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.

Gradient-based learning applied to document recognition

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.

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Going deeper with convolutions

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 …

I and i

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.

/pdf/histograms-of-oriented-gradients-for-human-detection-3nteinziuq.pdf

Histograms of oriented gradients for human detection

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.

/pdf/rich-feature-hierarchies-for-accurate-object-detection-and-22fvjlggyg.pdf

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

PNImodeler: Web Server for Inferring Protein Binding Nucleotides from Sequence Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Jinyong Im, Narankhuu Tuvshinjargal, Byungkyu Park, Wook Lee, and Kyungsook Han A MCI Decision Support System Based on Ontology . . . . . . . . . . . . . . . . . 368 Xiaowei Zhang, Yang Zhou, Bin Hu, Jing Chen, and Xu Ma Context Similarity Based Feature Selection Methods for Protein Interaction Article Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 Yifei Chen, Yuxing Sun, and Ping Hou XVI Table of

Bioinformatics Research and Applications

Various object representations have been widely used for many tasks such as object detection, recognition, and tracking. Most of them requires an intensive training process on large database which is collected in advance, and it is hard to add models of a previously unobserved object which is not in the database. In this paper, we investigate how to create a representation of a new and unknown object online, and how to apply it to practical applications like object detection and tracking. To make it viable, we utilize a sensor fusion approach using a camera and a single-line scan LIDAR. The proposed representation consists of an approximated geometry model and a viewpoint-scale invariant appearance model which makes to extremely simple to match the model and the observation. This property makes it possible to model a new object online, and provides a robustness to viewpoint variation and occlusion. The representation has benefits of both an implicit model (referred to as a view-based model) and an explicit model (referred to as a shape-based model). Intensive experiments using synthetic and real data demonstrate the viability of the proposed object representation in both modeling and detecting/tracking objects.

Online Approximate Model Representation Based on Scale-Normalized and Fronto-Parallel Appearance

Human Foot Reconstruction from Multiple Camera Images with Foot Shape Database(Image Recognition, Computer Vision)

Chirurgie orthopedique assistee par ordinateur

/pdf/constructing-worlds-from-real-scenes-5dd6ih7zkm.pdf

Takeo Kanade

Papers

Bioinformatics Research and Applications

Online Approximate Model Representation Based on Scale-Normalized and Fronto-Parallel Appearance

Human Foot Reconstruction from Multiple Camera Images with Foot Shape Database(Image Recognition, Computer Vision)

Chirurgie orthopedique assistee par ordinateur

Constructing Worlds from Real Scenes