Image processing

In modern world Digital Image Processing is most important and rapidly growing field because it plays a very essential role in numerous fields for example optics, computer science, mathematics, surface physics and visual psycho-physics in case of computer vision its applications include remote sensing, feature extraction, meteorology, face detection, finger-print detection, optical sorting, astronomy, argument reality, microscope imaging. Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image. Image processing basically includes the following three steps.

Advantages of DIP

The processing of images is faster and more cost-effective. One needs less time for processing, as well as less film and other photographing equipment.
It is more ecological to process images. No processing or fixing chemicals are needed to take and process digital images. However, printing inks are essential when printing digital images.
Copying a digital image is easy, and the quality of the image stays good unless it is compressed. For instance, saving an image as jpg format compresses the image. By re saving the image as jpg format, the compressed image will be re-compressed, and the quality of the image will get worse with every saving.
The expensive reproduction (compared with rastering the image with a repro camera) is faster and cheaper.
By changing the image format and resolution, the image can be used in a number of media.
Fixing and retouching of images has become easier.

IMAGE PROCESSING PROJECTS

Object Separator

This software Object Separator is designed by using image processing Technics for separate object in image according to their colors.

The object separator provides three output images after processing the original image which are HSV image,Red object image and Blue object image,following images clearly shows the behavior of the processed images.

Original Image

HSV Image

Blue Objects

Red Objects

Applications

For industrial purposes ex. cloth buttons separation , medical tablets separation etc.
Color detection

design by : kosala bandara

Rice Grain Quality Analyzer RiceQc 1.0

Machine Vision System And Mobile Application For Analyze Rice Grain Sample

There are several types of rice are available in the market. The quality of the rice is depended on several factors. One major factor is the percentage of broken rice grains in a sample. Rice quality is primarily assessed based on physical properties such as head rice recovery, chalkiness, grain size and shape, and grain color, and premium quality traits such as aroma have extra value.In this project, we are mainly going to analyze the physical quality of the rice grains by analyzing rice grain shape and size by using android mobile application integrated with machine vision system.

Process

Capture sample image by using mobile phone camera

Convert captured image to gray scale image (RGBtoGRAY)

Threshold the image by most suitable value and convert into the binary image, the image consists only 1 and 0 s (Black and White pixels).

Find contours by using edge detection method and save contours properties into the array. Finds edges in an image using the Canny algorithm.The function finds edges in the input image and marks them in the output map edges using the Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. Contours are drawn by green color line. Contours can be explained simply as a curve joining all the continuous points (along with the boundary), having same color or intensity. The contours are a useful tool for shape analysis and object detection and recognition.

Then by analyzing contours, the vision system provides the quality of the rice sample.

RiceQc 1.0 Interface

design by : kosala bandara

Machine Vision System For Real Time Object Tracking

A system has been developed for tracking the motion of objects in two dimensions in real-time. The system consists of a conventional CCD camera and Java Interface with OpenCV. The objective of video tracking is to associate target objects in consecutive video frames. Video tracking is the process of locating a moving object (or multiple objects) over time using a camera. It has a variety of uses, some of which are: human-computer interaction, security and surveillance, video communication and compression, augmented reality, traffic control, medical imaging and video editing.

Methodology

Input Image

RGB to HSV converted image

Color filtered image with threshold

Noise filtered image

Object detecting

System Interface

design by : Kosala Bandara

Machine Vision Adoption For Distance Measuring And Verifying Processes

A system has been developed for measure distances in industrial applications. This system consists of a conventional CCD camera and Java Interface.Measurement is a main-stay for automated inspection and has provided the platform for ever faster, more efficient and more accurate quality control. In addition to preventing defective product reaching the customer, vision measurements can also be directly linked to statistical process control methods to improve product quality, reduce wastage, improve productivity and streamline the process.

Therefore I developed a system for measuring a distance between pre-defined two objects, marks or sings etc. This system can farther improve according to customer requirements.

Methodology

System Graphical User Interface (GUI)

GUI

The red line connects the centers of the objects

design by : Kosala Bandara