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MACHINE VISION1MACHINE VISION : Acquisition of image data, followed by the processing and interpretation of these data by computer for some useful application like inspection, counting etc.INTRODUCTIONUSES OF ROBOT VISIONVision based guidance of a robot armInspection for close dimensional tolerance.Improved object recognitionImproved part location capabilitiesROBOT VISION REQUIREMENTSCheaper computational deviceIncreased speedBetter algorithmsINTRODUCTIONMACHINE VISION 1) Captures images2) Extracts useful information from theses imagesWhat is an image??An image is a reflection of a 3-D world on a 2-D planeAn image is captured at a given instant of time.Therefore 1 image or many images at regular intervals of time can be takenTYPES OF MACHINE VISION SYSTEMMost commonly used system.For measuring dimensions of parts.Verifying presence of components.Checking features of Flat or semi flat surfaces.2-D system3-D systemNot used frequentlyRequires special lighting techniquesSometimes 2 cameras are required to obtain a stereoscopic view of the scene.TYPES OF MACHINE VISION SYSTEMBinary system

The video signal is divided into white (1) or black(0) signals based on the threshold levelGrey scale systemThe brightness graduation is divided into 256 levels

CameraKeyboardMonitorStored programs/ algorithmsAuxiliarystorageI/FRobot controller(TASK)LightingA/DFrame GrabberComputer(processor)FunctionHardware1. Image acquisition & digitizing image data2. Image processing & analysis3. ApplicationsMachine VisionTechniquesSignal Conversion - Sampling - Encoding- Quantization- Image storage- LightingData reductionSegmentationFeature extractionObject recognition - Inspection - Material handling- Safety monitoring61. Image acquisition and digitizationImage acquisitionImage acquisition and digitization is accomplished using a video camera and a digitizing system to store the image data for subsequent analysis. VIDICON CAMERA

1. Image acquisition and digitizationImage acquisitionVIDICON CAMERA

1. Image acquisition and digitizationLightingThe scene captured by the vision camera must be well illuminated and illumination must be constant over time.

There are 4 categories of lighting systems:Front lightingBack lightingSide lightingStructured lighting1. Image acquisition and digitizationLighting1. Front lighting

CameraObjectLight sourceDark fieldLight fieldLight source is placed on the same side of the cameraProduces a reflected light from the object that allows inspection of surface features1. Image acquisition and digitizationLighting2. Back lightingLight source is placed behind the object being viewed by the camera.This creates a dark silhouette of the object that contrasts sharply with the light background.Used to inspect part dimensions and distinguish part outlines

Silhouette of a tensile test specimenLight sourceDiffuserObjectCameraObject shadow1. Image acquisition and digitizationLighting3. Side lighting

Light source is placed at the side of the surface to be illuminated.Generally used for finding out surface irregularities, flaws, defects on surfacesCameraLight sourceObject1. Image acquisition and digitizationLighting4. Structured lightingMakes use of patterns of light instead of diffused light.2 sheets of light meet at a point.

When the object is in the vicinity of the light, a different pattern is formed.This pattern is studied to extract information about the object.

FRONT VIEW-NO OBJECTTOP VIEW- PATTERN WITH NO OBJECTFRONT VIEW-WITH OBJECTTOP VIEW-PATTERN WITH OBJECT

Sampled PointsVoltage1. Image acquisition and digitizationAnalog to digital conversionA-D conversion is done in 3 steps1)Sampling2)Quantization 3) Encoding1. SamplingA process in which the analog signal obtained by scanning a single line is sampled at regular intervals to obtain a discrete time analog signals.Time

VoltageTimeMORE THE NUMBER OF SAMPLING POINTS, MORE IS THE NUMBER OF PIXELS1. Image acquisition and digitization1. Sampling (contd..)Example: A vision system uses a vidicon tube. An analogue video signal is generated for each line of the 512 lines comprising the faceplate. The sampling capability of the A-D converter is 100 Nano seconds. This is the cycle time required to complete the A-D conversion process for 1 pixel. Using the American standard of 33.33 milliseconds (1/30 sec) to scan the entire faceplate consisting of 512 lines, determine the number of pixels that can be processed per line. Thus, the sampling rate determines the number of pixels horizontally and no of scanning lines determine the no of pixels vertically1. Image acquisition and digitizationAnalog to digital conversion2. QuantizationQuantization is a process wherein the amplitude levels of the discrete voltage signals are assigned a value which corresponds to the grey scale used in the systemThe number of quantization level is dependent on the bit storage capacity of the A-D converter

If n= 8 bits, the converter would allow us to quantize 28 = 256 different values1. Image acquisition and digitizationAnalog to digital conversion3. EncodingEncoding is a process of converting quantized amplitude level into a digital code representing the amplitude level as a sequence of binary digits

EXAMPLE OF QUANTIZATION AND ENCODINGVOLTAGE RANGEBINARY NUMBERGREY SCALE0 - 0.01950000 000000.0195 - 0.03900000 000110.0390 - 0.05850000 001024.9610 - 4.98051111 11102544.9805 - 51111 1111255QUANTIZATIONENCODING2. Image processing and analysisImage processing is a procedure of extracting useful information from the image captured and digitized in the previous stepsData reduction

Segmentation

Feature extraction

Object recognitionThresholdingRegion growingEdge detectionSteps in Image processing and analysisDigital conversionWindowing2. Image processing and analysisData reductionMain objective of image data reduction is to reduce the volume of data.Steps in data reductionDigital conversionWindowingDigital conversion: Process of reducing the number of grey levels used by the machine vision systemExample: For an image digitized at 128 points per lines and 128 lines, determine The total number of bits to represent the grey level values required if an 8 bit converter is used to indicate various shades of gray and The reduction in data volume if only black and white values are digitized.Windowing: Only a portion of the total image is used for image processing and analysis.2. Image processing and analysisSegmentationThresholdingSegmentation techniques are intended to define and separate regions of interest having similar characteristics within the image.

Conversion of each pixel intensity level into a binary value, representing either white or black.It is done by comparing the intensity value at each pixel with a defined threshold value.If the pixel value is greater than the threshold, it is given the binary bit value of white, say 1. If it is less than the defined threshold, it is given the bit value of black, say 0.Thresholding2. Image processing and analysisSegmentationRegion growingRegion growing is a process wherein grid elements possessing similar attributes are grouped to form a regionProcedure:A pixel on the object is identified and assigned the value 1.The adjacent pixel is tracked for match in the attributes.The matching pixel is assigned 1 and non matching pixel with 0.The terms are repeated till the complete screen is covered resulting in growth and identification of region2. Image processing and analysisSegmentationRegion growing

Original imageAssigning values to pixelsSimplified imageNote that the procedure did not identify the hole. This can be resolved by decreasing the distance between grid pointsSegmentationEdge detection2. Image processing and analysisEdge detection is concerned with determining the location of boundaries between an object and its surroundings in an image. This is accomplished by identifying the contrast in light intensity that exists between adjacent pixels at the borders of the object.

Feature Extraction2. Image processing and analysisCharacterize an object in the Image by means of the object's features. Some of the features of an Object include the object's area, length, width, diameter, perimeter, centre of gravity, and aspect ratio. Feature extraction methods are designed to determine these features based on the area and boundaries of the object (using thresholding, edge detection, and other segmentation techniques). For example: the area of the object can be determined by counting the number of white (or black) pixels that make up the object. Its length can be found by measuring the distance (in terms of pixels) between the two extreme opposite edges of the part.Object Recognition2. Image processing and analysisFor any given application, the image must be interpreted based on the extracted features. The objective in these tasks is to identify the object in the image by comparing it with predefined models or standard values.

Template matching is the name given to various methods that attempt to compare one or more features of an image with the corresponding features of a model or template stored in computer memory.

The most basic template matching technique is one in which the image is compared pixel by pixel with a corresponding computer model. Within certain statistical tolerances, the computer determines whether the image matches the template. One of the technical difficulties with this method is the problem of aligning the part in the same position and orientation in front of the camera to allow the comparison to be made without complications in image processing. Object Recognition2. Image processing and analysisFeature Weighing A technique in which several features (e.g., area, length, and perimeter) are combined into a single measure by assigning a weight to each feature according to its relative importance in identifying the object. The score of the object in the image is compared with the score of an ideal object residing in computer memory to achieve proper identification3. ApplicationsDimensional measurement: These applications involve determining the size of certain dimensional features of parts.Dimensional gauging: This is similar to the preceding except that a gauging function rather than a measurement is performed.Verification of the presence of components in an assembled product. Verification of hole location and number of holes in a part: Operationally, this task is similar to dimensional measurement and verification of componentsDetection of surface flaws and defects: Flaws and defects on the surface of a part or material often reveal themselves as a change in reflected lightDetection of flaws in a printed label: The defect can be in the form of a poorly located label or poorly printed text numbering or graphics on the label.Inspection3. ApplicationsInvolves applications in which a vision system is teamed with a robot or similar machine to control the movement of the machine. Examples of these applications include seam tracking in continuous arc welding, part positioning and/or reorientation, bin picking, collision avoidance, machining operations, and assembly tasks.Visual guidance and controlPart identificationThe applications are those in which the vision system is used to recognize and perhaps distinguish parts or other objects so that some action can be taken. The applications include part sorting, counting different types of parts flowing past along a conveyor, and inventory monitoring. Reading of 2-D bar codes and character recognition.