Detection of **circular** shape from grey scale images is explored in this paper. The algorithm used for detection of circles is **Circular** **Hough** **Transform** which is one of the types of **Hough** **Transform**. The algorithm is implemented in VHDL. In this paper IEEE-754 single precision standard floating point number has been discussed and also the arithmetic operations such as addition, subtraction, multiplication and division have also been covered.

Copyright to IJIRSET www.ijirset.com 3549 Gradients were biased in the vertical direction for the outer iris/sclera boundary. Vertical and horizontal gradients were weighted equally for the inner iris/pupil boundary. The range of radius values to search for was set manually based on theoretical values depending on the database used. For all CASIA database versions, values of the iris radius range from 90 to 150 pixels, while the pupil radius ranges from 28 to 75 pixels. Since the pupil is always within the iris region, **Hough** **transform** for the detection of iris/sclera boundary was performed first, then the **Hough** **transform** for the iris/pupil boundary was performed within the iris region. This makes the circle detection process more efficient and accurate. After completion of this process, six parameters are stored, the radius, and x and y centre coordinates for both circles. Eyelids are treated as noise during segmentation. Their isolation is done by first fitting a line to the upper and lower eyelid. A second horizontal line is drawn, which intersects with the first line at the iris edge that is closest to the pupil as shown in figure 2,3,4and 5. This is done for both top and bottom of eye.

After that canny edge detector is applied on the grayscale image to compute the edges of sharp intensity. It can be easily seen that with the edges of circles there is some noise coming from background as the background is not plain. The output of canny edge detection gives us the edges of the objects.At this step, even if **circular** **Hough** **transform** is applied only coins will be detected and counted because of their **circular** shape but pen will not be detected because it is not **circular** in shape. The purpose of the proposed approach is to use **circular** **Hough** **transform** in such a way to get the results in which all the desired objects are detached from each other and after that using a generic image processing technique e.g. contour detection to count objects irrespective of their shapes. So for the purpose of simplification the pen contour is separated and filled internally to be prominent, even though the results can be achieved otherwise, we can see that the pen still has some noise as a part of the coin is attached to it, which can be removed by drawing the contour with a black boundary around it. In the proposed method, no morphological approach is applied. Contours of the circles are drawn on a new image which is an 8bits 3-channels image. In this paper, **Circular** **Hough** **transform** is used not just only to detect the **circular** shaped objects, but also to draw the closed contour circles. After the contour detection step, a lot of circles have open and unconnected boundaries. So by applying **circular** **hough** **transform**, retrieved the circles having a closed contour and after that circles are filled to to be visually prominent, but still the objects are not totally detached.

Optic Disc (OD) is considered as one of the main features of a retinal fundus image. Segmenting the OD is a key pre-processing element in many algorithms designed for automatic extraction of anatomical structures. Information about the OD can be used to examine the severity of some diseases such as glaucoma, proliferative diabetic retinopathy, disc edema, etc. An elliptical template based methodology is proposed for the segmentation of optic disc. The detection procedure comprises of two independent methodologies namely optic disc detection and boundary approximation. To improve the accuracy of Optic Disc detection, the candidate regions are first determined by clustering the brightest pixels in red plane of the fundus image. Different image contrast analysis methods are applied within that candidate region to locate the optic disc. Sub image having the optic disc can be separated for boundary detection using histogram. Boundary detection methodology estimates an elliptical approximation of the OD boundary by applying mathematical morphology, edge detection techniques and **circular** **Hough** **transform** along with **circular** template. Due to the exceptional ellipsity degree of the optic disc, elliptical template is proposed to increase the overlapping rate from 86% achieved with a **circular** template matching to 95%.

A very well known existing technique, the **Circular** **Hough** **Transform** (CHT) has been implemented for detection of iris and pupil boundaries (Gonzalez and Woods, 2007). Traditional segmentation techniques do not give accurate results with images containing noise. But, the fundamental structure of CHT algorithm is the reason of consuming high processing time and also utilizing high capacity of storage. These drawbacks of CHT algorithm make it hard to employing CHT algorithm for a practical usage that Iris Recognition System needs to apply for millions of users.

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1.2) Hough_Transform: One of the most commonly used algorithms to recognize different shapes in an image is **Hough** **Transform** [8]. **Hough** **Transform** was introduced by Paul **Hough** in 1962 and patented by IBM. In 1972 Richard Duda and Peter Hart modified **Hough** **Transform**, which is used universally today under the name Generalized **Hough** **Transform** [9]. An extended form of General **Hough** **Transform**, **Circular** **Hough** **Transform** (CHT) [8] is used to detect circles. The edge detected from the canny edge detector forms the input to extract the circle using the **Circular** **Hough** **Transform**. In **Circular** **Hough** **Transform**, voting procedure is carried out in a parameter space. The local maxima in accumulator space obtained by voting procedure are used to compute the **Hough** **Transform**. Parameter space is defined by the parametric representation used to describe circles in the picture plane, which is given by equation (3). An accumulator is an array used to detect the existence of the circle in the **Circular** **Hough** **Transform**. Dimension of the accumulator is equal to the unknown parameters of the circle.

This review presents a detailed survey of methods and results used for the automatic detection of DR stages. The process of analyzing retinal images involves series of steps. Image acquisition, image preprocessing, Candidate feature extraction and classification. All these steps include various techniques or algorithms. Some existing methods are compared to give a complete view of the field. Most of them use a threshold based method to segment the image and blood vessels are removed. Intensity features are formulated to classify the images. Unfortunately, the major limitation to this approach is that most of the false positives at the vessel segmentation step are actually lesions. These lesions are removed along with the detected vessels and cannot retrieve in subsequent processing. **Hough** **transform** is used to find features of any shape in an image. Regular curves such as lines, circles and ellipses can be detected using this method. **Circular** **hough** **transform** is used to find circles in imperfect image inputs. But its computational complexity and large memory requirement leads to slowness in performance.

In the proposed method, the images are collected from the DIARETDB1 [10] database that is publically available from the internet. First step is pre-processing of an image which is mainly used to remove the noise from the images. Optic disc detection is based on the **circular** **Hough** **transform**. Because almost all the optic disc are in **circular** structure. Blood vessel extractions are based on the thresholding method. After detection of optic disc and blood vessels, the detection of exudates are performed by adaptive thresholding. That is used to faint exudates also.

[16] Marc Lalonde, David Byns, Langis Gagnon, Normand Teasdale and Denis Laurendeau, “Real-time eye blink detection with GPU-based SIFT tracking”, IEEE 2007. [17] Paul Viola and Michael Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features” IEEE 2001. [18] Mandalapu Sarada Devi, Monali V. Choudhari and Dr.Preeti Bajaj, “Driver Drowsiness Detection Using Skin Color Algorithm and **Circular** **Hough** **Transform**”, IEEE 2011. [19] Mohammad Amin Assari and Mohammad Rahmati, “Driver Drowsiness Detection Using Face Expression Recognition”, IEEE 2011.

randomly on noise bearing images [20-24].In Probability- based **Circular** **Hough** **Transform** (PCHT) methods, only a proportion of image edge points (about 5 to 15%) are used as representative points in storage increase followed by a search. Therefore PCHT methods reduce the computational complexity [15-19]. Although many accurate and efficient algorithms have been developed, none of them can control the number of false-positives, especially in image with co-centric or overlapping circles. The efficiency of these algorithms in low-noise environments is less than that in noise-bearing environments and even equal to that of SCHT algorithm. On the other hand, when these algorithms are used to search for co-centric circles with different radius, the problem of system memory shortage and computation time increase will arise due to using nested loops.

The paper is organized as follows : section 2 named preliminaries concerns with the advantages of an hexagonal grid . It also defines naif, standard and supercover straight line. Section 3 analyzes the construction of naif, standard, supercover straight line. Section 4 defines new analytical straight lines based on hexagons and octogons. Section 5 focuses on their recognition with the **hough** **transform** method.

The simplest case of **Hough** **transform** is detecting straight lines. In general, the straight line y = mx + b can be represented as a point (b, m) in the parameter space. However, vertical lines pose a problem. They would give rise to unbounded values of the slope parameter m. Thus, for computational reasons, Dude and Hart [5] proposed the use of the Hesse normal form r=x cos (theta) +y sin (theta),

Because of its less limitation so many research methods have been proposed for Iris Recognition so far. Daugman [3], [1] was first proposed an algorithm for iris recognition. His algorithm is mainly based on Iris Codes. Integrodifferential operators are used to detect the centre and diameter of the iris. The image is converted from cartesian to polar **transform** and rectangular representation of the region of interest is generated. Feature extraction algorithm uses the complex valued 2D Gabor wavelets [7], [4] to generate the iris codes which are then matched using Hamming Distance. The algorithm gives the accuracy of more than 99.99%. Also the time required for iris identification is less than one second.

This processing is performed prior to using an edge-sensitive **Hough** circle detector. The approach can easily be adapted for other domains where image segmentation is desirable, or inverted for use when sub-pixel interpolation is desirable, such as: enhancing iris patterns and finger prints for biometric identification, following blood vessels and catheters in bioinformatics images, and even geospatial remote sensing. The fundamental idea is to examine regions around each pixel in terms of consistency, and to adaptively replace the pixel based on the most consistent region. This processing attenuates small first surface reflections and noise edges that are caused by fixed pattern noise, sensor noise, and compression artifacts.

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The edge pixels that pass the decision test, as described by previous step, are further tested at a finer level. Instead of testing all of eight vertices of the **Hough** space cubic cell only the center of the cell is tested against the right **circular** cone generated by the edge pixel. A **Hough** space cubic cell defined by opposite vertices ( , , ) ( , h k r 1 1 1 h k r 2 2 , ) 2 has its center point given by ( , h k r c c , ) c where h c ( h 1 h 2 ) 2 , and so on.

Traditional subspace methods which are based on the spatial time-frequency distribution (STFD) matrix have been investigated for direction-of-arrival (DOA) estimation of linear frequency modulation (LFM) signals. However, the DOA estimation performance may degrade substantially when multiple LFM signals are spectrally overlapped in time-frequency (TF) domain. In order to solve this problem, this paper proposes single-source TF points selection algorithm based on **Hough** **transform** and short-time Fourier **transform** (STFT). Firstly, the signal intersections in TF domain can be solved based on the **Hough** **transform**, and multiple-source TF points around the intersections are removed, so that the single-source TF points set is reserved. Then, based on the Euclidean distance operator, the single-source TF points set belonging to each signal can be obtained according to the property that TF points of the same signal have same eigenvector. Finally, the averaged STFD matrix is constructed for each signal, and DOA estimation is achieved based on multiple signal classification (MUSIC) algorithm. In this way, the proposed algorithm exhibit remarkable superiority in estimation accuracy and angular resolution over the state-of-the-art schemes and can achieve DOA estimation in the underdetermined cases. In addition, the proposed algorithm can still perform DOA estimation when multiple LFM signals intersect at one point. Numerical simulations demonstrate the validity of the proposed method.

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In this paper, it is shown that the lane detection warning system which is a mechanism designed to warn a driver when the vehicle begins to move out of its lane in that direction on freeways and arterial roads. The **Hough** **Transform**, CHEVP algorithm etc. are the techniques used for lane detection to warn the driver from lane departure. This work has also shown that lane detection using a single forward facing camera is also possible. This could prove valuable in safety application in vehicle where the driver is not paying attention to the road, falling asleep, etc. Experimental results expose the robustness and efficiency of the performance of the lane detection algorithm in various environments.

processing technology, word spotting becomes possible without determining the start and end points of the word. In order to detect a line in a graphical image, the mini- mum mean square method and **Hough** **transform** are well known as strong and easy methods. Each has its own feature.

Algorithm and **Hough** Line Detector. 59 4.10 The corner candidate using the **Hough** Corner Detector. 62 5.1 The sample of digital image 65 5.2 The result from the edge detector (the Edge Image) 66 5.3 The result of thinning process (the Minima Image) 67 5.4 The corner candidates produced from the thinning image of

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x cos θ + y sin θ = r (17) where θ is the angle made by the radius vector with the positive x-axis as shown in Figure 5. Equation (17) is exactly similar to equation (5) and thus the same architecture for straight line HT can be extended for **circular** HT. All the points lying on the same circle will give same radius value for different θ . Considering the co-ordinate system where the origin is coincident with the center of the circle, the θ scan range will be of [0, 2 π ]. This range can be divided into eight subspaces (a, b, c, d, e, f, g, h) and the θ scan range can be restricted to [0, π /4 ± δ ]. The values of r in different subspaces can be calculated according to the following equations,

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