Monday, April 16, 2007

T.G.S.I.o.B. : special issue - submission #1

"A minimalist approach to the dog fooding problem"

Author
The Monchier et al.

Category
Low power solutions

Abstract
Well, make the dog starve. CVD

2 comments:

gia said...

Authors:

Giacomo and the V Colonna

-------------------------
Abstract

In this work we propose a jointly -wavelet based- approach for estimating the blur present in single image of a dog eating his food.

The image blur is crucial in image processing, and both the food and the dog are relevant generic issues for every day life. All these three elements characterize our every day life.

The dog, in fact, is commonly assumed the human "best friend", while his food, but more generally speaking the food itself, is, as far as we know, essential for human beings survival.

In this work we consider jointly all these three aspects by a statistical markovian and marxistian/trotzkista approach to these new concepts and eventually we will focus also on some other interisting stuff that we will find during our experiments.


Up to now we do not have any clar idea on what will be going on.



Please, accept our paper.

Unknown said...

A review on "A minimalist approach to the dog fooding problem"

We maintain that the dog fooding problem is not solved by the method the author proposes. In fact, by starving the dog, the efficiency of the dog fooding process decreases because by partitioning the dog in eatable and noneatable portions, the eatable/noneatable ratio decreases. Therefore, the best strategy is to eat the dog as soon as possible. This is in accordance to the results in the well-known seminar work [1].

Besides this, we will still give high marks for no apparent reason.

Overall Rating: 1 strong accept
Importance / Relevance: 1 definitely interesting, wide impact
References to previous work: 2 references adequate
Clarity of presentation: 2 clear enough
Experimental Validation: 2 convincing experimental validation

References:
[1]: "It's best an egg today than an hen tomorrow", IEEE Transactions on Collective Wisdom, 1521.