Sung-Il Joo

Real-Time Depth-Based Hand Detection and Tracking

This paper illustrates the hand detection and tracking method that operates in real time on depth data. To detect a hand region, we propose the classifier that combines a boosting and a cascade structure. The classifier uses the features of depth-difference at the stage of detection as well as learning. The features of each candidate segment are to be computed by subtracting the averages of depth values of subblocks from the central depth value of the segment. The features are selectively employed according to their discriminating power when constructing the classifier. To predict a hand region in a successive frame, a seed point in the next frame is to be determined. Starting from the seed point, a region growing scheme is applied to obtain a hand region. To determine the central point of a hand, we propose the so-called Depth Adaptive Mean Shift algorithm. DAM-Shift is a variant of CAM-Shift (Bradski, 1998), where the size of the search disk varies according to the depth of a hand. We have evaluated the proposed hand detection and tracking algorithm by comparing it against the existing AdaBoost (Friedman et al., 2000) qualitatively and quantitatively. We have analyzed the tracking accuracy through performance tests in various situations.

Real-time Hand Region Detection and Tracking using Depth Information

In this paper, we propose a real-time approach for detecting and tracking a hand region by analyzing depth images. We build a hand model in advance. The model has the shape information of a hand. The detecting process extracts out moving areas in an image, which are possibly caused by moving a hand in front of a camera. The moving areas can be identified by analyzing accumulated difference images and applying the region growing technique. The extracted moving areas are compared against a hand model to get justified as a hand region. The tracking process keeps the track of center points of hand regions of successive frames. For this purpose, it involves three steps. The first step is to determine a seed point that is the closest point to the center point of a previous frame. The second step is to perform region growing to form a candidate region of a hand. The third step is to determine the center point of a hand to be tracked. This point is searched by the mean-shift algorithm within a confined area whose size varies adaptively according to the depth information. To verify the effectiveness of our approach, we have evaluated the performance of our approach while changing the shape and position of a hand as well as the velocity of hand movement.

ASM-Based Objectionable Image Detection in Social Network Services

This paper presents a method for detecting harmful images using an active shape model (ASM) in social network services (SNS). For this purpose, our method first learns the shape of a womans breast lines through principal component analysis and alignment, as well as the distribution of the intensity values of the corresponding control points. This method then finds actual breast lines with a learned shape and the pixel distribution. In this paper, to accurately select the initial positions of the ASM, we attempt to extract its parameter values for the scale, rotation, and translation. To obtain this information, we search for the location of the nipple areas and extract the location of the candidate breast lines by radiating in all directions from each nipple position. We then locate the mean shape of the ASM by finding the scale and rotation values with the extracted breast lines. Subsequently, we repeat the matching process of the ASM until saturation is reached. Finally, we determine objectionable images by calculating the average distance between each control point in a converged shape and a candidate breast line.

Detection of Pavement Region with Structural Patterns through Adaptive Multi-Seed Region Growing

In this paper, we propose an adaptive pavement region detection method that is robust to changes of structural patterns in a natural scene. In order to segment out a pavement reliably, we propose two step approaches. We first detect the borderline of a pavement and separate out the candidate region of a pavement using VRays. The VRays are straight lines starting from a vanishing point. They split out the candidate region that includes the pavement in a radial shape. Once the candidate region is found, we next employ the adaptive multi-seed region growing(A-MSRG) method within the candidate region. The A-MSRG method segments out the pavement region very accurately by growing seed regions. The number of seed regions are to be determined adaptively depending on the encountered situation. We prove the effectiveness of our approach by comparing its performance against the performances of seed region growing(SRG) approach and multi-seed region growing(MSRG) approach in terms of the false detection rate.

Using Depth Information for Real-Time Face Detection

This paper proposes a method for real-time face detection. The proposed method is to use depth information to detect faces in a manner that performs robustly even in response to changes of lighting and face sizes. And we use the depth difference features in depth image and apply this to the boosting algorithm for training and recognition. The depth difference features that this study proposes to use enable dynamic changes to be made to the window according to the face size. The conventional method of face detection was to define the size of the face to be detected in advance, and complete scanning for all the possible sizes. By contrast, this new method requires only a single scan to detect faces of all sizes, since it uses the central depth value to predict size based on 2nd polynomial model. These detected depth difference features are performed training and recognition step with boosting algorithm[1]. The boosting classifier performs recognition by connecting strong classifiers that are constituted by weak classifiers.

A novel method of artificial caption detection in videos using temporal and spatial information

The majority of the artificial captions in videos include semantic information related to the video. Most of the preceding studies on caption detection seeking to extract such semantic information relied on spatial information in still images and used temporal information in videos. By contrast, this study proposes a method of detecting the artificial caption region in videos using both temporal and spatial information simultaneously. This method broadly proceeds in two stages. Firstly, an improved text appearance map is generated to detect the caption candidate region, and the continuous candidate region is detected through the process of candidate region matching. Secondly, a disappearance test is conducted on the detected continuous candidate region to determine whether the caption disappears, and if the caption disappears, the caption candidate region is determined through a merging process based on temporal and spatial information. The experiment is conducted to demonstrate the efficiency of the proposed method for region detection in videos that include captions in a variety of sizes, formats and positions.

Adaptive real-time road detection using VRay and A-MSRG in complex environments

This paper proposes an adaptive detection method for detecting road regions that have ambiguous boundaries within natural images. The proposed method achieves reliable partitioning of the road region within a natural environment where noise is present through the following two stages. In the first stage, we separate out candidate regions of the road by detecting the road’s boundary through the Radial region split method using VRay(Vanishing point-constrained ray). In the second stage, we apply so called Adaptive-Multiple Seed Region Growing(A-MSRG) approach into the separated candidate region in order to identify the road region in real time. The A-MSRG is an enhanced version of the Seed Region Growing(SRG). For performance evaluation, this study assessed efficiency based on the results of region detection achieved through the proposed combination of the Radial region split method and A-MSRG. We also conducted comparisons against the existing SRG and MSRG methods to confirm the validity of the proposed method.

Detection of Artificial Caption using Temporal and Spatial Information in Video

The artificial captions appearing in videos include information that relates to the videos. In order to obtain the information carried by captions, many methods for caption extraction from videos have been studied. Most traditional methods of detecting caption region have used one frame. However video include not only spatial information but also temporal information. So we propose a method of detection caption region using temporal and spatial information. First, we make improved Text-Appearance-Map and detect continuous candidate regions through matching between candidate-regions. Second, we detect disappearing captions using disappearance test in candidate regions. In case of captions disappear, the caption regions are decided by a merging process which use temporal and spatial information. Final, we decide final caption regions through ANNs using edge direction histograms for verification. Our proposed method was experienced on many kinds of captions with a variety of sizes, shapes, positions and the experiment result was evaluated through Recall and Precision.