Models, code, and papers for "Mehmet Turkan":
In conventional sparse representations based dictionary learning algorithms, initial dictionaries are generally assumed to be proper representatives of the system at hand. However, this may not be the case, especially in some systems restricted to random initializations. Therefore, a supposedly optimal state-update based on such an improper model might lead to undesired effects that will be conveyed to successive iterations. In this paper, we propose a dictionary learning method which includes a general feedback process that codes the intermediate error left over from a less intensive initial learning attempt, and then adjusts sparse codes accordingly. Experimental observations show that such an additional step vastly improves rates of convergence in high-dimensional cases, also results in better converged states in the case of random initializations. Improvements also scale up with more lenient sparsity constraints.
Machine learning models, which are frequently used in self-driving cars, are trained by matching the captured images of the road and the measured angle of the steering wheel. The angle of the steering wheel is generally fetched from steering angle sensor, which is tightly-coupled to the physical aspects of the vehicle at hand. Therefore, a model-agnostic autonomous car-kit is very difficult to be developed and autonomous vehicles need more training data. The proposed vision based steering angle estimation system argues a new approach which basically matches the images of the road captured by an outdoor camera and the images of the steering wheel from an onboard camera, avoiding the burden of collecting model-dependent training data and the use of any other electromechanical hardware.