Fig. 1: Inpainting results on ultra high-resolution images.

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This paper provides a novel approach for a user oriented language model for face detection. Even though there are many open source or commercial libraries to solve the problem of face detection, they are still hard to use because they require specific knowledge on details of algorithmic techniques. This paper proposes a high-level language model for face detection with which users can develop systems easily and even without specific knowledge on face detection theories and algorithms. Important conditions are firstly considered to categorize the large problem space of face detection. The conditions identified here are then represented as expressions in terms of a language model so that developers can use them to express various problems. Once the conditions are expressed by users, the proposed associated interpreter interprets the conditions to find and organize the best algorithms to solve the represented problem with corresponding conditions. We show a proof-of-concept implementation and some test and analyze example problems to show the ease of use and usability.

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Research into computer vision techniques has far outpaced the development of interfaces (such as APIs) to support the techniques' accessibility, especially to developers who are not experts in the field. We present a new interface, specifically for segmentation methods, designed to be application-developer-friendly while retaining sufficient power and flexibility to solve a wide variety of problems. The interface presents segmentation at a higher level (above algorithms) and uses a task-based description derived from definitions of low-level segmentation. We show that through interpretation, the description can be used to invoke an appropriate method to provide the developer's requested result. Our proof-of-concept implementation interprets the model description and invokes one of six segmentation methods with automatically derived parameters, which we demonstrate on a range of segmentation tasks. We also discuss how the concepts presented for segmentation may be extended to other computer vision problems.