• International Journal of Advanced Culture Technology
• /
• v.7 no.4
• /
• pp.229-235
• /
• 2019

Due to the development of video and optical technology today, video equipments are being used in a variety of fields such as identification, security maintenance, and factory automation systems that generate products. In this paper, we investigate an algorithm that effectively recognizes an experimental object in an input image with a partial problem due to the mechanical problem of the input imaging device. The object recognition algorithm proposed in this paper moves and rotates the vertices constituting the outline of the experimental object to the positions of the respective vertices constituting the outline of the DB model. Then, the discordance values between the moved and rotated experimental object and the corresponding DB model are calculated, and the minimum discordance value is selected. This minimum value is the final discordance value between the experimental object and the corresponding DB model, and the DB model with the minimum discordance value is selected as the recognition result for the experimental object. The proposed object recognition method obtains satisfactory recognition results using only partial information of the experimental object.

• Journal of Sensor Science and Technology
• /
• v.30 no.2
• /
• pp.76-81
• /
• 2021

In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.34C no.10
• /
• pp.76-88
• /
• 1997

Based on the principles described in Part I, we have implemented a working prototype vision system using a feature structure called an LSG (local surface group) for generating object hypotheses. In order to verify an object hypothesis, we estimate the view of the hypothesized model object and render the model object for the computed view. The object hypothesis is then verified by finding additional features in the scene that match those present in the rendered image. Experimental results on synthetic and real range images show the effectiveness of the indexing scheme.

• IEIE Transactions on Smart Processing and Computing
• /
• v.3 no.6
• /
• pp.366-371
• /
• 2014

This paper reports a machine learning approach for image object detection. Object detection and localization in a wild image, such as a STL-10 image dataset, is very difficult to implement using the traditional computer vision method. A convolutional neural network is a good approach for such wild image object detection. This paper presents an object detection application using a convolutional neural network with pretrained feature vector. This is a very simple and well organized hierarchical object abstraction model.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.12
• /
• pp.4795-4815
• /
• 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 Institute of Electronics Engineers of Korea CI
• /
• v.47 no.6
• /
• pp.75-85
• /
• 2010

This paper presents a robust 3D model hashing based on object feature vector for 3D content authentication. The proposed 3D model hashing selects the feature objects with highest area in a 3D model with various objects and groups the distances of the normalized vertices in the feature objects. Then we permute groups in each objects by using a permutation key and generate the final binary hash through the binary process with the group coefficients and a random key. Therefore, the hash robustness can be improved by the group coefficient from the distance distribution of vertices in each object group and th hash uniqueness can be improved by the binary process with a permutation key and a random key. From experimental results, we verified that the proposed hashing has both the robustness against various mesh and geometric editing and the uniqueness.

• Journal of information and communication convergence engineering
• /
• v.9 no.4
• /
• pp.466-470
• /
• 2011

This paper proposes the new method based on fuzzy logic which recognizes between normal and abnormal. The object image was the Thyroid Gland cell image that was diagnosed as normal and abnormal(two types of abnormal : follicular neoplastic cell, and papillary neoplastic cell), respectively. The nuclei were successfully diagnosed as normal and abnormal. The multiple feature parameters (pre-obtained 16 feature parameters of image data) were used to extract the features of each nucleus. As a consequence of using fuzzy logic algorithm, proposed in this paper, average recognition rate of 98.25% was obtained.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.1
• /
• pp.142-148
• /
• 2014

A descriptor which is suitable for motion analysis by using the motion features of moving objects from the real time image sequence is proposed. To segment moving objects from the background, the background learning is performed. We extract motion trajectories of individual objects by using the sequence of the 1st order moment of moving objects. The center points of each object are managed by linked list. The descriptor includes the 1st order coordinates of moving object belong to neighbor of the pre-defined position in grid pattern, The start frame number which a moving object appeared in the scene and the end frame number which it disappeared. A video retrieval by the proposed descriptor combining global and local feature is more effective than conventional methods which adopt a single feature among global and local features.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2012.10a
• /
• pp.898-902
• /
• 2012

A descriptor which is suitable for motion analysis by using the motion features of moving objects from the real time image sequence is proposed. To segment moving objects from the background, the background learning is performed. We extract motion trajectories of individual objects by using the sequence of the $1^{st}$ order moment of moving objects. The center points of each object are managed by linked list. The descriptor includes the $1^{st}$ order coordinates of moving object belong to neighbor of the per-defined position in grid pattern, the start frame number which a moving object appeared in the scene and the end frame number which it disappeared. A video retrieval by the proposed descriptor combining global and local feature is more effective than conventional methods which adopt a single feature among global and local features.

• Journal of Information Processing Systems
• /
• v.14 no.5
• /
• pp.1176-1194
• /
• 2018

The object classification using the images' contents is a big challenge in computer vision. The superpixels' information can be used to detect and classify objects in an image based on locations. In this paper, we proposed a methodology to detect and classify the image's pixels' locations using enhanced bag of words (BOW). It calculates the initial positions of each segment of an image using superpixels and then ranks it according to the region score. Further, this information is used to extract local and global features using a hybrid approach of Scale Invariant Feature Transform (SIFT) and GIST, respectively. To enhance the classification accuracy, the feature fusion technique is applied to combine local and global features vectors through weight parameter. The support vector machine classifier is a supervised algorithm is used for classification in order to analyze the proposed methodology. The Pascal Visual Object Classes Challenge 2007 (VOC2007) dataset is used in the experiment to test the results. The proposed approach gave the results in high-quality class for independent objects' locations with a mean average best overlap (MABO) of 0.833 at 1,500 locations resulting in a better detection rate. The results are compared with previous approaches and it is proved that it gave the better classification results for the non-rigid classes.