Sistem Identifikasi Biometrik Ekpresi Wajah Menggunakan Metode Transformasi Hough

2020 ◽  
Vol 3 (3) ◽  
pp. 262-267
Author(s):  
Anita Sindar ◽  
Arjon Samuel Sitio
Keyword(s):  
Facial Expression ◽  
Hough Transform ◽  
Transformation Method ◽  
Detection Accuracy ◽  
Outer Circle ◽  
Hough Transformation ◽  
Mood Changes ◽  
Straight Line ◽  
Neighbor Relationship ◽  
Curved Objects

The movement of the eye ball affects the condition of the pupil to dilate to become smaller or vice versa, it indicates a person's mood changes very quickly. The image of the eyeball is not necessarily in accordance with the condition of a person's heart, so it is necessary to analyze the movement of the pupil of the eye. Facial expression using the Hough Transformation Method focuses on the movement of the pupil of the eye. The Hough transform works by looking for the neighbor relationship between pixels using straight line equations to detect lines and circular equations to detect circles. Hough line transform is a technique most commonly used to detect curved objects such as lines, circles, ellipses and parabolas. The detection accuracy of the pupil is influenced by the accuracy of the extraction of the edges of the eye. If the outer circle identification is not detected, Hough Transform will be identified. The segmentation step carried out can identify the pupil circle region with a detection success of 80-85%.

Multiple Planar Intersects Recognition by Hough Transform

2013 ◽  
Vol 347-350 ◽  
pp. 3190-3193
Author(s):  
Yu Ting Chen ◽  
Xiao Lu Pan ◽  
Wei Yu ◽  
Ling Li ◽  
Qi Biao Ni
Keyword(s):  
Hough Transform ◽  
Transformation Method ◽  
Depth Gradient ◽  
Hough Transformation ◽  
Depth Data ◽  
3D Space

Detect the intersect lines of multiple planes is one way to recognize and reconstruct the 3D space. First, analyzed the depth data grabbed by SR3000, sampled the depth data into gray bitmap and detect the depth gradient edge. By using the improved Hough Transformation method, get the parameters of spatial intersection lines.

Analisis Manajemen Pajak Pada Industri Penyedia Jasa Telekomunikasi

2010 ◽  
Vol 2 (1) ◽  
pp. 57-69
Author(s):  
Iim Ibrahim Nur
Keyword(s):  
Income Tax ◽  
Tax Compliance ◽  
Transformation Method ◽  
Tax Burden ◽  
Tax Allowance ◽  
Straight Line ◽  
Tax Management ◽  
Family Gathering ◽  
Straight Line Method ◽  
Depreciation Methods

Tax Management must be done throughout the company’s activities. In principle, tax management can be done via good tax compliance and minimizing tax burden. The latter can be achieved by transforming non-deductible expenses into deductible expenses. For example, PT Nyambung Teruuusss Tbk. (PT. NT) must change income Tax Art. 21 paid by the company into tax allowance with gross-up method, pooling company's cars at the office instead of letting these cars brought home by the employees, outbound training for employees instead of family gathering, and other methods including converting fringe benefits into allowance. Another method to minimize tax burden is to change depreciation methods into double-declining method instead of straight-line method. With nondeductible transformation method have saved PT NT Rp 5.26 billion of corporate income tax, while depreciation methods transformation is predicted to save the company Rp 735.66 billion for an eightyear period

Coarse-To-Fine 3D Randomized Hough Transform for Dim Target Detection

2014 ◽  
Vol 519-520 ◽  
pp. 1040-1045
Author(s):  
Ling Fan
Keyword(s):  
Target Detection ◽  
Hough Transform ◽  
Constant Velocity ◽  
Three Dimensional ◽  
Randomized Hough Transform ◽  
Straight Line ◽  
Memory Complexity ◽  
Coarse To Fine

This paper makes some improvements on Roberts representation for straight line in space and proposes a coarse-to-fine three-dimensional (3D) Randomized Hough Transform (RHT) for the detection of dim targets. Using range, bearing and elevation information of the received echoes, 3D RHT can detect constant velocity target in space. In addition, this paper applies a coarse-to-fine strategy to the 3D RHT, which aims to solve both the computational and memory complexity problems. The validity of the coarse-to-fine 3D RHT is verified by simulations. In comparison with the 2D case, which only uses the range-bearing information, the coarse-to-fine 3D RHT has a better practical value in dim target detection.

Road Detection Based on Edge Feature with GAC Model in Aerial Image

2021 ◽  
Author(s):  
Linying Zhou ◽  
Zhou Zhou ◽  
Hang Ning
Keyword(s):  
Transformation Method ◽  
Aerial Images ◽  
Aerial Image ◽  
Initial Contour ◽  
Detection Accuracy ◽  
Road Detection ◽  
Canny Operator ◽  
The Road ◽  
Gradient Magnitude ◽  
Edge Feature

Road detection from aerial images still is a challenging task since it is heavily influenced by spectral reflectance, shadows and occlusions. In order to increase the road detection accuracy, a proposed method for road detection by GAC model with edge feature extraction and segmentation is studied in this paper. First, edge feature can be extracted using the proposed gradient magnitude with Canny operator. Then, a reconstructed gradient map is applied in watershed transformation method, which is segmented for the next initial contour. Last, with the combination of edge feature and initial contour, the boundary stopping function is applied in the GAC model. The road boundary result can be accomplished finally. Experimental results show, by comparing with other methods in [Formula: see text]-measure system, that the proposed method can achieve satisfying results.

scholarly journals A radio burst detection method based on the Hough transform

2020 ◽  
Vol 494 (2) ◽  
pp. 1994-2003
Author(s):  
Shifan Zuo ◽  
Xuelei Chen
Keyword(s):  
Parameter Space ◽  
Radio Burst ◽  
Hough Transform ◽  
Single Point ◽  
Fast Method ◽  
Noise Levels ◽  
Time Frequency ◽  
Straight Line ◽  
Fast Radio Burst ◽  
Truncation Threshold

ABSTRACT We present a simple and fast method for incoherent dedispersion and fast radio burst (FRB) detection based on the Hough transform, which is widely used for feature extraction in image analysis. The Hough transform maps a point in the time–frequency data to a straight line in the parameter space and points on the same dispersed f−2 curve to a bundle of lines all crossing at the same point, thus the curve is transformed to a single point in the parameter space, enabling an easier way for the detection of radio burst. By choosing an appropriate truncation threshold, in a reasonably radio quiet environment, i.e. with radio frequency interferences present but not dominant, the computing speed of the method is very fast. Using simulation data of different noise levels, we studied how the detected peak varies with different truncation thresholds. We also tested the method with some real pulsar and FRB data.

Randomized Hough Transform

2011 ◽  
pp. 1343-1350 ◽  
Author(s):  
Lei Xu ◽  
Erkki Oja
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Hough Transform ◽  
Random Sampling ◽  
Computational Cost ◽  
Detection Accuracy ◽  
The Past ◽  
Randomized Hough Transform ◽  
Dynamic Storage ◽  
High Computational Cost

Proposed in 1962, the Hough transform (HT) has been widely applied and investigated for detecting curves, shapes, and motions in the fields of image processing and computer vision. However, the HT has several shortcomings, including high computational cost, low detection accuracy, vulnerability to noise, and possibility of missing objects. Many efforts target at solving some of the problems for decades, while the key idea remains more or less the same. Proposed in 1989 and further developed thereafter, the Randomized Hough Transform (RHT) manages to considerably overcome these shortcomings via innovations on the fundamental mechanisms, with random sampling in place of pixel scanning, converging mapping in place of diverging mapping, and dynamic storage in place of accumulation array. This article will provides an overview on advances and applications of RHT in the past one and half decades.

scholarly journals Parallel Hough Transform-Based Straight Line Detection and Its FPGA Implementation in Embedded Vision

Sensors ◽  
2013 ◽  
Vol 13 (7) ◽  
pp. 9223-9247 ◽  
Author(s):  
Xiaofeng Lu ◽  
Li Song ◽  
Sumin Shen ◽  
Kang He ◽  
Songyu Yu ◽  
...  
Keyword(s):  
Hough Transform ◽  
Fpga Implementation ◽  
Line Detection ◽  
Embedded Vision ◽  
Straight Line ◽  
Straight Line Detection
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