Sketch Recongition User Interfaces: Guidelines for Design and Development

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Akshay's Blog

Summary

This system in general is user study of a sketch system which mainly focuses on the interfaces of such systems. The system used in this study is a free-hand drawing sketch system which can recognize the shapes drawn by the user and immediately they are translated into power point objects, when the user switches the screens.

After the evaluation of the system the author describes a set of rules which according to the author should be considered when designing a sketch recognition system. Points mentioned in short are.

1. display recognition results only when the author is done sketching.
2. provide obvious indications to distinguish free sketching from recognition.
3. restrict recognition to single domain until automatic domain detection becomes feasible.
4. incorporate pen-based editing.
5. sketching and edition should use distinct pen motion.
6. SkRUIs require large buttons.
7. the pen must always respond in real-time.

The author at the end also makes the assumption that iterative design techniques paper-prototyping, heuristic evaluation, low-fidelity prototypes or wizard of oz techniques are not possible when designing such applications.


Discussion

This paper is thinking on different line of making the sketch system more usable. It define a nice framework for the usability of these sketch systems but some more work is definitely required refining the framework.

Fluid Sketches: Continous Recognition and Morphing of Simple Hand-Drawn Shapes

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Akshay's Blog

Summary

This paper mainly talks about the beautification technique which is different from the techniques which have been used by the other sketch recognition systems. In this systems as the user draws a sketch on the system the points of the systems are moved from the original location to the location of the recognized shapes.

Currently the system only supports only two types of shape which are mainly circle and rectangle. The important here in this paper is that the user is getting an immediate feedback by the sketch of the shape it's being recognized into. This interpretation of the system is not final and can change during the course of drawing.

A user study was conducted with eleven subjects and qualitative data was gathered for the system. In general the system was appreciated by the users and subjects could easily pick the concepts of the fluid sketches.

Discussion

This system is very basic, which recognizes only two shapes. It can act as a proof of a concept but it's possibility of becoming a more general system for sketch recognition is still a question, since the system will have to work around many hurdles for full fledged sketch system which can recognize many shapes.

What Are Intelligence? And Why?

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Daniel's blog

Summary

The author takes an interesting route to define the evolution of intelligence of human beings as different from animals. Based on the evolutionary evidences the rise of the human intelligence can be defined by various theories.

the primal tool maker: says that human intelligence was evolved from making effective tools. the killer frisbee theory suggests that human made frisbee to kill animals. There are various other theories. the killer climate, the primal frugivor, the primal psychologist, the primal linguist.

Then the author discusses the intelligence in other living beings. The mathematical ability of the horse clever hans which was for quite some time misunderstood as intelligence was later found out to be the ability of horse to pick up answers from the audience. The birds and bees does show some sort of intelligence, like the bees telling the direction of the flower field and parrot recognizing human speech and ability to communicate. Monkeys have also shown some of intelligence.

The evolution of the human intelligence has a more bilogical perspective attached to it and its spans over a million years.

Discussion

I loved this paper!. Human brain and intelligence has always intrigued me. The ability of the humans to perform various abstract calculations in no time is remarkable.

I agree with the idea of the author here that AI as a discipline should have more common in biology than mathematics and physics.

Magic Paper: Sketch-Understanding Research

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Manoj's Blog

Summary

In this paper the author describes the basic history of sketch recognition. It's correlation and deviance with the problem of speech recognition. The author here his work in sketch recognition as towards the creation of the 'magic paper' which is as intuitive to use as natural paper and yet it's able to understand what the user has drawn to give him a feel of 'intelligent paper'.

The author thinks that the problem is important even in front of some very good modeling tools because according to a study in cognitive science whose result is that people tend to be more creative and innovative when they are working with more natural utensils than a CAD system.

Author here discusses the difficulties in sketch recognition which are unique to the field or which are common among other fields such as speech recognition.

The author then tries to define a framework in understanding sketch which a popular LADDER system uses quite effectively.

Discussion

A light-weight discussion in the field of sketch recognition. It more sets a tone for research in the field of sketch recognition than any technicalities or innovations.

Interactive Learning of Structural Shape Descriptions from Automatically Generated Near-miss Examples

Comments

Akshay's Blog

Summary

This paper talks about an improvement in the LADDER system which is not related to the recognition of sketch but an improvement in the usability of the LADDER system. LADDER required users to provide shape descriptions for the sketch recognition. With a large vocabulary of constraints and shapes it becomes a difficult task for the user to write those description accurately.

When defining constraints either manually be developer or generated by the system. They can become over or under constrained resulting in false positives or false negatives. Here the author uses a near-miss strategy to help correct over and under constrained descriptions. For over constrained descriptions it removes a constraint and generates a shape which takes advantage of the removed constraint. The user than provides a feedback whether this shape is acceptable or not. Similarly for under-constrained descriptions it adds a constraint and generated a shape demonstrates the effect of the modification.

Discussion

A very useful feature added to the LADDER framework makes it more easier for the user to write the descriptions by visually seeing the possibilities and effect of constraints rather than to extensively think for each description and possibilities.

I think the near-miss strategy works by adding/removing one constraint. What happens if the description is under/over constrained by more than on constraint? Is the process iterative?