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Capturing Data Through Image Segmentation 2014

RIT researchers are developing some of the most sophisticated computing technologies to process static and advanced-video images through image segmentation—a process defined as the partitioning of an image or video stream into pixel sets of specific objects and object components. Distinguishing those objects and components has inherent challenges: recognizing textures, color gradation, and object groupings, determining correct placement of objects, as well as being able to confidently rely on computer applications to systematically recognize, isolate, and process these and other distinguishing characteristics of objects within images. Using image segmentation provides a means for more accurate analysis of images— whether monitoring natural resources adjacent to urban areas for flood prevention, observing buildings or facilities and detecting variations or damage to infrastructure after a natural disaster, or finding anomalies in areas for target detection and surveillance.

Eli Saber, professor of electrical engineering in RIT’s Kate Gleason College of Engineering, developed a complex algorithm that prompts the segmentation technology. This platform technology is being used across various industries from biomedical applications to security and surveillance, from entertainment and resource recovery to advanced printing. The algorithm and subsequent processing technology has been successfully used to improve the analysis of multiple static images, compressing data within the images to build three-dimensional models. Saber and his research team have begun expanding the concept to video imaging, capturing multiple moving images, and extracting data.

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Saber has collaborated with David Messinger, director of the Digital Imaging and Remote Sensing Laboratory in the Chester F. Carlson Center for Imaging Science, and the two have developed applications to address image segmentation demands utilizing this multidimensional, computing algorithm—called MAPGSEG (multi-resolution adaptive and progressive gradient-based color image segmentation)—developed at RIT in conjunction with Hewlett-Packard Company.

“Partitioning generates a reduced and relevant data set for high-level operations such as rendering, indexing, classification, compression, content-based retrieval, and multimedia applications,” says Saber, who leads the Image, Video and Computer Vision Laboratory in the engineering college.

This type of partitioning, or segmentation, comes naturally to humans. The human eye views and distinguishes numerous images daily, and the brain processes the information in real time. Developing a simulated environment to perform similar tasks is the basis of the MAPGSEG algorithm. It can selectively access and manipulate individual content in images based on desired level of detail. MAPGSEG is a solution that computationally meets the demands of many practical applications involving segmentation and can be a reasonable compromise between quality and speed that lays the foundation to do fast and intelligent object/region-based, real-world applications of color imagery, Saber adds.

Today, some of those segmentation applications of color imagery are being adapted for biomedical imaging, object recognition, and surveillance. Saber and Messinger are bringing to fruition improvements to, and unique solutions for, these applications and others.

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