In this present paper, a new counting algorithm for tangerine yield estimation is adapted to obtain better results with respect to partially / semi partially occluded tangerine and its clusters. To optimize tangerine counting, and to minimize typical background noises from orchards (i.e. bare soil, weeds, and man-made objects), a tangerine fruit counting algorithm is implemented, and compared b...

counting the population of insect pests is a key task for planning a successful integrated pest management program. most image processing and machine vision techniques in the literature are very site-specific and cannot be easily re-usable because their performances are highly related to their ground truth data. in this article a new unsupervised image processing method is proposed which is gen...

We study the parameterized complexity of the propositional model counting problem #SAT for CNF formulas. As the parameter we consider the treewidth of the following two graphs associated with CNF formulas: the consensus graph and the conflict graph. Both graphs have as vertices the clauses of the formula; in the consensus graph two clauses are adjacent if they do not contain a complementary pai...

background and objectives: numerous procedures in biology and medicine require the counting of cells. direct enumer- ation of colony forming units (cfus) is time-consuming and dreary accurate cell counting on plates with high numbers of cfus is error prone. in this study we report a new indirect cell counting method that was developed based on the use of redsafe fluorometric assay. the usefulne...

This paper introduces a new counting code. Its design was motivated by distributed video coding where, for decoding, error correction methods are applied to improve predictions. Those error corrections sometimes fail which results in decoded values worse than the initial prediction. Our code exploits the fact that bit errors are relatively unlikely events: more than a few bit errors in a decode...

We introduce ApproxCount, an algorithm that approximates the number of satisfying assignments or models of a formula in propositional logic. Many AI tasks, such as calculating degree of belief and reasoning in Bayesian networks, are computationally equivalent to model counting. It has been shown that model counting in even the most restrictive logics, such as Horn logic, monotone CNF and 2CNF, ...

TPA: A New Method for Approximate Counting by Sarah Schott Department of Mathematics Duke University