Connected Components Labeling represents a fundamental step for many Computer Vision and Image Processing pipelines. Since the first appearance of the task in the sixties, many algorithmic solutions to optimize the computational load needed to label an image have been proposed. Among them, block-based scan approaches and decision trees revealed to be some of the most valuable strategies.However, due to the cost of the manual construction of optimal decision trees and the computational limitations of automatic strategies employed in the past, the application of blocks and decision trees has been restricted to small masks, and thus to 2D algorithms.With this paper we present a novel heuristic algorithm based on decision tree learning methodology, called Entropy Partitioning Decision Tree (EPDT). It allows to compute nearoptimal decision trees for large scan masks. Experimental results demonstrate that algorithms based on the generated decision trees outperform state-of-the-art competitors.
Digital images and image streams represent two major categories of media captured, delivered, and shared on the Web. Techniques for their analysis, classification, and processing are fundamental building blocks in today's digital media applications ranging from mobile image transformation apps to professional digital production suites. To efficiently process such digital media (1) independent of hardware requirements, (2) at different data complexity scales, while (3) yielding high-quality results, poses several challenges for software frameworks and hardware systems, in particular for mobile devices. With respect to these aspects, using service-based architectures is a common approach to strive for. However, unlike geodata, there is currently no standard approach for service definition, implementation, and orchestration in the domain of digital images and videos. This paper presents an approach for service-based image-processing and provisioning of processing techniques by the example of imageabstraction techniques. The generality and feasibility of the proposed system is demonstrated by different client applications that have been implemented for the Android operating system, for Google's G-Suite Software-as-a-Service infrastructure, as well as for desktop systems. The performance of the system is discussed at the example of complex, resource-intensive image-abstraction techniques, such as watercolor rendering.
With the spread of smart phones capable of taking high-resolution photos and the development of high-speed mobile data infrastructure, digital visual media is becoming one of the most important forms of modern communication. With this development, however, also comes a devaluation of images as a media form with the focus becoming the frequency at which visual content is generated instead of the quality of the content. In this work, an interactive system using image-abstraction techniques and an eye tracking sensor is presented, which allows users to experience diverting and dynamic artworks that react to their eye movement. The underlying modular architecture enables a variety of different interaction techniques that share common design principles, making the interface as intuitive as possible. The resulting experience allows users to experience a game-like interaction in which they aim for a reward, the artwork, while being held under constraints, e.g., not blinking. The conscious eye movements that are required by some interaction techniques hint an interesting, possible future extension for this work into the field of relaxation exercises and concentration training.
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