Hybrid Parallel Image Processing Algorithm for Binary Images with Image Thinning Technique

Image thinning is the most essential pre-processing technique that plays major role in image processing applications such as image analysis and pattern recognition. It is a process that reduces a thick binary image into thin skeleton. In the present paper we have used hybrid parallel thinning algorithm to obtain the skeleton of the binary image. The result skeleton contains one pixel width which preserves the topological properties and retains the connectivity.

Identification of Hemorrhages in Iris Using Hybrid Morphological Method

In the field of ophthalmology, hemorrhage is the term used more often because of increasing diabetic patients. It’s a challenge amidst the ophthalmologist to distinguish the hemorrhage from the blood vessels, these lands in various problems. In the past various techniques were employed for the detection of the hemorrhage but they were not so accurate and often encountered misclassification between hemorrhage and blood vessels. Precise detection and classification of hemorrhage and blood vessel is very important in the diagnosis of many problems. This paper depicts a mechanized procedure for recognizing hemorrhages in fundus pictures. The acknowledgment of hemorrhages is one of the critical factors in the early finish of diabetic retinopathy. The algorithm proceeds through several steps such as image enhancement, image subtraction, morphological operations such as image thresholding, image strengthening, image thinning, erosion, morphological closing, image complement to suppress blood vessels and to highlight the hemorrhages

Identifikasi embrio dalam telur berbasis image processing

One of ways to increase the success of hatching eggs is by selecting and separating the eggs embryonated (fertile) with eggs are not embryonated (infertile) by way of observation (candling). This system utilizes digital image processing as an identification process. By this system, it is expected that the identification results will be more accurate results than conventional monitoring, so as to increase the results of hatching. This system utilizes a flashlight as a medium, so that the egg's internal condition can be seen which then takes pictures by the webcam. After that the digital image processing is done by converting the original image (RGB) to binary image by providing a thresholding value (T), the T value is very influential in the next image processing, opening and closing, thinning the image (thinning), and contour detection. Then from the final process of contour detection produces the number of detection of blood vessels that are considered as embryos as a determinant of the outcome category of identification. From the experiments carried out the percentage of conformity between the original condition of the egg with the results obtained in the system that is 88.88%, in determining the yield category (fertile/infertile) with an error of 11.12%. For the suitability of the estimated percentage of hatchlings themselves have a success of 61.11% with an error of 38.89%. These results are influenced by many factors like the condition of the eggs and supporting devices in the system.

Detection method for auto guide vehicle’s walking deviation based on image thinning and Hough transform

Auto guide vehicle’s position deviation always appears in its walking process. Current edge approaches applied in the visual navigation field are difficult to meet the high-level requirements of complex environment in factories since they are easy to be affected by noise, which results in low measurement accuracy and unsteadiness. In order to avoid the defects of edge detection algorithm, an improved detection method based on image thinning and Hough transform is proposed to solve the problem of auto guide vehicle’s walking deviation. First, the image of lane line is preprocessed with gray processing, threshold segmentation, and mathematical morphology, and then, the refinement algorithm is employed to obtain the skeleton of the lane line, combined with Hough detection and line fitting, the equation of the guide line is generated, and finally, the value of auto guide vehicle’s walking deviation can be calculated. The experimental results show that the methodology we proposed can deal with non-ideal factors of the actual environment such as bright area, path breaks, and clutters on road, and extract the parameters of the guide line effectively, after which the value of auto guide vehicle’s walking deviation is obtained. This method is proved to be feasible for auto guide vehicle in indoor environment for visual navigation.