… to pose invariance and classification performance. In this contribution, we analyze optimal
… by evaluating the performance of the resulting invariant recognition approach. In section 2, …

… for human face recognition. Our findings show that DCNNs optimized for face identification
are tuned to the same facial features used by humans for face recognition. Sensitivity to these …

… Then, a feature is selected if this ratio is higher than a threshold. The optimum threshold value
is computed by a trial and error search in which the performance over the training samples …

… object detection as an optimization problem that has been solved with PSO, an evolutionary
optimization method. … Experimental results demonstrated that accurate object detection and …

… of object recognition, a particularly interesting and challenging question is whether unsupervised
learning can be used to learn invariant features. … of input images from optimal codes Z∗ …

… Then, the goal is to find in this space, an optimal decision hyperplane, in the sense of a
criterion that we are going to define. Note that for a same training set, different transformations Φ …

… We used a concurrent iterative optimization algorithm to add feature points only where the
interpolation lines are not well represented by a straight line between their ends. The block …

… , which is achieved by introducing and learning a new rotation-invariant layer on the …
optimizes the multinomial logistic regression objective, our RICNN model is trained by optimizing a …

… is based on local affine invariant features enabling recognition of partially occluded objects.
T he … A t this stage of our research we made no attempt to design an 'optimal' footprint. The …

… parameters in the optimization by using diagonal … of feature selection and has compared
different techniques. This comparison shows that likelihood is well suited for object recognition …