We describe a technique that can be used for the fusion of multiple sources of information as well as for the evaluation and selection of alternatives under multi-criteria. Three important properties contribute to the uniqueness of the technique introduced. The first is the ability to do all necessary operations and aggregations with information that is of a nonnumeric linguistic nature. This f...

this research provides an optimization tool for use by supply chain managers in the design and operation of manufacturing- distribution networks under uncertain demand conditions. the problem under consideration consists of determining the supply chain infrastructure; raw material purchases, shipments, and inventories; and finished product production quantities, inventories, and shipments neede...

This paper proposes a novel formulation for the multiobject tracking-by-detection paradigm for two (or more) input detectors. Using full-body and heads detections, the fusion helps to recover heavily occluded persons and to reduce false positives. The assignment of the two input features to a person and the extraction of the trajectories is commonly solved from one binary quadratic program (BQP...

We aim at developing a geometry-based retrieval system for multi-object images. We model both shape and topology of image objects including holes using a structured representation called curvature tree (CT); the hierarchy of the CT reflects the inclusion relationships between the objects and holes. To facilitate shape-based matching, triangle-area representation (TAR) of each object and hole is...

The existing cosegmentation methods use intra-group information to extract a common object from a single image group. Observing that in many practical scenarios there often exist multiple image groups with distinct characteristics but related to the same common object, in this paper we propose a multi-group image cosegmentation framework, which not only discoveries intra-group information withi...

In this paper a new method is introduced for path planning of an autonomous vehicle. In this method, the environment is considered cluttered and with some uncertainty sources. Thus, the state of detected object should be estimated using an optimal filter. To do so, the state distribution is assumed Gaussian. Thus the state vector is estimated by a Kalman filter at each time step. The estimation...

In many real applications, especially those involving data objects with complicated semantics, it is generally desirable to discover the relation between patterns in the input space and labels corresponding to different semantics in the output space. This task becomes feasible with MIML (Multi-Instance Multi-Label learning), a recently developed learning framework, where each data object is rep...

We study multi-label prediction for structured output sets, a problem that occurs, for example, in object detection in images, secondary structure prediction in computational biology, and graph matching with symmetries. Conventional multilabel classification techniques are typically not applicable in this situation, because they require explicit enumeration of the label set, which is infeasible...

We present a method for one-handed, task-based manipulation of objects. Our approach uses a mid-level, multi-phase approach to organize the problem into three phases. This provides an appropriate control strategy for each phase and results in cyclic finger motions that, together, accomplish the task. The exact trajectory of the object is never specified since the goal is defined by the final or...