Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
The nature of statistical learning theory
The nature of statistical learning theory
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
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
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Fast Template Matching Algorithm with Adaptive Skipping Using Inner-Subtemplates' Distances
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Low Distortion Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Probabilistic Object Recognition Using Multidimensional Receptive Field Histograms
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Face detection using discriminating feature analysis and Support Vector Machine
Pattern Recognition
Video Event Recognition Using Kernel Methods with Multilevel Temporal Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Class of Algorithms for Fast Digital Image Registration
IEEE Transactions on Computers
Object detection using spatial histogram features
Image and Vision Computing
Adaptive integrated image segmentation and object recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Deformable shape finding with models based on kernel methods
IEEE Transactions on Image Processing
Regression application based on fuzzy ν-support vector machine in symmetric triangular fuzzy space
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
The forecasting model based on fuzzy novel ν-support vector machine
Expert Systems with Applications: An International Journal
Leukocyte image segmentation using simulated visual attention
Expert Systems with Applications: An International Journal
A multiple camera methodology for automatic localization and tracking of futsal players
Pattern Recognition Letters
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A new method for specific object detection in two-dimensional color images is proposed in this paper. The proposed method uses color histograms of an object on the hue and saturation (HS) color space as detection features. To represent color information by histograms as accurately as possible, a non-uniform partition of HS space is proposed. The whole detection process consists of three stages. In the first stage, the input image is repeatedly sub-sampled by a factor, resulting in a pyramid of images. Scanning on all of the scaled images with a pre-defined window size is performed, where histograms of each window are fed as inputs to a fuzzy classifier. The fuzzy classifier used is a self-organizing Takagi-Sugeno (TS)-type fuzzy network with support vector learning (SOTFN-SV). SOTFN-SV is a fuzzy system constituted by TS-type fuzzy if-then rules. It is constructed by the hybridization of fuzzy clustering and support vector machine. Many candidate objects are detected in this stage. In the second stage, a splitting K-means clustering method is proposed and applied to the detections from Stage 1 so that detections with nearby positions are grouped into the same cluster. The number of clusters is generated automatically by the clustering method according to cluster variances. Final object position is determined from the clusters. In the final stage, size of a detected object is determined. To verify performance of the proposed method, experiments on five specific object detections are conducted and comparisons with different types of detectors are made.