Object Recognition Examples

Object Recognition
One of the major applications of image processing and computer vision is object recognition. The term “object recognition” is often is used in broad sense, so it is important to take an insight to understand its meaning. One of the common features of many techniques in image processing is to analyze one or more images to recognize the objects present in it. This feature gives them some knowledge about the appearance of the objects to be found (the model, which has been created in advance). One of the special cases is that the model database contains only one object class and so the task is much easier to decide whether an instance of this specific object class is present and its location. Other features that need an attention

It includes industrial applications (more commonly, the term “machine vision” is used), security/tracking applications, also searching and detection applications. Some are mentioned below:
• Position measurement: It is used mostly in industrial objects. The information about the position is useful in production environment for placing parts or transporting, gripping and processing on assembly line etc. For example, it is crucial to locate components like ICs prior to placing them on PCBs (printed circuit board) As an example, it is necessary to accurately locate electrical components such as ICs before placing them on a PCB (printed circuit board). This is done to ensure proper soldering for all connections for the production of electronic devices (e.g. TVs, mobile phones, laptops, etc.). The object pose refers to x-y position of the object along with its rotation and scale.
• Inspection: one of the classical usages of machine vision would be for quality control in production environments. By inspecting the surfaces of industrial parts one can detect the defects for e.g. inspection of welds or threads of screws. This requires determining the position of the parts in advance thereby involving object

Quality of fruits is determined using these parameters. Color indicates ripeness, defects, freshness etc. At present, human vision is used to inspect food products using parameters like the color and size etc. But this has human limitation as human eyes cannot distinguish slight changes lie in color after prolonged observations. Moreover, the background of the object and color intensity. For consistent decisions, image analysis using color is used in the machine vision technology to determine quality of fruits is one of the best techniques.