Rapid detection of landmines and explosive hazards is a critical issue for modern military operations. Due to the varied nature of the objects of interest and the complexity of the surroundings, one approach is to utilize the superior recognition capabilities of the human brain in the detection process. We are developing frameworks and algorithms to process image video data from an RGB camera, mounted on a moving vehicle, and to provide cueing capability for a human-in-the-loop detection system. Feedback from the human operator is embedded into the system memory to aid future detection processes (causal). Due to the inherent variation of different objects of interest terms of color and texture appearance, and the inherent variation and complexity of the surroundings, we introduce a classification algorithm that operates on local decision boundaries. Each decision boundary is learned using the support vector machine (SVM) technique. Given test data, the K-nearest neighbor (KNN) method is used to pre-select the nearest training set to localize the scope of SVM training, giving us the local decision boundary.

This paper proposes a method for segmenting an unstructured dirt road in color space images using color and texture analysis. A support vector machine (SVM) classifier was trained on samples of on and off road patches from a similar road. Image patches were classified at sparse intervals at a fixed distance from the vehicle. Each patch is described by the Histogram of oriented gradients (HOG), the Local Binary Patters (LBP), a histogram of the color channel, and a histogram of a non linear color transform. The classified patches were transformed to the next frame of the sequence using the scale invariant feature transform (SIFT) to reduce reclassification of image patches. Morphological opening and closing were used to transform the points into a mask, and reduce errors. Experimental results indicated that the algorithm can accurately segment road images given a set of training data from similar road utilizing only color imagery.

In this paper we describe a method for detecting buried objects of interest using a forward looking infrared camera (FLIR) installed on a moving vehicle. Infrared (IR) detection of buried targets is based on the thermal gradient between the object and the surrounding soil. The processing of FILR images consists in a spot-finding procedure that includes edge detection, opening and closing. Each spot is then described using texture features such as histogram of gradients (HOG) and local binary patterns (LBP) and assigned a target confidence using a support vector machine (SVM) classifier. Next, each spot together with its confidence is projected and summed in the UTM space. To validate our approach, we present results obtained on 6 one mile long runs recorded with a long wave IR (LWIR) camera installed on a moving vehicle.

This paper explores several novel approaches to solve the Morris water maze task. In this spatial memory task, the robot must learn how to associate perceptual information with a particular location to aid in navigating to the goal. A self-organizing feature map (SOFM) is used to discretize the perceptual space. The robot must then learn to associate these perceptual states with an action used to navigate through the environment. Two navigational approaches are proposed. The first approach involves computing a probabilistic graph between SOFM nodes and then searching the graph to locate a path to the goal. The second approach uses temporal difference learning to learn the association between an SOFM node and an action that will direct it to the goal. The paper compares the effectiveness of these two approaches and discusses their respective utility.