An Effective Slant Detection and Correction Method Based on the Tilted Rectangle Method for Telugu Manuscript Terms

2021 ◽  
Vol 12 (4) ◽  
pp. 25-37
Author(s):  
Vijaya Kumar V. ◽  
G. Bindu Madhavi ◽  
V. Krishna Vakula
Keyword(s):  
Efficient Method ◽  
Correction Method ◽  
Reverse Direction ◽  
Connected Components ◽  
Connected Component ◽  
Slant Angle ◽  
Shear Transformation ◽  
Experimental Findings ◽  
The Given ◽  
Complex Characters

This paper proposes an efficient method called tilted rectangle (TR) for detecting and correcting of slant angle of the manuscript Telugu words (MTW). Telugu language is one of India's common languages spoken by over 80 million individuals. The complex characters are attached with some extra marks known as “maatras” and “vatthus,” and it is challenging to detect slant angle. The proposed TR method initially performs preprocessing and identifies a connected component within the given Telugu manuscript word. Then, it estimates the slant angle of each connected component by deriving connected slant lines on the boundary of each connected component. After this process, the proposed TR method estimates the entire word's overall slant angle from the average of estimated slant angle and height of all connected components. The correction of the word's slant angle is done in the reverse direction by applying a simple shear transformation. With 1000 manuscript records of three different kinds, the algorithm is tested. Experimental findings indicate the efficacy of the approach proposed.

STATISTICAL CHARACTERISTICS OF SLANT ANGLES IN HANDWRITTEN NUMERAL STRINGS AND EFFECTS OF SLANT CORRECTION ON SEGMENTATION

2010 ◽  
Vol 24 (01) ◽  
pp. 97-116 ◽  
Author(s):  
JAVAD SADRI ◽  
CHING Y. SUEN ◽  
TIEN D. BUI
Keyword(s):  
Efficient Method ◽  
Weighted Average ◽  
Correction Method ◽  
Statistical Characteristics ◽  
Geometric Features ◽  
Correction Algorithm ◽  
Connected Component ◽  
Slant Angle ◽  
Segmentation Accuracy ◽  
First Time

A novel and efficient method for correction of slant angles in handwritten numeral strings is proposed. For the first time, the statistical distribution of slant angles in handwritten numerals is investigated and the effects of slant correction on the segmentation of handwritten numeral strings are shown. In our proposed slant correction method, utilizing geometric features, a Component Slant Angle (CSA) is estimated for each connected component independently. A weighted average is then used to compute the String Slant Angle (SSA), which is applied uniformly to correct the slant of all the components in numeral strings. Our experimental results have revealed novel statistics for slant angles of handwritten numeral strings, and also showed that slant correction can significantly improve extraction of segmentation features and segmentation accuracy of touching numerals. Comparison between our slant correction algorithm and similar algorithms in the literature show that our algorithm is more efficient, and on average it has a faster running time.

A RUN-BASED ONE-AND-A-HALF-SCAN CONNECTED-COMPONENT LABELING ALGORITHM

2010 ◽  
Vol 24 (04) ◽  
pp. 557-579 ◽  
Author(s):  
LIFENG HE ◽  
YUYAN CHAO ◽  
KENJI SUZUKI
Keyword(s):  
Binary Image ◽  
Experimental Results ◽  
Connected Components ◽  
Connected Component ◽  
Run Length ◽  
Highly Efficient ◽  
Connected Component Labeling ◽  
Labeling Algorithm ◽  
The Given

This paper presents a run- and label-equivalence-based one-and-a-half-scan algorithm for labeling connected components in a binary image. Major differences between our algorithm and conventional label-equivalence-based algorithms are: (1) all conventional label-equivalence-based algorithms scan all pixels in the given image at least twice, whereas our algorithm scans background pixels once and object pixels twice; (2) all conventional label-equivalence-based algorithms assign a provisional label to each object pixel in the first scan and relabel the pixel in the later scan(s), whereas our algorithm assigns a provisional label to each run in the first scan, and after resolving label equivalences between runs, by using the recorded run data, it assigns each object pixel a final label directly. That is, in our algorithm, relabeling of object pixels is not necessary any more. Experimental results demonstrated that our algorithm is highly efficient on images with many long runs and/or a small number of object pixels. Moreover, our algorithm is directly applicable to run-length-encoded images, and we can obtain contours of connected components efficiently.

Elimination of Support Effects in Modal Testing

2012 ◽  
Vol 226-228 ◽  
pp. 48-51
Author(s):  
Jun Ren ◽  
Shu Sheng Bi ◽  
Wei Wang ◽  
Guang Hua Zong
Keyword(s):  
Correction Method ◽  
Modal Testing ◽  
Mass Loading ◽  
Data Simulation ◽  
Support Effects ◽  
Loading Effects ◽  
Measured Frequency Response ◽  
Support Conditions ◽  
Stiffness And Damping ◽  
The Given

In modal testing, the measured Frequency Response Functions (FRFs) are often inaccurate due to the adverse mechanical effects, such as mass-loading effects of transducers, shaker-structure interaction and the support effects. This paper deals with the elimination of the support effects from measured FRFs using dynamic substructure method. Both stiffness and damping are considered in the support effects. The validity of the method is proved in a simulated modal test. It is shown that with the given support conditions the affected FRFs can be corrected provided that some additional FRFs concerned with the support point are also measured. Finally, performance of this method is assessed with noisy measured data. Simulation shows that 1% white noise is acceptable in the proposed correction method.

The size of the connected components of excursion sets of χ2, t and F fields

1999 ◽  
Vol 31 (03) ◽  
pp. 579-595 ◽  
Author(s):  
J. Cao
Keyword(s):  
Positron Emission Tomography ◽  
Two Dimensions ◽  
Emission Tomography ◽  
Connected Components ◽  
Gaussian Fields ◽  
Connected Component ◽  
Test Statistic ◽  
Excursion Sets ◽  
Positron Emission ◽  
Excursion Set

The distribution of the size of one connected component and the largest connected component of the excursion set is derived for stationary χ2, t and F fields, in the limit of high or low thresholds. This extends previous results for stationary Gaussian fields (Nosko 1969, Adler 1981) and for χ2 fields in one and two dimensions (Aronowich and Adler 1986, 1988). An application of this is to detect regional changes in positron emission tomography (PET) images of blood flow in human brain, using the size of the largest connected component of the excursion set as a test statistic.

scholarly journals Dental Images’ Segmentation Using Threshold Connected Component Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Vincent Majanga ◽  
Serestina Viriri
Keyword(s):  
Deep Learning ◽  
Region Of Interest ◽  
Component Analysis ◽  
Connected Components ◽  
Connected Component ◽  
Low Contrast ◽  
Connected Component Analysis ◽  
Feature Representations ◽  
Learning Techniques ◽  
Performance Results

Recent advances in medical imaging analysis, especially the use of deep learning, are helping to identify, detect, classify, and quantify patterns in radiographs. At the center of these advances is the ability to explore hierarchical feature representations learned from data. Deep learning is invaluably becoming the most sought out technique, leading to enhanced performance in analysis of medical applications and systems. Deep learning techniques have achieved great performance results in dental image segmentation. Segmentation of dental radiographs is a crucial step that helps the dentist to diagnose dental caries. The performance of these deep networks is however restrained by various challenging features of dental carious lesions. Segmentation of dental images becomes difficult due to a vast variety in topologies, intricacies of medical structures, and poor image qualities caused by conditions such as low contrast, noise, irregular, and fuzzy edges borders, which result in unsuccessful segmentation. The dental segmentation method used is based on thresholding and connected component analysis. Images are preprocessed using the Gaussian blur filter to remove noise and corrupted pixels. Images are then enhanced using erosion and dilation morphology operations. Finally, segmentation is done through thresholding, and connected components are identified to extract the Region of Interest (ROI) of the teeth. The method was evaluated on an augmented dataset of 11,114 dental images. It was trained with 10 090 training set images and tested on 1024 testing set images. The proposed method gave results of 93 % for both precision and recall values, respectively.

Feature based Text Extraction System using Connected Component Method

2016 ◽  
Vol 7 (1) ◽  
pp. 41-57 ◽  
Author(s):  
Nitigya Sambyal ◽  
Pawanesh Abrol
Keyword(s):  
Character Segmentation ◽  
Connected Components ◽  
Test Cases ◽  
Connected Component ◽  
Font Size ◽  
Text Extraction ◽  
Component Technique ◽  
Edge Detectors ◽  
Feature Based

Text detection and segmentation system serves as important method for document analysis as it helps in many content based image analysis tasks. This research paper proposes a connected component technique for text extraction and character segmentation using maximally stable extremal regions (MSERs) for text line formation followed by connected components to determined separate characters. The system uses a cluster size of five which is selected by experimental evaluation for identifying characters. Sobel edge detector is used as it reduces the execution time but at the same time maintains quality of the results. The algorithm is tested along a set of JPEG, PNG and BMP images over varying features like font size, style, colour, background colour and text variation. Further the CPU time in execution of the algorithm with three different edge detectors namely prewitt, sobel and canny is observed. Text identification using MSER gave very good results whereas character segmentation gave on average 94.572% accuracy for the various test cases considered for this study.

scholarly journals Shear-Transformation Zone Activation during Loading and Unloading in Nanoindentation of Metallic Glasses

Materials ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1477 ◽  
Author(s):  
Karina E. Avila ◽  
Stefan Küchemann ◽  
Iyad Alabd Alhafez ◽  
Herbert M. Urbassek
Keyword(s):  
Dynamics Simulation ◽  
Minor Effect ◽  
Rate Analysis ◽  
Loading And Unloading ◽  
Shear Transformation ◽  
Shear Transformation Zones ◽  
Experimental Findings ◽  
A Minor ◽  
Increasing Temperature ◽  
Activity Cluster

Using molecular dynamics simulation, we study nanoindentation in large samples of Cu–Zr glass at various temperatures between zero and the glass transition temperature. We find that besides the elastic modulus, the yielding point also strongly (by around 50%) decreases with increasing temperature; this behavior is in qualitative agreement with predictions of the cooperative shear model. Shear-transformation zones (STZs) show up in increasing sizes at low temperatures, leading to shear-band activity. Cluster analysis of the STZs exhibits a power-law behavior in the statistics of STZ sizes. We find strong plastic activity also during the unloading phase; it shows up both in the deactivation of previous plastic zones and the appearance of new zones, leading to the observation of pop-outs. The statistics of STZs occurring during unloading show that they operate in a similar nature as the STZs found during loading. For both cases, loading and unloading, we find the statistics of STZs to be related to directed percolation. Material hardness shows a weak strain-rate dependence, confirming previously reported experimental findings; the number of pop-ins is reduced at slower indentation rate. Analysis of the dependence of our simulation results on the quench rate applied during preparation of the glass shows only a minor effect on the properties of STZs.

scholarly journals SELF-STABILIZING COMPUTATION OF 3-EDGE-CONNECTED COMPONENTS

2011 ◽  
Vol 22 (05) ◽  
pp. 1161-1185
Author(s):  
ABUSAYEED SAIFULLAH ◽  
YUNG H. TSIN
Keyword(s):  
Time Complexity ◽  
Computer Network ◽  
Connected Components ◽  
Connected Component ◽  
External Agent ◽  
Tree Construction ◽  
Legitimate State ◽  
Time And Space Complexity ◽  
Better Than ◽  
Made In

A self-stabilizing algorithm is a distributed algorithm that can start from any initial (legitimate or illegitimate) state and eventually converge to a legitimate state in finite time without being assisted by any external agent. In this paper, we propose a self-stabilizing algorithm for finding the 3-edge-connected components of an asynchronous distributed computer network. The algorithm stabilizes in O(dnΔ) rounds and every processor requires O(n log Δ) bits, where Δ(≤ n) is an upper bound on the degree of a node, d(≤ n) is the diameter of the network, and n is the total number of nodes in the network. These time and space complexity are at least a factor of n better than those of the previously best-known self-stabilizing algorithm for 3-edge-connectivity. The result of the computation is kept in a distributed fashion by assigning, upon stabilization of the algorithm, a component identifier to each processor which uniquely identifies the 3-edge-connected component to which the processor belongs. Furthermore, the algorithm is designed in such a way that its time complexity is dominated by that of the self-stabilizing depth-first search spanning tree construction in the sense that any improvement made in the latter automatically implies improvement in the time complexity of the algorithm.

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