• Journal of Digital Convergence
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• v.11 no.12
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• pp.459-465
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• 2013

This paper is a study on an object extraction method which using color features of an object and background in the image. A human recognizes an object through the color difference of object and background in the image. So we must to emphasize the color's difference that apply to extraction result in this image. Therefore, we have converted to HSV color images which similar to human visual system from original RGB images, and have created two each other images that applied Median Filter and we merged two Median filtered images. And we have applied the Mean Shift algorithm which a data clustering method for clustering color features. Finally, we have normalized 3 image channels to 1 image channel for binarization process. And we have created object map through the binarization which using average value of whole pixels as a threshold. Then, have extracted major object from original image use that object map.

• The Journal of Korea Robotics Society
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• v.1 no.1
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• pp.25-35
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• 2006

Improving practicality of SLAM requires various sensors to be fused effectively in order to cope with uncertainty induced from both environment and sensors. In this case, combining sonar and vision sensors possesses numerous advantages of economical efficiency and complementary cooperation. Especially, it can remedy false data association and divergence problem of sonar sensors, and overcome low frequency SLAM update caused by computational burden and weakness in illumination changes of vision sensors. In this paper, we propose a SLAM method to join sonar sensors and stereo camera together. It consists of two schemes, extracting robust point and line features from sonar data and recognizing planar visual objects using multi-scale Harris corner detector and its SIFT descriptor from pre-constructed object database. And fusing sonar features and visual objects through EKF-SLAM can give correct data association via object recognition and high frequency update via sonar features. As a result, it can increase robustness and accuracy of SLAM in indoor environment. The performance of the proposed algorithm was verified by experiments in home -like environment.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.91-99
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• 2018

Sketch-based 3D object retrieval is a convenient way to search for various 3D data using human-drawn sketches as query. In this paper, we propose a new method of using Sketch CNN, Wasserstein CNN and Wasserstein center loss for sketch-based 3D object search. Specifically, Wasserstein center loss is a method of learning the center of each object category and reducing the Wasserstein distance between center and features of the same category. To do this, the proposed 3D object retrieval is performed as follows. Firstly, Wasserstein CNN extracts 2D images taken from various directions of 3D object using CNN, and extracts features of 3D data by computing the Wasserstein barycenters of features of each image. Secondly, the features of the sketch are extracted using a separate Sketch CNN. Finally, we learn the features of the extracted 3D object and the features of the sketch using the proposed Wasserstein center loss. In order to demonstrate the superiority of the proposed method, we evaluated two sets of benchmark data sets, SHREC 13 and SHREC 14, and the proposed method shows better performance in all conventional metrics compared to the state of the art methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.6
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• pp.122-129
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• 2016

There have been a lot of researches on object recognition in computer vision. The SURF(Speeded Up Robust Features) algorithm based on feature detection is faster and more accurate than others. However, this algorithm has a shortcoming of making an error due to feature point mismatching when extracting feature points. In order to increase a success rate of object recognition, we have created an object recognition system based on SURF and RANSAC(Random Sample Consensus) algorithm and proposed the pattern recognition filtering. We have also presented experiment results relating to enhanced the success rate of object recognition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.3
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• pp.197-205
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• 1992

In this paper object recognition using neural network is studied. The recognition is accomplished by matching linear line segments which are formed by local features extracted from the curvature points. Since there is similarities among segments. The boundary of models is not distinct in feature space. Due to these indistinctness the ambiguity of recognition occurs, and the recognition rate becomes degraded according to the limitation of boundary decision capability of neural network for similar of features. Object recognition and to improve recognition rate. Local features are used to represent the object effectively. The validity of the object recognition system is demonstrated by experiments for the occluded and varied objects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4795-4815
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• 2020

Drone detection can be considered as a specific sort of small object detection, which has always been a challenge because of its small size and few features. For improving the detection rate of drones, we design a Deeper SSD network, which uses large-scale input image and deeper convolutional network to obtain more features that benefit small object classification. At the same time, in order to improve object classification performance, we implemented the up-sampling modules to increase the number of features for the low-level feature map. In addition, in order to improve object location performance, we adopted the down-sampling modules so that the context information can be used by the high-level feature map directly. Our proposed Deeper SSD and its variants are successfully applied to the self-designed drone datasets. Our experiments demonstrate the effectiveness of the Deeper SSD and its variants, which are useful to small drone's detection and recognition. These proposed methods can also detect small and large objects simultaneously.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.760-765
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• 2009

Since the features in object-images such as color and shape cannot clearly express the characteristic of objects, those features lead to vagueness of object-image recognition. Recently there have been studied on object-image recognition based on knowledge base in order to reduce the vagueness. However, because images are represented by numerical information but knowledge bases are represented by conceptual information, combining two kinds of information is difficult. In this paper, we compose knowledge base by using ontology to reduce the gap between the two kinds of information, and propose a method for object-image recognition to reduce the vagueness by using information on inner-object. Moreover, we confirm the usefulness of the proposed method through the experiments on object-image recognition in fruit domain.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1425-1430
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• 2018

In this paper, we propose a CNN based deep learning algorithm for semantic segmentation of images. In order to improve the accuracy of semantic segmentation, we combined pixel level object classification and image level object classification. The image level object classification is used to accurately detect the characteristics of an image, and the pixel level object classification is used to indicate which object area is included in each pixel. The proposed network structure consists of three parts in total. A part for extracting the features of the image, a part for outputting the final result in the resolution size of the original image, and a part for performing the image level object classification. Loss functions exist for image level and pixel level classification, respectively. Image-level object classification uses KL-Divergence and pixel level object classification uses cross-entropy. In addition, it combines the layer of the resolution of the network extracting the features and the network of the resolution to secure the position information of the lost feature and the information of the boundary of the object due to the pooling operation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.3
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• pp.18-29
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• 2012

Viola-Jones algorithm, a very effective real-time object detection method, uses Haar-like features to constitute weak classifiers. A Haar-like feature is made up of at least two rectangles each of which corresponds to either positive or negative areas and the feature value is computed by subtracting the sum of pixel values in the negative area from that of pixel values in the positive area. Compared to the conventional Haar-like feature which is made up of more than one rectangle, in this paper, we present a couple of new rectangular features whose feature values are computed either by the sum or by the variance of pixel values in a rectangle. By the use of these rectangular features in combination with the conventional Haar-like features, we can select additional features which have been excluded in the conventional Viola-Jones algorithm where every features are the combination of contiguous bright and dark areas of an object. In doing so, we can enhance the performance of object detection without any computational overhead.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.6
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• pp.486-491
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• 2013

This paper proposes a fast and efficient Haar-like feature selection algorithm for training classifier used in object detection. Many features selected by Haar-like feature selection algorithm and existing AdaBoost algorithm are either similar in shape or overlapping due to considering only feature's error rate. The proposed algorithm calculates similarity of features by their shape and distance between features. Fast and efficient feature selection is made possible by removing selected features and features with high similarity from feature set. FERET face database is used to compare performance of classifiers trained by previous algorithm and proposed algorithm. Experimental results show improved performance comparing classifier trained by proposed method to classifier trained by previous method. When classifier is trained to show same performance, proposed method shows 20% reduction of features used in classification.