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The vibratory bowl feeder is the oldest and still most common approach to the automated feeding (orienting) of industrial parts. In this paper we consider a class of vibratory bowl filters that can be described by removing polygonal sections from the track; we refer to this class of filters as traps.For an n-sided polygonal part and an m-sided polygonal trap, we give an O(n 2 m log n) algorithm to decide whether the part in a specific orientation will safely move across the trap or will fall through the trap and thus be filtered out. For an n-sided convex polygonal part and m-sided convex polygonal trap, this bound is improved to O((n + m) log n).Furthermore, we show how to design various trap shapes, ranging from simple traps to general polygons which will filter out all but one of the different stable orientations of a given part. Although the run times of our design algorithms are exponential in the number of trap parameters, many industrial part feeders use few-parameter traps (balconies, canyons, slots); in these cases the running times of our algorithms range from linear to low degree polynomial.

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A common task in automated manufacturing processes is to orient parts prior to assembly. W e consider sensorless orientation of a polygonal part by a sequence of fences. We show that any polygonal part can be oriented by a sequence of fences placed along a conveyor belt, thereby settling a conjecture by Wiegley et al. 17 , and present the rst polynomial-time algorithm to compute the shortest such sequence. The algorithm is easy to implement and runs in time On 3 log n, where n is the numb e r o f v ertices of the part. 1 Introduction Many automated manufacturing processes require parts to be oriented prior to assembly. A part feeder takes in a stream of identical parts in arbitrary orientations and outputs them in a uniform orientation. Part feeders often use data obtained from some kind of sensing device to accomplish their task. We consider the problem of sensorless orientation of parts, in which the initial pose of the part is assumed to be unknown. In sensorless manipulation, parts are positioned and or oriented using passive mechanical compliance. The input is a description of the part shape and the output is a sequence of open-loop actions that moves a part from an unknown initial pose into a unique nal pose. Among the sensorless part feeders considered in literature are the parallel-jaw gripper 6, 9 , a single pushing jaw 2, 10, 11, 13 , a conveyor belt with a sequence of stationary fences placed along its sides 5, 14, 17 , a conveyor belt with a single rotational fence 1JOC 1 , a tilting tray 8, 12 , and vibratory plates and programmable vector elds 3, 4. The pushing jaw 2 , 1 0 , 11, 13 orients a part by an alternating sequence of pushes and jaw reorientations. The problem of sensorless orientation by a pushing jaw is to nd a sequence of push directions that will move the part from an arbitrary initial orientation into a single known nal orientation. Such a sequence is referred to as a push plan. Goldberg 9 showed that any polygonal part can be oriented by a sequence of pushes. Chen and Ierardi 6 proved that any polygonal part with n vertices can be oriented by On pushes. They showed that this bound is tight b y constructing pathological n-gons that require n pushes to be oriented. Goldberg gave an algorithm for computing the shortest push plan for a polygon. His algorithm runs in On 2 time.

A vastly growing number of productions from the entertainment industry are aiming at 3D movie theatres. These productions use a two-view format, primarily intended for eye-wear assisted viewing in a well defined environment. To get this 3D content into the home environment, where a large variety of 3D viewing conditions exists (e.g different display sizes, display types, viewing distance), we need a flexible 3D format that can adjust the depth effect. Such a format is the image plus depth format in which a video frame is enriched with depth information of all pixels in the video. This format can be extended with an additional layer for occluded video and associated depth, that contains what is behind objects in the video. To produce 3D content in this extended format, one has to deduce what is behind objects. There are various axes along which this occluded data can be obtained. This paper presents a method to automatically detect and fill the occluded areas exploiting the temporal axis. To get visually pleasing results, it is of utmost importance to make the inpainting globally consistent. To do so, we start by analyzing data along the temporal axis and compute a confidence for each pixel. Then pixels from the future and the past that are not visible in the current frame are weighted and accumulated based on computed confidences. These results are then fed to a generic multi-source framework that computes the occlusion layer based on the available confidences and occlusion data.