last vergen reports

8/7/2019 last vergen reports

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Reports of research papers..pagesRoad Sign Detection and Recognition ............. 2Real Time Road Sign Recognition System Using Artificial.5

Neural Networks for Bengali Textual Information Box

A First Approach to Learning a Model of Traffic Signs using9

Connectionist and syntactic methods

Sign finder: Using Color to detect, localize and identify...15

Informational signs

Road Traffic Sign Detection and Classification.18

Our Approach ..22


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b. The ratio of the area of the blob to the area of the bounding boxis restricted to prevent blobs that are too thin from being

accepted.3. Blobs that conform to the rules are considered to be candidate signs

and are tracked from image to image.4. If a blob is seen in five successive frames, it is confirmed as a

candidate and goes on to the recognition phase of the algorithm.

2- Recognition:

y Recognition is achieved by template matching.

1) A preprocessing step is first applied to each candidate sign:

y It masks out the background surrounding the sign which would otherwise

interfere with the template matching.y We make use of the results of the sign detection phase that already

constructed a mask for the sign.

y Using this mask results in good segmentation of the sign region from thebackground (Figure 2).

y The masked candidate signs are scaled to a standard size (48x48 pixels)2) The masked candidate signs are compared with stored signs of the same size.

y The stored sign templates are taken from video sequences similar to those beingrecognized.

y Because there is a lot of variation in the signs, several stored templates may beneeded for each canonical sign.


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3) Bangla Optical Character Recognition Using MLP:

An artificial neural network based approach is used for Bangla opticalcharacters recognition (OCR) of text in road signs.

The Bangla OCR module has three sub-modules:3.1 Character Segmentation:

The text is partitioned into its coherent parts. To getindividual characters the text must pass in sequences ofprocesses: line segmentation, word segmentation, andcharacter segmentation.

3.2 Feature Extraction:It is step for extracting feature column matrix which it keyof recognition phase.

3.3 Character Recognition by MLP Neural Network: The segmented characters are recognized using

Multilayer Perceptron (MLP) Neural Network. For segmented Bangla character recognition, the three

layers feed forward supervised neural networks aredesigned.

In these three layers MLP neural network Log-Sigmoidand Hyperbolic-Sigmoid transfer functions are used as

activation functions. Two 3-layer neural networks are created;

o One is for all Bangla characters recognitionincluding vowels, consonants and conjunctives;

o Another is for modifiers of Bangla characters. Each MLP network has 400 neurons in input layer

according to the feature column matrix.

4) Confirmation of textual road sign:

The recognized text is matched with a pre-memorized Bangla roadsign text list to confirm whether it is a Bangladeshi road sign textor not.

If it is a member of Bangla road signs' text list, then it is sent to theconversion phase to convert text into Times New Roman textFormat.

5) Speech Synthesis:

Process of converting written text into spoken language (digitalaudio stream).


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Experimental Results and Discussion

y The performance and accuracy rate of the proposed system are measured bytesting four major modules with a set of real time video frames, which containsdifferent types of Bangla road signs.

y The modules of our proposed Bangla Road Sign Recognition System areimplemented using Visual Basic 6.0 and MATLAB 7.0.

y The following definitions are applied:Success Rate = (Total of Success/ Total Number of Input Sample) x100 %Failure Rate=(Total Number ofFailure/Total number of Input Sample) x100%Efficiency= (100-Failure Rate) %

y In this system, the total computing time required for recognizing a road sign aftercapturing an image up to speech synthesis is 0.98s in non complied Matlabenvironment.

y That means that before capturing a new frame the previous frame completely passall of phases of the system. As a result, the time complexity of the system is quitelow.

y After testing this system, the obtained accuracy rate was evaluated at 91.48%.


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A First Approach to Learning a Model of Traffic Signs

using Connectionist and syntactic methods

Overview

The approach is proposed by miguel sainz and alberto sanfeliu. The main purpose of thisresearch is to develop a system for learning and recognizing traffic signs using neuralnetwork and syntactic methods. This paper use tow level of learning, first:segmentation learning based on neural network, second: model learning based ongrammatical inference. Recognition process uses the results of the learning process asinput for identifying the traffic sings. The recognition of traffic sings in scene is donein tow steps, first: the sign is located in the scene by using a connectionistsegmentation method, second: the sign is coded and analyzed to determine whichtraffic sign it is. The system has been tested successfully only for the first step but thesecond step is currently under development.

Approach

1. The Learning Process:

1.1 Segmentation Learning Based On Neural Network

1. The input of this step is color image obtained from TV camera on thecar.

2. The human Operator decides how many different labels the system willconsider (in the traffic sign recognition problem we have considered thefollowing 5 labels (road-road lines- sky -grass-traffic sign)).

3. then he marks and labels some areas in a set of images "Segmentation

areas "4. Segmentation module consist of 3 layered neural net, this net is trained by

the back propagation method.5. Once the net is trained we perform a validation test over a set of test

images to check the learning performances, In the case of low efficiency ornon-satisfactory we can modify the samples or the net parameters toimprove the learning of Segmentation module.


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1.2 Model Learning

1. In this level the operator marks the areas on the scenes where themodel is locate these areas called model areas

2. It is necessary to preprocess the image before the learning processstarts .

i. the preprocessinghas 3 parts :ii. Optimization of the areas.1.2.2.2 Normalization of the sizes (to get the same information

from any of the different images area, we shouldnormalize the size of the model areas).

1. 2.2.3 coding into symbols the content of the sample areas (wecode each pixel of the model area into one of thefollowing 4 symbols red(R),white(W),black(B) , the

remaining of colors ($). improve the shape of the trafficsign (remove holes and smoothing the contour ) by

applied morphological process) .1.2.3 Extract the primitive chains by reading the primitives of the coded

sample.1.2.4 Validation test is applied to evaluate how good the system ,if it is not

good the operator may restart the model learning level .

2-The Recognition ProcessThe Recognition Process Divided Into 3 Steps and 3rd Step Divided Into 2 Phases

2.1 Location Of The Traffic Sign By Using Segmentation Model see figure 3 .2.2 Morphological Process Is Applied To Remove Noise And Fill Up Grapes.2.3 The Third Step Is The Recognition Of Traffic Sign This Step Is Divided

Into Phases


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2.3.1 Finding A Distance Measure Between The ExtractedSymbol Chain And Each Inferred Grammar Of Traffic Sign

Models Error correcting parser is used.2.3.2 Analyze The Symbol Inside The Sign.

Results:


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we see 2 road scenes on the right side and 2 segment rode scenes ,as we see the

Segmentation process gives very good results

we see traffic sign from the scenes and the results of coding them into grammarsymbols


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Signfinder: Using Color to detect, localize and identify

Informational signs

Overview :The Approach Is Proposed By A.L. Yuille, D. Snow, And M. Nitzberg .The Main Purpose Of This Search Paper Is Describe An Approach That Is Able To Detect AndLocate Certain Important Classes Of Signs. The Signs Are Then Automatically TransformedTo A Standard (Frontal) Viewpoint . Most Street Signs Obey The Following Assumptions,First :The Signs Have Stereotypical Boundary Shapes, Second : The Writing On The Sign HasOne Uniform Color And The Rest Of The Sign Has A Second Uniform Color , Third : TheText Font Is In A Standard Font Set.We Start By Selecting Simple Tests Which Can Be RunIn Parallel Over The Image. These Tests Locate Seed Positions For Hypotheses. The SeedsInitiate Region Growing To Segment Two Color Regions (E.G. Signs). A Specialized EdgeDetector Is Used On The Segmented Regions To Determine The Precise Location Of The SignBoundaries And To Con_Rm (Or Deny) That The Region Is Really A Sign. From TheBoundaries We Can Calculate An Affine Transformation To Transform The Sign To A

Standard (F

rontal) Viewpoint .

Approach :

These Steps Have Been Applied On 100 Red And White Stop Signs . Steps In General Are AsFollows:1. Get A Database Of Color Images With Signs In Them In Different Situations Of Light ,

Shadow , Vision Of The Place And Distortions2. In Order To Locate The Position Of A Sign Inside Image ,We Must

2.1. Identify The Seed Regions (Which There Are Two Color Peaks).

In Order To Determine Seed We Use Statistical Analysis Of The Colors In Order To Learn AdjacentSets Of Red And White Pixels With Unknown Illuminant (I.E. For Stop Signs). And Then ApplyMultiplicative Model To Deduce The Set Of Illuminant Colors.

.

2.2.Then Apply The Algorithm To The Growth Of The Regions .

Growth Of The Seed Region Is Through The Integration Neighboring Pixels

Of The Seed Where The Properties Of Colors Similar To The Properties OfThe Colors In The Seed Region .

2.3 Tested It (I.E. Hypothesis Regions Which Obtained By Applying The GrowingAlgorithm In The Previous Step) .

By Detecting Straight Line Edges (To Find Boundary , This PaperUse SpeciallyTailored Edge Detectors And A Variant Of The Hough Transform To Detect TheBoundaries And The Corners Of The Sign) In Order To Obtain Information On TheGeometric Shape Of The Sign , Which Allows Us To Know The Direction Of The


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Sign Then Normalize The Sign To A Front Parallel Viewpoint At Fixed Scale (This

Makes Reading Directly).

Results :The Algorithm Worked At Close To One Hundred Percent Effectiveness On OurDataset . This Section Shows The Final Results On Some Of The More DifficultImages In Our Database. These Involve Partial Occlusion, Heavy Shadowing, AndDifficult Illuminant Colors And Pose.1 . Sign With Partial Occlusion

a) The image containing the occluded sign. b) The final result of the algorithm

2. Sign with large shadowing.

a) The image containing the shadowed sign. b) The final result of the algorithm.

3- Sign at difficult viewpoint and illuminant


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a) The image containing the sign at difficult viewpoint and illuminant. b) The final result of thealgorithm.

Road Traffic Sign Detection and Classification

Overview:The approach proposed by A. Escalera, L. E. Moreno ,M. A. Salichs and J. M.Armingol. The algorithm presented here has two modules. The first one, localizes the

sign in the image depending on the color and the form. The second one,Recognizesthe sign through a neural network.A system capable of performing such a task would be very valuable and would havedifferent applications. It could be used as an assistant for drivers, alerting them aboutthe presence of some specific sign or some risky situation.

Approach:

The algorithm is divided in two modules described as follow:

Module 1: TRAFFICSIGNDETECTION:

Step 1: Color ThresholdingThe functions that give the red, green, and blue levels of each point of the image.


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Step 2: The Optimal Corner Detector

Have two methods:1- The first is corner detectors that work on the codification

of the edge of an object:o Divide the image into regions, extract the subsequent

of the edges of these regions then codification.2- Corner detectors that work directly on the image:

o Find the corner using convolution of the image withmask.

Step 3: Corner Extraction:

Algorithm steps to obtain a corner of an image as following:y It obtains the convolution for every type of mask

y It selects the points above a threshold. This threshold is obtained from an ideal result.

y It calculates the center of mass. Although the detection mask is built to obtain themaximum value of the convolution exactly in the corner, because the image is neverideal and the threshold, a sole isolated point will never appear labeled as a corner.

Step 4: Example of different signs detection

y Triangular Signs DetectionBy seeking in the image the three kinds of corners that form the triangle.By proving they are forming an equilateral triangle.

Following these steps:1- Corner detection.2- The study of the position of the corners

y Rectangular Signs DetectionBy seeks the four kinds of 90 corners that form the sign and that are locateddefining a rectangle. Following these Steps:

1- Corner detection.2- The study of the position of the corners

y Circular Signs DetectionMasks to locate some portions of a circumference can be built, and thecircumference they belong to can be found from the convolution.

The masks built for the 90 corners are an approximation of small-circumference arcslocated in the 45 , 135 , 225 , and 315 angles.


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Module 1: TRAFFICSIGNCLASSIFICATION

By using multilayer perceptron NN, the size of the input layer corresponds to an

image of 30* 30 pixels, and the output layer is ten . Present the image as the inputpattern. Two neural networks were trained because the detection algorithm is differentaccording to the form of the sign. The studied NN's were three, the number anddimension of their hidden layers being different.

Step 1: Image NormalizationNormalize the image obtained by the detection module to the dimensions 30*30 using ,

the relation between the dimension we need and the ones we have obtained is calculated, thepixels are repeated or discarded depending on that relation (using the nearest neighbormethod).

Step 2: Training PatternsNine ideal signs were chosen for the net training .The training patterns are obtained fromthese signs through the following modifications.1- The slope accepted for a sign is 6. From every one of the nine signs, another five wereobtained by covering that draft range.2- Three Gaussian noise levels were added to each of the previous signs. This way, during thetraining of the net, low weights were associated with the background pixels of the inner partof the sign.3- Four different thresholds were applied to the resulting image; the system is adapted tovarious lighting conditions that the real images will present.4- After making a decision about the net dimensions, a new set of training patterns was made,taking into account a displacement of three pixels to the left and to the right. Then, from thechosen ideal patterns, 1620 training patterns were obtained.

Experiments & Results:

The dimensions of the three studied NN's are as follows:30*30 is input size to the network, 10 is the output and between them is the hiddenlayer of Triangular, Rectangular and Circular respectively1) 30*30/30/10;2) 30* 30/30/15/10;3) 30* 30/15/5/10.The three NN's were trained with the patterns obtained from the first three conditions.

In order to compare the results, some test images were chosen, as shown inF

ig. 9.The best results corresponded to the third network and are shown in Table III (0minimum value, 100 maximum).

The algorithm has been implemented in an ITI 150/40 in a PC486 33 MHz with LocalBus. The speed of the detection phase is 220 ms for a 256*256 image.The neural network runs in the PC CPU and takes 1.2 s. The implementation of theneural network in a digital signal processor (DSP) is undergoing research, and theexpected speed is between 3040 ms.


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Fig.9 Ideal signs and test images.

Our Approach:Module 1: concept ofneural network1-Diffentions:

Aneural network is an interconnected group ofneurons. The prime examples

are biological neural networks, especially the human brain.

An artificial neural network is a mathematical or computational model for

information processing based on a connectionist approach to computation.

The original inspiration for the technique was from examination of

bioelectrical networks in the brainformed by neurons and their synapses. In

a neural network model, simple nodes (or "neurons", or "units") are

connected together to form a network ofnodes hence the term "neural

network.

Composed of many neurons that co-operate to perform the desired

function.

2-Neural network in real life: In real life applications, neural networks perform particularly well on the

following common tasks:

Function approximation (aka regression analysis)

Time series prediction

Classification

Pattern recognition

Problems with noisy data

Prediction.

Classification.

Data association.

Data conceptualization.

Filtering.

Planning Recognizing and matching complicated, vague, or incomplete

patterns.

Prediction: learning from past experience

Pick the best stocks in the market.


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Predict weather.

Identify people with cancer risk .

Extrapolation based on historical data.

Classification

Image processing.

Predict bankruptcy for credit card companies.

Risk assessment.

Feature extraction, image matching.

Recognition

Pattern recognition: SNOOPE (bomb detector in U.S. airports).

Character recognition.

Handwriting: processing checks.

Data association

Not only identify the characters that were scanned but identify when

the scanner is not working properly.

Data Conceptualization

Infer grouping relationships e.g. extract from a database the names of

those most likely to buy a particular product.

Data Filtering

E.g. take the noise out of a telephone signal, signal smoothing .

Planning

Unknown environments.

Sensor data is noisy.

Fairly new approach to planning .

Noise Reduction

Recognize patterns in the inputs and produce noiseless outputs.

3-What is neuron?I. A simple neuron :

An artificial neuron is a device with many inputs and one output.

The neuron has two modes ofoperation:

1. The training mode:

the neuron can be trained tofire (or not), for particular input

patterns.

2. The using mode. when a taught input pattern is detected at the input, its

associated output becomes the current output.

II. A more complicated neuron:

The difference from the previous model is that the inputs are weighted; the

effect that each input has at decision making is dependent on the weight of

the particular input.


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a two-layer network having a sigmoid first layer and a linear second layer can

be trained to approximate most functions arbitrarily well. Single-layer

networks cannot do this.

Fig.3 Three-

LayerNetwork.

How to pick anArchitecture?

Problem specifications help define the network in the following ways:

Number ofnetwork inputs = number ofproblem inputs.

Number ofneurons inoutput layer = number ofproblem output.

Output layer transfer function choice at least partly determined by

problem specificationofthe outputs.

Module 3: Learning rules:

The purpose of the learning rule is to train the network to perform some task. Thereare many types of neural network learning rules They fall into three broad

categories.

Supervised Learning :

The learning rule is provided with a set ofexamples(the training set) ofproper

network behavior:

where p is an input to the network and t is the corresponding correct( target) output.

As the inputs are applied to the network, the network outputs are compared to the

targets.

reinforcement (or graded) learning

Is similar to supervised learning, except that, instead ofbeing provided with the

correct output for each network input, the algorithm is only given a grade. The

grade (or score) is a measure of the network performance over some sequence

of inputs. This type of learning is currently much less common than supervised

learning.

unsupervised learning


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The weights and biases are modified in response tonetwork inputs only. There

are no target outputs available. At first glance this might seem to be impractical.

How can you train a network if you don`t know what it is supposed to do? Most

of these algorithms perform some kind of clustering operation. They learn to

categorize the input patterns into a finite number of classes. This is especially

useful in such applications as vector quantization.

Module 4: PerceptronArtificial Neural NetworkThe perceptron is a type ofartificial neural network invented in 1957 at the

Cornell Aeronautical Laboratory by Frank Rosenblatt. Consists of one or more

layers ofartificial neurons; the inputs are fed directly to the outputs via a series

ofweights.

Perceptron Architecture:

Single-layer Perceptron:

o Consists ofa single layer ofoutput nodes.

o The inputs are fed directly to the outputs via a series of

weights (feed-forward network).

o Single-unit perceptrons are only capable of learning linearly.

Multi-layer Perceptron:

o Consists of multiple layers of computational units, usually

interconnected in a feed -forward way.

o Each neuron in one layer has directed connections to the

neurons ofthe subsequent layer.

o In many applications the units of these networks apply a

sigmoid function as an activationfunction.

o Use a variety of learning techniques, the most popular

being back-propagation .

o In back-propagation the output values are compared with the

correct answer to compute the value of some predefined

error-function.

o The error is thenfed back through the network.

o The algorithm adjusts the weights ofeach connection inorder

to reduce the value of the error function by some small

amount. After repeating this process for a sufficiently large

number of training cycles the network will usually converge to

some state where the error of the calculations is small. In this

case one says that the network has learneda certain target

function


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