The nature of statistical learning theory
The nature of statistical learning theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Automatic Detection and Tracking of Human Motion with a View-Based Representation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Pedestrian Detection Using Wavelet Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Detection and Location of People in Video Images Using Adaptive Fusion of Color and Edge Information
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Object Detection in Images by Components
Object Detection in Images by Components
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
Journal of Cognitive Neuroscience
Subset selection for efficient SVM tracking
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Stereo- and neural network-based pedestrian detection
IEEE Transactions on Intelligent Transportation Systems
People Detection by a Mobile Robot Using Stereo Vision in Dynamic Indoor Environments
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
3D shape recovery from image focus using kernel regression in eigenspace
Image and Vision Computing
Detection of multiple people by a mobile robot in dynamic indoor environments
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
TED: A texture-edge descriptor for pedestrian detection in video sequences
Pattern Recognition
PCA document reconstruction for email classification
Computational Statistics & Data Analysis
A self-constructing cascade classifier with AdaBoost and SVM for pedestriandetection
Engineering Applications of Artificial Intelligence
Learning from the web: recognition method based on object appearance from internet images
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
Novel and efficient pedestrian detection using bidirectional PCA
Pattern Recognition
Document categorization based on minimum loss of reconstruction information
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Minimizer of the Reconstruction Error for multi-class document categorization
Expert Systems with Applications: An International Journal
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In this paper, we present an object detection system and its application to pedestrian detection in still images, without assuming any a priori knowledge about the image. The system works as follows: in a first stage a classifier examines each location in the image at different scales. Then in a second stage the system tries to eliminate false detections based on heuristics. The classifier is based on the idea that Principal Component Analysis (PCA) can compress optimally only the kind of images that were used to compute the principal components (PCs), and that any other kind of images will not be compressed well using a few components. Thus the classifier performs separately the PCA from the positive examples and from the negative examples; when it needs to classify a new pattern it projects it into both sets of PCs and compares the reconstructions, assigning the example to the class with the smallest reconstruction error. The system is able to detect frontal and rear views of pedestrians, and usually can also detect side views of pedestrians despite not being trained for this task. Comparisons with other pedestrian detection systems show that our system has better performance in positive detection and in false detection rate. Additionally, we show that the performance of the system can be further improved by combining the classifier based on PCA reconstruction with a conventional classifier using a Support Vector Machine.