Grayscale Image Analysis Using Morphological Filtering

Grayscale Image Analysis Using Morphological Filtering

1S. Sindhuja , 2M. Santhosh

1PG Scholar, Arasu Engineering College, Kumbakonam

2M.Tech, (PhD), Assistant Professor, Arasu Engineering College, Kumbakonam

Abstract –The capability of extracting moving objects from a video sequence captured using a static camera is a typical first step in visual surveillance. The idea of background subtraction is to subtract or difference the current image from a reference background model. This paper proposes a new method to detect moving object based on background subtraction. First of all, we establish a reliable background updating model based on statistical and use a dynamic optimization threshold method to obtain a more complete moving object. Here we have written the core processor Microblaze is designed in VHDL (VHSIC hardware description language), implemented using XILINX ISE 8.1 Design suite the algorithm is written in Impulse C Language and tested in SPARTAN-3 FPGA kit by interfacing a test circuit with the PC using the RS232 cable. The test results are seen to be satisfactory. The area taken and the speed of the algorithm are also evaluated.

1. INTRODUCTION

   Image segmentation is one of the most important categories of image processing. The purpose of image segmentation is to divide an original image into homogeneous regions. It can be applied as a pre-processing stage for other image processing methods. There exist several approaches for image segmentation methods for image processing. The after sheds transformation is studied in this thesis as a particular method of a region-based approach to the segmentation of an image. The complete transformation incorporates a pre-processing and post- processing stage that deals with embedded problems such as edge ambiguity and the output of a large number of regions. Multiscale Morphological Gradient (MMG) and Region Adjacency Graph (RAG) are two methods that are pre- processing and post-processing stages, respectively. RAG incorporates dissimilarity criteria to merge adjacent homogeneous regions.

   In this thesis, the proposed system has been applied to a set of co-aligned images, which include a pair of intensity and range images. It is expected that the hidden edges within the intensity image can be detected by observing

   range data or vice versa. Also it is expected that the contribution of the range image in region merging can compensate for the dominance of shadows within the intensity image regardless of the original intensity of the object.

   Image processing and analysis is an important area in the field of robotics. This is particularly true for the operation of autonomous vehicles. The operation of an autonomous vehicle is based on first acquiring data that describe its environment. Indeed, the motion planning and control of a fully autonomous vehicle requires an intelligent controller to be able to make decisions to allow the autonomous vehicle to maneuver in an unknown field based on these data. These data sets include range data, 2D images, and position measurements. This data is used to identify and avoid obstacles and to map the surrounding terrain.

   The elements of an image analysis system are shown in Fig.1. Image analysis usually starts with a pre-processing stage, which includes operations such as noise reduction. For the actual recognition stage, segmentation should be done before it to extract out only the part that has useful information. Image segmentation is a primary and critical component of image analysis. The quality of the final results of an image analysis could depend on the segmentation step. On the other hand, segmentation is one of the most difficult tasks in image processing, especially automatic image segmentation.

   Fig.1 Elements of Image Analysis

   The goal of the segmentation process is to define areas within the image that have some properties that make them homogeneous. The definition of those properties should satisfy the general condition that the union of neighboring regions should not be homogeneous if we consider the same

   set of properties. After segmentation, we can usually establish that the discontinuities in the image correspond to boundaries between regions. The methods most commonly used for image segmentation can be categorized into four classes.

2. MATHEMATICAL MORPHOLOGY

   The term morphology refers to the study of shapes and structures from a general scientific perspective. Also, it can be interpreted as shape study using mathematical set theory. In image processing, morphology is the name of a specific methodology for analyzing the geometric structure inherent within an image. The morphological filter, which can be constructed on the basis of the underlying morphological operations, are more suitable for shape analysis than the standard linear filters since the latter sometimes distort the underlying geometric form of the image.

   Some of the salient points regarding the morphological approach are as follows:

   1. Morphological operations provide for the systematic alteration of the geometric content of an image while maintaining the stability of the important geometric characteristics.

   2. There exists a well-developed morphological algebra that can be employed for representation and optimization.

   3. It is possible to express digital algorithms in terms of a very small class of primitive morphological operations.

   4. There exist rigorous representations theorems by means of which one can obtain the expression of morphological filters in terms of the primitive morphological operations.

   In general, morphological operators transform the original image into another image through the interaction with the other image of a certain shape and size, which is known as the structuring element. Geometric features of the images that are similar in shape and size to the structuring element are preserved, while other features are suppressed. Therefore, morphological operations can simplify the image data, preserving their shape characteristics and eliminate irrelevancies. In view of applications, morphological operations can be employed for many purposes, including edge detection, segmentation, and enhancement of images.

   This chapter begins with binary morphology that is based on the set theory. Then, grayscale morphology can be regarded as the extension of binary morphology to a three- dimensional space since a grayscale image can be considered as a set of points in 3D space. The basic geometric characteristics of the primitive morphology operators are introduced in this chapter. A systematic introduction of theoretical foundations of mathematical morphology, its main image operations, and their applications can be found in and.

   Mathematical morphology defined in a Euclidean setting is called Euclidean morphology and that defined in a digital setting is called digital morphology. In general, their relationship is akin to that between continuous signal processing and digital signal processing. The actual implementation of morphological operators will be in the digital setting, so in this thesis focusing on digital image, we only consider the digital morphological setting.

   1. Binary Dilation

      Definition: Binary Dilation

      With A and B as sets in Z2, the dilation of A by B (usually A is an image and B is the structuring element), denoted by AB, is defined as

      It can be shown that dilation is equivalent to a union of translation of the original image with respect to the structuring element:

      Fig.2 Illustration of Binary Dilation on Diital Setting

      Dilation is found by placing the center of the template over each of the foreground pixels of the original image and then taking the union of all the resulting copies of the structuring element, produced by using the translation. From Fig.2, it is clear how dilation modifies the original image with respect to the shape of the structuring element.

      Dilation generally has an effect of expanding an image; so consequently, small holes inside foreground can be filled.

      In another sense, dilation can be a morphological operation on a binary image defined as:

      This equation is based on obtaining the reflection of B about its origin and shifting this reflection by z. The dilation of A by B is the set of all displacements, z, such that B and A overlap by at least one element. Based on this interpretation, the equation above may be written as

      Although dilation is based on set operations, whereas convolution is based on arithmetic operations, the basic process — flipping B about its origin and successively displacing it so that it slides over set (image) A — is analogous to the convolution process. Even though dilation of an image A by structuring element B can be defined in several ways, all definitions have the same meaning and results in the same output. Fig.3 illustrates the dilation operation using a binary image. The original image is dilated with an 11×11 disk type structuring element.

      Fig.3 Binary Dilation Example

   2. Binary Erosion

      Definitions: Binary Erosion

      Erosion of a binary image A by structuring element B, denoted by AB, is defined

      Whereas dilation can be represented as a union of translates, erosion can be represented as an intersection of the negative translates. So, the given definition of erosion above can be redefined as

      Fig.4 Illustration of Binary Erosion on Digital Setting

      The erosion of the original image by the structuring element can be described intuitively by template translation as seen in the dilation process. Erosion shrinks the original image and eliminates small enough peaks (Note: the terms

      expand for dilation and shrink for erosion refer to the effects on the foreground). Fig.4 clearly illustrates these effects. The original image is eroded with 7×7 disk-shape structuring element.

   3. Binary Opening

      Definition: Binary Opening

      The opening of a binary image A by the structuring element B, denoted by AB, is defined as

      From the definition, the original image A is first eroded and then dilated by the same structuring element B. In terms of set theory, this opening process can also be defined as

      Fig.5 Illustration of Binary Opening Process

      The whole procedure of opening can be interpreted as rolling the structuring element about the inside boundary of the image.

      Fig.6 Binary Erosion Example

      The effects of the opening process on the original image are smoothing, reducing noise from quantization or the sensor and pruning extraneous structures. These effects result from

      the fact that the structuring element cannot fit into the regions. Therefore, it can be said that the result of the opening process heavily depends on the shape of structuring elements. Fig.7 presents an example of the opening process.

      Fig.7 Binary Opening Example

      The effects of the opening mentioned before are clearly shown in the Figure 2.8. The vortices of the triangle foreground have been cut out because the image is opened with square type structuring element, whereas those of the square are preserved.

   4. Binary Closing

   Definitions: Binary Closing

   Closing of a binary image A by a structuring element B, denoted by AB, is defined as

   In the closing operation, dilation and erosion are applied successively in that order. Note that this order is reversed for the opening process.

   In another aspect, the closing process on a binary image can be defined as:

   The closing operation can be described as in Fig.8, as rolling the structuring element on the outer boundary of the image.

   Fig.8 Illustration of Binary Closing Process

   The closing process has the effect of filling small holes in the original image, smoothing as the opening process does, and filling up the bay in the foreground.

3. EXPERIMENTAL SETUP

   The Xilinx Platform Studio (XPS) is the development environment or GUI used for designing the hardware portion of your embedded processor system. B. Embedded Development Kit Xilinx Embedded Development Kit (EDK) is an integrated software tool suite for developing embedded systems with Xilinx MicroBlaze and PowerPC CPUs. EDK includes a variety of tools and applications to assist the designer to develop an embedded system right from the hardware creation to final implementation of the system on an FPGA. System design consists of the creation

   of the hardware and software components of the embedded processor system and the creation of a verification component is optional.

   A typical embedded system design project involves: hardware platform creation, hardware platform verification (simulation), software platform creation, software application creation, and software verification. Base System Builder is the wizard that is used to automatically generate a hardware platform according to the user specifications that is defmed by the MHS (Microprocessor Hardware Specification) file. The MHS file defines the system architecture, peripherals and embedded processors. The Platform Generation tool creates the hardware platform using the MHS file as input. The software platform is defmed by MSS (Microprocessor Software Specification) file which defines driver and library customization parameters for peripherals, processor customization parameters, standard 110 devices, interrupt handler routines, and other software related routines. The MSS file is an input to the Library Generator tool for customization of drivers, libraries and interrupts handlers.

   Fig.9 Embedded Development Kit Design Flow

   The creation of the verification platform is optional and is based on the hardware platform. The MHS file is taken as an input by the Simgen tool to create simulation files for a specific simulator. Three types of simulation models can be generated by the Simgen tool: behavioral, structural and timing models. Some other useful tools available in EDK are Platform Studio which provides the GUI for creating the MHS and MSS files. Create / Import IP Wizard which allows the creation of the designer's own peripheral and import them into EDK projects. Platform Generator customizes and generates the processor system in the form of hardware netlists. Library Generator tool configures libraries, device drivers, file systems and interrupt handlers for embedded processor system. Bitstream Initializer tool initializes the instruction memory of processors on the FPGA shown in fig.9. GNU Compiler tools are used for compiling and linking application executables for each processor in the system [6]. There are two options available

   for debugging the application created using EDK namely: Xilinx Microprocessor Debug (XMD) for debugging the application software using a Microprocessor Debug Module (MDM) in the embedded processor system, and Software Debugger that invokes the software debugger corresponding to the compiler being used for the processor. C. Software Development Kit Xilinx Platform Studio Software Development Kit (SDK) is an integrated development environment, complimentary to XPS, that is used for C/C++ embedded software application creation and verification. SDK is built on the Eclipse opensource framework. Soft Development Kit (SDK) is a suite of tools that enables you to design a software application for selected Soft IP Cores in the Xilinx Embedded Development Kit (EDK).The software application can be written in a "C or C++" then the complete embedded processor system for user application will be completed, else debug & download the bit file ito FPGA. Then FPGA behaves like processor implemented on it in a Xilinx Field Programmable Gate Array (FPGA) device.

4. TABULATION RESULT

   The Algorithm is implemented in Micro blaze Processor and the results are furnished in the tabulation below

5. CONCLUSION

The proposed method is inherently parallel, since computations for each pixel of each sequence frame can be done concurrently with no need for communications. This can help in lowering execution times for high-resolution sequences. Moreover, the approach is suitable to be adopted in a layered framework, where, operating at region-level, it can improve detection results allowing to more efficiently tackle the camouage problem and to distinguish morphological Image by the morphological operator. This is a very desirable operative mode, considering that a very actual visual segmentation with high accuracy is achieved.

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