Artificial Intelligence and Image Processing

The evolution of artificial intelligence since the 1950s is discussed, especially as it is being applied in radiology to image processing. Developments in artificial intelligence are now being used to provide a new approach to image processing. Initially, the computer dealt with numeric representations using languages such as FORTRAN and BASIC. Now symbolic languages such as LISP and PROLOG have expanded the use of the computer into nonnumeric symbolic reasoning that is just being applied to image understanding. This paper explains the new languages and their application to image understanding

PALANG PINTU OTOMATIS BERBASIS WEBCAM DENGAN PROGRAM RASPBERRY PI 3

Artificial intelligence is highly demand in this modern world, human cannot be separated with the technology in now days, and the evolution of the technology, especially for artificial intelligence, are unstoppable. The artificial intelligence application is more wider, business transportation sector is one of the many sector are involve to using this technology. One of the example is the automation and detection license plate of the vehicle. Like in this research, the detection of license plate are using raspberry pi 3b connected with Arduino uno using serial communication, and for the image catcher, using 720p webcamera. In this research, we are using deep neural network method to do image processing, it refer to 300 images of data training, the training data using google collab, with yolov4-tinyy metho

Character Translation on Plate Recognition with Intelligence Approaches

In recent years, the number of automobiles in Indonesia has expanded. This rise has a knock-on impact on street crime. On this problem based, a preventative road safety prevention system is required. This research contribution is to develop an efficient algorithm for detecting vehicle license plates. This study's technique incorporates artificial intelligence technology with character translation. Yolov3 and Yolov4 are the artificial intelligence systems employed in this study. The detection of objects in the form of license plates is the result of this approach. In artificial intelligence, object detection results are utilized as input for image processing. The image processing method is used to translate characters. Optical Character Recognition (OCR) is used to decode the characters in the image precisely. The artificial intelligence data training resulted in a 76.53% and 89.55% mean average precision (mAP) level. Using OCR, the system is capable of character translation. These results give an opportunity to develop more complex image-processing applications

Activities of the Remote Sensing Information Sciences Research Group

Topics on the analysis and processing of remotely sensed data in the areas of vegetation analysis and modelling, georeferenced information systems, machine assisted information extraction from image data, and artificial intelligence are investigated. Discussions on support field data and specific applications of the proposed technologies are also included

Application of Fuzzy Logic on Image Edge Detection

In this paper a novel method for an application of digital image processing, Edge Detection is developed. The contemporary Fuzzy logic, a key concept of artificial intelligence helps to implement the fuzzy relative pixel value algorithms and helps to find and highlight all the edges associated with an image by checking the relative pixel values and thus provides an algorithm to abridge the concepts of digital image processing and artificial intelligence. Exhaustive scanning of an image using the windowing technique takes place which is subjected to a set of fuzzy conditions for the comparison of pixel values with adjacent pixels to check the pixel magnitude gradient in the window. After the testing of fuzzy conditions the appropriate values are allocated to the pixels in the window under testing to provide an image highlighted with all the associated edges

Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining

Advanced Information Processing Methods and Their Applications

This Special Issue has collected and presented breakthrough research on information processing methods and their applications. Particular attention is paid to the study of the mathematical foundations of information processing methods, quantum computing, artificial intelligence, digital image processing, and the use of information technologies in medicine

A Survey on Image Mining Techniques: Theory and Applications

Image mining is a vital technique which is used to mine knowledge straightforwardly from image. Image segmentation is the primary phase in image mining. Image mining is simply an expansion of data mining in the field of image processing. Image mining handles with the hidden knowledge extraction, image data association and additional patterns which are not clearly accumulated in the images. It is an interdisciplinary field that integrates techniques like computer vision, image processing, data mining, machine learning, data base and artificial intelligence. The most important function of the mining is to generate all significant patterns without prior information of the patterns. Rule mining has been adopting to huge image data bases. Mining has been done in accordance with the integrated collections of images and its related data. Numerous researches have been carried on this image mining. This paper presents a survey on various image mining techniques that were proposed earlier in literature. Also, this paper provides a marginal overview for future research and improvements. Keywords— Data Mining, Image Mining, Knowledge Discovery, Segmentation, Machine Learning, Artificial Intelligence, Rule Mining, Datasets