• Journal of Computational Design and Engineering
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• v.3 no.3
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• pp.191-197
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• 2016

Since 3D measurement technologies have been widely used in manufacturing industries edge detection in a depth image plays an important role in computer vision applications. In this paper, we have proposed an edge detection process in a depth image based on the image based smoothing and morphological operations. In this method we have used the principle of Median filtering, which has a renowned feature for edge preservation properties. The edge detection was done based on Canny Edge detection principle and was improvised with morphological operations, which are represented as combinations of erosion and dilation. Later, we compared our results with some existing methods and exhibited that this method produced better results. However, this method works in multiframe applications with effective framerates. Thus this technique will aid to detect edges robustly from depth images and contribute to promote applications in depth images such as object detection, object segmentation, etc.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.25-35
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• 2014

This paper proposes a method of boosting-based classification for the purpose of real-time face detection. The proposed method uses depth images to ensure strong performance of face detection in response to changes in lighting and face size, and uses the depth difference feature to conduct learning and recognition through the I-MCTBoost classifier. I-MCTBoost performs recognition by connecting the strong classifiers that are constituted from weak classifiers. The learning process for the weak classifiers is as follows: first, depth difference features are generated, and eight of these features are combined to form the weak classifier, and each feature is expressed as a binary bit. Strong classifiers undergo learning through the process of repeatedly selecting a specified number of weak classifiers, and become capable of strong classification through a learning process in which the weight of the learning samples are renewed and learning data is added. This paper explains depth difference features and proposes a learning method for the weak classifiers and strong classifiers of I-MCTBoost. Lastly, the paper presents comparisons of the proposed classifiers and the classifiers using conventional MCT through qualitative and quantitative analyses to establish the feasibility and efficiency of the proposed classifiers.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.1
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• pp.9-16
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• 2002

This paper proposes a new 3D modeling technique using disparity-motion relationship and feature points. To generate the 3D model from real scene, generally, we need to compute depth of model vertices from the dense correspondence map over whole images. It takes much time and is also very difficult to get accurate depth. To improve such problems, in this paper, we only need to find the correspondence of some feature points to generate a 3D model of object without dense correspondence map. The proposed method consists of three parts, which are the extraction of object, the extraction of feature points, and the hierarchical 3D modeling using classified feature points. It has characteristics of low complexity and is effective to synthesize images with virtual view and to express the smoothness of Plain regions and the sharpness of edges.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.1
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• pp.13-20
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• 2016

In this paper, we propose an object recognition system that can effectively find out its category, its instance name, and several attributes from the color and depth images of an object with hierarchical feature learning. In the preprocessing stage, our system transforms the depth images of the object into the surface normal vectors, which can represent the shape information of the object more precisely. In the feature learning stage, it extracts a set of patch features and image features from a pair of the color image and the surface normal vector through two-layered learning. And then the system trains a set of independent classification models with a set of labeled feature vectors and the SVM learning algorithm. Through experiments with UW RGB-D Object Dataset, we verify the performance of the proposed object recognition system.

• Journal of Biosystems Engineering
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• v.28 no.1
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• pp.65-76
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• 2003

The analysis of cow body parameters is important to provide some useful information fur cow management and cow evaluation. Present methods give many stresses to cows because they are invasive and constrain cow postures during measurement of body parameters. This study was conducted to develop the stereo vision system fur non-invasive analysis of cow body features. Body feature parameters of 16 heads at two farms(A, B) were measured using scales and nineteen stereo images of them with walking postures were captured under outdoor illumination. In this study, the camera calibration and inverse perspective transformation technique was established fer the stereo vision system. Two calibration results were presented for farm A and fm B, respectively because setup distances from camera to cow were 510 cm at farm A and 630cm at farm B. Calibration error values fer the stereo vision system were within 2 cm for farm A and less than 4.9 cm for farm B. Eleven feature points of cow body were extracted on stereo images interactively and five assistant points were determined by computer program. 3D world coordinates for these 15 points were calculated by computer program and also used for calculation of cow body parameters such as withers height. pelvic arch height. body length. slope body length. chest depth and chest width. Measured errors for body parameters were less than 10% for most cows. For a few cow. measured errors for slope body length and chest width were more than 10% due to searching errors fer their feature points at inside-body positions. Equation for chest girth estimated by chest depth and chest width was presented. Maximum of estimated error fur chest girth was within 10% of real values and mean value of estimated error was 8.2cm. The analysis of cow body parameters using stereo vision system were successful although body shape on the binocular stereo image was distorted due to cow movements.

• Journal of Korea Multimedia Society
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• v.12 no.6
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• pp.794-803
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• 2009

In this paper, we will present a container auto-landing system, the system use the stereo camera to measure the container depth information. And the container region can be detected by using its hough line feature. In the line feature detection algorithm, we will detect the parallel lines and perpendicular lines which compose the rectangle region. Among all the candidate regions, we can select the region with the same aspect-ratio to the container. The region will be the detected container region. After having the object on both left and right images, we can estimate the distance from camera to object and container dimension. Then all the detect dimension information and depth inform will be applied to reconstruct the virtual environment of crane which will be introduce in this paper. Through the simulation result, we can know that, the container detection rate achieve to 97% with simple background. And the estimation algorithm can get a more accuracy result with a far distance than the near distance.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.2067-2078
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• 2017

In this paper, a new face tracking algorithm is proposed. The CamShift (Continuously adaptive mean SHIFT) algorithm shows unstable tracking when there exist objects with similar color to that of face in the background. This drawback of the CamShift is resolved by the proposed algorithm using Kinect's pixel-by-pixel depth information and the skin detection method to extract candidate skin regions in HSV color space. Additionally, even when the target face is disappeared, or occluded, the proposed algorithm makes it robust to this occlusion by the feature point matching. Through experimental results, it is shown that the proposed algorithm is superior in tracking performance to that of existing TLD (Tracking-Learning-Detection) algorithm, and offers faster processing speed. Also, it overcomes all the existing shortfalls of CamShift with almost comparable processing time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1595-1613
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• 2017

The IR camera and laser-based IR projector provide an effective solution for real-time collection of moving targets in RGB-D videos. Different from the traditional RGB videos, the captured depth videos are not affected by the illumination variation. In this paper, we propose a novel feature extraction framework to describe human activities based on the above optical video capturing method, namely spatial-temporal texture features for 3D human activity recognition. Spatial-temporal texture feature with depth information is insensitive to illumination and occlusions, and efficient for fine-motion description. The framework of our proposed algorithm begins with video acquisition based on laser projection, video preprocessing with visual background extraction and obtains spatial-temporal key images. Then, the texture features encoded from key images are used to generate discriminative features for human activity information. The experimental results based on the different databases and practical scenarios demonstrate the effectiveness of our proposed algorithm for the large-scale data sets.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.197-207
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• 2014

In this paper, I propose an automatic extraction method of golf swing features using a practical TOF camera Kinect. I extracted 7 key swing frames and features using joints and depth information from a Kinect. I tested the proposed method on 50 swings from 10 players and showed the performace. It is meaningful that 3D swing features are extracted automatically using an inexpensive and simple system and specific numerical feature values can be used for the building of automatic swing analysis system.

• Journal of Computing Science and Engineering
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• v.12 no.1
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• pp.1-11
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• 2018

Human activities recognition is a challenging task due to its complexity of human movements and the variety performed by different subjects for the same action. This paper presents a recognition algorithm by using skeleton information generated from depth maps. Concatenating motion features and temporal constraint feature produces feature vector. Reducing dictionary scale proposes an improved fast classifier based on sparse representation. The developed method is shown to be effective by recognizing different activities on the UTD-MHAD dataset. Comparison results indicate superior performance of our method over some existing methods.