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Takeo Kanade

Bio: Takeo Kanade is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Motion estimation & Image processing. The author has an hindex of 147, co-authored 799 publications receiving 103237 citations. Previous affiliations of Takeo Kanade include National Institute of Advanced Industrial Science and Technology & Hitachi.


Papers
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Proceedings ArticleDOI
03 Jan 1998
TL;DR: A content-based 3D neuroradiologic image retrieval system is being developed at the Robotics Institute of CMU, directly dealing with multimodal 3D images (MR/CT); image similarity based on anatomical structures of the human brain; and combining both visual and collateral information for indexing and retrieval.
Abstract: A content-based 3D neuroradiologic image retrieval system is being developed at the Robotics Institute of CMU. The special characteristics of this system include: directly dealing with multimodal 3D images (MR/CT); image similarity based on anatomical structures of the human brain; and combining both visual and collateral information for indexing and retrieval. A testbed has been implemented for using detected salient visual features for indexing and retrieving 3D images.

39 citations

01 Jan 2003
TL;DR: This paper summarizes the work and the understanding on volumetric pathological neuro image retrieval under the framework of classification-driven feature selection for improved image indexing feature discriminating power as well as reduced computational cost during on-line pathological neuroimage retrieval.
Abstract: This paper summarizes our work and our understanding on volumetric pathological neuroimage retrieval under the framework of classification-driven feature selection. The main effort concerns off-line image feature space reduction for improved image indexing feature discriminating power as well as reduced computational cost during on-line pathological neuroimage retrieval. Keywrods: 3D image, feature selection, brain asymmetry, midsagittal plane, image classification,indexing, retrieval

39 citations

Proceedings ArticleDOI
03 Dec 2003
TL;DR: An autonomous blimp for a surveillance system, which is circling around a specified target with only one camera, is designed, and an extension of Lucas-Kanade algorithm for detection and tracking of features with rotation and scaling is provided, and a simplified structure-from-motion algorithm is applied to improve the accuracy of state estimation.
Abstract: An autonomous blimp for a surveillance system, which is circling around a specified target with only one camera, is designed in this paper. For this purpose, an extension of Lucas-Kanade algorithm for detection and tracking of features with rotation and scaling is provided, and a simplified structure-from-motion algorithm is applied to improve the accuracy of state estimation. A tracking controller is designed for the blimp which is an underactuated system. The desired path of the blimp is also generated from image information of a target. The blimp flies around the target automatically, after a commander sets it. Some experiments are performed indoors by using an aerial blimp.

39 citations

Proceedings ArticleDOI
05 Dec 2002
TL;DR: A robust subspace approach to extracting layers from images reliably is presented by taking advantage of the fact that homographies induced by planar patches in the scene form a low dimensional linear subspace, which provides a constraint for detecting outliers in the local measurements, thus making the algorithm robust to outliers.
Abstract: Representing images with layers has many important applications, such as video compression, motion analysis, and 3D scene analysis. The paper presents a robust subspace approach to extracting layers from images reliably by taking advantage of the fact that homographies induced by planar patches in the scene form a low dimensional linear subspace. Such a subspace provides not only a feature space where layers in the image domain are mapped onto denser and better-defined clusters, but also a constraint for detecting outliers in the local measurements, thus making the algorithm robust to outliers. By enforcing the subspace constraint, spatial and temporal redundancy from multiple frames are simultaneously utilized, and noise can be effectively reduced. Good layer descriptions are shown to be extracted in the experimental results.

39 citations

Book ChapterDOI
19 Sep 1999
TL;DR: This work characterize such anatomical variations to achieve accurate registration between 3-D images of human anatomies and shows how innate differences in the appearance and location of anatomical structures between individuals make accurate registration difficult.
Abstract: Registration between 3-D images of human anatomies enables cross-subject diagnosis. However, innate differences in the appearance and location of anatomical structures between individuals make accurate registration difficult. We characterize such anatomical variations to achieve accurate registration.

39 citations


Cited by
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Journal ArticleDOI
01 Jan 1998
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Abstract: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.

42,067 citations

Proceedings ArticleDOI
07 Jun 2015
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Abstract: We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

40,257 citations

Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Proceedings ArticleDOI
20 Jun 2005
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Abstract: We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.

31,952 citations

Proceedings ArticleDOI
23 Jun 2014
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.
Abstract: Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also present experiments that provide insight into what the network learns, revealing a rich hierarchy of image features. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn.

21,729 citations