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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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Book
01 Jan 2009
TL;DR: A polymorphic data type that captures a broad range of temporal media is presented, leading to an algebraic theory of polymorphic temporal media that is valid for underlying media types that satisfy specific constraints.
Abstract: Temporal media is information that is directly consumed by a user, and that varies with time. Examples include music, digital sound files, computer animations, and video clips. In this paper we present a polymorphic data type that captures a broad range of temporal media. We study its syntactic, temporal, and semantic properties, leading to an algebraic theory of polymorphic temporal media that is valid for underlying media types that satisfy specific constraints. The key technical result is an axiomatic semantics for polymorphic temporal media that is shown to be both sound and complete.

2 citations

Journal ArticleDOI
TL;DR: An automatic image analysis system for drug susceptibilityTesting that provides results within 3 hours using a drug susceptibility testing micro uidic (DSTM) device and addresses the issue of overlapping cells by incorporating a graph-based cell separation algorithm.
Abstract: In recent years, a rapid increase in bacterial strains resistant to modern antibiotics has been observed. This alarming rise in drug-resistant organisms has emphasized the importance of identifying new effective antimicrobial agents. Since traditional approaches for drug susceptibility testing are time-consuming and labor-intensive, more ef cient methods are urgently needed. Here, we report an automatic image analysis system for drug susceptibility testing that provides results within 3 hours using a drug susceptibility testing micro uidic (DSTM) device. The device consists of ve sets of four micro uidic channels prepared by soft lithography. The channels are in close proximity to allow simultaneous observations. The antimicrobial agent and bacterial suspension to be tested are added to the channel and incubated for 3 hours. Previously, microscopic images of the DSTM device were analyzed manually by an expert to evaluate the susceptibility of a strain. In this work, we present an automatic computer vision algorithm for processing images and performing analysis. The algorithm enhances the quality of the input image, detects cells in each channel, extracts a variety of cell-related characteristics, and estimates drug susceptibility using a pre-trained support vector machine. We address the issue of overlapping cells by incorporating a graph-based cell separation algorithm. The minimum concentration of a drug for which the proposed method predicted susceptibility represents the minimum inhibitory concentration (MIC). The novel method was implemented as a standalone application and tested on a dataset containing images of 101 clinically isolated strains of Pseudomonas aeruginosa incubated in the presence of ve different drugs. The estimated MICs correlated well with the results obtained using the conventional broth microdilution method.

2 citations

Book ChapterDOI
20 Nov 2016
TL;DR: This work proposes a second order linear regression method that is both compact and robust against strong rotations, and provides a closed form solution, making the method fast to train.
Abstract: Recent methods for facial landmark location perform well on close-to-frontal faces but have problems in generalising to large head rotations. In order to address this issue we propose a second order linear regression method that is both compact and robust against strong rotations. We provide a closed form solution, making the method fast to train. We test the method’s performance on two challenging datasets. The first has been intensely used by the community. The second has been specially generated from a well known 3D face dataset. It is considerably more challenging, including a high diversity of rotations and more samples than any other existing public dataset. The proposed method is compared against state-of-the-art approaches, including RCPR, CGPRT, LBF, CFSS, and GSDM. Results upon both datasets show that the proposed method offers state-of-the-art performance on near frontal view data, improves state-of-the-art methods on more challenging head rotation problems and keeps a compact model size.

2 citations

Proceedings ArticleDOI
12 Nov 1981
TL;DR: This paper describes one such system under development, MAPS (Map Assisted Photo interpretation System), and gives some general rationales for its design and implementation.
Abstract: This paper is concerned with the use of a database to support automated photo interpretation. The function of the database is to provide an environment in which to perform photo interpretation utilizing software tools, and represent domain knowledge about the scenes being interpreted. Within the framework of the database, image interpretation systems use knowledge stored as map, terrain, or scene descriptions to provide structural or spatial constraints to guide human and machine processing. We describe one such system under development, MAPS (Map Assisted Photo interpretation System), and give some general rationales for its design and implementation.© (1981) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

1 citations


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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