• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.1976-1995
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• 2023

The anchor-free object detector CenterNet regards the object as a center point and predicts it based on the Gaussian circle region. For each object's center point, CenterNet directly regresses the width and height of the objects and finally gets the boundary range of the objects. However, the critical range of the object's center point can not be accurately limited by using the Gaussian circle region to constrain the prediction region, resulting in many low-quality centers' predicted values. In addition, because of the large difference between the width and height of different objects, directly regressing the width and height will make the model difficult to converge and lose the intrinsic relationship between them, thereby reducing the stability and consistency of accuracy. For these problems, we proposed a center point prediction method based on the Gaussian elliptic region and a size component regression method based on the small solution space. First, we constructed a Gaussian ellipse region that can accurately predict the object's center point. Second, we recode the width and height of the objects, which significantly reduces the regression solution space and improves the convergence speed of the model. Finally, we jointly decode the predicted components, enhancing the internal relationship between the size components and improving the accuracy consistency. Experiments show that when using CenterNet as the improved baseline and Hourglass-104 as the backbone, on the MS COCO dataset, our improved model achieved 44.7%, which is 2.6% higher than the baseline.

• Journal of KIBIM
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• v.13 no.4
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• pp.96-105
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• 2023

This study focuses on developing a building object recognition technology for efficient use in the remodeling of buildings constructed without drawings. In the era of the 4th industrial revolution, smart technologies are being developed. This research contributes to the architectural field by introducing a deep learning-based method for automatic object classification and recognition, utilizing point cloud data. We use a TD3D network with voxels, optimizing its performance through adjustments in voxel size and number of blocks. This technology enables the classification of building objects such as walls, floors, and roofs from 3D scanning data, labeling them in polygonal forms to minimize boundary ambiguities. However, challenges in object boundary classifications were observed. The model facilitates the automatic classification of non-building objects, thereby reducing manual effort in data matching processes. It also distinguishes between elements to be demolished or retained during remodeling. The study minimized data set loss space by labeling using the extremities of the x, y, and z coordinates. The research aims to enhance the efficiency of building object classification and improve the quality of architectural plans by reducing manpower and time during remodeling. The study aligns with its goal of developing an efficient classification technology. Future work can extend to creating classified objects using parametric tools with polygon-labeled datasets, offering meaningful numerical analysis for remodeling processes. Continued research in this direction is anticipated to significantly advance the efficiency of building remodeling techniques.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.463-470
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• 2006

It is difficult to track and extract the movement of an object through a camera exactly because of noises and changes of the light. The fast searching algorithm is necessary to extract the object and to track the movement for realtime image. In this paper, we propose the correct and fast algorithm using the variable searching area and the background image change method to robustic for the change of background image. In case the threshold value is smaller than reference value on an experimental basis, change the background image. When it is bigger, we decide it is the point of the time of the object input and then extract boundary point of it through the pixel check. The extracted boundary points detect precise movement of the object by creating area block of it and searching block that maintaining distance. The designed and embodied system shows more than 95% accuracy in the experimental results.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.1
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• pp.49-60
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• 2002

We propose an algorithm for extracting the boundary of a desired object with shape information obtained from sample images. Considering global shape obtained from sample images and edge orientation as well as edge magnitude, the Proposed method composed of two steps finds the boundary of an object. The first step is the approximate segmentation that extracts a rough boundary with a probability map and an edge map. And the second step is the detailed segmentation for finding more accurate boundary based on the SEEL (seed-point extraction and edge linking) algorithm. The experiment results using IR images show robustness to low-quality image and better performance than conventional segmentation methods.

• Journal of Biomedical Engineering Research
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• v.26 no.6
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• pp.357-363
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• 2005

Active contour models have been extensively used to segment, match, and track objects of interest in computer vision and image processing applications, particularly to locate object boundaries. With conventional methods an object boundary can be extracted by controlling the internal energy and external energy based on energy minimization. However, this still leaves a number of problems, such as initialization and poor convergence in concave regions. In particular, a contour is unable to enter a concave region based on the stretching and bending characteristic of the internal energy. Therefore, this study proposes a method that controls the internal energy by moving the local perpendicular bisector point of each control point on the contour, and determines the object boundary by minimizing the energy relative to the external energy. Convergence at a concave region can then be effectively implemented as regards the feature of interest using the internal energy, plus several objects can be detected using a multi-detection method based on the initial contour. The proposed method is compared with other conventional methods through objective validation and subjective consideration. As a result, it is anticipated that the proposed method can be efficiently applied to the detection of the pulmonary parenchyma region in medical images.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.403-406
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• 2001

We propose an algorithm for extracting the boundary of an object. In order to take full advantage of global shape, our approach uses global shape parameters derived from Point Distribution Model (PDM). Unlike PDM, the proposed method models global shape using curvature as well as edge. The objective function of applying the shape model is formulated using Bayesian rule. We can extract the boundaries of an object by evaluating iteratively the solution maximizing the objective function. Experimental results show that the proposed method can reduce computation cost than the PDM and it is robust to noise, pose variation, and some occlusion.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.81-87
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• 2022

In this paper, we propose a watershed algorithm that applies a high-frequency enhancement filter to emphasize the boundary and a local adaptive threshold to search for minimum points. The previous method causes the problem of over-segmentation, and over- segmentation appears around the boundary of the object, creating an inaccurate boundary of the region. The proposed method applies a high-frequency enhancement filter that emphasizes the high-frequency region while preserving the low-frequency region, and performs a minimum point search to consider local characteristics. When merging regions, a fixed threshold is applied. As a result of the experiment, the proposed method reduced the number of segmented regions by about 58% while preserving the boundaries of the regions compared to when high frequency emphasis filters were not used.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.317-322
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• 2018

We use edge detection technique for the input image to extract the entire edges of the object in the image and then select only the edges that construct the outline of the object. By examining the positional relation between these pixels composing the outline, a simplified version of the outline of the object in the input image is generated by removing unnecessary pixels while maintaining the condition of connection of the outline. For each pixel constituting the outline, its direction is calculated by examining the positional relation with the next pixel. Then, we group the consecutive pixels with same direction into one and then change them to a line segment instead of a point. Among those line segments composing the outline of the object, a line segment whose length is smaller than a predefined minimum length of acceptable line segment is removed by merging it into one of the adjacent line segments. As a result, an outline composed of line segments of over a certain length is obtained through this process.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.23-34
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• 2004

In this paper we propose a feature point tracking algorithm using optical flow under non-prior taming active feature model (NPT-AFM). The proposed algorithm mainly focuses on analysis non-rigid objects[1], and provides real-time, robust tracking by NPT-AFM. NPT-AFM algorithm can be divided into two steps: (i) localization of an object-of-interest and (ii) prediction and correction of the object position by utilizing the inter-frame information. The localization step was realized by using a modified Shi-Tomasi's feature tracking algoriam[2] after motion-based segmentation. In the prediction-correction step, given feature points are continuously tracked by using optical flow method[3] and if a feature point cannot be properly tracked, temporal and spatial prediction schemes can be employed for that point until it becomes uncovered again. Feature points inside an object are estimated instead of its shape boundary, and are updated an element of the training set for AFH Experimental results, show that the proposed NPT-AFM-based algerian can robustly track non-rigid objects in real-time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8A
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• pp.679-688
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• 2012

In this paper, performance improved hole-filling algorithm including boundary noise removing pre-process which can be used for an arbitrary view synthesis with given two views is proposed. Boundary noise usually occurs because of the boundary mismatch between the reference image and depth map and common-hole is defined as the occluded region. These boundary noise and common-hole created while synthesizing a virtual view result in some defects and they are usually very difficult to be completely recovered by using only given two images as references. The spiral weighted average algorithm gives a clear boundary of each object by using depth information and the gradient searching algorithm is able to preserve details. In this paper, we combine these two algorithms by using a weighting factor ${\alpha}$ to reflect the strong point of each algorithm effectively in the virtual view synthesis process. The experimental results show that the proposed algorithm performs much better than conventional algorithms.