computer science

Takeo Kanade

Takeo Kanade is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Takeo Kanade rather than just read about it. In short: Takeo Kanade (金出武雄, Kanade Takeo; born October 24, 1945 in Hyōgo) is a Japanese computer scientist and one of the world's foremost researchers in computer vision. He is U.A. and Helen Whitaker Professor at Carnegie Mellon School of Computer Science.

Key takeaways

Takeo Kanade belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Takeo Kanade to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Takeo Kanade from memory before moving on to harder problems.

Reference excerpt

Takeo Kanade (金出武雄, Kanade Takeo; born October 24, 1945 in Hyōgo) is a Japanese computer scientist and one of the world's foremost researchers in computer vision. He is U.A. and Helen Whitaker Professor at Carnegie Mellon School of Computer Science. He has approximately 300 peer-reviewed academic publications and holds around 20 patents.

Honors and achievements In 1990 he was an inaugural Fellow of the Association for the Advancement of Artificial Intelligence In 1997, he was elected to the US National Academy of Engineering for contributions to computer vision and robotics. In 1997, he was elected to the American Academy of Arts and Sciences In 1999 he was inducted as a Fellow of the Association for Computing Machinery. In 2008 Kanade received the Bower Award and Prize for Achievement in Science from The Franklin Institute in Philadelphia, Pennsylvania. A special event called TK60: Celebrating Takeo Kanade's vision was held to commemorate his 60th birthday. This event was attended by prominent computer vision researchers. Elected member of American Association of Artificial Intelligence, Robotics Society of Japan, and Institute of Electronics and Communication Engineers of Japan Marr Prize, 1990 for the paper Shape from Interreflections which he co-authored with Shree K. Nayar and Katsushi Ikeuchi Longuet-Higgins Prize for lasting contribution in computer vision at CVPR 2006 for the paper "Neural Network-Based Face Detection" coauthored with H. Rowley and S. Baluja CVPR 2008 for the paper "Probabilistic modeling of local appearance and spatial relationships for object recognition" coauthored with H Schneiderman The other awards he has received include the C&C Award, the Joseph Engelberger Award, FIT Funai Accomplishment Award, the Allen Newell Research Excellence Award, and the JARA Award. He has served for many government, industrial, and university advisory boards, including the Aeronautics and Space Engineering Board (ASEB) of the National Research Council, NASA's Advanced Technology Advisory Committee, PITAC Panel for Transforming Healthcare Panel, and the Advisory Board of Canadian Institute for Advanced Research. In 2016 Kanade received the Kyoto Prize in Information Sciences. In 2019 he was the recipient of Armenia's Global High-Tech Award. In 2023 he was awarded the BBVA Foundation Frontiers of Knowledge Award.

Notable works Lucas–Kanade method One of the earliest face detectors Tomasi–Kanade factorization method Virtualized Reality Multi-baseline stereo and the world's first full-image video-rate stereo machine VLSI computational sensors Shape recovery from line drawings (known as Origami World theory and skew symmetry) Kanade–Lucas–Tomasi feature tracker

External links

Takeo's Home Page at the Robotics Institute, CMU. Envisioning Robotics Online Archival Exhibit Think like an amateur, do as an expert 2016 Kyoto Prize Public Lecture

References

Illustrations

Worked examples

Example 1 — a first encounter with Takeo Kanade

Start with the simplest possible case. Write down what Takeo Kanade claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Takeo Kanade before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Takeo Kanade ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Takeo Kanade

In research: Takeo Kanade appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Takeo Kanade in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Takeo Kanade is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1945 births, Computer vision researchers, Fellows of the Association for Computing Machinery, so understanding it makes those chapters shorter.
In everyday life: Look for Takeo Kanade outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Takeo Kanade in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Takeo Kanade means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Takeo Kanade out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Takeo Kanade in simple terms?

Why does Takeo Kanade matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Takeo Kanade?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Takeo Kanade.