This paper reviews P´olya urn models and their connection to random trees. Basic results are presented, together with proofs that underly the historical evolution of the accompanying thought process. Extensions and generalizations are given according to chronology: • P´olya-Eggenberger’s urn • Bernard Friedman’s urn • Generalized P´olya urns • Extended urn schemes • Invertible urn schemes ...

Recovering structure from motion even using information from multiple image frames is diicult, in part because motion error can introduce large, correlated errors in the structure estimate. A method is proposed for recursively recovering structure from motion that can deal with this problem. Encouraging results on real images and synthetic data are presented.

Fine-grained image classification is a difficult problem, and previous studies mainly overcome this problem by locating multiple discriminative regions in different scales then aggregating complementary information explored from the located regions. However, introduces heavy overhead not suitable for real-world application. In paper, we propose recursive multi-scale channel-spatial attention mo...

We present a method to increase the dynamical range of a Residue Number System (RNS) by adding virtual RNS layers on top of the original RNS, where the required modular arithmetic for a modulus on any non-bottom layer is implemented by means of an RNS Montgomery multiplication algorithm that uses the RNS on the layer below. As a result, the actual arithmetic is deferred to the bottom layer. The...

Fully supervised deep neural networks for segmentation usually require a massive amount of pixel-level labels which are manually expensive to create. In this work, we develop multi-task learning method relax constraint. We regard the problem as sequence approximation subproblems that recursively defined and in increasing levels accuracy. The handled by framework consists 1) task learns from gro...

We discuss recent work on the algorithmic analysis of systems involving recursion and probability. Recursive Markov chains extend ordinary finite state Markov chains with the ability to invoke other Markov chains in a potentially recursive manner. They offer a natural abstract model of probabilistic programs with procedures, and generalize other classical well-studied stochastic models, eg. Mul...

Recursive inverse filtering with non-stationary filters is becoming a useful tool in a range of applications, from multi-dimensional inverse problems to wave extrapolation. I formulate causal non-stationary convolution and combination and their adjoints in such a way that it is apparent that the corresponding non-stationary recursive filters are true inverse processes. Stationary recursive inve...

Hyperspectral anomaly detection (HAD) plays an important role in military and civilian applications has attracted a lot of research. The well-known Reed–Xiaoli (RX) algorithm is the benchmark HAD methods. Based on RX model, many variants have been developed. However, most them ignore spatial characteristics hyperspectral images (HSIs). In this paper, we combine extended multi-attribute profiles...