• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.249-268
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• 2014

Relevance feedback is an effective tool to bridge the gap between superficial image contents and medically-relevant sense in content-based medical image retrieval. In this paper, we propose an interactive medical image search framework based on pairwise constraint propagation. The basic idea is to obtain pairwise constraints from user feedback and propagate them to the entire image set to reconstruct the similarity matrix, and then rank medical images on this new manifold. In contrast to most of the algorithms that only concern manifold structure, the proposed method integrates pairwise constraint information in a feedback procedure and resolves the small sample size and the asymmetrical training typically in relevance feedback. We also introduce a long-term feedback strategy for our retrieval tasks. Experiments on two medical image datasets indicate the proposed approach can significantly improve the performance of medical image retrieval. The experiments also indicate that the proposed approach outperforms previous relevance feedback models.

• Journal of the Korean Society of Physical Medicine
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• v.7 no.4
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• pp.533-539
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• 2012

PURPOSE: The purpose of this study was to investigate the effect of pelvic floor muscle contraction with image feedback on Abdominal muscle thickness. METHODS: Twenty three adults participated in this study. Abdominal muscle thickness was measured by ultrasound in three condition(rest, pelvic floor muscle contraction, pelvic floor muscle contraction with image feedback). Subjects was contraction pelvic floor muscle by general method. And ultrasound(convex probe, 3.5MHz) was used to image feedback for selective pelvic floor muscle contraction. One-way ANOVA was used to compare abdominal muscle thickness in three condition. RESULTS: There was no significant difference in external oblique(p=.514) and internal oblique muscle(p=.250) thickness by three condition. There was significant difference in transverse abdominis thickness by three condition (Transverse abdominis thickness was highest while Pelvic floor muscle contraction than pelvic floor muscle contraction with image feedback and rest.)(p=.000). CONCLUSION: This study shows that pelvic floor muscle contraction with image feedback increase the thickness of transverse abdominis lesser than general pelvic floor muscle contraction.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.2
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• pp.90-98
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• 2015

This research propose with image-based visual a new approach to design a feedback control of mobile robot. because mobile robot must be recharged periodically, it is necessary to detect and move to docking station. Generally, laser scanner is used for detect of position of docking station. CCD Camera is also used for this purpose. In case of using camera, the position-based visual servoing method is widely used. But position-based visual servoing method requires the accurate calibration and it is hard and complex work. Another method using cameras is inmage-based visual feedback. Recently, image based visual feedback is widely used for robotic application. But it has a problem that cannot have linear trajectory in the 3-dimensional space. Because of this weak point, image-based visual servoing has a limit for real application. in case of 2-dimensional movement on the plane, it has also similar problem. In order to solve this problem, we point out the main reason of the problem of the resolved rate control method that has been generally used in the image-based visual servoing and we propose an image-based visual feedback method that can reduce the curved trajectory of mobile robot in th cartesian space.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3149-3165
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• 2013

Content based image retrieval is increasingly gaining popularity among image repository systems as images are a big source of digital communication and information sharing. Identification of image content is done through feature extraction which is the key operation for a successful content based image retrieval system. In this paper content based image retrieval system has been developed by adopting a strategy of combining multiple features of shape, color and relevance feedback. Shape is served as a primary operation to identify images whereas color and relevance feedback have been used as supporting features to make the system more efficient and accurate. Shape features are estimated through second derivative, least square polynomial and shapes coding methods. Color is estimated through max-min mean of neighborhood intensities. A new technique has been introduced for relevance feedback without bothering the user.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.940-943
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• 1996

In teleoperating, as seeing the monitor screen obtained from a camera instituted in the working environment, human operator generally controls the slave arm. Because we can see only 2-D image in a monitor, human operator does not know the depth information and can not work with high accuracy. In this paper, we proposed a traded control method using an visual sensor for the purpose of solving this problem. We can control a teleoperation system with precision when we use the proposed algorithm. Not only a human operator command but also an autonomous visual sensor feedback command is given to a slave arm for the purpose of coincidence current image features and target image features. When the slave arm place in a distant place from the target position, human operator can know very well the difference between the desired image features and the current image features, but calculated visual sensor command have big errors. And when the slave arm is near the target position, the state of affairs is changed conversely. With this visual sensor feedback, human does not need coincide the detail difference between the desired image features and the current image features and proposed method can work with higher accuracy than other method without, sensor feedback. The effectiveness of the proposed control method is verified through series of experiments.

• 전기의세계
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• v.26 no.4
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• pp.54-60
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• 1977

The feedback sources of oculomotor control system consist of three types of feedback path originating from retinal image displacement, in the proprioceptive fibers of the extraocular muscles, in the efference copy within the C.N.S. From above feedback loops, the retinal image feedback path is a main subject in this experiment. The electrical output of eye ball motion detecting with a photo-electric matrix method is fed into galvanometer through the external feedback path, and the stability was also examined.

• Journal of Korea Multimedia Society
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• v.6 no.4
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• pp.746-752
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• 2003

In this paper, we proposed an image retrieval system with a simple and effective relevance feedback, called RAP(Reward and Punishment) algorithm. First, color and texture features were extracted from the images. Next, the extracted feature values were used for image retrieval in various forms. We applied the relevance feedback to the initial retrieved images from the image retrieval system, and compared its result with that of the conventional system. In the experiment using the test image database of 16 class 512 images, the proposed system showed the better retrieval performance of about 10∼l7 % than that of the conventional INRIA system in each relevance feedback step.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.116.5-116
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• 2002

In remote control of telerobotics equipment, the real-time visual feedback is necessary in order to facilitate real-time control. Because of the network congestion and the associated delays, the real-time image feedback is generally difficult in the public networks like internet. If the remote user is not able to receive the image feedback within a certain time, the work performance may tend to decrease, and it makes difficulties to control of the telerobotics equipment. In this paper, we propose an improved visual feedback scheme over the internet for telerobotics system. The size of a remote site image and its quality are adjusted for efficient transmission. The constructed system has a be...

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.61-67
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• 2000

Generally, relevance, feedback reflecting user's intention has been used to refine the refine the query conditions in image retrieval. However, in this paper, the usage of the relevance feedback is extended to the image database categorization so as to be accommodated to the user independent image retrieval. In our approach, to guarantee a desirable user-satisfactory performance descriptors and the elements of the descriptors corresponding unique features associatiated with of each image are weighted using the relevance feedback where experts can more lead rather than beginners do. In this paper, we propose a proper image description scheme consisting of global information, local information, descriptor weights and element weights based on color and texture descriptors. In addition, we also introduce an appropriate learning method based on the reliability scheme preventing wrong learning from abusive feedback.

• Journal of the Semiconductor & Display Technology
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• v.7 no.4
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• pp.13-18
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• 2008

This paper explores a content-based image retrieval framework with relevance feedback based on genetic algorithm (GA). This framework adopts GA to learn the user preferences using the similarity functions defined for all available descriptors. The objective of the GA-based learning methods is to learn the user preferences using the similarity functions and to find a descriptor combination function that best represents the user perception. Experiments were performed to validate the proposed frameworks. The experiments employed the natural image databases and color and texture descriptors to represent the content of database images. The proposed frameworks were compared with the other two relevance feedback methods regarding effectiveness in image retrieval tasks. Experiment results demonstrate the superiority of the proposed method.