Inclusion-Exclusion Principle

Andreescu, Titu; Feng, Zuming

doi:10.1007/978-0-8176-8154-8_6

Cited by 9 publications

(10 citation statements)

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“…To merge counts from both sides, we compute the intersection of the connected components from both sides (see Figure ). Then, we compute the total counts by using the inclusion–exclusion principle (Andreescu & Feng, ). Essentially, we sum the counts from all the connected components, compute the intersection area among them (among 1,2, ..., the total number of intersecting clusters) and add/subtract the weighted parts accordingly.…”

Section: Methodsmentioning

confidence: 99%

“…Figure in the right shows how the connected components from the back are intersecting with the front ones. Apple counts from the intersecting clusters are merged using the inclusion-exclusion principle(Andreescu & Feng, 2004). 3D, three-dimensional [Color figure can be viewed at wileyonlinelibrary.com] F I G U R E 2 4 A trunk prediction instance (right) and its ground truth (left).…”

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confidence: 99%

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Semantic mapping for orchard environments by merging two‐sides reconstructions of tree rows

Dong

Roy

Isler

2019

Journal of Field Robotics

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Measuring semantic traits for phenotyping is an essential but labor‐intensive activity in horticulture. Researchers often rely on manual measurements which may not be accurate for tasks, such as measuring tree volume. To improve the accuracy of such measurements and to automate the process, we consider the problem of building coherent three‐dimensional (3D) reconstructions of orchard rows. Even though 3D reconstructions of side views can be obtained using standard mapping techniques, merging the two side‐views is difficult due to the lack of overlap between the two partial reconstructions. Our first main contribution in this paper is a novel method that utilizes global features and semantic information to obtain an initial solution aligning the two sides. Our mapping approach then refines the 3D model of the entire tree row by integrating semantic information common to both sides, and extracted using our novel robust detection and fitting algorithms. Next, we present a vision system to measure semantic traits from the optimized 3D model that is built from the RGB or RGB‐D data captured by only a camera. Specifically, we show how canopy volume, trunk diameter, tree height, and fruit count (FC) can be automatically obtained in real orchard environments. The experiment results from multiple data sets quantitatively demonstrate the high accuracy and robustness of our method.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Semantic mapping for orchard environments by merging two‐sides reconstructions of tree rows

Dong

Roy

Isler

2019

Journal of Field Robotics

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show abstract

“…To merge counts from both sides, we compute the intersection of the connected components from both sides. Afterwards, we compute the total counts by using the inclusion-exclusion principle [23]. Essentially, we sum up the counts from all the connected components, compute the intersections area among them (among 1, 2 .…”

Section: B Real Datasetmentioning

confidence: 99%

Registering Reconstructions of the Two Sides of Fruit Tree Rows

Roy

Dong

Isler

2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We consider the problem of building accurate three dimensional (3D) reconstructions of orchard rows. This problem arises in many applications including yield mapping and measuring traits (e.g. trunk diameters) for phenotyping. While 3D reconstructions of side views can be obtained using standard methods, merging the two side-views is difficult due to the lack of overlap between the two partial reconstructions. We present a novel method that utilizes global features to constrain the solution. Specifically, we use information from the silhouettes and the ground plane for alignment. The method is evaluated using multiple simulated and real datasets.

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“…The events A i are not disjoint and the computation of the union is consequently not trivial. To compute this probability, we propose to use the Inclusion-Exclusion Principle [24], which is defined as:…”

Section: Neighbor Busy Time Fractionmentioning

confidence: 99%

Association Optimization in Wi-Fi Networks based on the Channel Busy Time Estimation

Amer

Busson

Lassous

2018

2018 IFIP Networking Conference (IFIP Networking) and Workshops

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With the centralized management paradigm offered by the recent IEEE 802.11 products, it is now easier and more efficient to optimize associations between access points (APs) and stations. Most of the optimization approaches consider a saturated network. Even if such traffic conditions are rare, the optimization of the association step under this assumption has the benefit to fairly share the bandwidth between stations. Nevertheless, traffic demands may be very different from one station to another and it may be more useful to optimize associations according to the stations' demands. In this paper, we propose an optimization of the association step based on the stations' throughputs and the channel busy time fraction (BTF). The latter is defined as the proportion of time the channel is sensed busy by an AP. Associations are optimized in order to minimize the greatest BTF in the network. This original approach allows the Wi-Fi manager/controller to unload the most congested AP, increase the throughput for most of the stations, and offer more bandwidth to stations that need it. We present a local search technique that finds local optima to this optimization problem. This heuristic relies on an analytical model that predicts BTF for any configuration. The model is based on a Markov network and a Wi-Fi conflict graph. NS-3 simulations including a large set of scenarios highlight the benefits of our approach and its ability to improve the performance in congested and non-congested Wi-Fi networks.

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Inclusion-Exclusion Principle

Cited by 9 publications

References 0 publications

Semantic mapping for orchard environments by merging two‐sides reconstructions of tree rows

Semantic mapping for orchard environments by merging two‐sides reconstructions of tree rows

Registering Reconstructions of the Two Sides of Fruit Tree Rows

Association Optimization in Wi-Fi Networks based on the Channel Busy Time Estimation

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