• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.161-171
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• 2014

When the Hough transform is applied to identify an instance of a given model, the output is typically a histogram of votes cast by a set of image features into a parameter space. The next step is to threshold the histogram of counts to hypothesize a given match. The question is "What is a reasonable choice of the threshold?" In a standard implementation of the Hough transform, the threshold is selected heuristically, e.g., some fraction of the highest cell count. Setting the threshold too low can give rise to a false alarm of a given shape(Type I error). On the other hand, setting the threshold too high can result in mis-detection of a given shape(Type II error). In this paper, we derive two conditional probability functions of cell counts in the accumulator array of the generalized Hough transform(GHough), that can be used to select a scientific threshold at the peak detection stage of the Ghough.

• KIISE Transactions on Computing Practices
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• v.22 no.3
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• pp.127-132
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• 2016

Several methods have been proposed to apply artificial neural networks to text compression in the past. However, the networks and targets are both limited to the small size due to hardware capability in the past. Modern GPUs have much better calculation capability than CPUs in an order of magnitude now, even though CPUs have become faster. It becomes possible now to train greater and complex neural networks in a shorter time. This paper proposed a method to transform the distribution of original data with a probabilistic neural predictor. Experiments were performed on a feedforward neural network and a recurrent neural network with gated-recurrent units. The recurrent neural network model outperformed feedforward network in compression rate and prediction accuracy.

• Journal of KIISE:Databases
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• v.37 no.2
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• pp.82-91
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• 2010

Data compression technique is traditional and effective to reduce network traffic. Generally, sensor data exhibit strong correlation in both space and time. Many algorithms have been proposed to utilize these characteristics. However, each sensor just utilizes neighboring information, because its communication range is restrained. Information that includes the distribution and characteristics of whole sensor data provide other opportunities to enhance the compression technique. In this paper, we propose an orthogonal approach for compression algorithm based on a novel feedback diffusion algorithm in sensor networks. The base station or a super node generates the Huffman code for compression of sensor data and broadcasts it into sensor networks. Every sensor that receives the information compresses their sensor data and transmits them to the base station. We define this approach as feedback-diffusion. In order to show the superiority of our approach, we compare it with the existing aggregation algorithms in terms of the lifetime of the sensor network. As a result, our experimental results show that the whole network lifetime was prolonged by about 30%.

• 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 the Korea Computer Industry Society
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• v.2 no.6
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• pp.827-836
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• 2001

Data is represented by file structure in Computer System. But the file size is to be larger, it is hard to control and transmit. Therefore, in recent years, many researchers have developed new algorithms for the data compression. And now, we introduce a new Dynamic Compression Technique, making up for the weaknesses of huffman's. The huffman compression technique has two weaknesses. The first, it needs two steps of reading, one for acquiring character frequency and the other for real compression. The second, low compression rate caused by storing tree information. These weaknesses can be solved by our new Dynamic Relocatable Method, reducing the reading pass by relocating data file to dynamic form, and then storing tree information from pipeline structure. The first, it needs two steps of reading, one for acquiring character frequency and the other for real compression. The second, low compression rate caused by storing tree information. These weaknesses can be solved by our new Dynamic Relocatable Method, reducing the reading pass by relocating data file to dynamic form, and then storing tree information from pipeline structure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.5C
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• pp.382-390
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• 2005

Huffman codes are widely used for image and video data transmission. As the increase of real-time data, a lot of studies on effective decoding algorithms and architectures have been done. In this paper, we proposed a balanced binary search tree for Huffman decoding and compared the performance of the proposed architecture with that of previous works. Based on definitions of the comparison of codewords with different lengths, the proposed architecture constructs a balanced binary tree which does not include empty internal nodes, and hence it is very efficient in the memory requirement. Performance evaluation results using actual image data show that the proposed architecture requires small number of table entries, and the decoding time is 1, 5, and 2.41 memory accesses in minimum, maximum, and average, respectively.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.5
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• pp.112-117
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• 2007

This paper presents specialized DSP instructions and their hardware architecture for audio coding algorithms, such as the MPEG-2/4 Advanced Audio Coding(AAC), Dolby AC-3, MPEG-2 Backward Compatible(BC), etc. The proposed architecture is specially designed and optimized for the MDCT/IMDCT(Inverse Modified Discrete Cosine Transform), and Huffman decoding of the AAC decoding algorithm. Performance comparisons show a significant improvement compared with TMS320C62x and ASDSP21060 for the MDCT/IMDCT computation. In addition, the dedicated Huffman decoding accelerator performs decoding and preparing operand in only one cycle. The proposed DPU(Data Processing Unit) consists of 107,860 gates and achieves 150 MIPS.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.12
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• pp.2953-2960
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• 2014

In the field of image recognition, circle detection is one of the most widely used techniques. Conventional algorithms are mainly based on Hough transform, which is the most straightforward algorithm for detecting circles and for providing enough robust algorithm. However, it suffers from large memory requirements and high computational loads, and sometimes tends to detect incorrect circles. This paper proposes an optimal circle detection and approximation method which is applicable for inspecting fiber optic connector endface. The proposed method finds initial center coordinates and radius based on the initial edge lines. Then, by introducing the simplified K-means algorithm, the proposed method investigates a substitute-circle by minimizing the area of non-overlapped regions. Through extensive simulations, it is shown that the proposed method can improve the error rate by as much as 67% and also can reduce the computing time by as much as 80%, compared to the Hough transform provided by the OpenCV library.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.135-142
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• 2020

In this paper, we describe the iris localization method in RGB images. Most of the iris localization methods are developed for infrared images, thus an iris localization method in RGB images is required for various applications. The proposed method consists of four stages: i) detection of the candidate irises using circular Hough transform (CHT) from an input image, ii) detection of a pupil center based on deep learning, iii) determine the iris using the pupil center, and iv) correction of the iris region. The candidate irises are detected in the order of the number of intersections of the center point candidates after generating the Hough space, and the iris in the candidates is determined based on the detected pupil center. Also, the error due to distortion of the iris shape is corrected by finding a new boundary point based on the detected iris center. In experiments, the proposed method has an improved accuracy about 27.4% compared to the CHT method.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.45-54
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• 2009

In this paper, we propose an adult images detection algorithm using a mean intensity filter and an improved 2D Hough Transform. This paper is composed of three major steps including a training step, a recognition step, and a verification step. The training step generates a mean nipple variance filter that will be used for detecting nipple candidate regions in the recognition step. To make the mean variance filter, we converts an input color image into a gray scale image and normalize it, and make an average intensity filter for nipple areas. The recognition step first extracts edge images and finds connected components, and decides nipple candidate regions by considering the ratio of width and height of a connected component. It then decides final nipple candidates by calculating the similarity between the learned nipple average intensity filter and the nipple candidate areas. Also, it detects breast lines of an input image through the improved 2D Hough transform. The verification step detects breast areas and identifies adult images by considering the relations between nipple candidate regions and locations of breast lines.