Genetic Programming for Multiple Class Object Detection
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
DEXA '99 Proceedings of the 10th International Conference on Database and Expert Systems Applications
Recent advances in visual and infrared face recognition: a review
Computer Vision and Image Understanding
A domain-independentwindow approach to multiclass object detection using genetic programming
EURASIP Journal on Applied Signal Processing
Optimizing parameters of a motion detection system by means of a distributed genetic algorithm
Image and Vision Computing
Object detection using neural networks and genetic programming
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
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IEEE Transactions on Intelligent Transportation Systems
A learning-based spatial processing method for the detection of point targets
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
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The detection of objects in high-resolution aerial imagery has proven to be a difficult task. In the authors' application, the amount of image clutter is extremely high. Under these conditions, detection based on low-level image cues tends to perform poorly. Neural network techniques have been proposed in object detection applications due to proven robust performance characteristics. A neural network filter was designed and trained to detect targets in thermal infrared images. The feature extraction stage was eliminated and raw gray levels were utilized as input to the network. Two fundamentally different approaches were used to design the training sets. In the first approach, actual image data were utilized for training. In the second case, a model-based approach was adopted to design the training set vectors. The training set consisted of object and background data. The neuron transfer function was modified to improve network convergence and speed and the backpropagation training algorithm was used to train the network. The neural network filter was tested extensively on real image data. Receiver operating characteristic (ROC) curves were determined in each case. The detection and false alarm rates were excellent for the neural network filters. Their overall performance was much superior to that of the size-matched contrast-box filter, especially in the images with higher amounts of visual clutter