Sketch

About: Sketch is a research topic. Over the lifetime, 6561 publications have been published within this topic receiving 98001 citations.

Participatory design and “democratizing innovation”

Abstract: We sketch an alternative “ddemocratizing innovation”” practice more in line with the original visions of participatory design based on our experience of running Malmo Living Labs - an open innovation milieu where new constellations, issues and ideas evolve from bottom-up long--term collaborations amongst diverse stakeholders..

Sketching interfaces: toward more human interface design

Abstract: Researchers at University of California, Berkeley and Carnegie Mellon University have designed, implemented, and evaluated SILK (Sketching Interfaces Like Krazy), an informal sketching tool that combines many of the benefits of paper-based sketching with the merits of current electronic tools. With SILK, designers can quickly sketch an interface using an electronic pad and stylus, and SILK recognizes widgets and other interface elements as the designer draws them. Unlike paper-based sketching, however, designers can exercise these elements in their sketchy state. For example, a sketched scroll-bar is likely to contain an elevator or thumbnail, the small rectangle a user drags with a mouse. In a paper sketch, the elevator would just sit there, but in a SILK sketch, designers can drag it up and down, which lets them test component or widget behavior. SILK also supports the creation of storyboards-the arrangement of sketches to show how design elements behave, such as how a dialog box appears when the user activates a button. Storyboards are important because they give designers a way to show colleagues, customers, or end users early on how an interface will behave.

Detecting salient blob-like image structures and their scales with a scale-space primal sketch: a method for focus-of-attention

Abstract: This article presents: (i) a multiscale representation of grey-level shape called the scale-space primal sketch, which makes explicit both features in scale-space and the relations between structures at different scales, (ii) a methodology for extracting significant blob-like image structures from this representation, and (iii) applications to edge detection, histogram analysis, and junction classification demonstrating how the proposed method can be used for guiding later-stage visual processes. The representation gives a qualitative description of image structure, which allows for detection of stable scales and associated regions of interest in a solely bottom-up data-driven way. In other words, it generates coarse segmentation cues, and can hence be seen as preceding further processing, which can then be properly tuned. It is argued that once such information is available, many other processing tasks can become much simpler. Experiments on real imagery demonstrate that the proposed theory gives intuitive results.

Frame representations and the declarative/procedural controversy

Abstract: Publisher Summary This chapter presents some criteria for evaluating ideas for representation. It also presents a rough sketch of a particular version of a frame representation, and discusses the ways in which it can deal with the issues raised. The proceduralists assert that human knowledge is primarily a knowing how. The human information processor is a stored program device, with its knowledge of the world embedded in the programs. The declarativists do not believe that knowledge of a subject is intimately bound with the procedures for its use. They see intelligence as resting on two bases: a quite general set of procedures for manipulating facts of all sorts, and a set of specific facts describing particular knowledge domains. In thinking, the general procedures are applied to the domain-specific data to make deductions. Often this process has been based on the model of axiomatic mathematics. The facts are axioms and the thought process involves proof procedures for drawing conclusions from them.

The sketchy database: learning to retrieve badly drawn bunnies

Abstract: We present the Sketchy database, the first large-scale collection of sketch-photo pairs. We ask crowd workers to sketch particular photographic objects sampled from 125 categories and acquire 75,471 sketches of 12,500 objects. The Sketchy database gives us fine-grained associations between particular photos and sketches, and we use this to train cross-domain convolutional networks which embed sketches and photographs in a common feature space. We use our database as a benchmark for fine-grained retrieval and show that our learned representation significantly outperforms both hand-crafted features as well as deep features trained for sketch or photo classification. Beyond image retrieval, we believe the Sketchy database opens up new opportunities for sketch and image understanding and synthesis.