Parsimonious Labeling

We propose a new family of discrete energy minimization problems, which we call parsimonious labeling. Specifically, our energy functional consists of unary potentials and high-order clique potentials. While the unary potentials are arbitrary, the clique potentials are proportional to the {\em diversity} of set of the unique labels assigned to the clique. Intuitively, our energy functional encourages the labeling to be parsimonious, that is, use as few labels as possible. This in turn allows us to capture useful cues for important computer vision applications such as stereo correspondence and image denoising. Furthermore, we propose an efficient graph-cuts based algorithm for the parsimonious labeling problem that provides strong theoretical guarantees on the quality of the solution. Our algorithm consists of three steps. First, we approximate a given diversity using a mixture of a novel hierarchical $P^n$ Potts model. Second, we use a divide-and-conquer approach for each mixture component, where each subproblem is solved using an effficient $\alpha$-expansion algorithm. This provides us with a small number of putative labelings, one for each mixture component. Third, we choose the best putative labeling in terms of the energy value. Using both sythetic and standard real datasets, we show that our algorithm significantly outperforms other graph-cuts based approaches

Impact of industrial effluents on ground water and soil quality in the vicinity of industrial area of Panipat city, India

The present paper is aimed towards the assessment of heavy metal contamination of agricultural soil due to irrigation with contaminated ground water affected by textile industrial effluents at Panipat city in India. Samples of ground water and irrigated soils from textile industrial area were analyzed for various heavy metals, viz. Mn, Ni, Fe, Cu, Cd, Pb and Zn, using Atomic Absorption Spectrophotometry. Metal transfer factors from ground water to irrigated agricultural soil and from soil to ground water were calculated for heavy metals. The findings deal with the distribution of heavy metals in ground water of industrial area and irrigated agricultural soil. Transfer factors for heavy metals from effluent to ground water were observed to be 0.436, 1.180, 6.461, 2.401, 2.790, 3.178 and 0.634 for Cd, Cu, Fe, Mn, Ni, Pb and Zn respectively. These were found to be very high from ground water to agriculture soil due to the natural shale value of heavy metals in soil system. Thus, untreated industrial effluents can cause an environmental threat to ground water resources and affects soil quality and agricultural plant productivity