Robust Histogram Construction from Color Invariants for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Online Detection and Classification of Moving Objects Using Progressively Improving Detectors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A survey of skin-color modeling and detection methods
Pattern Recognition
Selection and Fusion of Color Models for Image Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive object detection and recognition based on a feedback strategy
Image and Vision Computing
Combining color and shape information for illumination-viewpoint invariant object recognition
IEEE Transactions on Image Processing
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This paper presents a novel approach of object detection and localization for service robots, which combines color-based and gradient-based detectors and automatically adapts the color model according to the variation of lighting conditions. Exploiting complementary visual features, the fusion of color-based and gradient-based detectors can achieve both robust detection and accurate localization. In real world environment, the color-based detection according to an offline-learned general model may fail. From a new linear color variation model proposed in this paper, our approach can generate a specific model for the target object in the image and achieve self-adaptation of color detector for robust detection and accurate localization. The experiments show that the proposed method can significantly increase the detection rate for target object in various real world environments.