Image recognition of line drawing involves feature extraction and feature comparison. Works on the extraction required the representation of the image to be compared. Combining these two requirements, a framework that develops a new extraction algorithm of a chain code representation is presented. In addition, new corner detection is presented as pre-processing to the line drawing input in order to derive the chain code. This paper presents a new framework that consist of five steps namely pre-processing and image processing, new corner detection algorithm, chain code generator, feature extraction algorithm, and recognition process. Heuristic approach that is applied in the corner detection algorithm accepts input of thinned binary image and produce a modified thinned binary image consisted of J character to represent corners in the image. Using the modified thinned binary image, a new chain code scheme that is based on Freeman chain code is proposed and an algorithm is developed to generate a single chain code series that is representing the line drawing input. The feature extraction algorithm is then extracting the three pre-defined features of the chain code for recognition purpose. The features are corner properties, distance between corners, and angle from a corner to the connected corner. The explanation of steps in the framework is supported with two line drawings. The results show that the framework successfully recognizes line drawing into five categories namely not similar line drawing, and four other categories that are similar but with attributes of rotation angle and scaling ratio.
IRJET- Classification of Hindi Maatras by Encoding SchemeIRJET Journal
ย
This document presents a novel encoding scheme for classifying Hindi modifiers or matras. It describes existing techniques for modifier segmentation that use projection profiles and character heights. The proposed method segments modifiers using three encoding levels to assign a distinctive code for each modifier. For ascenders, the codes are based on the middle portion, skeletonized shape, and ends. For descenders, the codes consider width, last pixel, and lower right space. The method was tested on printed and handwritten modifiers, achieving over 90% accuracy for both ascenders and descenders. The encoding approach allows for direct classification of modifiers without feature extraction.
Unimodal Multi-Feature Fusion and one-dimensional Hidden Markov Models for Lo...IJECEIAES
ย
The objective of low-resolution face recognition is to identify faces from small size or poor quality images with varying pose, illumination, expression, etc. In this work, we propose a robust low face recognition technique based on one-dimensional Hidden Markov Models. Features of each facial image are extracted using three steps: ๏ฌrstly, both Gabor ๏ฌlters and Histogram of Oriented Gradients (HOG) descriptor are calculated. Secondly, the size of these features is reduced using the Linear Discriminant Analysis (LDA) method in order to remove redundant information. Finally, the reduced features are combined using Canonical Correlation Analysis (CCA) method. Unlike existing techniques using HMMs, in which authors consider each state to represent one facial region (eyes, nose, mouth, etc), the proposed system employs 1D-HMMs without any prior knowledge about the localization of interest regions in the facial image. Performance of the proposed method will be measured using the AR database.
Recognition of Persian handwritten characters has been considered as a significant field of research for
the last few years under pattern analysing technique. In this paper, a new approach for robust handwritten
Persian numerals recognition using strong feature set and a classifier fusion method is scrutinized to
increase the recognition percentage. For implementing the classifier fusion technique, we have considered
k nearest neighbour (KNN), linear classifier (LC) and support vector machine (SVM) classifiers. The
innovation of this tactic is to attain better precision with few features using classifier fusion method. For
evaluation of the proposed method we considered a Persian numerals database with 20,000 handwritten
samples. Spending 15,000 samples for training stage, we verified our technique on other 5,000 samples,
and the correct recognition ratio achievedapproximately 99.90%. Additional, we got 99.97% exactness
using four-fold cross validation procedure on 20,000 databases.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
The automatic license plate recognition(alpr)eSAT Journals
ย
Abstract Every country uses their own way of designing and allocating number plates to their country vehicles. This license number plate is then used by various government offices for their respective regular administrative task like- traffic police tracking the people who are violating the traffic rules, to identify the theft cars, in toll collection and parking allocation management etc. In India all motorized vehicle are assigned unique numbers. These numbers are assigned to the vehicles by district-level Regional Transport Office (RTO). In India the license plates must be kept in both front and back of the vehicle. These plates in general are easily readable by human due to their high level of intelligence on the contrary; it becomes an extremely difficult task for the computers to do the same. Many attributes like illumination, blur, background color, foreground color etc. will pose a problem. Index Terms: Automatic license plate recognition (ALPR) system, proposed methodology, reference
Role Model of Graph Coloring Application in Labeled 2D Line Drawing ObjectWaqas Tariq
ย
Several researches had worked on the development of sketch interpreters. However, very few of them gave a complete cycle of the sketch interpreter which can be used to transform an engineering sketch to a valid solid object. In this paper, a framework of the complete cycle of the sketch interpreter is presented. The discussion in this paper will stress on the usage of line labeling and graph coloring application in the validation of two dimensional (2D) line drawing phase. Both applications are needed to determine whether the given 2D line drawing represent possible or impossible object. In 2008, previous work by Matondang et al., has used line labeling algorithm to validate several 2D line drawings. However, the result shows that line labeling algorithm is not sufficient, as the algorithm does not have a validation technique for the result. Therefore, in this research study, it is going to be shown that if a 2D line drawing is valid as a possible object by using the line labeling algorithm, then it can be colored using graph coloring concept with a determine-able minimum numbers of color needed. This is equal in vice versa. The expected output from this phase is a valid-labeled of 2D line drawing with different colors at each edge and ready for the reconstruction phase. As a preliminary result, a high programming language MATLAB R2009a and several primitive 2D line drawings has been used and presented in this paper to test the graph coloring concept in labeled 2D line drawing.
Hybrid method for automating generation of reticulated structures (lattice st...IJECEIAES
ย
A reticulated structure is an interconnexion of bars used to create industrial products. They are rigid and lighter than traditional structures. So they can be the best choice when material gain is an optimization purpose. Generating a reticulated structure automatically is a feature helping industrial players in the design phase. This generation depends on the kind of the conception domain. In this paper we propose a solution that generates a reticulated structure in an arbitrary domain with zero or several holes. The proposed solution is a hybrid method using a technique generating a reticulated structure in a convex conception domain and suggesting a criterion to validate generated segments. Our new algorithm uses a method of computational geometry. We also present a study of the behaviour of a reticulated structure generated using our tool by calculating the deformation energy of this structure.
The automatic license plate recognition(alpr)eSAT Journals
ย
Abstract Every country uses their own way of designing and allocating number plates to their country vehicles. This license number plate is then used by various government offices for their respective regular administrative task like- traffic police tracking the people who are violating the traffic rules, to identify the theft cars, in toll collection and parking allocation management etc. In India all motorized vehicle are assigned unique numbers. These numbers are assigned to the vehicles by district-level Regional Transport Office (RTO). In India the license plates must be kept in both front and back of the vehicle. These plates in general are easily readable by human due to their high level of intelligence on the contrary; it becomes an extremely difficult task for the computers to do the same. Many attributes like illumination, blur, background color, foreground color etc. will pose a problem. Index Terms: Automatic license plate recognition (ALPR) system, proposed methodology, reference
IRJET- Classification of Hindi Maatras by Encoding SchemeIRJET Journal
ย
This document presents a novel encoding scheme for classifying Hindi modifiers or matras. It describes existing techniques for modifier segmentation that use projection profiles and character heights. The proposed method segments modifiers using three encoding levels to assign a distinctive code for each modifier. For ascenders, the codes are based on the middle portion, skeletonized shape, and ends. For descenders, the codes consider width, last pixel, and lower right space. The method was tested on printed and handwritten modifiers, achieving over 90% accuracy for both ascenders and descenders. The encoding approach allows for direct classification of modifiers without feature extraction.
Unimodal Multi-Feature Fusion and one-dimensional Hidden Markov Models for Lo...IJECEIAES
ย
The objective of low-resolution face recognition is to identify faces from small size or poor quality images with varying pose, illumination, expression, etc. In this work, we propose a robust low face recognition technique based on one-dimensional Hidden Markov Models. Features of each facial image are extracted using three steps: ๏ฌrstly, both Gabor ๏ฌlters and Histogram of Oriented Gradients (HOG) descriptor are calculated. Secondly, the size of these features is reduced using the Linear Discriminant Analysis (LDA) method in order to remove redundant information. Finally, the reduced features are combined using Canonical Correlation Analysis (CCA) method. Unlike existing techniques using HMMs, in which authors consider each state to represent one facial region (eyes, nose, mouth, etc), the proposed system employs 1D-HMMs without any prior knowledge about the localization of interest regions in the facial image. Performance of the proposed method will be measured using the AR database.
Recognition of Persian handwritten characters has been considered as a significant field of research for
the last few years under pattern analysing technique. In this paper, a new approach for robust handwritten
Persian numerals recognition using strong feature set and a classifier fusion method is scrutinized to
increase the recognition percentage. For implementing the classifier fusion technique, we have considered
k nearest neighbour (KNN), linear classifier (LC) and support vector machine (SVM) classifiers. The
innovation of this tactic is to attain better precision with few features using classifier fusion method. For
evaluation of the proposed method we considered a Persian numerals database with 20,000 handwritten
samples. Spending 15,000 samples for training stage, we verified our technique on other 5,000 samples,
and the correct recognition ratio achievedapproximately 99.90%. Additional, we got 99.97% exactness
using four-fold cross validation procedure on 20,000 databases.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
The automatic license plate recognition(alpr)eSAT Journals
ย
Abstract Every country uses their own way of designing and allocating number plates to their country vehicles. This license number plate is then used by various government offices for their respective regular administrative task like- traffic police tracking the people who are violating the traffic rules, to identify the theft cars, in toll collection and parking allocation management etc. In India all motorized vehicle are assigned unique numbers. These numbers are assigned to the vehicles by district-level Regional Transport Office (RTO). In India the license plates must be kept in both front and back of the vehicle. These plates in general are easily readable by human due to their high level of intelligence on the contrary; it becomes an extremely difficult task for the computers to do the same. Many attributes like illumination, blur, background color, foreground color etc. will pose a problem. Index Terms: Automatic license plate recognition (ALPR) system, proposed methodology, reference
Role Model of Graph Coloring Application in Labeled 2D Line Drawing ObjectWaqas Tariq
ย
Several researches had worked on the development of sketch interpreters. However, very few of them gave a complete cycle of the sketch interpreter which can be used to transform an engineering sketch to a valid solid object. In this paper, a framework of the complete cycle of the sketch interpreter is presented. The discussion in this paper will stress on the usage of line labeling and graph coloring application in the validation of two dimensional (2D) line drawing phase. Both applications are needed to determine whether the given 2D line drawing represent possible or impossible object. In 2008, previous work by Matondang et al., has used line labeling algorithm to validate several 2D line drawings. However, the result shows that line labeling algorithm is not sufficient, as the algorithm does not have a validation technique for the result. Therefore, in this research study, it is going to be shown that if a 2D line drawing is valid as a possible object by using the line labeling algorithm, then it can be colored using graph coloring concept with a determine-able minimum numbers of color needed. This is equal in vice versa. The expected output from this phase is a valid-labeled of 2D line drawing with different colors at each edge and ready for the reconstruction phase. As a preliminary result, a high programming language MATLAB R2009a and several primitive 2D line drawings has been used and presented in this paper to test the graph coloring concept in labeled 2D line drawing.
Hybrid method for automating generation of reticulated structures (lattice st...IJECEIAES
ย
A reticulated structure is an interconnexion of bars used to create industrial products. They are rigid and lighter than traditional structures. So they can be the best choice when material gain is an optimization purpose. Generating a reticulated structure automatically is a feature helping industrial players in the design phase. This generation depends on the kind of the conception domain. In this paper we propose a solution that generates a reticulated structure in an arbitrary domain with zero or several holes. The proposed solution is a hybrid method using a technique generating a reticulated structure in a convex conception domain and suggesting a criterion to validate generated segments. Our new algorithm uses a method of computational geometry. We also present a study of the behaviour of a reticulated structure generated using our tool by calculating the deformation energy of this structure.
The automatic license plate recognition(alpr)eSAT Journals
ย
Abstract Every country uses their own way of designing and allocating number plates to their country vehicles. This license number plate is then used by various government offices for their respective regular administrative task like- traffic police tracking the people who are violating the traffic rules, to identify the theft cars, in toll collection and parking allocation management etc. In India all motorized vehicle are assigned unique numbers. These numbers are assigned to the vehicles by district-level Regional Transport Office (RTO). In India the license plates must be kept in both front and back of the vehicle. These plates in general are easily readable by human due to their high level of intelligence on the contrary; it becomes an extremely difficult task for the computers to do the same. Many attributes like illumination, blur, background color, foreground color etc. will pose a problem. Index Terms: Automatic license plate recognition (ALPR) system, proposed methodology, reference
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Automatic rectification of perspective distortion from a single image using p...ijcsa
ย
Perspective distortion occurs due to the perspective projection of 3D scene on a 2D surface. Correcting the distortion of a single image without losing any desired information is one of the challenging task in the field of Computer Vision. We consider the problem of estimating perspective distortion from a single still image of an unstructured environment and to make perspective correction which is both quantitatively accurate as well as visually pleasing. Corners are detected based on the orientation of the image. A method based on plane homography and transformation is used to make perspective correction. The algorithm infers frontier information directly from the images, without any reference objects or prior knowledge of the camera parameters. The frontiers are detected using geometric context based segmentation. The goal of this paper is to present a framework providing fully automatic and fast perspective correction.
Face Alignment Using Active Shape Model And Support Vector MachineCSCJournals
ย
The document proposes improvements to the classical Active Shape Model (ASM) algorithm for face alignment. The improvements include: 1) Combining Sobel filtering and 2D profiles to build texture models, 2) Applying Canny edge detection for enhancement, 3) Using Support Vector Machines (SVM) to classify landmarks for more accurate localization, and 4) Automatically adjusting 2D profile lengths based on image size. Experimental results on two face databases show the proposed ASM-SVM method achieves better alignment accuracy than the classical ASM and other methods.
A Method to Determine End-Points ofStraight Lines Detected Using the Hough Tr...IJERA Editor
ย
This document presents a method for determining the end points of lines detected using the Hough transform. The Hough transform detects lines of unspecified length by finding equations that describe lines, but does not provide information about the actual end points. The presented method tracks points from the original image that contributed to lines detected in the Hough transform space. Consecutive points are grouped into sub-lines if there are enough points to constitute a significant segment and points are far enough from other groups along the same line. Sample results demonstrating the method are shown. The method involves grouping contributing points into valid sub-lines based on minimum length and separation criteria.
In This paper we presented new approach for cursive Arabic text recognition system. The objective is to propose methodology analytical offline recognition of handwritten Arabic for rapid implementation.The first part in the writing recognition system is the preprocessing phase is the preprocessing phase to prepare the data was introduces and extracts a set of simple statistical features by two methods : from a window which is sliding long that text line the right to left and the approach VH2D (consists in projecting every character on the abscissa, on the ordinate and the diagonals 45ยฐ and 135ยฐ) . It then injects the resulting feature vectors to Hidden Markov Model (HMM) and combined the two HMM by multi-stream approach.
A MULTI-STREAM HMM APPROACH TO OFFLINE HANDWRITTEN ARABIC WORD RECOGNITIONijnlc
ย
This document presents a multi-stream HMM approach for offline handwritten Arabic word recognition. It extracts two sets of features from each word using a sliding window approach and VH2D projection approach. These features are input to separate HMM classifiers, and the outputs are combined in a multi-stream HMM to provide more reliable recognition. The system is evaluated on 200 words, achieving a recognition rate of 83.8% using the multi-stream approach compared to 78.2% and 76.6% for the individual classifiers.
1) The document describes a proposed note to coin converter machine that would detect fake notes and convert real notes into coins for users.
2) The machine would use image processing techniques like HSI color modeling and thresholding in MATLAB to identify the denomination of inserted notes.
3) If a note is determined to be real, the equivalent number and type of coins would be dispensed based on the note's value. However, if a note is identified as fake, it would be ejected without providing any coins.
The document describes a C++ program called ListMyPolygons that allows users to input polygon details, store the polygon vertices in matrices, and perform transformations on the polygons. It discusses the design of the matrix, polygon, and user interface classes. The matrix class stores vertex coordinates and supports operations for transformations. Polygon classes are derived from an abstract base class and store vertex matrices. The user interface allows inputting polygons, reviewing/modifying them via transformations, and deleting polygons. While the program works for most polygons, determining regular polygons from vertices proved unreliable.
Face recognition using selected topographical features IJECEIAES
ย
This paper represents a new features selection method to improve an existed feature type. Topographical (TGH) features provide large set of features by assigning each image pixel to the related feature depending on image gradient and Hessian matrix. Such type of features was handled by a proposed features selection method. A face recognition feature selector (FRFS) method is presented to inspect TGH features. FRFS depends in its main concept on linear discriminant analysis (LDA) technique, which is used in evaluating features efficiency. FRFS studies feature behavior over a dataset of images to determine the level of its performance. At the end, each feature is assigned to its related level of performance with different levels of performance over the whole image. Depending on a chosen threshold, the highest set of features is selected to be classified by SVM classifier.
IRJET - A Detailed Review of Different Handwriting Recognition MethodsIRJET Journal
ย
This document provides a detailed review of different methods for handwriting recognition, including incremental, semi-incremental, convolutional neural network, line and word segmentation, part-based, and slope and slant correction methods. It discusses the processes, benefits, and limitations of each approach. The document is intended as a review for researchers studying handwriting recognition techniques.
Offline Signiture and Numeral Recognition in Context of ChequeIJERA Editor
ย
Signature is considered as one of the biometrics. Signature Verification System is required in almost all places where it is compulsory to authenticate a person or his/her credentials to proceed further transaction especially when it comes to bank cheques. For this purpose signature verification system must be powerful and accurate. Till date various methods have been used to make signature verification system powerful and accurate. Research here is related to offline signature verification. Shape Contexts have been used to verify whether 2 shapes are similar or not. It has been used for various applications such as digit recognition, 3D Object recognition, trademark retrieval etc. In this paper we present a modified version of shape context for signature verification on bank cheques using K-Nearest Neighbor classifier.
Automatic Recognition of Isolated And Interacting Manufacturing Features In M...IJERA Editor
ย
Manufacturing features play an important role between design information and manufacturing activities.
Recently, various efforts have been concentrated in development of automatic feature recognition systems.
However, only limited number of features could be recognized, intersecting features were generally not
involved. This paper presents a simple system, in which manufacturing features are easily detected using a
Chain of Faces and Base of Faces (CF-BF) graph. A feature is modeled by a series/parallel association of
opened Chain of Faces (OCF) or Closed chain of Faces (CCF) that rest on a Base Face (BF). The feature is
considered Perfect Manufacturing Feature (PMF) if all Faces that participate in constitution of OCF/CCF are
blank faces, else it is an Imperfect Manufacturing Feature (IMF). In order to establish news Virtual Faces to
satisfy this necessaries condition, a judicious analysis of orientation of frontier faces that rest on BF is
performed. The technique was tested on several parts taken from literature and the results were satisfying.
Vehicle License Plate Recognition (VLPR) is an important system for harmonious traffic. Moreover this system is helpful in many fields and places as private and public entrances, parking lots, border control and theft control. This paper presents a new framework for Sudanese VLPR system. The proposed framework uses Multi Objective Particle Swarm Optimization (MOPSO) and Connected Component Analysis (CCA) to extract the license plate. Horizontal and vertical projection will be used for character segmentation and the final recognition stage is based on the Artificial Immune System (AIS). A new dataset that contains samples for the current shape of Sudanese license plates will be used for training and testing the proposes framework.
IRJET - License Plate Detection using Hybrid Morphological Technique and ...IRJET Journal
ย
This document presents a license plate detection and recognition system using hybrid morphological techniques and neural networks. The system first uses the Viola-Jones algorithm to detect candidate license plate regions in video frames. The Kanade-Lucas-Tomasi algorithm is then used to track potential plates across frames. Candidate regions are classified using AlexNet and SVM to confirm plates. Morphological operations extract the exact plate region. Experimental results on vehicle image datasets show the approach provides improved license plate detection compared to existing methods.
IRJET- Lane Segmentation for Self-Driving Cars using Image ProcessingIRJET Journal
ย
This document discusses a lane segmentation system for self-driving cars using image processing. The system uses a camera mounted on a vehicle to capture live video frames, which are then processed by a Raspberry Pi computer. Image processing techniques like morphological transformations and Hough line transforms are used to detect lane lines from the frames. The morphological transformations involve operations like erosion and dilation to preprocess the images. Hough transforms represent lines from detected image edges as points in parameter space to effectively identify lane lines. Some challenges of the system include detecting lanes in various lighting and weather conditions. The advantages are that it could help reduce accidents and enable driverless vehicles.
ARABIC HANDWRITTEN CHARACTER RECOGNITION USING STRUCTURAL SHAPE DECOMPOSITIONsipij
ย
This document summarizes a statistical framework for recognizing 2D shapes represented as arrangements of curves or strokes. It presents a hierarchical model with two layers: 1) At the lower level, each curve is represented by a point distribution model describing its shape variability. Curves are assigned stroke labels. 2) At the top level, shapes are represented by the geometric arrangement of curve center points (using another point distribution model) and a string of stroke labels. Recognition involves assigning stroke labels to curves, and recovering stroke and shape deformation parameters using expectation maximization. The method is applied to Arabic handwritten character recognition.
This document provides an overview of assembly modelling including interference checking, tolerance analysis, mass property calculations, mechanism simulation, and interference checking. It discusses assembly modelling approaches like bottom-up and top-down, assembly planning considerations, mating conditions, tolerance systems, and methods for tolerance analysis including worst-case, statistical, and Monte Carlo simulation methods. Mass properties like mass, center of gravity, and moments of inertia are also covered. Mechanism simulation advantages and disadvantages are outlined as well as the purpose of interference checking in assemblies.
Vehicle logo recognition using histograms of oriented gradient descriptor and...TELKOMNIKA JOURNAL
ย
Most of vehicle have the similar structures and designs. It is extremely complicated and difficult to identify and classify vehicle brands based on their structure and shape. As we requirea quick and reliable response, so vehicle logos are an alternative method of determining the type of a vehicle. In this paper, we propose a method for vehicle logo recognition based on feature selection method in a hybrid way. Vehicle logo images are first characterized by histograms of oriented gradient descriptors and the final features vector are then applied feature selection method to reduce the irrelevant information. Moreover, we release a new benchmark dataset for vehicle logo recognition and retrieval task namely, VLR-40. The experimental results are evaluated on this database which show the efficiency of the proposed approach.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document proposes a method for detecting vehicle license plates using vertical edge detection for toll gate applications. It involves binarizing the input image using adaptive thresholding, applying an unwanted line elimination algorithm to enhance the image, and then using vertical edge detection algorithm (VEDA) to detect vertical edges. Candidate plate regions are then extracted and the desired plate is selected. This methodology is intended for use in real-time applications like electronic toll collection systems, where the plate number is extracted and authorization is checked before automatically deducting payment using RFID tags.
The document discusses image recognition using convolutional neural networks (CNNs). It explains that CNNs consist of multiple layers of small neuron collections that look at small portions of an input image called receptive fields. The results are tiled to overlap and represent the original image better. CNNs learn filters through training rather than relying on hand-engineered features. Convolution involves calculating the overlap between functions as one is translated, and is used in CNNs to identify patterns across translated versions of inputs like images. Pointwise nonlinearities are applied between CNN layers to introduce nonlinearity.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Automatic rectification of perspective distortion from a single image using p...ijcsa
ย
Perspective distortion occurs due to the perspective projection of 3D scene on a 2D surface. Correcting the distortion of a single image without losing any desired information is one of the challenging task in the field of Computer Vision. We consider the problem of estimating perspective distortion from a single still image of an unstructured environment and to make perspective correction which is both quantitatively accurate as well as visually pleasing. Corners are detected based on the orientation of the image. A method based on plane homography and transformation is used to make perspective correction. The algorithm infers frontier information directly from the images, without any reference objects or prior knowledge of the camera parameters. The frontiers are detected using geometric context based segmentation. The goal of this paper is to present a framework providing fully automatic and fast perspective correction.
Face Alignment Using Active Shape Model And Support Vector MachineCSCJournals
ย
The document proposes improvements to the classical Active Shape Model (ASM) algorithm for face alignment. The improvements include: 1) Combining Sobel filtering and 2D profiles to build texture models, 2) Applying Canny edge detection for enhancement, 3) Using Support Vector Machines (SVM) to classify landmarks for more accurate localization, and 4) Automatically adjusting 2D profile lengths based on image size. Experimental results on two face databases show the proposed ASM-SVM method achieves better alignment accuracy than the classical ASM and other methods.
A Method to Determine End-Points ofStraight Lines Detected Using the Hough Tr...IJERA Editor
ย
This document presents a method for determining the end points of lines detected using the Hough transform. The Hough transform detects lines of unspecified length by finding equations that describe lines, but does not provide information about the actual end points. The presented method tracks points from the original image that contributed to lines detected in the Hough transform space. Consecutive points are grouped into sub-lines if there are enough points to constitute a significant segment and points are far enough from other groups along the same line. Sample results demonstrating the method are shown. The method involves grouping contributing points into valid sub-lines based on minimum length and separation criteria.
In This paper we presented new approach for cursive Arabic text recognition system. The objective is to propose methodology analytical offline recognition of handwritten Arabic for rapid implementation.The first part in the writing recognition system is the preprocessing phase is the preprocessing phase to prepare the data was introduces and extracts a set of simple statistical features by two methods : from a window which is sliding long that text line the right to left and the approach VH2D (consists in projecting every character on the abscissa, on the ordinate and the diagonals 45ยฐ and 135ยฐ) . It then injects the resulting feature vectors to Hidden Markov Model (HMM) and combined the two HMM by multi-stream approach.
A MULTI-STREAM HMM APPROACH TO OFFLINE HANDWRITTEN ARABIC WORD RECOGNITIONijnlc
ย
This document presents a multi-stream HMM approach for offline handwritten Arabic word recognition. It extracts two sets of features from each word using a sliding window approach and VH2D projection approach. These features are input to separate HMM classifiers, and the outputs are combined in a multi-stream HMM to provide more reliable recognition. The system is evaluated on 200 words, achieving a recognition rate of 83.8% using the multi-stream approach compared to 78.2% and 76.6% for the individual classifiers.
1) The document describes a proposed note to coin converter machine that would detect fake notes and convert real notes into coins for users.
2) The machine would use image processing techniques like HSI color modeling and thresholding in MATLAB to identify the denomination of inserted notes.
3) If a note is determined to be real, the equivalent number and type of coins would be dispensed based on the note's value. However, if a note is identified as fake, it would be ejected without providing any coins.
The document describes a C++ program called ListMyPolygons that allows users to input polygon details, store the polygon vertices in matrices, and perform transformations on the polygons. It discusses the design of the matrix, polygon, and user interface classes. The matrix class stores vertex coordinates and supports operations for transformations. Polygon classes are derived from an abstract base class and store vertex matrices. The user interface allows inputting polygons, reviewing/modifying them via transformations, and deleting polygons. While the program works for most polygons, determining regular polygons from vertices proved unreliable.
Face recognition using selected topographical features IJECEIAES
ย
This paper represents a new features selection method to improve an existed feature type. Topographical (TGH) features provide large set of features by assigning each image pixel to the related feature depending on image gradient and Hessian matrix. Such type of features was handled by a proposed features selection method. A face recognition feature selector (FRFS) method is presented to inspect TGH features. FRFS depends in its main concept on linear discriminant analysis (LDA) technique, which is used in evaluating features efficiency. FRFS studies feature behavior over a dataset of images to determine the level of its performance. At the end, each feature is assigned to its related level of performance with different levels of performance over the whole image. Depending on a chosen threshold, the highest set of features is selected to be classified by SVM classifier.
IRJET - A Detailed Review of Different Handwriting Recognition MethodsIRJET Journal
ย
This document provides a detailed review of different methods for handwriting recognition, including incremental, semi-incremental, convolutional neural network, line and word segmentation, part-based, and slope and slant correction methods. It discusses the processes, benefits, and limitations of each approach. The document is intended as a review for researchers studying handwriting recognition techniques.
Offline Signiture and Numeral Recognition in Context of ChequeIJERA Editor
ย
Signature is considered as one of the biometrics. Signature Verification System is required in almost all places where it is compulsory to authenticate a person or his/her credentials to proceed further transaction especially when it comes to bank cheques. For this purpose signature verification system must be powerful and accurate. Till date various methods have been used to make signature verification system powerful and accurate. Research here is related to offline signature verification. Shape Contexts have been used to verify whether 2 shapes are similar or not. It has been used for various applications such as digit recognition, 3D Object recognition, trademark retrieval etc. In this paper we present a modified version of shape context for signature verification on bank cheques using K-Nearest Neighbor classifier.
Automatic Recognition of Isolated And Interacting Manufacturing Features In M...IJERA Editor
ย
Manufacturing features play an important role between design information and manufacturing activities.
Recently, various efforts have been concentrated in development of automatic feature recognition systems.
However, only limited number of features could be recognized, intersecting features were generally not
involved. This paper presents a simple system, in which manufacturing features are easily detected using a
Chain of Faces and Base of Faces (CF-BF) graph. A feature is modeled by a series/parallel association of
opened Chain of Faces (OCF) or Closed chain of Faces (CCF) that rest on a Base Face (BF). The feature is
considered Perfect Manufacturing Feature (PMF) if all Faces that participate in constitution of OCF/CCF are
blank faces, else it is an Imperfect Manufacturing Feature (IMF). In order to establish news Virtual Faces to
satisfy this necessaries condition, a judicious analysis of orientation of frontier faces that rest on BF is
performed. The technique was tested on several parts taken from literature and the results were satisfying.
Vehicle License Plate Recognition (VLPR) is an important system for harmonious traffic. Moreover this system is helpful in many fields and places as private and public entrances, parking lots, border control and theft control. This paper presents a new framework for Sudanese VLPR system. The proposed framework uses Multi Objective Particle Swarm Optimization (MOPSO) and Connected Component Analysis (CCA) to extract the license plate. Horizontal and vertical projection will be used for character segmentation and the final recognition stage is based on the Artificial Immune System (AIS). A new dataset that contains samples for the current shape of Sudanese license plates will be used for training and testing the proposes framework.
IRJET - License Plate Detection using Hybrid Morphological Technique and ...IRJET Journal
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This document presents a license plate detection and recognition system using hybrid morphological techniques and neural networks. The system first uses the Viola-Jones algorithm to detect candidate license plate regions in video frames. The Kanade-Lucas-Tomasi algorithm is then used to track potential plates across frames. Candidate regions are classified using AlexNet and SVM to confirm plates. Morphological operations extract the exact plate region. Experimental results on vehicle image datasets show the approach provides improved license plate detection compared to existing methods.
IRJET- Lane Segmentation for Self-Driving Cars using Image ProcessingIRJET Journal
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This document discusses a lane segmentation system for self-driving cars using image processing. The system uses a camera mounted on a vehicle to capture live video frames, which are then processed by a Raspberry Pi computer. Image processing techniques like morphological transformations and Hough line transforms are used to detect lane lines from the frames. The morphological transformations involve operations like erosion and dilation to preprocess the images. Hough transforms represent lines from detected image edges as points in parameter space to effectively identify lane lines. Some challenges of the system include detecting lanes in various lighting and weather conditions. The advantages are that it could help reduce accidents and enable driverless vehicles.
ARABIC HANDWRITTEN CHARACTER RECOGNITION USING STRUCTURAL SHAPE DECOMPOSITIONsipij
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This document summarizes a statistical framework for recognizing 2D shapes represented as arrangements of curves or strokes. It presents a hierarchical model with two layers: 1) At the lower level, each curve is represented by a point distribution model describing its shape variability. Curves are assigned stroke labels. 2) At the top level, shapes are represented by the geometric arrangement of curve center points (using another point distribution model) and a string of stroke labels. Recognition involves assigning stroke labels to curves, and recovering stroke and shape deformation parameters using expectation maximization. The method is applied to Arabic handwritten character recognition.
This document provides an overview of assembly modelling including interference checking, tolerance analysis, mass property calculations, mechanism simulation, and interference checking. It discusses assembly modelling approaches like bottom-up and top-down, assembly planning considerations, mating conditions, tolerance systems, and methods for tolerance analysis including worst-case, statistical, and Monte Carlo simulation methods. Mass properties like mass, center of gravity, and moments of inertia are also covered. Mechanism simulation advantages and disadvantages are outlined as well as the purpose of interference checking in assemblies.
Vehicle logo recognition using histograms of oriented gradient descriptor and...TELKOMNIKA JOURNAL
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Most of vehicle have the similar structures and designs. It is extremely complicated and difficult to identify and classify vehicle brands based on their structure and shape. As we requirea quick and reliable response, so vehicle logos are an alternative method of determining the type of a vehicle. In this paper, we propose a method for vehicle logo recognition based on feature selection method in a hybrid way. Vehicle logo images are first characterized by histograms of oriented gradient descriptors and the final features vector are then applied feature selection method to reduce the irrelevant information. Moreover, we release a new benchmark dataset for vehicle logo recognition and retrieval task namely, VLR-40. The experimental results are evaluated on this database which show the efficiency of the proposed approach.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document proposes a method for detecting vehicle license plates using vertical edge detection for toll gate applications. It involves binarizing the input image using adaptive thresholding, applying an unwanted line elimination algorithm to enhance the image, and then using vertical edge detection algorithm (VEDA) to detect vertical edges. Candidate plate regions are then extracted and the desired plate is selected. This methodology is intended for use in real-time applications like electronic toll collection systems, where the plate number is extracted and authorization is checked before automatically deducting payment using RFID tags.
The document discusses image recognition using convolutional neural networks (CNNs). It explains that CNNs consist of multiple layers of small neuron collections that look at small portions of an input image called receptive fields. The results are tiled to overlap and represent the original image better. CNNs learn filters through training rather than relying on hand-engineered features. Convolution involves calculating the overlap between functions as one is translated, and is used in CNNs to identify patterns across translated versions of inputs like images. Pointwise nonlinearities are applied between CNN layers to introduce nonlinearity.
The document describes feedback received on rough drafts of pages from a magazine called INPUT. For the front page, feedback included using better photos and varying fonts more. For the contents page, it was suggested to add color and reduce text. Feedback on the double page spread noted the title/subheading were good but fonts could be bolder and columns thicker. The document also discusses what was learned about photography, computer programs, and managing time from the magazine construction process.
iWebkit is a file package that helps users create iPhone, iPod Touch, and iPad compatible websites or web apps without needing HTML knowledge. It includes tutorials and pre-made styles and elements to build a full-featured mobile site quickly. The kit uses simple HTML that is easy to edit and compatible across devices. It provides various interface elements like navigation bars, buttons, lists and forms to construct the look of a mobile app in a few minutes.
The slide deck for our recent talk at the alt.Net meetup:
Note: These slides make almost no sense without the presentation, although some have requested the slides, so here they are.
If there are any questions regarding the slides, feel free to contact either Abhaya or Joshua.
Microservice scars:
PageUp is on a journey from monolith to microservices.
This talk is to discuss the lessons we learnt from our first microservice. It has been running in production for 9 months - looking back, we have scars, and we've learnt a lot - lets have a retro!
We will cover all sorts of topics ranging from the technical details of our approach, in terms of technology stack, continuous deployments, to the soft skills - stakeholder management, team dynamic. We talk through our experience, and what we took from it. Something for everyone.
Abhaya Chauhan is a Senior Technical Advisor at PageUp - led the team for PageUp's first microservice.
He is focused on ensuring the company is ready for scale. Reducing time to market and bringing agility back to our product. He loves to focus on delivering pragmatically, and showing value.@AbhayaChauhan
www.abhayachauhan.com
Joshua Toth is a Full Stack Developer at PageUp - A member of the team that produced PageUp's first microservice. He loves learning about new technologies and tackling whatever challenge is presented. He has an interest in security and devops as a culture.@TothJoshuaJ
TothJoshuaJ@gmail.com
This document discusses TensorFlow.rb, a Ruby wrapper for TensorFlow that allows Ruby programmers to use TensorFlow for machine learning tasks. It summarizes Jason Toy's background in machine learning and Ruby, describes common machine learning problems and algorithms, provides an overview of deep learning compared to traditional machine learning, and details the status and goals of the TensorFlow.rb project. The document encourages contributors to the open source project and thanks current contributors.
The document discusses an image recognition plugin that allows macros to search for and interact with images on websites. It provides two commands: IMAGESEARCH to find an image on a page, and IMAGECLICK to simulate clicking on an image. These commands use image recognition technology to locate images based on their visual characteristics rather than coordinates. The document also provides examples of using these commands to automate interactions with flash-based chat applications.
Tek Era - Image Recognition & Augmented RealityRimple Sanchla
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Have you ever heard your prints and creatives interacting? Here it is! Now you can connect printed content with the dynamic world of the internet by creating a direct, real time connection between the printed content and its associated online web content.
O documento analisa dois enunciados gerais e propรตe alteraรงรตes para tornรก-los mais especรญficos. O primeiro enunciado "Reforรงar o trabalho articulado" รฉ considerado muito vago e รฉ proposto um enunciado mais especรญfico que menciona o departamento de lรญnguas e atividades do Plano Nacional de Leitura. O segundo enunciado "Reforรงar a produรงรฃo de instrumentos de apoio" tambรฉm รฉ considerado vago e รฉ proposto um enunciado mais especรญfico que menciona guiรตes de pesquisa, es
This document provides an overview of UAV image recognition technology and applications. It defines UAVs and describes the key technologies that have enabled their development, such as autopilots, GPS, and miniaturized components. It outlines the UT UAV group's work on autonomous target recognition for competition, including detecting, analyzing, and determining the position of targets in images. The group's system achieves 85% detection accuracy and aims to reduce position error below 50 feet. Potential applications of UAVs discussed include monitoring oil pipelines and ranches as well as aiding wildfire response. Strict regulations govern UAV use due to safety concerns.
The document discusses using image processing and morphology operations to better characterize features for focus exposure matrices compared to traditional CD SEM measurements. It shows that parameters like orientation, area, and elongation extracted from binary images provide better separation of dose and focus conditions than CD alone. A minimal mean distance classifier is proposed to recognize imaging sets based on these multi-dimensional morphology measurements. Further steps mentioned include collecting more focus exposure data and recipe optimization to fully validate the approach on production images.
Mobile serach Image And Object RecognitionAndres Padilla
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This document discusses mobile image and object recognition technology. It describes how object recognition works by characterizing objects with local descriptors from interest points, and how this allows for robust identification of objects in cluttered scenes regardless of scale, orientation, noise or partial occlusion. Main applications are recognizing products from images, like CD covers, logos, and products with distinctive packaging to provide users with information like prices. The technology involves a mobile application, recognition engine, and content provider. It works by having the mobile app take an image, send it to the recognition engine which identifies the object and returns a product ID to lookup details from the content provider.
Picto vision - using image recognition to turn sketches into communicationDavid Wright
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A pitch deck - a raw one - but combining computer vision with sketches could make for an interesting communication format. Also - touch screen sketches - might be a more practical format - than gesture.
This document discusses the application of face and image recognition systems. It begins by defining a facial recognition system as a computer application that can automatically identify or verify a person from a digital image or video frame. It notes that machine performance does not degrade with increasing data like human performance, and machines do not experience fatigue. It then lists nine common applications of these systems, including law enforcement to identify suspects, security/counterterrorism to detect known terrorists, corrections to track inmates, and banking/airports for identity verification to reduce fraud.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/industry-analysis/video-interviews-demos/introducing-ieee-low-power-image-recognition-challenge-lpir
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Yung-Hsiang Lu, Associate Professor at Purdue University, delivers the presentation "Introducing the IEEE Low-Power Image Recognition Challenge (LPIRC)" at the September 2015 Embedded Vision Alliance Member Meeting. Yung-Hsiang describes the objectives and details of the competition, the 2015 LPIRC results, and the upcoming 2016 LPIRC plans.
The document presents an approach called Compact Descriptor through Invariant Kernel Projection (CDIKP) which develops highly compact 20-dimensional local feature descriptors for mobile platforms. CDIKP descriptors are more compact than other state-of-the-art descriptors like SIFT and SURF while not requiring pre-training unlike PCA-SIFT. The descriptors can robustly recognize natural and artificial scenes on mobile phones for applications like visual tags and product logos recognition.
Tracking number plate from vehicle usingijfcstjournal
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This document presents a new algorithm in MATLAB to extract vehicle number plates from images in various lighting conditions. The algorithm uses preprocessing techniques like grayscale conversion, dilation, and edge detection. It then segments the region of interest containing the number plate and extracts it. Individual characters are then segmented and recognized using template matching. The algorithm achieves 99% accuracy on images taken from a fixed angle and distance under controlled conditions. It is less accurate for images with problematic backgrounds or lighting. The algorithm provides an automated way to extract number plates for applications like traffic monitoring, parking management, and stolen vehicle identification.
Segmentation and recognition of handwritten digit numeral string using a mult...ijfcstjournal
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In this paper, the use of Multi-Layer Perceptron (MLP) Neural Network model is proposed for recognizing
unconstrained offline handwritten Numeral strings. The Numeral strings are segmented and isolated
numerals are obtained using a connected component labeling (CCL) algorithm approach. The structural
part of the models has been modeled using a Multilayer Perceptron Neural Network. This paper also
presents a new technique to remove slope and slant from handwritten numeral string and to normalize the
size of text images and classify with supervised learning methods. Experimental results on a database of
102 numeral string patterns written by 3 different people show that a recognition rate of 99.7% is obtained
on independent digits contained in the numeral string of digits includes both the skewed and slant data.
Content Based Image Retrieval Approach Based on Top-Hat Transform And Modifie...cscpconf
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In this paper a robust approach is proposed for content based image retrieval (CBIR) using texture analysis techniques. The proposed approach includes three main steps. In the first one, shape detection is done based on Top-Hat transform to detect and crop object part of the image. Second step is included a texture feature representation algorithm using color local binary patterns (CLBP) and local variance features. Finally, to retrieve mostly closing matching images to the query, log likelihood ratio is used. The performance of the proposed approach is evaluated using Corel and Simplicity image sets and it compared by some of other well-known approaches in terms of precision and recall which shows the superiority of the proposed approach. Low noise sensitivity, rotation invariant, shift invariant, gray scale invariant and low computational complexity are some of other advantages.
This document summarizes an automatic number plate recognition system. The system uses a camera to capture images of vehicle license plates. It then pre-processes the images by converting them to grayscale, applying noise removal filters, and cropping the license plate region. Morphological operations like dilation and erosion are used to extract the license plate. Individual characters are segmented using edge detection operators. Character recognition is performed by comparing the characters to stored templates using optical character recognition. The system was able to successfully recognize license plate characters through these image processing and recognition steps.
Bangla Optical Digits Recognition using Edge Detection MethodIOSR Journals
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Abstract:This paper is based on Bangla Optical Digit Recognition (ODR) by the Edge detection technique. In this method, Bangla digit image converted into gray-scale which distributed by an M by N array form. Here input data are considered off-line printed digitโs image which collected from computer generated image, scanned documents or printed text. After addressing the gray-scale image against a variable in the form of an M by N array, where the value of array pointers are shown 255 for total white space, 0 (zero) for total dark space and value between 255 and 0 for mix of white and dark space of the image. At the next process, four edgestouch points as well as each touch pointโs ratio use as parameters to determine each Bangla digit uniquely. Keywords-Edge, image,gray-scale, Matrix,ODR.
License Plate Recognition using Morphological Operation. Amitava Choudhury
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This paper describes an efficient technique of locating and
extracting license plate and recognizing each segmented
character. The proposed model can be subdivided into four
parts- Digitization of image, Edge Detection, Separation of
characters and Template Matching. In this work, we propose a
method which is based on morphological operations where
different Structuring Elements (SE) are used to maximally
eliminate non-plate region and enhance plate region.
Character segmentation is done using Connected Component
Analysis. Correlation based template matching technique is
used for recognition of characters. This system is
implemented using MATLAB7.4.0. The proposed system is
mainly applicable to Indian License Plates.
IRJET - Kirsch Compass Kernel Edge Detection for Vehicle Number Plate Det...IRJET Journal
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This document describes a method for vehicle number plate detection using image processing techniques. It involves preprocessing the captured vehicle image by converting it to grayscale and binary, then using Kirsch compass kernel edge detection to locate the number plate region. Morphological operations like dilation and erosion are performed for processing. The number plate is extracted using bounding box technique and characters within are segmented. Individual characters are displayed and can be recognized using template matching. The described method aims to accurately detect vehicle number plates for applications like parking access control.
Feature Extraction and Feature Selection using Textual Analysisvivatechijri
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After pre-processing the images in character recognition systems, the images are segmented based on
certain characteristics known as โfeaturesโ. The feature space identified for character recognition is however
ranging across a huge dimensionality. To solve this problem of dimensionality, the feature selection and feature
extraction methods are used. Hereby in this paper, we are going to discuss, the different techniques for feature
extraction and feature selection and how these techniques are used to reduce the dimensionality of feature space
to improve the performance of text categorization.
IRJET - Automatic Licence Plate Detection and RecognitionIRJET Journal
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This document describes a system for automatic license plate detection and recognition. The system uses image processing techniques in MATLAB to capture an image of a vehicle license plate, preprocess the image by converting it to grayscale and reducing noise, segment the license plate from the image, and recognize the characters on the plate using optical character recognition. The system is proposed to identify vehicles entering a university campus and check if they are registered in the university's database. The document outlines the methodology, which involves preprocessing, segmentation, character separation, and character recognition steps. It also discusses related work on license plate detection algorithms and presents experimental results demonstrating the system's ability to accurately extract license plate numbers from images.
This document proposes a new method for corner detection in images using difference chain coding as a measure of curvature. The method involves extracting a one-pixel thick boundary from the image, chain encoding it to determine slope, smoothing the boundary to remove noise, and calculating difference codes to determine points of high curvature change, which indicate corners. Preliminary results show the method is simple, efficient, and performs comparably to standard corner detection techniques like Harris and Yung.
The document presents a new approach called Linear Curvature Empirical Coding (LCEC) for image retrieval. LCEC aims to improve upon existing curvature-based coding approaches by linearly representing the curvature scale space plot and then applying empirical coding to select descriptive shape features. The linear representation considers variations across all smoothing factors rather than discarding information below a threshold. Empirical coding is used to select features based on variation density rather than just magnitudes. The results show LCEC performs better than previous methods for image retrieval.
Empirical Coding for Curvature Based Linear Representation in Image Retrieval...iosrjce
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The document presents a new approach called Linear Curvature Empirical Coding (LCEC) for image retrieval. LCEC aims to improve upon existing curvature-based coding approaches by linearly representing the curvature scale space plot and then applying empirical coding to select descriptive shape features. The linear representation considers variations across all smoothing factors rather than discarding information below a threshold. Empirical coding is used to select features based on variation density rather than just magnitude. The results show LCEC performs better than previous methods for image retrieval.
This document presents a simple signature recognition system that uses invariant central moment and modified Zernike moment for feature extraction. The system is divided into preprocessing, feature extraction, and recognition/verification stages. In preprocessing, the input signature image is converted to grayscale and binary, and the region of interest is extracted. Feature extraction uses invariant central moments and Zernike moments to extract shape features. Recognition and verification is performed using a backpropagation neural network for its high accuracy and low computational complexity. The system was tested on a database of 500 signatures from 50 individuals and achieved suitable performance for signature verification.
This document summarizes a research paper about a simple signature recognition system designed using MATLAB. The system extracts features from signatures using invariant central moment and modified Zernike moment for invariant feature extraction. It is divided into preprocessing, feature extraction, and recognition/verification. Preprocessing prepares the signature image for processing. Feature extraction uses invariant central moments and Zernike moments. Recognition uses a backpropagation neural network for classification. The system was tested on a database of 500 signatures from 50 individuals, achieving high accuracy and low computational complexity.
IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mechanical and civil engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mechanical and civil engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
An Efficient Model to Identify A Vehicle by Recognizing the Alphanumeric Char...IJMTST Journal
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Automatic Engine Number Recognition (AENR) is the digital image processing and an important aspect/role to identify the theft vehicles by recognizing characters, digits and special symbols. There is increase in the theft of vehicles, so to identify these theft vehicles, the proposed system is introduced. The proposed system controls the theft vehicles by recognizing a digits and characters in the number plate and chassis region and stores in the database in ASCII format to check the theft vehicles are registered or unregistered. Both system consists of 4 common phases: - Preprocessing, Character Extraction (ROI), Character Segmentation, and Character Recognition. This paper proposes a new scheme for engine number and chassis number extraction from the pre-processed image of the vehicleโs engine and chassis region using preprocess techniques, Region of Interest(ROI), Binarization, thresholding, template matching.
Feature extraction is becoming popular in face recognition method. Face recognition is the interesting and growing area in real time applications. In last decades many of face recognitions methods has been developed. Feature extraction is the one of the emerging technique in the face recognition methods. In this method an attempt to show best faces recognition method. Here used different descriptors combination like LBP and SIFT, LBP and HOG for feature extraction. Using a single descriptor is difficult to address all variations so combining multiple features in common. Find LBP and SIFT features separately from the images and fuse them with a canonical correlation analysis and same procedure also done using LBP and HOG. The SIFT features have some limitations they donรยขรขโยฌรขโยขt work well with lighting changes, quite slow, and mathematically complicated and computationally heavy. The combinations of HOG and LBP features make the system robust against some variations like illumination and expressions. Also, face recognition technique used a different classifier to extract the useful information from images to solve the problems. This paper is organized into four sections. Introduction in the first section. The second section describes feature descriptors and the third section describes proposed methods, final sections describes experiments result and conclusion phase.
Numeral recognition is an important research direction in field of pattern recognition, and it has
broad application prospects. Aiming at four arithmetic operations of general printed formats, this article
adopts a multiple hybrid recognition method and is applied to automatically calculating. This method mainly
uses BP neural network and template matching method to distinguish the numerals and operators, in order
to increase the operation speed and recognition accuracy. Sample images of four arithmetic operations are
extracted from printed books, and they are used for testing the performance of proposed recognition
method. The experiments show that the method provides correct recognition rate of 96% and correct
calculation rate of 89%.
Comparative study of two methods for Handwritten Devanagari Numeral RecognitionIOSR Journals
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Abstract : In this paper two different methods for Numeral Recognition are proposed and their results are
compared. The objective of this paper is to provide an efficient and reliable method for recognition of
handwritten numerals. First method employs Grid based feature extraction and recognition algorithm. In this
method the features of the image are extracted by using grid technique and this feature set is then compared
with the feature set of database image for classification. While second method contains Image Centroid Zone
and Zone Centroid Zone algorithms for feature extraction and the features are applied to Artificial Neural
Network for recognition of input image. Machine text recognition is important research area because of its
applications in many areas like Bank, Post office, Hospitals etc.
Keywords: Handwritten Numeral Recognition, Grid Technique, ANN, Feature Extraction, Classification.
A design of license plate recognition system using convolutional neural networkIJECEIAES
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This document describes a license plate recognition system using convolutional neural networks. The system uses several preprocessing techniques like Sobel edge detection, morphological operations, and connected component analysis to localize, isolate, and segment characters from license plate images. A convolutional neural network with a four-layer architecture is then used for character recognition. Various hyperparameters of the CNN model like the number of feature maps, connection patterns, and learning parameters are optimized using 10-fold cross-validation. The proposed system achieves 74.7% accuracy in the preprocessing stage and 94.6% accuracy in the character recognition stage using CNN.
Similar to The Framework of Image Recognition based on Modified Freeman Chain Code (20)
The chapter Lifelines of National Economy in Class 10 Geography focuses on the various modes of transportation and communication that play a vital role in the economic development of a country. These lifelines are crucial for the movement of goods, services, and people, thereby connecting different regions and promoting economic activities.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
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A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
ย
(๐๐๐ ๐๐๐) (๐๐๐ฌ๐ฌ๐จ๐ง ๐)-๐๐ซ๐๐ฅ๐ข๐ฆ๐ฌ
๐๐ข๐ฌ๐๐ฎ๐ฌ๐ฌ ๐ญ๐ก๐ ๐๐๐ ๐๐ฎ๐ซ๐ซ๐ข๐๐ฎ๐ฅ๐ฎ๐ฆ ๐ข๐ง ๐ญ๐ก๐ ๐๐ก๐ข๐ฅ๐ข๐ฉ๐ฉ๐ข๐ง๐๐ฌ:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
๐๐ฑ๐ฉ๐ฅ๐๐ข๐ง ๐ญ๐ก๐ ๐๐๐ญ๐ฎ๐ซ๐ ๐๐ง๐ ๐๐๐จ๐ฉ๐ ๐จ๐ ๐๐ง ๐๐ง๐ญ๐ซ๐๐ฉ๐ซ๐๐ง๐๐ฎ๐ซ:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
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The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
ย
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
ย
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
ย
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
The Framework of Image Recognition based on Modified Freeman Chain Code
1. H. Hasan, H. Haron & S. Z. Mohd Hashim
International Journal of Image Processing (IJIP), Volume (5) : Issue (5) : 2011 542
The Framework of Image Recognition Based on
Modified Freeman Chain Code
Haswadi Hasan haswadi@utm.my
Faculty of Computer Science and Information System (FSKSM)
Universiti Teknologi Malaysia
Skudai, 81310, Malaysia
Habibollah Haron habib@utm.my
Faculty of Computer Science and Information System (FSKSM)
Universiti Teknologi Malaysia
Skudai, 81310, Malaysia
Siti Zaiton Mohd Hashim sitizaiton@utm.my
Faculty of Computer Science and Information System (FSKSM)
Universiti Teknologi Malaysia
Skudai, 81310, Malaysia
Abstract
Image recognition of line drawing involves feature extraction and feature comparison; works on
the extraction required the representation of the image to be compared and analysed. Combining
these two requirements, a framework that implements a new extraction algorithm of a chain code
representation is presented. In addition, new corner detection is presented as pre-processing to
the line drawing input in order to derive the chain code. This paper presents a new framework
that consists of five steps namely pre-processing and image processing, new corner detection
algorithm, chain code generator, feature extraction algorithm, and recognition process. Heuristic
approach that is applied in the corner detection algorithm accepts thinned binary image as input
and produces a modified thinned binary image containing J characters to represent corners in the
image. Using the modified thinned binary image, a new chain code scheme that is based on
Freeman chain code is proposed and an algorithm is developed to generate a single chain code
series that is representing the line drawing input. The feature extraction algorithm is then extracts
the three pre-defined features of the chain code for recognition purpose. The features are corner
properties, distance between corners, and angle from a corner to the connected corner. The
explanation of steps in the framework is supported with two line drawings. The results show that
the framework successfully recognizes line drawing into five categories namely not similar line
drawing, and four other categories that are similar but with attributes of rotation angle and scaling
ratio.
Keywords: Corner Detection, Chain Code, Line Drawing, Feature Extraction, Recognition
1. INTRODUCTION
Image recognition of line drawing involves comparison and analysis of more than one line
drawing against the reference. The recognition includes derivation of features from the input
image, therefore, pre-processing, image processing and data representation stages are required
to analyze the image in producing the feature of the line drawing. Thinning and corner detection
algorithms are among basic steps in image processing while chain code is one of line drawing
representation. Combining these steps, a framework is presented that consists of pre-processing,
image processing, data representation, feature extraction and finally the recognition process.
This paper is divided into five sections. Section 1 presents an introduction on image recognition
and steps involved. Next in Section 2, the framework and steps from previous works on image
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recognition are discussed, and then the proposed framework and steps taken are detailed in
Section 3. Section 4 examines the experimental result of the framework that is supported with line
drawing examples. Finally, conclusion and discussion is presented in Section 5.
2. FRAMEWORK IN IMAGE RECOGNITION
As mentioned earlier, steps in image recognition are including pre-processing, image processing,
data representation and feature extraction. This section presents few previous works on image
recognition and steps involved. In each step, new algorithm or representation is proposed and
they are discussed in this section as motivation and comparison in the development of a new
framework in image recognition.
The literature review is divided into three categories namely works about the framework on image
recognition and its feature identification and extraction, input of the feature extraction especially
the chain code representation, and the steps in image processing of the input image especially
corner detection.
The previous works summarizes all works that accepts chain code as its input or data
representation, and detecting corner based on chain code. The framework in [1-3] provides basis
in identifying features of image recognition of this work. The works by [2-5] show the application
of chain code in image recognition and feature extraction. The corner detection in [6]
demonstrates the application of chain code in detecting corner while [7] points out how corner
detection apply chain code scheme as curvature. Works by [8] proposes new chain code scheme
in image retrieval.
Based on these three categories of previous works, it shows that the image recognition and
feature extraction can possibly include corner detection as part of pre-processing and image
processing step. The study also shows that chain code is relevant scheme and representation in
image recognition and feature extraction. They give motivation and ideas on new framework in
image recognition particularly for line drawing that combines feature extraction, corner detection
and chain code representation.
3. THE PROPOSED FRAMEWORK
3.1 The Framework
This section presents the framework and its steps. Fig. 1 shows the framework diagram. The
dotted box in the figure represents the input and output of the process contained in the solid box.
There are five steps in the framework namely pre-processing, corner detection, chain code
generation, feature extraction, and recognition. First, data preparation and pre-processing
involving image processing tools in producing thinned binary image are performed. The derived
thinned binary image (TBI) is then altered by the corner detection algorithm to produce a new
modified TBI that contains additional J characters representing corner of the image. By defining
new chain code scheme, a series of chain code is derived from the modified TBI. This chain code
series is then analyzed by the feature extraction algorithm based on three pre-defined features of
line drawing namely corner properties, distance of edge between corners, and angle between
corners. The extracted features are then used in the recognition process. The following sub-
sections explain in detail each step while their experimental results are presented in Section 4.
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International Journal of Image Processing (IJIP), Volume (5) : Issue (5) : 2011 544
FIGURE 1: The Proposed Line Drawing Recognition Framework.
3.2 Data Definition and Pre-Processing
Defining data set of line drawing involves several assumptions such as the line drawing is a two-
dimensional regular line drawing and the source of the line drawing would be from origami world.
Pre-processing involves resizing and thinning the image, cleaning or removing noise and
unwanted pixel, and reconnecting lines if necessary. This process is performed manually using
tools such as Microsoft Paint and simple script based on MATLAB function. Since it is beyond the
scope of discussion, they are not discussed in this paper. This step produces a TBI of the line
drawing.
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3.3 Development of Corner Detection Algorithm
This step reads cleaned TBI and creates a modified TBI with J character in the image. The
module involves two phases. First, starting point to traverse is determined. Second, cluster
grouping is performed that is labelling the TBI with temporary label โGโ, creating vector,
generating vector list, and elimination of path and vector. The result of corner list in term of new
TBI with J character is produced.
Any gate location is marked with G character and cluster member with + character. Any cluster in
image will be grouped so that cluster exit gate which defines start and stop point for branch can
be searched. Tracker movement is depends on the number of gates left untouched on current
cluster before moving on to another cluster, while the current cluster is always referring to the
latest cluster found.
To determine corner position in every cluster, vector creation is executed using cluster gate list
that is either a beginning or end of a branch. Since connection between gates is not included in
the list, path tracing between gates is inevitable. Combined with the need to include line angle
namely inclination or degree of slope as its properties, edge detection is concurrently performed
here since it involves scanning for slope changes. A box or window is maintained as reference
slope and moved throughout the path trace while the actual branch slope will be calculated from
the branch start until current point. When the fluctuation between the slopes has passed over the
accepted limit, a corner is declared as found.
Line vectors are created based on cluster gates to find corners located in every cluster by line
extrapolation. Every cluster member will be cross-marked its point distance from line vectors
attached cluster and the location with lowest value (lowest distance) will be chosen as a corner in
the cluster. Newly appointed corner is tested for its connectivity with all gates in corresponding
cluster to ensure that corner is enough for the cluster or additional corner is required. Now, all
corner locations have been found and will be marked with J character in the line drawing to form
a modified TBI.
3.4 Definition of New Chain Code Scheme and Development of Its Generator
This framework section proposed a new modified Freeman chain code scheme (MFCC). The
development of this chain code scheme is parallel with the development of the chain code
generator. The scheme is based on Freeman chain code but with additional character started
from A, B, CโฆZ not to represent the direction of the pixel but the corner label passed during
tracing. The chain code scheme is defined to have classification for outer loop and inner loop of
the line drawing: outer loop is derivation of codes in the series of the boundary while inner loop is
for remaining inner lines. The MFCC is a single chain code series and the reverse traversal of the
code will produce the same source line drawing.
Using TBI with label corner J (0, 1, J) as input, tracking point will be set at the most bottom left
pixel of image as starting location. The tracing will start from here and repeatedly tests for current
position and neighbouring pixels for next move direction. The unlabelled corner previously
marked its location with J, will be assigned a label that follows the latest used label. For starting
point, the corner will be assigned 'A' character as its label and the corner is recorded in the chain
code. Every corner found during tracing process is recorded in a First In Last Out (FILO) stack for
rollback ability when the tracing reaches the end of any path. The boundary of line drawing will be
used first for path traversing until the tracking point reaches back the starting point.
After traversing the outer loop, next is extracting chain code for inner lines. Positions for tracker
should be at the same starting point as in outer loop counterpart. All remaining paths are
traversed and all corner found and direction taken during tracing is recorded. When the tracker
reached dead end, where the end should be a corner, corner stack is consulted for previous
corner and if there is still corner left, the tracker will be set at that corner position. If there are no
more corners left in the stack, then line drawing traversal and MFCC generation is deemed
complete.
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3.5 Defining the Features and Development of the Extraction Algorithm
This step involves two stages namely defining features to be used in the recognition, and
extracting the features of the line drawing based on the generated MFCC. Identifying features of
the line drawing is based on the geometric and topological properties of a line drawing. The three
features are number of branches at each corner (F1), distance between corners (F2), and angle
between corners (F3). Before extracting the features, layer where the corner resides in the line
drawing must be determined starting from the boundary layer (outer loop) and moving into the
inner loop based on corner linkage.
For F1, two properties is extracted namely the number of branches and the interconnectivity
between corners. A table is created to represent these features. For F2 and F3, the calculation of
these features is performed by a heuristic approach producing two values namely distance
(length) in pixel for each corner, and branch angle at each corner. In MFCC, a branch will be
found between 2 corner markers with its distance and angle will be derived based on directional
codes defining the branch. Pythagoras theorem will be used to calculate the distance (F2) while
the angle (F3) is using tangent formulation. Value of F1, F2 and F3 will be stored in corner
property list, where the list is unique for each compared MFCC.
3.6 Recognition Process
Recognition involves comparison between two line drawings and it is based on the values of F1,
F2 and F3. All features must be considered matched or accepted so that the recognition session
to be declared successful. For F1, comparison of properties for each corner between two line
drawings is performed. The list of corner properties with the number of branches at each corner
for tested line drawing is rotated by one displacement until the quantity in both lists is matched.
Corner labels are also important to be matched, but the pairing is limited to be performed on outer
loop corners only since the labelling is in sequential order for this segment.
After F1 analysis is satisfied, analysis for F2 and F3 is performed by calculating the means and
variances of both distance and angle. For F2, mean value represents ratio (%) of the scaling
process while mean for F3 represents the degree of rotation occurred between two line drawings.
Variances for both F2 and F3 are used to measure on how far a set of distance and angle values
are spread out among them against a preset limit. Thus for these features, variances are used as
rejection/acceptance criterion in the recognition process.
4. EXPERIMENTAL RESULT
Result on two line drawings tested on the framework is presented in this section. The discussion
on input and output of four steps in the framework of first line drawing (LD1) namely pre-
processing, corner detection, generating the chain code and deriving features are presented.
Next, second line drawing (LD2) and its features is presented as input of the fifth step, recognition
process. The details of the first four steps for LD2 are not given because of its similarity in steps
taken in LD1. After the features of LD1 and LD2 are obtained, the recognition process is
conducted and conclusion of the recognition is displayed. The following sub-sections show the
input and output of each step in the framework.
4.1 Pre-processing
This step reads input of image as shown in Fig. 2(a), produced temporary thinned image as
shown in Fig. 2(b), and lastly output the thinned binary image as shown in Fig. 3(a). The detail of
the process can be referred in Section 3.2.
4.2 Corner Detection
This second step reads the thinned binary image (TBI) and produces the thinned binary image
with J as shown in Fig. 3(c). The character J indicates corner of the image detected by the
algorithm mentioned in Section 3.3. As the intermediate process, the clustering produces a
cluster map which shows the cluster member and its gates, marked with + and G, respectively, as
depicted in Fig. 3(b).
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(a) Line Drawing 1 (LD1) (b) The thinned image of LD1
FIGURE 2: The line drawing and its thinned image.
(a) Thinned binary image (b) Clustered pixels found in TBI (c) Thinned binary image with J
FIGURE 3: The TBI and its modification until final version with J character.
4.3 Chain Code Generation
Third step in the framework reads the TBI with J and produces the modified Freeman Chain Code
(MFCC). Temporary binary image with additional code as defined in the MFCC scheme is created
by the algorithm. Fig. 4(a) and 4(b) respectively show the temporary TBI created and the MFCC
derived from the TBI. The temporary TBI includes the additional corner label A-H of the TBI that is
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related to the derived MFCC shown in Fig. 4(b). Based on the algorithm, if Fig. 4(a) is traversed
from A (most bottom left pixel) and continue the traversal, it will lead to the character B until H.
A000000000B000000000000000000000C
222222222222222222222D22222222222
2E44444444444F44444444445G6656665
6665665666566656656665666AGF66666
666666666666666666H11111111111DH5
555555555B
(a) Temporary TBI (b) The MFCC Chain Code
FIGURE 4: The MFCC Chain Code Generation.
4.4 Feature Extraction
Fourth step in the framework reads the MFCC and produce a table consist of values of Feature 1
(F1), Feature 2 (F2) and Feature 3 (F3) as shown in Table 1. For F1, number in bracket in
Current column indicates the number of branches from the corner, while Target column
represents the connectivity of neighbouring corner from the corner. For F2 and F3, their values of
distance and angle are shown in respective column. Distance represents the value of distance
from Current corner to Target corner in unit pixel while Angle symbolizes angle value to point to
Target corner from Current corner.
Feature 1 Feature 2 Feature 3 Feature 1 Feature 2 Feature 3
Corner Properties Distance
(unit pixel)
Angle
(ยฐ)
Corner Properties Distance
(unit pixel)
Angle
(ยฐ)Current Target Current Target
A (2)
B 9.00 0.00
E (2)
D 12.00 270.00
G 32.99 75.96 F 11.00 180.00
B (3)
A 9.0 180.00
F (3)
E 11.00 0.00
C 21.00 0.00 G 11.05 185.19
H 14.14 45.00 H 23.00 270.00
C (2)
B 21.00 180.00
G (2)
F 11.05 5.19
D 21.00 90.00 A 32.98 255.96
D (3)
C 21.00 270.00
H (3)
F 23.00 90.00
E 12.00 90.00 B 14.14 225.00
H 15.56 225.00 D 15.56 45.00
TABLE 1: Value of features for LD1.
4.5 Recognition
As mentioned earlier in this section, for recognition purpose more than one line drawing is
required as comparison. For example, if there are two line drawings to be compared, recognition
process reads two set of features values for every given line drawings. The recognition produces
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one of recognition result enumerated in Table 2. The result from recognition also provides angle
of rotation and ratio of scaling of LD2 from LD1 based on mean values found during variance
calculation in recognition algorithm.
Result
Similar image
Rotation Scaling
1 No No
2 Yes No
3 No Yes
4 Yes Yes
5 Not similar image
TABLE 2: Category of recognition result.
Fig. 5(a) and 5(b) show example of line drawing 1 (LD1) and 2 (LD2), respectively. The features
of LD1 are as shown in Table 1 while features of LD2 are shown in Table 3. These tables will be
used in recognition process.
(a) Line Drawing1 (LD1)
(as shown in Figure 2(a))
(b) Line Drawing 2 (LD2)
(for recognition purpose)
FIGURE 5: The input of recognition process with corners labelled.
Feature 1 Feature 2 Feature 3 Feature 1 Feature 2 Feature 3
Corner Properties Distance
(unit pixel)
Angle
(ยฐ)
Corner Properties Distance
(unit pixel)
Angle
(ยฐ)Current Target Current Target
A (2)
B 15.56 45.00
E (2)
D 8.60 324.46
G 15.56 135.00 F 24.70 211.76
B (3)
A 15.56 225.00
F (2)
E 24.70 31.76
C 9.90 45.00 G 7.07 315.00
H 12.00 180.00
G (3)
F 7.07 135.00
C (2)
B 9.90 225.00 A 15.56 315.00
D 8.49 135.00 H 10.00 0.00
D (3)
C 8.49 315.00
H (3)
G 10.00 180.00
E 8.60 144.46 B 12.00 0.00
H 18.38 225.00 D 18.38 45.00
TABLE 3: Value of features for LD2
Table 4 shows the comparison of features F1, F2 and F3. For F1, the label for outer loop corners
will be rotated as well as the sum of branches in order to find the pairing of the corners. The value
of Distance Ratio is the scaling ratio between two distance values of F2 in Table 1 and 3. The
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Distance Ratio represents the scaling factor of the new line drawing. For F3, the angle difference
(Angle Diff.) is calculated based on the values of angle in Table 1 and 3. The values are then
normalised. The normalised values indicate the rotation angle of the operation. Table 4 shows the
value of mean and variance for F2 and F3. For F2, the value mean 0.768 indicates that the 76.8%
scaling had happened for LD2. For F3, the value 44.40 indicates that line drawing 2 also have
been rotated 44
0
with the rotation direction will be conferred from the actual angle difference
value since the information is lost when angle normalization is executed. The exact answer would
be the LD1 has been resized to 75% and 45ยฐCW rotation to form LD2.
The value of variance is to measure structure lines properties uniformity. This value should be
small enough to show that the line drawing 2 is really based on the line drawing 1. To judge how
small the variance value must be is by comparing it to generic error margin set so that it must be
lower than this value. If error margin is set to 0.1 for F2 and 15 for F3, the comparison can be
allowed that LD2 is similar to LD1 after 76% scaled and 450
rotated clockwise. This acceptance is
because variance value for F2 is 0.00093 and F3 is 1.863 that is less than error margin set.
F1 (LD1) F1 (LD2) F2 F3
Corner Corner Distance Ratio Angle Diff.
Current Target Current Target Actual Normalized
A
B
F
G 0.786 -315.00 45.00
G E 0.749 44.20 45.20
B
A
G
F 0.786 45.00 45.00
C A 0.741 -315.00 45.00
C
B
A
G 0.741 45.00 45.00
D B 0.741 45.00 45.00
D
C
B
A 0.741 45.00 45.00
E C 0.825 45.00 45.00
E
D
C
B 0.825 45.00 45.00
F D 0.771 45.00 45.00
F
E
D
C 0.771 -315.00 45.00
G E 0.779 40.73 40.73
G
F
E
D 0.779 -319.27 40.73
A F 0.749 44.20 44.20
H
F
H
D 0.800 45.00 45.00
D B 0.771 45.00 45.00
B G 0.707 45.00 45.00
Mean 0.768 44.40
Variance 0.00093 1.863
TABLE 4: Feature Comparison Table in Recognition Process
5. DISCUSSION AND CONCLUSION
The results show that the framework successfully detects corner, generates chain code, extracts
features, and finally recognizes the line drawing via comparison. The proposed heuristic corner
detection algorithm provides a simpler way since it does not involve complex mathematical
equation in the calculation. The advantage of the proposed MFCC is that a line drawing can be
represented as one single code series. This overcomes problems in representing a line drawing
by more than single series of chain code such as proposed by Freeman chain code. The
proposed extraction algorithm successfully reads and derives features which is considered simple
and yet produced accurate result. The proposed three features is enough for recognition purpose.
The advantage of recognition based on the MFCC is storage saving and increasing complexity of
input drawing to be compared. Finally, an integrated system which can compare and recognize
altered line drawing from its original is presented.
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6. ACKNOWLEDGMENT
The authors honourably show appreciation to Universiti Teknologi Malaysia (UTM) and Malaysian
Ministry of Higher Education (MoHE) for the support in making this research successful.
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