Fast algorithms for fitting active appearance models to unconstrained images

Fitting algorithms for Active Appearance Models (AAMs) are usually considered to be robust but slow or fast but less able to generalize well to unseen variations. In this paper, we look into AAM fitting algorithms and make the following orthogonal contributions: We present a simple “project-out” optimization framework that unifies and revises the most well-known optimization problems and solutions in AAMs. Based on this framework, we describe robust simultaneous AAM fitting algorithms the complexity of which is not prohibitive for current systems. We then go on one step further and propose a new approximate project-out AAM fitting algorithm which we coin extended project-out inverse compositional (E-POIC). In contrast to current algorithms, E-POIC is both efficient and robust. Next, we describe a part-based AAM employing a translational motion model, which results in superior fitting and convergence properties. We also show that the proposed AAMs, when trained “in-the-wild” using SIFT descriptors, perform surprisingly well even for the case of unseen unconstrained images. Via a number of experiments on unconstrained human and animal face databases, we show that our combined contributions largely bridge the gap between exact and current approximate methods for AAM fitting and perform comparably with state-of-the-art face alignment algorithms

Mobile robot scheduling for cycle time optimization in flow-shop cells, a case study

The typical production system in carton box production companies is cell-production. These cells normally benefit from a mobile robot which serves the machines according to a given schedule. One of the main problems of such companies is finding the order of robot moves in a way that the time required for completing all jobs is minimized. In the studied case in this research, each cell contains three machines of which, two or three of them might be activated for production process depending on the product type. These machines are equipped with a one-capacitated input and output buffer. Considering the fact that the machines are capable of performing any operation, the assignments of the jobs to them may have several alternatives. The one-capacitated buffers make the robot scheduling more complex as they act as extra stations to be served by the robot (contribute to exponential increase in job assignments permutation). This study aims to deal with this complexity and provide a decision-making toolbox for business owners to determine and employ the best robot moving schedule according to the characteristics of the problem. The mentioned approach significantly contributes to decision-maker’s effective time management and results in adopting a better production scheme for each production cycle. In line with this prospect, this research proposes a sequential part production matrix (SPPM) to determine feasible robot move strategies through which the best scheduling scheme is introduced for different problem configurations. Additionally, a metaheuristic algorithm is proposed to determine the best robot move strategy for cases with more active machines in a cell as manual determination of the robot move strategies becomes exhaustive in such cases.</p