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IMAGE PROCESSING PowerPoint Presentation
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IMAGE PROCESSING

IMAGE PROCESSING

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IMAGE PROCESSING

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  1. IMAGE PROCESSING Image processing is a method of performing specific operations on an image in order to get an enhanced image or to extract some information that is required from it. It is a type of signal processing in which input is an image and output may be image or information defining the features associated with that image. Nowadays, image processing is amongst the most rapidly growing technologies and is an essential part of machine vision systems. It forms a core research area within the computer science discipline. Related Article: IMAGE ACQUISITION COMPONENTS Image processing includes the following three steps: Importing the image via image acquisition tools;  Analyzing and manipulating the image;  Output in which result can be a modified image or a report that is based on the image analysis  There are essentially two aspects in the image processing pipeline for it to be carried out successfully. Computational Hardware Image Processing Software IMAGE PROCESSING SOFTWARE  

  2. In terms of practical implementation, one construct for machine vision software can be described as an application that “configures” the system components and how they execute machine vision functions and tasks. These software applications are the heart of the system. These apps tend to have graphical user interfaces (GUI) devoted to “ease of use” with intuitive and graphically manipulated application configuration steps. Smart cameras and systems using dedicated and/or proprietary physical configurations almost always feature software that has this fixed (though often quite thorough), configurable collection of tools that can be selected to operate in a constrained but usually user-definable sequence to execute a complete machine vision application. In the case of smart cameras, the configuration software usually runs on a computer external to the vision system. Other systems with proprietary computing platforms might have the entire graphical user interface built into the system, again with the software providing configuration of the application. Furthermore, “configurable” software applications are readily available for open system architectures running standard operating systems like Windows or Linux. Still, the function of the application is to provide a software platform in which the machine vision engineer can manipulate hardware and select/configure tools to perform an application. Also Read, CAMERA FUNDAMENTALS IN MACHINE VISION At the other end of the implementation spectrum, is software designed for open architecture machine vision systems that are fully programmable. Typically targeting users with suitable levels of experience in computer programming in languages like C, C++, C#, .NET and others, these software products might be called “software development kits” (SDK) or “libraries,” and contain an extensive selection of low- and medium-level operators (“algorithms”) that when properly combined perform tasks ranging from very basic to extremely complex. In many cases also, libraries designed specifically for machine vision application development feature an integrated development environment (IDE) or even a configurable software application built on the underlying tools found in the library. These product extensions can make the development process much easier in applications that use these libraries without sacrificing the full functionality of the individual tool or algorithm, and in some cases offering a path to migrate the configured application code or script to a lower-level programming language automatically. Related Article: The Ultimate Guide to Machine Vision Camera Selection

  3. COMPUTATIONAL HARDWARE All of the previously mentioned software modules require hardware to work on. These can be done on embedded systems as well as with dedicated computers. Smart cameras are cameras that have the hardware required installed into them. These devices are purpose-built for specialized applications where space constraints require a compact footprint. An alternate approach could be using dedicated PCs for your computations. Such dedicated systems are useful when working with high-resolution images. Alternatively, when training a machine vision model, which generally requires a lot of computational power, the process could be frustrating if done without the right hardware. This intensive part of the neural network is made up of various matrix multiplications. This can be accomplished simply by performing all the operations at the same time, instead of taking them one after the other. This is where a GPU can be very beneficial, with several thousand cores designed to compute with almost 100% efficiency. Turns out these processors are suited to perform the computation of neural networks as well. The fight between CPUs and GPUs favors the latter because of the large number of cores of GPUs offsetting the 2–3x faster speed of CPU clocks – ~3500 (GPU) vs ~16 (CPU). The GPU cores are technically a more streamlined version of the complex CPU cores. However, having so many of them enables GPUs to have a higher level of parallelism and thus better performance.

  4. CONCLUSION All of the above-mentioned parts are essential to guarantee that your system will provide you with the best results. A weak link in any of these components can make your system loose accuracy and effectiveness.