Jaekyoung Moon

A Region of Interest Based Image Segmentation Method using a Biologically Motivated Selective Attention Model

We propose a new method for a region of interest (ROI) based image segmentation that uses biologically motivated selective attention model. One of the most important issues in image segmentation based on a region of interest (ROI) is how to decide upon a semantic object region according to a specific purpose. The proposed saliency map model in conjunction with a top-down Fuzzy adaptive resonance theory (ART) model for human interaction can generate a scan path that contains a plausible area in a natural scene. In order to extract an interesting region generated by the saliency map model, we propose a new region of interest (ROI) extraction algorithm using scale salient information and multiple features such as a intensity, edge, R+G-, and B+Y-color to reflect more exact salient regions. Computer experimental results show that the proposed model can successfully segment an ROI boundary in natural scenes and computer graphics.

Biologically Motivated Face Selective Attention System

In this paper, we propose a biologically motivated face preference selective attention system to identify a face within complex natural scenes. In order to localize a face in natural scenes, we have developed a task-specific selective attention model which integrates the conventional bottom-up saliency map with punishment and rewarding functions, with top-down attention and bias signals, according to a given task. The color-filtered intensity, color opponent, and edge of the winner color opponent features are intensified for the biasing of skin color in order to identify a face. Computer experimental results have shown that the proposed model successfully identifies multiple faces within a complex environment. In addition, we have implemented a robot vision system which will be used for an autonomous mental development system.

Online Multi-view Range Image Registration using Geometric and Photometric Feature Tracking

An on-line registration technique is presented to register multi-view range images for the 3D reconstruction of real objects. Using a range camera, we first acquire range images and photometric images continuously. In the range images, we divide object and background regions using a predefined threshold value. For the coarse registration of the range images, the centroid of the images are used. After refining the registration of range images using a projection-based technique, we use a modified KLT(Kanade-Lucas-Tomasi) tracker to match photometric features in the object images. Using the modified KLT tracker, we can track image features fast and accurately. If a range image fails to register, we acquire new range images and try to register them continuously until the registration process resumes. After enough range images are registered, they are integrated into a 3D model in offline step. Experimental results and error analysis show that the proposed method can be used to reconstruct 3D model very fast and accurately.

An automotive detector using biologically motivated selective attention model for a blind spot monitor

The conventional side-view and rear-view mirrors are not enough for drivers safety in an automobile. A driver may not be able to recognize the vehicle in a blind spot. In this paper, we propose an automotive detector algorithm using biologically motivated selective attention model for a blind spot monitor. This method decides a region of interest (ROI) which includes the blind spot from the successive image frames obtained by side-view cameras. It can detect the dangerous situations in the ROI using novelty points from the biologically motivated selective attention model, and alerts the driver whether there is dangerous object for changing the lane in driving. The proposed algorithm is based on deciding the ROI using difference from intensity histogram of a Gaussian smoothed image and finding the novelty points from the biologically motivated selective attention model. From variations of those novelty points, we determine whether a vehicle is approaching or not.

Defect depth estimation using magnetic flux leakage measurement for in-line inspection of pipelines

Magnetic Flux Leakage (MFL) methods are widely employed for the nondestructive evaluation (NDE) of gas pipelines. In the application of MFL pipeline inspection technology, corrosion anomalies are detected and identified via their leakage filed due to changes in wall thickness. The gas industry is keenly interested in automating the interpretation process, because a large amount of data to be analyzed is generated for in-line inspection. This paper presents a novel approach to the tasks of data segmentation, feature extraction and depth estimation from gas pipelines. Also, we will show that the proposed method successfully identifying artificial defects.

Completion of Occluded Objects in a Video Sequence using Spatio-Temporal Matching

Video Completion refers to a computer vision technique which restores damaged images by filling missing pixels with suitable color in a video sequence. We propose a new video completion technique to fill in image holes which are caused by removing an unnecessary object in a video sequence, where two objects cross each other in the presence of camera motion. We remove the closer object from a camera which results in image holes. Then these holes are filled by color information of some others frames. First of all, spatio-temporal volumes of occluding and occluded objects are created according to the centroid of the objects. Secondly, a temporal search technique by voxel matching separates and removes the occluding object. Finally. these holes are filled by using spatial search technique. Seams on the boundary of completed pixels we removed by a simple blending technique. Experimental results using real video sequences show that the proposed technique produces new completed videos.