest location in relation to the anchor. In order to reduce disorientation and annoyance from the preview information, we propose that users will focus on the small area nearside around a pointer, and a small number of hyperlink previews in that focused area will appear differently depending on the distances between the pointer and the hyperlinks: the nearer the distance is, the richer the content of the information scent is. We also propose that users can navigate the link paths by controlling the pointer and the zooming interface, so that users may go backward and forward seamlessly along several possible link paths. We found that combining the pointer and a zoom significantly improved performance for navigational tasks.

Deep neural network using color and synthesized three-dimensional shape for face recognition

We present an approach for face recognition using synthesized three-dimensional (3-D) shape information together with two-dimensional (2-D) color in a deep convolutional neural network (DCNN). As 3-D facial shape is hardly affected by the extrinsic 2-D texture changes caused by illumination, make-up, and occlusions, it could provide more reliable complementary features in harmony with the 2-D color feature in face recognition. Unlike other approaches that use 3-D shape information with the help of an additional depth sensor, our approach generates a personalized 3-D face model by using only face landmarks in the 2-D input image. Using the personalized 3-D face model, we generate a frontalized 2-D color facial image as well as 3-D facial images (e.g., a depth image and a normal image). In our DCNN, we first feed 2-D and 3-D facial images into independent convolutional layers, where the low-level kernels are successfully learned according to their own characteristics. Then, we merge them and feed into higher-level layers under a single deep neural network. Our proposed approach is evaluated with labeled faces in the wild dataset and the results show that the error rate of the verification rate at false acceptance rate 1% is improved by up to 32.1% compared with the baseline where only a 2-D color image is used.

HDO: A novel local image descriptor

This paper presents a simple, yet powerful local image descriptor, called the histograms of dominant orientations (HDO). The HDO consists of two components, namely the dominant orientation and its coherence, which represents how intensively gradients in the local region are distributed along the dominant orientation. For a given image patch, we incorporate these two components into a 1-D histogram and define it as our HDO descriptor. Compared to previous approaches suffering from the presence of clutters and significant distortions, our HDO descriptor has a great ability to preserve the underlying image structure, and it can thus be successfully applied to various applications (e.g., object detection). The proposed method has been extensively tested on several challenging data sets and results show that our HDO descriptor is effective for object detection in images.

Novel LCDs with IR-sensitive backlights

— In this paper, a novel multi-touch LCD architecture with hover-sensing capability is described. To detect multiple touch points and hover points simultaneously, a sensitive backlight, which is a backlight integrated with an IR sensor array, is introduced. The sensitive backlight uses visible light to display contents on a display screen and is also used to detect reflected IR light from objects on or near the display screen. The captured image from the sensitive backlight is used to extract touch and hover information. The proposed display architecture maintains the slim form factor of an LCD with no loss of display quality, while making it possible to sense multiple touches and hovers simultaneously.

Randomized decision bush: Combining global shape parameters and local scalable descriptors for human body parts recognition

This paper presents a novel method which combines global shape parameters and scalable local descriptors for accurate body parts recognition from a single depth image in real-time. Human poses are of extremely large variation in aspects of visual shapes, because human can take poses from daily activities to gymnastic actions. In order to cover wide-range of the human poses, the proposed algorithm employs a unified structure which combines pose clustering and body parts classification. We name the proposed method Randomized Decision Bush (RDB). Specifically, global shape parameters which can discriminate coarse level shapes are utilized for pose clustering while scalable local shape descriptors are employed for accurate classification. RDB splits the various human poses into multiple clusters which contain similar shapes of the poses. As a result, it provides robust clustering which enables fine level classification within the cluster. The experimental results show improvements on recognizing body parts due to the pose clustering and classification with scalable local descriptors. Additionally, we significantly reduce the complexity of training a large number of human shapes.

Real-time 3D human pose recognition from reconstructed volume via voxel classifiers

This paper presents a human pose recognition method which simultaneously reconstructs a human volume based on ensemble of voxel classifiers from a single depth image in real-time. The human pose recognition is a difficult task since a single depth camera can capture only visible surfaces of a human body. In order to recognize invisible (self-occluded) surfaces of a human body, the proposed algorithm employs voxel classifiers trained with multi-layered synthetic voxels. Specifically, ray-casting onto a volumetric human model generates a synthetic voxel, where voxel consists of a 3D position and ID corresponding to the body part. The synthesized volumetric data which contain both visible and invisible body voxels are utilized to train the voxel classifiers. As a result, the voxel classifiers not only identify the visible voxels but also reconstruct the 3D positions and the IDs of the invisible voxels. The experimental results show improved performance on estimating the human poses due to the capability of inferring the invisible human body voxels. It is expected that the proposed algorithm can be applied to many fields such as telepresence, gaming, virtual fitting, wellness business, and real 3D contents control on real 3D displays.

Real-time hand shape recognition by orientation invariant data learning for smart TV

The paper proposes a novel recognition system for hand shapes at a distance for smart TV. Two types of hand shapes are selected for the needs of TV browsing. The proposed method provides robust recognition performance on various hand orientations and guarantees real-time computation.