Empirical Performance Evaluation of Graphics Recognition Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empirical Evaluation Techniques in Computer Vision
Empirical Evaluation Techniques in Computer Vision
Input Guided Performance Evaluation
Proceedings of the Theoretical Foundations of Computer Vision, TFCV on Performance Characterization in Computer Vision
Evaluation of Salient Point Techniques
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Evaluation of global image thresholding for change detection
Pattern Recognition Letters
A Method for Objective Edge Detection Evaluation and Detector Parameter Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning for adaptive image interpretation
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
Modeling, evaluation and control of a road image processing chain
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
A hierarchical approach to color image segmentation using homogeneity
IEEE Transactions on Image Processing
On performance analysis of optical flow algorithms
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
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Tuning a complete image processing chain (IPC) is not a straightforward task. The first problem to overcome is the evaluation of the whole process. Until now researchers have focused on the evaluation of single algorithms based on a small number of test images and ad hoc tuning independent of input data. In this paper, we explain how the design of experiments applied on a large image database enables statistical modeling for IPC significant parameter identification. The second problem is then considered: how can we find the relevant tuning and continuously adapt image processing to input data? After the tuning of the IPC on a typical subset of the image database using numerical optimization, we develop an adaptive IPC based on a neural network working on input image descriptors. By testing this approach on an IPC dedicated-to-road obstacle detection, we demonstrate that this experimental methodology and software architecture can ensure continuous efficiency. The reason is simple: the IPC is globally optimized, from a large number of real images and with adaptive processing of input data.