Art In Image Processing Research Articles

State of the art in image processing & big data analytics: issues and challenges

Image processing, in the contemporary domain, is now emerging as a novel and an innovative space in computing research and applications. Today, the discipline of “computer science” may be termed as “image science”, why because in every aspect of computer application, either science or humanities or management, image processing plays a vital role in varied ways. It is broadly now used in all the industries, organizations, administrative divisions; various social organizations, economic/business institutions, healthcare, defense and so on. Image processing takes images as input and image processing techniques are used to process the images and the output is modified images, video, or collection of text, or features of the images. The resultant output by most image processing techniques creates a huge amount of data which is categorized as Big-data. In this technique, bulky information is processed and stored as either structured or unstructured data as a result of processing images through computing techniques. In turn, Big Data analytics for mining knowledge from data created through image processing techniques has a huge potential in sectors like education, government organizations, healthcare institutions, manufacturing units, finance and banking, centers of retail business. This paper focuses on highlighting the recent innovations made in the field of image processing and Big Data analytics. The integration and interaction of the two broad fields of image processing and Big Data have great potential in various areas. Research challenges identified in the integration and interaction of these two broad fields are discussed and some possible research directions are suggested.

Image Based Techniques for Crack Detection, Classification and Quantification in Asphalt Pavement: A Review

Pavement condition information is a significant component in Pavement Management Systems. The labeling and quantification of the type, severity, and extent of surface cracking is a challenging area for weighing the asphalt pavements. This paper presents a widespread review on various platform and image processing approaches for asphalt surface interpretation. The main part of this study presents a comprehensive combination of the state of the art in image processing based on crack interpretation related to asphalt pavements. An attempt is made to study the existing methodologies from different points of views accompanied by extensive comparisons on three stages of methods—distress detection, classification, and quantification to facilitate further research studies. This paper presents a survey of the developed pavement inspection systems up to date. Additionally, emerging and evolution technologies considered to automate the processes are discussed.

Peyman Milanfar plenary: Data-adaptive Filtering and the State of the Art in Image Processing

Peyman Milanfar plenary: Data-adaptive Filtering and the State of the Art in Image Processing

Advances in Computational Image Processing for Microscopy

The field of electron microscopy applied to structural biology has made significant progress in the last 5 years, with many labs attempting the solution of ever more challenging problems. This work has demanded significant new resources for the computational analysis of the data collected from the microscope. The purpose of this paper is to review briefly the state of the art in image processing of microscope data and to serve as an introduction to this special issue of the journal.