No abstract

In order to reduce the security risk of a commercial aircraft, passengers are not allowed to take certain items in their carry-on baggage. For this reason, human operators are trained to detect prohibited items using a manually controlled baggage screening process. In this paper, we propose the use of an automated method based on multiple Xray views to recognize certain regular objects with highly defined shapes and sizes. The method consists of two steps: 'monocular analysis', to obtain possible detections in each view of a sequence, and 'multiple view analysis', to recognize the objects of interest using matchings in all views. The search for matching candidates is efficiently performed using a lookup table that is computed off-line. In order to illustrate the effectiveness of the proposed method, experimental results on recognizing regular objects -clips, springs and razor blades-in pen cases are shown achieving around 93% accuracy for 120 objects. We believe that it would be possible to design an automated aid in a target detection task using the proposed algorithm.

In order to reduce the security risk of a commercial aircraft, passengers are not allowed to take certain items in carry-on baggage. For this reason, human operators are trained to detect prohibited items using a manually controlled baggage screening process. In this paper, we propose the use of a method based on multiple X-ray views to detect some regular prohibited items with very defined shapes and sizes. The method consists of two steps: 'structure estimation', to obtain a geometric model of the multiple views from the object to be inspected (baggage); and 'parts detection', to detect the parts of interest (prohibited items). The geometric model is estimated using a structure from a motion algorithm. The detection of the parts of interest is performed by an ad-hoc segmentation algorithm (object dependent) followed by a general tracking algorithm based on geometric and appearance constraints. In order to illustrate the effectiveness of the proposed method, experimental results on detecting regular objects − razor blades and guns − are shown yielding promising results.

No abstract

This paper presents an extension of the Latency Time (LT) scheduling algorithm for assigning tasks with arbitrary execution times on a multiprocessor with shared memory. The Extended Latency Time (ELT) algorithm adds to the priority function the synchronization associated with access to the shared memory. The assignment is carried out associating with each task a time window of the same size as its duration, which decreases for every time unit that goes by. The proposed algorithm is compared with the Insertion Scheduling Heuristic (ISH). Analysis of the results established that ELT has better performance with fine granularity tasks (computing time comparable to synchronization time), and also, when the number of processors available to carry out the assignment increases.