• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.79-84
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• 2019

Video image based technology is being used in various fields with continuous development. The demand for vision system technology that analyzes and discriminates image objects acquired through cameras is rapidly increasing. Image processing is one of the core technologies of vision systems, and is used for defect inspection in the semiconductor manufacturing field, object recognition inspection such as the number of tire surfaces and symbols. In addition, research into license plate recognition is ongoing, and it is necessary to recognize objects quickly and accurately. In this paper, propose a recognition model through the rotational alignment of objects after checking the angle value of the tilt of the object in the input video image for the recognition of inclined objects such as numbers or symbols marked on the surface. The proposed model can perform object recognition of the rotationally sorted image after extracting the object region and calculating the angle of the object based on the contour algorithm. The proposed model extracts the object region based on the contour algorithm, calculates the angle of the object, and then performs object recognition on the rotationally aligned image. In future research, it is necessary to study template matching through machine learning.

• Journal of the Optical Society of Korea
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• v.5 no.3
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• pp.99-109
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• 2001

This paper presents a phase-only modulation scheme for a three-dimensional (3-D) image matching system to improve optical efficiency of the system. The 3-D image matching system is based on the two mask heterodyne scanning. A hologram of the 3-D reference object is first created and then the phase of the hologram is extracted. The phase of the hologram is represented as one mask with the other mask being a plane wave. The superposition of each beam modulated by the two masks generated a scanning beam pattern. This beam pattern scans the 3-D target object to be recognized. The output of the scanning system gives out the correlation of the phase-only hologram of the reference object and the complex hologram of the target object. Since a hologram contains 3-D information of an object as a form of fringe pattern, the correlation of holograms matches whole 3-D aspect of the objects. Computer simulations are performed with additive gaussian noise and without noise for the complex hologram modulation scheme and the phase-only hologram modulation scheme. The computer simulation results show that the phase-only hologram modulation scheme improves the optical efficiency. Thus the system with the phase-only hologram modulation scheme is more robust than the system with the complex hologram modulation scheme.

• The Journal of the Convergence on Culture Technology
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• v.4 no.1
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• pp.291-297
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• 2018

In this paper, we present an automatic image object tracking system for Studio cameras on the stage. For object tracking, we use the OpenCV-based Python program using PC, Raspberry Pi 3 and mobile devices. There are many methods of image object tracking such as mean-shift, CAMshift (Continuously Adaptive Mean shift), background modelling using GMM(Gaussian mixture model), template based detection using SURF(Speeded up robust features), CMT(Consensus-based Matching and Tracking) and TLD methods. CAMshift algorithm is very efficient for real-time tracking because of its fast and robust performance. However, in this paper, we implement an image object tracking system for studio cameras based CMT algorithm. This is an optimal image tracking method because of combination of static and adaptive correspondences. The proposed system can be applied to an effective and robust image tracking system for continuous object tracking on the stage in real time.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.54-61
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• 2008

This paper proposes a new algorithm for detecting and recognizing overlapped objects among a stack of arbitrarily located objects using a signature representation scheme. The proposed algorithm consists of two processes of detecting overlap of objects and of determining the boundary between overlapping objects. To determine overlap of objects, in the first step, the edge image of object region is extracted and those areas in the object region are considered as the object areas if an area is surrounded by a closed edge. For each object, its signature image is constructed by measuring the distances of those edge points from the center of the object, along the angle axis, which are located at every angle with reference to the center of the object. When an object is not overlapped, its features which consist of the positions and angles of outstanding points in the signature are searched in the database to find its corresponding model. When an object is overlapped, its features are partially matched with those object models among which the best matching model is selected as the corresponding model. The boundary among the overlapping objects is determined by projecting the signature to the original image. The performance of the proposed algorithm has been tested with the task of picking the top or non-overlapped object from a stack of arbitrarily located objects. In the experiment, a recognition rate of 98% has been achieved.

• Journal of KIISE:Databases
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• v.36 no.2
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• pp.73-83
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• 2009

In this paper we propose an object similarity matching method based on shape characteristics of an object in an image. The proposed method extracts edge points from edges of objects and generates a log polar histogram with respect to each edge point to represent the relative placement of extracted points. It performs the matching in such a way that it compares polar histograms of two edge points sequentially along with edges of objects, and uses a well-known k-NN(nearest neighbor) approach to retrieve similar objects from a database. To verify the proposed method, we've compared it to an existing Shape-Context method. Experimental results reveal that our method is more accurate in object matching than the existing method, showing that when k=5, the precision of our method is 0.75-0.90 while that of the existing one is 0.37, and when k=10, the precision of our method is 0.61-0.80 while that of the existing one is 0.31. In the experiment of rotational transformation, our method is also more robust compared to the existing one, showing that the precision of our method is 0.69 while that of the existing one is 0.30.

• Journal of Platform Technology
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• v.12 no.2
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• pp.23-33
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• 2024

The development of mixed reality (MR) technology has a great influence on the research and development of medical support equipment. In particular, it supports to respond effectively to emergencies occurring in the field. MR technology enables access to first aid and field support by combining virtual information with the real world so that users can see virtual objects in the real world. However, due to the nature of the equipment, there is a limitation in accurately matching virtual objects based on user vision. To improve these limitations, this paper proposes a 3D biometric object recognition and matching algorithm in the MR environment. As a result of the experiment, when a virtual object is rendered and visualized while equipped with an optical-based HMD from the user's side, it was possible to reduce the user's field of view error and eliminate the joint-loss phenomenon during skeleton recognition. The proposed method can reduce errors between the real user's field of view and the virtual image and provide a basis for reducing errors that occur in the process of virtual object recognition and matching. It is expected that this study will contribute to improving the accuracy of the telemedicine support system for first aid.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.3
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• pp.303-317
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• 2002

In this paper, we design and implement E-COIRS enabling users to query with concepts and image features used for further refining the concepts. For example, E-COIRS supports the query "retrieve images containing black home appliance to north of reception set. "The query includes two types of concepts: IS-A and composite. "home appliance"is an IS-A concept, and "reception set" is a composite concept. For evaluating such a query. E-COIRS includes three important components: a visual image indexer, thesauri and a query processor. Each pair of objects in an mage captured by the visual image indexer is converted into a triple. The triple consists of the two object identifiers (oids) and their spatial relationship. All the features of an object is referenced by its old. A composite concept is detected by the triple thesaurus and IS-A concept is recolonized by the fuzzy term thesaurus. The query processor obtains an image set by matching each triple in a user with an inverted file and CS-Tree. To support efficient storage use and fast retrieval on high-dimensional feature vectors, E-COIRS uses Cell-based Signature tree(CS-Tree). E-COIRS is a more advanced content-based image retrieval system than other systems which support only concepts or image features.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.1-6
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• 2019

One method for image recognition is template matching. In conventional template matching, the block matching algorithm (BMA) is performed while changing the two-dimensional translational displacement of the template within a given matching image. The template size and shape do not change during the BMA. Since only two-dimensional translational displacement is considered, the success rate decreases if the size and direction of the object do not match in the template and the matching image. In this paper, a variable is added to adjust the two-dimensional direction and size of the template, and the optimal value of the variable is automatically calculated in the block corresponding to each two-dimensional translational displacement. Using the calculated optimal value, the template is automatically transformed into an optimal template for each block. The matching error value of each block is then calculated based on the automatically deformed template. Therefore, a more stable result can be obtained for the difference in direction and size. For ease of use, this study focuses on designing the algorithm in a closed form that does not require additional information beyond the template image, such as distance information.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.1
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• pp.7-12
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• 2011

In this paper, we propose the technique detecting interest object region effectively in the images from periscope of submarine based on self-generated template. First, we extract the sea-sky line, and divide it into sky and sea area from background region based on the sea-sky line. In each divided background region, the blocks which can be represented in each background region are set as a reference template. After dividing an image into several same size of blocks, we apply multi template matching to the divided search blocks and histogram template to divide the image into object region and background region. Proposed algorithm is adapted to various images in which objects exist in the background of sea and sky. We verified that proposed algorithm performed properly without given informmed prby prior learning.ropso, regardless of the slope of sea-sky line and the locmed p of object based on sea-sky line, we verified that the objects region was segmented effectively from the input image.

• The Journal of Information Technology
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• v.7 no.4
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• pp.1-8
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• 2004

The depth information of 3D image lost by projecting 3D-object to 2D-screen for earning image. If depth information is restored and is used to recognize 3D-object, we can make the more effective recognition system. We often use shape from stereo algorithm in order to restore this information. In this paper, we suggest 3-D object recognition system in which the 3-D Hough transform domain is employed to represent the 3-D objects. In this system, we use the moving vector of object to reduce matching time and In second matching step, the unknown input image is compared with the reference images, which is made with octree codes. Octree codes are used in volume-based representation of a three dimensional object. The result of simulation show that the proposed 3-D object recognition system provides satisfactory performance.