Classification of Occluded Objects using Fast Recurrent Processing

Recurrent neural networks are powerful tools for handling incomplete data problems in computer vision, thanks to their significant generative capabilities. However, the computational demand for these algorithms is too high to work in real time, without specialized hardware or software solutions. In this paper, we propose a framework for augmenting recurrent processing capabilities into a feedforward network without sacrificing much from computational efficiency. We assume a mixture model and generate samples of the last hidden layer according to the class decisions of the output layer, modify the hidden layer activity using the samples, and propagate to lower layers. For visual occlusion problem, the iterative procedure emulates feedforward-feedback loop, filling-in the missing hidden layer activity with meaningful representations. The proposed algorithm is tested on a widely used dataset, and shown to achieve 2$\times$ improvement in classification accuracy for occluded objects. When compared to Restricted Boltzmann Machines, our algorithm shows superior performance for occluded object classification.Comment: arXiv admin note: text overlap with arXiv:1409.8576 by other author

A hand shape recognizer from simple sketches

Hand shape recognition is one of the most important techniques used in human-computer interaction. However, it often takes developers great efforts to customize their hand shape recognizers. In this paper, we present a novel method that enables a hand shape recognizer to be built automatically from simple sketches, such as a 'stick-figure' of a hand shape. We introduce the Hand Boltzmann Machine (HBM), a generative model built upon unsupervised learning, to represent the hand shape space of a binary image, and formulate the user provided sketches as an initial guidance for sampling to generate realistic hand shape samples. Such samples are then used to train a hand shape recognizer. We evaluate our method and compare it with other state-of-the-art models in three aspects, namely i) its capability of handling different sketch input, ii) its classification accuracy, and iii) its ability to handle occlusions. Experimental results demonstrate the great potential of our method in real world applications. © 2013 IEEE.published_or_final_versio