• Journal of Convergence for Information Technology
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• v.7 no.3
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• pp.73-81
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• 2017

The generalized Hough transform (GHough) is a useful technique for detecting and locating 2-D model. However, GHough requires a 4-D parameter array and a large amount of time to detect objects of unknown scale and orientation because it enumerates all possible parameter values into a 4-D parameter space. Several n-to-1 mapping algorithms were proposed to reduce the parameter space from 4-D to 2-D. However, these algorithms are very likely to fail due to the random votes cast into the 2-D parameter space. This paper proposes to use internal gradient information in addition to the model boundary points to reduce the number of random votes cast into 2-D parameter space. Experimental result shows that our proposed method can reduce both the number of random votes cast into the parameter space and the execution time effectively.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.143-151
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• 2017

The generalized Hough transform(GHough) can be used effectively for detecting and extracting an arbitrary-shaped 2-D model in an input image. However, the main drawbacks of the GHough are both heavy computation and an excessive storage requirement. Thus, most of the researches so far have focused on reducing both the time and space requirement of the GHough. But it is still not clear how well their improved algorithms will perform under various noise in an input image. Thus, this paper proposes a new framework that can measure the performance of the GHough quantitatively. For this purpose, we view the GHough as a detector in signal detection theory and the ROC curve will be used to specify the performance of the GHough. Finally, we show that we can evaluate the GHough under various noise conditions in an input image.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.156-158
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• 2003

Analysis of high-resolution satellite image becomes important for cartography, surveillance, and remote sensing. However, there are lots of problems to be solved for automatic analysis of high-resolution satellite image especially in urban area. The problems are originated from the increased complexity due to the unnecessary details and shadows, and time-varying illuminations. Because of such obstacles, it seems impossible to make automatic analysis. This paper proposes a way of change detection of buildings in urban area by using digital vector map. The proposed way makes the buildings on the vector map parameterized, and searches them in the preprocessed high-resolution image by using generalized Hough transform. The searched building objects are overlaid on the satellite image. The overlaid image can help to detect the change of building rapidly.

• Journal of IKEEE
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• v.3 no.2 s.5
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• pp.178-185
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• 1999

Geological structures such as fault and fracture patterns provide important information about preliminary exploration of mineralized areas and geological characterization. They may be recognized and interpreted from satellite images as line-like features usually referred to as lineaments. A proposed filtering method taking the sums azimuth angle into account is utilized, by which linear edges from low contrast areas where features extend parallel to the sun direction and in mountain shadow can be effectively extracted. Then, generalized Hough transform is applied to extract lineaments which correspond to fault and fracture patterns.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.493-498
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• 2007

In this paper, a 2-dimensional accurate edge-based matching algorithm using subpixel edges is proposed that combines the Generalized Hough Transform(GHT) and the Chamfer matching to complement the weakness of either method. First, the GHT is used to find the approximate object positions and orientations, and then these positions and orientations are used as starting parameter values to find more accurate position and orientation using the Chamfer matching with distance interpolation. Finally, matching accuracy is further refined by using a subpixel algorithm. Testing results demonstrate that greater matching accuracy is achieved using subpixel edges rather than edge pixels.

• Journal of KIISE:Software and Applications
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• v.30 no.10
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• pp.973-982
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• 2003

This paper presents an efficient method for automatically detecting objects in a given image. The GHT is a robust template matching algorithm for automatic object detection in order to find objects of various shapes. Many different templates are applied by the GHT in order to find objects of various shapes and size. Every boundary detected by the GHT scan be used as an initial outline for more precise contour-finding techniques. The main weakness of the GHT is the excessive time and memory requirements. In order to overcome this drawback, the proposed algorithm uses a multiresolution search by scaling down the original image to half-sized and quarter-sized images. Using the information from the first iterative GHT on a quarter-sized image, the range of nuclear sizes is determined to limit the parameter space of the half-sized image. After the second iterative GHT on the half-sized image, nuclei are detected by the fine search and segmented with edge information which helps determine the exact boundary. The experimental results show that this method gives reduction in computation time and memory usage without loss of accuracy.

• Journal of Internet Computing and Services
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• v.11 no.6
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• pp.31-39
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• 2010

The Generalized Hough Transform(GHT) is the most used algorithm for circle detection with high accuracy. However, it requires many computation time, because many different templates are applied in order to find circles of various size. In the case of circle detection and tracking in video, the classical approach applies GHT for each frame in video and thus needs much high processing time for all frames. This paper proposes the fast GHT algorithm in video, using two consecutive frames are similar. In the proposed algorithm, a change-driven method conducts GHT only when two consecutive frames have many changes, and trajectory-based method does GHT in candidate areas and with candidate radius using circles detected in a previous frame. The algorithm can reduce computation time by reducing the number of frames, the edge count, and the number of searching circles, as factors which affects the speed of GHT. Our experimental results show that the algorithm successfully detects circles with less processing time and no loss of accuracy in video acquisited by a fixed camera and a moving camera.

• Korean Journal of Remote Sensing
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• v.14 no.2
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• pp.175-195
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• 1998

Automatic lineament extraction algorithms had been developed by various researches for geological purpose using remotely sensed data. However, most of them are designed for a certain topographic model, for instance rugged mountainous region or flat basin. Most of common topographic characteristic in Korea is a mountainous region along with alluvial plain, and consequently it is difficult to apply previous algorithms directly to this area. A new algorithm of automatic lineament extraction from remotely sensed images is developed in this study specifically for geological applications. An algorithm, named as DSTA(Dynamic Segment Tracing Algorithm), is developed to produce binary image composed of linear component and non-linear component. The proposed algorithm effectively reduces the look direction bias associated with sun's azimuth angle and the noise in the low contrast region by utilizing a dynamic sub window. This algorithm can successfully accomodate lineaments in the alluvial plain as well as mountainous region. Two additional algorithms for estimating the individual lineament vector, named as ALEHHT(Automatic Lineament Extraction by Hierarchical Hough Transform) and ALEGHT(Automatic Lineament Extraction by Generalized Hough Transform) which are merging operation steps through the Hierarchical Hough transform and Generalized Hough transform respectively, are also developed to generate geological lineaments. The merging operation proposed in this study is consisted of three parameters: the angle between two lines($\delta$$\beta$), the perpendicular distance($(d_ij)$), and the distance between midpoints of lines(dn). The test result of the developed algorithm using Landsat TM image demonstrates that lineaments in alluvial plain as well as in rugged mountain is extremely well extracted. Even the lineaments parallel to sun's azimuth angle are also well detected by this approach. Further study is, however, required to accommodate the effect of quantization interval(droh) parameter in ALEGHT for optimization.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.67-78
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• 1996

The purpose of this paper is object recognition even in the presence of occlusion by using generalized Hough transform(GHT). The GHT can be considered as a kind of model based object recognition algorithm and is executed in the following two stages. The first stage is to store the information of the model in the form of R-table (Reference table). The next stage is to identify the existence of the objects in the image by using the R-table. The improved GHT method is proposed for the practical vision system. First, in constructing the R-table, we extracted the partial arc from the portion of the whole object boundary, and this partial arc can be used for constructing the R-table. Also, clustering algorithm is employed for compensating an error arised by digitizing an object image. Second, an efficient method is introduced to avoid Ballard's use of 4-D array which is necessary for estimating position, orientation and scale change of an object. Only 2-D array is enough for recognizing an object. Especially, scale token method is introduced for calculating the scale change which is easily affected by camera zoom. The results of our test show that the improved hierarchical GHT method operates stably in the realistic vision situation, even in the case of object occlusion.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.3
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• pp.81-89
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• 2007

This paper presents a parallel distributed implementation of the GHT (generalized Hough transform) for the fast processing on the MPI-based PC cluster. We tried to achieve the higher speedup mainly by alleviating the communication overhead through the pipelined broadcast and accumulator array partition strategy and by time overlapping of the communication and the computation over entire process. Experimental results show that nearly linear speedup is reachable by the proposed method on the MPI-based PC clusters connected through 100Mbps Ethernet switch.