Hough Transform for Distinctive Edge Detection to Images in Fingerprint Recognition Matching Transformation

Biometric Identification is a globally renowned procedure, which has been utilised to achieve a successful and accurate level of identification. In the sea of biometrics, fingerprints are deemed more popular when it comes to verification. This results from the presence of the ridges on the fingerprints that are completely exclusive to each individual. Besides that, fingerprints are expansively employed to ascertain and authenticate people individually. Therefore, this study had proposed to employ distinctive Edge Detection techniques together with the Hough Transform to match the images of the fingerprints in a fingerprint matching system. The Hough Transform is a superior procedure carried out to get an accurate series of finer points or lines. The finer points or lines would then distinguish the fingerprints. Nevertheless, it was still a challenge to extract finer points or lines from the fingerprints under uninhibited conditions. Therefore, this paper was organised based on four distinctive steps. First, different Edge Detection operators were employed to perform the fingerprint matching algorithm. Next, the fingerprint matching algorithm was applied twice to the same Edge Detection operators. Thirdly, the Edge Detection operators had been substituted with the Transformation Method for the same matching procedure. For example, the proposed fingerprint matching algorithm comprised of the Hough Transform and same Edge Detection operators. Finally, distinct Edge Detection operators based on the decision making algorithm were used to calculate and determine the percentage of matching. Therefore, this study proved that the prints obtained via the Prewitt Edge Detection together with Hough Transform were in an agreement.

A Hybrid Method for Payload Enhancement in Image Steganography Based on Edge Area Detection

In this research a new method for increasing the embedding capacity in images based on the edge area is proposed. The new approach combines Canny and Prewitt edge detection techniques using OR binary operation. The secret message is concealed using the Least Significant Bit (LSB) method. Embedding capacity, PSNR, SSIM, and MSE values are used as evaluation metrics. Based on the resulted values, the proposed method showed higher embedding capacity while keeping the PSNR, SSIM, MSE values without major changes of other methods which means keeping the imperceptibility quality of the stego image.

Door Detection Based on Geometrical Features and Harris Corner

Door is a very significant element as it enables a person to enter a house or room. Though identifying doorway is an easy task for a regular person, for robots or visually impaired people it is a challenging task. To overcome this challenge, we have proposed a door detection method. Our proposed method is based on Prewitt edge detection method and Harris corner detector. Here, we are using a number of predefined rules to detect the doorframe correctly. To establish the robustness of our proposed method, we have formed a substantial dataset of scene images that are captured in various unfamiliar environments. Our experimental results validate that our proposed method is robust against changes in viewpoint, shapes, occlusions, illumination, colors, sizes, orientations, and textures of the door. The experimental results show that our proposed method reaches 87.45% accuracy as well as achieves lower false positive rate and lower computational time.

Automatic Diabetic Retinopathy Diagnosis using Prewitt Edge Detection & Color Mapping from Fundus Imaging

Diabetic Retinopathy is a diabetic disease that directly affects the vision that causes damaged blood vessels at the back end of the eyes. It a complicated disease that cannot be recognized from normal eyes; a fundus imaging can reflect the impairments over the retina that causes partial or complete blindness that cannot be cured. It is mandatory for a routine examination that may lead to prevent from complete blindness because it can be prevented from current damaged blood vessels but it cannot be revert or treated. In the field of image processing; various diseases can be diagnosed automatically that saves humans life along with easiness for medical professionals. If a person pertains diabetes for a long time may have highest possibility for diabetic retinopathy. Here, the system has been proposed that can diagnose this disease with high level of accuracy with minimal false alarm rate. System uses Prewitt Edge Detection and Color Mapping techniques for recognizing diabetic retinopathy symptoms or damaged blood vessels from fundus imaging. Prewitt is highly sensitive for extracting impairments along with blood vessels and system is able to mask the unwanted area by using color correction tool.

Automatic Sign Language Gesture Recognition using Prewitt & Morphological Dilation

Sign languages have their own linguistic structure, grammar and characteristics, and are independent of the rules that govern spoken languages. They are visual languages that rely on hand gestures as well as on bodily and facial expressions. Sign languages in different countries are vastly different from one another, so enabling easy communication is important: not just to break the barrier between hearing and deaf individuals, but also between people who do not sign in the same language. In India, sign language plays an important role in the field of communication among dumb and deaf people. There are different signs associated for communication in every country as per their convenient gestures. Automatic sign language gesture recognition is an approach for recognizing gestures and converts it to its actual meaning and convey either through speech or text as per requirements. Here the system is based on Prewitt Edge Detection that possesses the gestures of sign language and helps to recognize and assign their meanings. The Prewitt is second order derivative that has been used in image processing and computer vision, in the form of edge detection or extraction algorithms where it creates gradient of horizontal and vertical magnitude. System also uses certain pre-processing filtration technique such as morphological dilation for better feature extraction.

Development of automated liquid filling system based on the interactive design approach

The automatic liquid filling system is used in different applications such as production of detergents, liquid soaps, fruit juices, milk products, bottled water, etc. The automatic bottle filling system is highly expensive. Where, the common filling systems required to complex changes in hardware and software in order to modify volume of liquid. There are many important variables in the filling process such as volume of liquid, the filling time, etc. This paper presents a new approach to develop an automatic liquid filling system. The new proposed system consists of a conveyor subsystem, filling stations, and camera to detect the level of the liquid at any instant during the filling process. The camera can detect accurately the level of liquid based on the imaging process technique (Edge Detection Approach). In order to achieve the aim of this work, Arduino board is used as the controller unit in the automatic operation of developed filling system. The developed automatic liquid filling system is designed to be not expensive compared to the other available filling systems on the markets. The system is also easy to operate and user-friendly,where only simple steps are required to operate the filling system or modify the working condition.It was found, based on results, that the Prewitt edge detection is the optimal method that should be applied to obtain high accuracy of results and quick response of developed system.

Optimization of Pattern Recognition of Hijaiyah Letters using Normalized Cross Correlation Tchniques

The development of analysis in digital image increasingly developed with various methods, one of which is in recognition of letter patterns. Each letter written using handwriting must have different writing patterns, such as the thickness and shape of the letter pattern. This research will be doing on the pattern recognition of hijaiyah letters of handwriting by applying the Normalized Cross Correlation (NCC) technique. NCC is a technique used to match two images. Before the NCC process, it should be done with the preprocessing using convolution and without convolution using the binary image. The convolution technique used was the Sobel and Prewitt edge detection with the aimed to get the edge of an object and compared the number of matching letters between using edge detection and without edge detection. The tests were done by using the different sized image of 32x32 pixels, 64x64 pixels and then match it against a similar sample data, a different sample data, a different objects font sample data and a different sample data of original image size. The results show that the matching of the letter pattern depends on the size of the image that is more matches to the image of 32x32 pixels. The binary image had better matching numbers than the convolution techniques. While in convolution techniques, Prewitt edge detection had the higher accuracy and matching results compared to the image using Sobel edge detection.