Automatic image analysis of histopathology specimens would help the early detection of blood cancer. The first step for automatic image analysis is segmentation. However, touching cells bring the difficulty for traditional segmentation algorithms. In this paper, we propose a novel algorithm which can reliably handle touching cells segmentation. Robust estimation and color active contour models ...

In this paper an optimization approach is used to solve the problem of nding the minimum distance between concave objects, without the need for partitioning the objects into convex sub-objects. Since the optimization problem is not unimodal (i.e., has more than one local minimum point), a global optimization technique, namely a Genetic Algorithm, is used to solve the concave problem. In order t...

The present experiment was designed to examine the roles of painted linear perspective cues, and the convexity bias that are known to influence human observers' perception of three-dimensional (3D) objects and scenes. Reverse-perspective stimuli were used to elicit a depth-inversion illusion, in which far points on the stimulus appear to be closer than near points and vice versa, with a 2 (Type...

The maximization of one-dimensional piecewise linear concave (OPLC) functions arises in the line search associated with the maximization of piecewise linear concave functions (e.g. Kelley cutting plane method). The OPLC line search is usually done by the next-break-point method, where one goes from break point to break point up to the optimum. If the number of break points is large this method ...

In this paper, we introduce a class of local indicators that enable to compute efficiently optimal transport plans associated to arbitrary weighted distributions of N demands and M supplies in R in the case where the cost function is concave. Indeed, whereas this problem can be solved linearly when the cost is a convex function of the distance on the line (or more generally when the cost matrix...

Discrete strip-concave functions considered in this paper are, in fact, equivalent to an extension of Gelfand-Tsetlin patterns to the case when the pattern has a not necessarily triangular but convex configuration. They arise by releasing one of the three types of rhombus inequalities for discrete concave functions (or “hives”) on a “convex part” of a triangular grid. The paper is devoted to a ...

We consider the convex optimization problem P : minx{f(x) : x ∈ K} where f is convex continuously differentiable, and K ⊂ R is a compact convex set with representation {x ∈ R : gj(x) ≥ 0, j = 1, . . . ,m} for some continuously differentiable functions (gj). We discuss the case where the gj ’s are not all concave (in contrast with convex programming where they all are). In particular, even if th...

Two fundamental problems of machine learning misclassi cation minimization and feature selection are formulated as the minimization of a concave function on a polyhedral set Other formulations of these problems utilize linear programs with equilibrium constraints which are generally intractable In contrast for the proposed concave minimization formulation a successive linearization algorithm wi...

We introduce a technique called Concave, Adaptive Value Estimation (CAVE) to approximate recourse functions found in stochastic programs. CAVE constructs a sequence of concave (or convex) piecewise-linear approximations using sample gradients of the recourse function at di erent points in the domain. The result is a nonlinear approximation that is more responsive than traditional stochastic qua...

An important objective of health monitoring systems for tall buildings is to diagnose the state of the building and to evaluate its possible damage. In this paper, we use our prototype to evaluate our data-mining approach for the fault monitoring. The offset cancellation and high-pass filtering techniques are combined effectively to solve common problems in numerical integration of acceleration...