2016/2
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Recent Submissions
- ItemA tabu search approach for the reconstruction of binary images without empty interior region(Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky, 2016) Billionnet, A.; Jarray, F.; Tlig, G.; Zagrouba, E.In this paper, we are concerned with a discrete tomography problem. We seek to reconstruct a binary image from its orthogonal projections, i.e, its horizontal and vertical line sums without interior black holes. We provide a tabu search approach to minimize the number of holes while satisfying the projections. We test our approach on some random binary images. Computational results show that the algorithm proposed produces near-optimal solutions for all test problems.
- ItemNorth Atlantic right whale localization and recognition using very deep and leaky Neural Network(Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky, 2016) Kabani, A.; El-Sakka, M. R.We describe a deep learning model that can be used to recognize individual right whales in aerial images. We developed our model using a data set provided by the National Oceanic and Atmospheric Administration. The main challenge we faced when working on this data set is that the size of the training set is very small (4,544 images) with some classes having only 1 image. While this data set is by far the largest of its kind, it is very di cult to train a deep neural network with such a small data set. However, we were able to overcome this challenge by dividing this problem into smaller tasks and by reducing the viewpoint variance in the data set. First, we localize the body and the head of the whale using deep learning. Then, we align the whale and normalize it with respect to rotation. Finally, a network is used to recognize the whale by analyzing its callosities. The top-1 accuracy of the model is 69.7% and the top-5 accuracy is 85%. The solution we describe in this paper was ranked 5th (out of 364 teams) in a challenge to solve this problem.
- ItemTwo-dimensional jumping finite automata(Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky, 2016) James Immanuel, S.; Thomas, D. G.In this paper, we extend a newly introduced concept called the jumping nite automata for accepting string languages to two-dimensional jumping nite automata for accepting two-dimensional languages. We discuss some of the basic properties of these automata and compare the family of languages accepted by these automata with the family of Siromoney matrix languages and also recognizable picture languages (REC). We also discuss some of the closure properties of these automata along with some of their decidability properties.
- ItemA modified Block Matching 3D algorithm for additive noise reduction(Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky, 2016) Alkinani, M. H.; El-Sakka, M. R.This paper presents a patch-based image ltering algorithm for addi- tive noise reduction. Our algorithm is a modi cation to the block matching 3D algorithm, where an adaptive thresholding was used for the collaborative hard- thresholding step. The collaborative Wiener ltering step was also modi ed by assigning more weights for similar patches. Experimental results show that our algorithm outperforms the original block matching 3D algorithm at various noise levels.
- ItemMathematics for applications in imaging – foreword(Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky, 2016) Brimkov, V. E.; Barneva, R. P.