• 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.

• Journal of IKEEE
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• v.16 no.3
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• pp.182-188
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• 2012

Here we propose an image quality assessment (IQA) based on histogram of oriented gradients (HOG). This method makes use of the characteristic that the histogram of gradient image describes the state of input image. In the proposed method, the image quality is derived by the slope of the HOG obtained from the target image. The line representing the HOG is measured by a random sample consensus (RANSAC) on the HOG. Simulation results based on the LIVE image quality assessment database suggest that the proposed method aligns better with how the human visual system perceives image quality than several state-of-the-art IQAs.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.671-678
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• 2006

In this paper, we propose a method for robustly estimating camera motion using fuzzy classification from the correspondences between two images. We use a RANSAC(Random Sample Consensus) algorithm to obtain accurate camera motion estimates in the presence of outliers. The drawback of RANSAC is that its performance depends on a prior knowledge of the outlier ratio. To resolve this problem the proposed method classifies samples into three classes(good sample set, bad sample set and vague sample set) using fuzzy classification. It then improves classification accuracy omitting outliers by iteratively sampling in only good sample set. The experimental results show that the proposed approach is very effective for computing a homography.

• Journal of the Korea Society of Computer and Information
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• v.27 no.4
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• pp.27-36
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• 2022

In this paper, to improve the conditions of the fish processing site, we propose a method to predict the cutting point of fish according to the target weight using AI machine vision. The proposed method performs image-based preprocessing by first photographing the top and front views of the input fish. Then, RANSAC(RANdom SAmple Consensus) is used to extract the fish contour line, and then 3D external information of the fish is obtained using 3D modeling. Next, machine learning is performed on the extracted three-dimensional feature information and measured weight information to generate a neural network model. Subsequently, the fish is cut at the cutting point predicted by the proposed technique, and then the weight of the cut piece is measured. We compared the measured weight with the target weight and evaluated the performance using evaluation methods such as MAE(Mean Absolute Error) and MRE(Mean Relative Error). The obtained results indicate that an average error rate of less than 3% was achieved in comparison to the target weight. The proposed technique is expected to contribute greatly to the development of the fishery industry in the future by being linked to the automation system.

• Journal of KIISE
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• v.43 no.3
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• pp.346-352
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• 2016

The fishing industry requires many workers to manually carry out the jobs of sorting and cutting fishes. There are therefore many dangerous situations in their working environment and the throughput is inefficiently low. This paper introduces an automatic fin cutting system based on RANSAC that is able to increase the throughput of fish processing jobs. The system proposed in this paper first detects the edges of a fish using a high-pass filter. The boundary lines between fin and body are then detected by adjusting parameters and the threshold of the noise filters. Finally, the optimal cutting lines are detected using RANSAC. Through an experiment with a sample of 50 fishes, this paper shows that the proposed system detects the cutting lines with about 90% accuracy.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.93-97
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• 2010

This paper presents the improved KLT(Kanade-Lucas-Tomasi) of registration of Image sequence captured by camera mounted on unmanned helicopter assuming without camera attitude information. It consists of following procedures for the proposed image registration. The initial interested points are detected by characteristic curve matching via dynamic programming which has been used for detecting and tracking corner points thorough image sequence. Outliers of tracked points are then removed by using Random Sample And Consensus(RANSAC) robust estimation and all remained corner points are classified as inliers by homography algorithm. The rectified images are then resampled by bilinear interpolation. Experiment shows that our method can make the suitable registration of image sequence with large motion.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.709-712
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• 2005

Ground processing for geostationary weather satellite such as GOES, MTSAT includes the process called image navigation. Image navigation means the retrieval of satellite navigational parameters from images and requires landmark detection by matching satellite images against landmark chips. For an automated preprocessing, a matching must be performed automatically. However, if match results contain errors, the accuracy of image navigation deteriorates. To overcome this problem, we propose the use of a robust estimation technique, called Random Sample Consensus (RANSAC), to automatically detect mismatches. We tested GOES-9 satellite images with 30 landmark chips. Landmark chips were extracted from the world shoreline database. To them, matching was applied and mismatch results were detected automatically by RANSAC. Results showed that all mismatches were detected correctly by RANSAC with a threshold value of 2.5 pixels.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.2
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• pp.180-187
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• 2007

In this paper, we propose an automatic method which combines two or more images acquired by camera on the air-vehicle into a larger image mosaics. The shift, scaling, rotation factors between two images can be calculated by using the correspondences between the points of the images. In order to estimate these factors, we find the relative positions of two images with respect to each other by using the SIFT descriptor and the RANSAC algorithm. After estimating the factors, the images can be merged into a single image mosaic by warping the target image. To avoid seams when mosaics are constructed from overlapped images, we apply the average gray level value of points within a overlapped zone. We have tested our proposed method on various image sets and have confirmed that our method produced good result subjectively.

• Journal of KIISE
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• v.44 no.4
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• pp.392-398
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• 2017

The point-cloud representation of an object is performed by scanning a space through a laser scanner that is extracting a set of points, and the points are then integrated into the same coordinate system through a registration. The set of the completed registration-integrated point clouds is classified into meaningful regions, shapes, and noises through a mathematical analysis. In this paper, the aim is the matching of a curved area like a cylinder shape in 3D point-cloud data. The matching procedure is the attainment of the center and radius data through the extraction of the cylinder-shape candidates from the sphere that is fitted through the RANdom Sample Consensus (RANSAC) in the point cloud, and completion requires the matching of the curved region with the Catmull-Rom spline from the extracted center-point data using the Principal Component Analysis (PCA). Not only is the proposed method expected to derive a fast estimation result via linear and curved cylinder estimations after a center-axis estimation without constraint and segmentation, but it should also increase the work efficiency of reverse engineering.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.3
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• pp.317-327
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• 2016

The extraction of significant feature points from a video is directly associated with the suggested method's function. In particular, the occlusion regions in trees or people, or feature points extracted from the background and not from objects such as the sky or mountains are insignificant and can become the cause of undermined matching or recognition function. This paper classifies the feature points required for building recognition by using multi-frames in order to improve the recognition function(algorithm). First, through SIFT(scale invariant feature transform), the primary feature points are extracted and the mismatching feature points are removed. To categorize the feature points in occlusion regions, RANSAC(random sample consensus) is applied. Since the classified feature points were acquired through the matching method, for one feature point there are multiple descriptors and therefore a process that compiles all of them is also suggested. Experiments have verified that the suggested method is competent in its algorithm.