I hereby certify that I am responsible for the work submitted in this project, that the original work is mine except as noted in the references and acknowledgments, and that the original work contained herein has not been taken or performed by unspecified sources or persons. This article describes a component detection system capable of detecting the components of an LCD TV based on a captured image. For the second contributor, edge detection focus on the Carmy algorithm is used to provide more accurate detection.
Her efforts in publishing reference journals in computer vision have greatly aided the development of this thesis. His extensive experience in computer vision has been deeply motivated in the development of the specialty. For example, different objects are placed on the same black cardboard as the background.
The project will develop a component detection system capable of detecting components in LCD TV sets based on the captured image. Pixel value calculation is used for image comparison where the target image and the base image are compared based on the pixel value. They must inspect each component of the manufactured product and ensure that there are no missing components or parts.
Based on the author's experience during the author's industrial internship training in one of the most prominent electronics companies, they are currently holding a thick pencil and manually inspecting and marking the corresponding components based on the picture shown in the standard operating procedure is attached.
PROBLEM STATEMENT
Once this human error occurs, it costs a lot of money to spend and time consuming to solve the problem.
OBJECTIVE
OTHER SIDE-OBJECTIVES
SCOPE OF STUD Y
To make the project easier, let's assume that these items have been thoroughly inspected and are in good functional condition. Based on human vision as our reference, it is easier said than done to develop a vision system with the same capabilities as human vision.
HUMAN VISION
By focusing on the visual system, the object stimulates the eye nerves and information is sent through the nerves and finally reaches our brain. In Wikipedia, under the heading Optic nerve, the eye nerves, called optic nerves, are made up of axons of retinal ganglion cells and supporting cells. Because of what we practice, we can give feedback as soon as a question is asked.
Based on Warren (1994) and Cardoso (1997), memory has been variously characterized as an information retention process in which our experiences are archived and then restored when recalled. For the next two subsections, the human vision architecture is made as our reference for achieving a good computer vision system.
IMAGE COMPARISON
Pixel is the remembered color value for each of these swatches that represent tiny square areas. When all this image data (millions of numbers representing tiny color sample values, each called a pixel) is recombined and rendered in correct order and column order on printed paper or a computer screen, our human brain recognizes the original image again. For this paper, we denote an original image as X and the pixel value is calculated at the position (x, y) by X(x, y).
By hand, we denote a real-time image captured by the input sensor as Y and the pixel value calculated using the same algorithm as the original image, Y(x, y). If there is any difference in the pixel values after comparing with the real-time image, an error has occurred. The concept applied is the same as the usual game, spot the difference, between two pictures placed side by side.
We have to ask what computers see and how they know where the points of difference are. Donovan (2006) is very clear: "The distance between the object and the viewer determines the scale of the object. An object detection or analysis system must be able to detect the object despite this change in scale and the resulting loss of object detail that occurs as the size of the object decreases" (p 2).
However, in this project, the distance between input sensor and target image remains fixed. The pixels value calculation is very useful to calculate any difference on the pixels value between images. This concept comes as the first step after the input sensor captures an image, sends it and then compares it with the reference image or based image obtained from the database.
Later, by learning to compare between images, the computer vision system learns to recognize a particular object; in this project is the scope of how a computer learns to recognize components.
OBJECT DETECTION
34; the detected object is only considered correct if the estimated position of the object is within LlR pixels of the object's true position." (p.l0,104). However, there are some factors that affect the system's ability to ' recognize an object, influence, or in this project scope, component As stated by Donovan, an object's appearance is the result of the combined effects of its shape, reflectance properties, attitude, distance from the viewer (i.e. relative size or scale ), and the lighting characteristics of the environment." (p.l2) Detection requires the ability to identify the location of the object, which may sometimes be partially enclosed in the scene or background.
The location of the components remains the same within the manufactured product for the entire manufacture. The poses of the components remain as the product is placed horizontally on the production lines. The pixels and resolution used to capture the target image and the base image are unchanged.
The image captured for the basic image and the real-time image use the 8-megapixel webcam.
RECENT PROJECT ON DETECTION SYSTEM .1 Object detection and analysis using coherency fdtering
A system developed by Donovan (2006) can recognize objects in test images, focusing on pose, scale, exposure, occlusion, and image noise. Nguyen (2010) develops an improved template matching method that combines spatial and orientational information in a simple and efficient way (e.g., generalized distance transform (GDT) and orientation map (OM) are proposed to ensure that the system can consider edge strength and orientation for reliable and robust matching.
In GDT, it considers the horizontal and vertical gradients of the image by controlling a positive constant variable. Later, he calculated GDT not only based on spatial distance but also based on edge strength. Additional information is available in the OM to match the image to the test image.
The template matching method detects people, cars and maple leaves from images to evaluate the system. For people detection, they use images in the upper right standing poses from the front and back viewpoints. In short, the performance of the proposed template matching method in detecting people, cars and maple leaves is improving the detection performance by increasing the true positive and negative rate.
PROJECT METHODOLOGY
- PLANNING
- ANALYSIS
- DESIGN
- IMPLEMENTATION
- TESTING
- INSTALLATION I FINAL SYSTEM
The planning phase is the fundamental process of understanding why the project needs to be built and determining how the project management process would work. Based on the identified problem, clear objectives and a proposed project title were derived. A literature review was prepared, as well as a project timeline and milestone (project Gantt chart) were developed.
The analysis phase is the first phase of the iterative phases of the prototyping methodology. In the analysis phase, research is conducted into the current system; identifying improvements over the current system and concepts for developing the system. The design phase determines how the system will work, in terms of hardware, software and network infrastructure, the user interface, forms and the specific programs, databases and files needed.
In other words, the steps in the design phase determine exactly how the system will work. All programming, code generation and system improvement according to user requirements will be done throughout this phase. For example, the first prototype has a malfunction to distinguish between the AC cord and the surface of the LCD array.
Therefore, an edge detection algorithm is built into the system to distinguish the AC cable from the LCD surface. After making the first prototype, the system is practically used in the field of quality inspection. The test is performed as part of the quality control area to ensure that the system is adaptable to the actual condition.
As for the final year project, this project has been developed to the first system prototype. If time and cost permit, the project will continue until the component is known. The algorithms used by the system will be discussed in the results and discussion chapter of this paper.
PROPOSED COMPONENT DETECTION MODEL
Image processing and analyzing
A grayscale image is composed entirely of shades of gray, varying from black at the weakest intensity to white at the strongest. This method is used to overcome the same color as that of the AC wire and the LCD surface to improve the first prototype of the system. If a difference occurs, the system triggers the alarm indicating that the current LCD TV on the carrier is incomplete or has an unidentified error.
Finally, the system proceeds to detect the three components from the captured image if it has passed the image comparison stage. Otherwise, the product is considered transferred and the stopper at the end of the conveyor belt releases the LCD TV.
Image interpretation
DEVELOPMENT TOOLS (HARDW ARE AND SOFTWARE)
Otherwise, the product is considered transferred and the stopper at the end of the conveyor belt releases the LCD TV. b) 32-inch LCD TV receiver.
STUDY- INTER VIEW
SYSTEM SET-UP
SYSTEM TRAINING
SYSTEM TESTING
CONCLUSION
RECOMMENDATION
Degree of Master of Engineering, Department of Electrical and Computer Engineering, McGill University, Montreal, Canada. 2010 An Improved Template Matching Method for Object Detection Advanced Multimedia Research Laboratory, ICT Research Institute, School of Computer Science and Software Engineering, University of Wollongong, Australia.
