Few studies have explored the complex circuit simulation of stochastic and unary computing systems, which are referred to under the umbrella term of bit-stream processing. The computer simulation of multi-level cascaded circuits with reconvergent paths has not been largely examined in the context of bit-stream processing systems. This study addresses this gap and proposes a contingency table -based reconvergent path-aware simulation method for fast and efficient simulation of multi-level circuits. The proposed method exhibits significantly better runtime and accuracy.

Stochastic computing (SC) is an emerging paradigm that has come to the fore in computer vision applications in the last decade. Complex arithmetic circuitry is reduced to simple logic gates, fed with uniform random bit-streams. Due to the requirement of long bit-streams, the computer-aided simulation of SC systems is facing run-time and memory-use challenges. This work presents an efficient approach for emulating SC-based systems. The proposed simulation technique does not utilize actual bit-streams but produces similar results as if the traditional stochastic bit-streams were processed. The data are processed with the aid of a correlation-controlled contingency table (CT) construct. Our technique emulates three state-of-the-art stochastic bit-streams, namely, bit-streams with binomial distribution, pseudo-random, and low-discrepancy bit-streams. We validate the proposed technique by emulating three new SC image processing designs. We propose novel SC designs for (i) template matching, (ii) image compositing, and (iii) bilinear interpolation. Our experimental results show that our simulation technique provides comparable accuracy to processing actual bit-streams, but at a significantly lower run-time and memory usage.

The rapid computerized simulation of stochastic computing (SC) systems is a challenging problem. A method for agile simulation of SC image processing is proposed in this work. The input operands are processed with the aid of a correlation-controlled contingency table (CT) construct without using actual stochastic bit-streams. The proposed approach underlines the validity of CT simulation with 1) image compositing; 2) pattern detection; and 3) bilinear interpolation case studies. Using the corresponding error models, we emulate the state-of-the-art pseudo-random and quasi-random bit-streams. Experimental results show that the proposed approach achieves similar computation accuracy to the traditional SC simulation while performing runtime- and memory-efficient computations. The execution time reduces more than$200\times $for the image compositing task when emulating random bit-streams with CT. Pattern detection and bilinear interpolation further showed$76\times $and$22\times $lower memory usage, respectively, when employing CT.

This study compares the performance of local binary patterns (LBP) and scale invariant feature transform (SIFT) with support vector machines (SVM) in automatic classification of discrete facial expressions. Facial expression recognition is a multiclass classification problem and seven classes; happiness, anger, sadness, disgust, surprise, fear and comtempt are classified. Using SIFT feature vectors and linear SVM, 93.1% mean accuracy is acquired on CK+ database. On the other hand, the performance of LBP-based classifier with linear SVM is reported on SFEW using strictly person independent (SPI) protocol. Seven-class mean accuracy on SFEW is 59.76%. Experiments on both databases showed that LBP features can be used in a fairly descriptive way if a good localization of facial points and partitioning strategy are followed.