Handbook of Pattern Recognition and Computer Vision

Chen, C H; Pau, L. F.; Wang, P S P

doi:10.1142/1802

Cited by 47 publications

(11 citation statements)

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“…For the first three runs, we applied CSLBP⁄, a hybrid shape descriptor, composed of Center Symmetric Local Binary Pattern (CSLBP) feature [108], Entropy descriptor [109], and optional Chain Code (CC). The difference between the three runs comes from the number of view projections and the existence of the optional CC: 16 views for CSLBP in Run 1, 24 views for CSLBP in Run 2 and Run 3, while no CC for Run 1 and Run 2 and CC addition in Run 3.…”

Section: Query By Model Retrieval Methodsmentioning

confidence: 99%

A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries

et al. 2015

Computer Vision and Image Understanding

118

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a b s t r a c tLarge scale 3D shape retrieval has become an important research direction in content based 3D shape retrieval. To promote this research area, two Shape Retrieval Contest (SHREC) tracks on large scale com prehensive and sketch based 3D model retrieval have been organized by us in 2014. Both tracks were based on a unified large scale benchmark that supports multimodal queries (3D models and sketches). This benchmark contains 13680 sketches and 8987 3D models, divided into 171 distinct classes. It was compiled to be a superset of existing benchmarks and presents a new challenge to retrieval methods as it comprises generic models as well as domain specific model types. Twelve and six distinct 3D shape retrieval methods have competed with each other in these two contests, respectively. To measure and compare the performance of the participating and other promising Query by Model or Query by Sketch 3D shape retrieval methods and to solicit state of the art approaches, we perform a more comprehensive comparison of twenty six (eighteen originally participating algorithms and eight additional state of the art or new) retrieval methods by evaluating them on the common benchmark. The benchmark, results, and evaluation tools are publicly available at our websites

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Section: Query By Model Retrieval Methodsmentioning

confidence: 99%

A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries

et al. 2015

Computer Vision and Image Understanding

118

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“…Сглаживание сводится к устранению дефектов изображения, вносимых устройствами. Для сглаживания применяется множество алгоритмов [4]. В рамках данной работы применялся алгоритм Normalized Box Filter [4].…”

Section: сглаживаниеunclassified

High-efficient search and analysis of road lanes

Морозов¹

2015

ScienceRise

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Разработан и реализован высокоэффективный алгоритм обнаружения и обработки элементов дорожной разметки. Рассмотрены ключевые этапы работы алгоритма. Реализована программа, которая подтвердила высокие технические качества работы алгоритма в заданных режимах эксплуатации. Показана применимость разработки для повышения безопасности движения транспортных средств Ключевые слова: низкоуровневая обработка изображений, распознавание образов, алгоритмы компьютерного зрения, дорожная разметка It is developed and implemented high-efficient algorithm for searching and processing elements of road lanes. There are described the most common steps in algorithm logic. The test program, which was written specially for this algorithm, has shown high detection quality. Such program can be used for drive r assistance systems

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“…Texture, which can be defined as a function of local variation of pixel intensities [13], is a useful image characteristic that has been successfully utilized in many automated image analysis algorithms. Many old and recent works on classification of optical and SAR satellite images in urban environments showed that textural features can yield high classification accuracies [10, 14, 15].…”

Section: Developed Proceduresmentioning

confidence: 99%

Rapid Urban Mapping Using SAR/Optical Imagery Synergy

Corbane

Faure

Baghdadi³

et al. 2008

Sensors

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This paper highlights the potential of combining Synthetic Aperture Radar (SAR) and optical data for operational rapid urban mapping. An algorithm consisting of a completely unsupervised procedure for processing pairs of co-registered SAR/optical images is proposed. In a first stage, a texture analysis is conducted independently on the two images using eight different chain-based Gaussian models. In a second stage, the resulting texture images are partitioned by an unsupervised fuzzy K-means approach. Finally, a fuzzy decision rule is used to aggregate the results provided by the classification of texture images obtained from the pair of SAR and optical images. The method was tested and validated on images of Bucharest (Romania) and Cayenne (French Guiana). These two study areas are of different terrain relief, urban settlement structure and land cover complexity. The data set included Radarsat-1/ENVISAT and SPOT-4/5 images. The developed SAR/optical information fusion scheme improved the capabilities of urban areas extraction when compared with the separate use of SAR and optical sensors. It also proved to be suitable for monitoring urbanization development. The encouraging results thus confirm the potential of combining information from SAR and optical sensors for timely urban area analysis, as required in cases of disaster management and planning in urban sprawl areas.

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Handbook of Pattern Recognition and Computer Vision

Cited by 47 publications

References 0 publications

A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries

A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries

High-efficient search and analysis of road lanes

Rapid Urban Mapping Using SAR/Optical Imagery Synergy

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