Automatic 3D object recognition and reconstruction based on neuro-fuzzy modelling

Samadzadegan, Farhad; Azizi, Ali; Hahn, Michael; Lucas, C.

doi:10.1016/j.isprsjprs.2005.02.010

Cited by 30 publications

(13 citation statements)

References 14 publications

(18 reference statements)

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“…Following this idea, Samadzadegan et al proposed a novel approach for objects recognition, based on neuro-fuzzy modeling. They extract structural, textural and spectral information and integrate them in a fuzzy reasoning process to which learning capability of neural networks is introduced [30]. Zimmermann et al produced Digital Surface Model (DSM) data from stereo images.…”

Section: Boykov and Jolly Proposed An Interactive Graph Cuts (Igc)mentioning

confidence: 99%

A Multiple-Objects Recognition Method Based on Region Similarity Measures: Application to Roof Extraction from Orthophotoplans

Idrissi¹,

Merabet²,

Ruichek³

et al. 2015

ijacsa

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Abstract-In this paper, an efficient method for automatic and accurate detection of multiple objects from images using a region similarity measure is presented. This method involves the construction of two knowledge databases: The first one contains several distinctive textures of objects to be extracted. The second one is composed with textures representing background. Both databases are provided by some examples (training set) of images from which one wants to recognize objects. The proposed procedure starts by an initialization step during which the studied image is segmented into homogeneous regions. In order to separate the objects of interest from the image background, an evaluation of the similarity between the regions of the segmented image and those of the constructed knowledge databases is then performed. The proposed approach presents several advantages in terms of applicability, suitability and simplicity. Experimental results obtained from the method applied to extract building roofs from orthophotoplans prove its robustness and performance over popular methods like K Nearest Neighbours (KNN) and Support Vector Machine (SVM).

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Section: Boykov and Jolly Proposed An Interactive Graph Cuts (Igc)mentioning

confidence: 99%

A Multiple-Objects Recognition Method Based on Region Similarity Measures: Application to Roof Extraction from Orthophotoplans

Idrissi¹,

Merabet²,

Ruichek³

et al. 2015

ijacsa

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“…Fuzzy sets and fuzzy operators are the "subjects" and "verbs" of fuzzy logic (Samadzadegan et al, 2005). In order to create a useful statement, complete sentences have to be formulated.…”

Section: Inferencementioning

confidence: 99%

A fuzzy decision making system for building damage map creation using high resolution satellite imagery

Rastiveis

Samadzadegan

Reinartz

2013

Nat. Hazards Earth Syst. Sci.

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Recent studies have shown high resolution satellite imagery to be a powerful data source for post-earthquake damage assessment of buildings. Manual interpretation of these images, while being a reliable method for finding damaged buildings, is a subjective and time-consuming endeavor, rendering it unviable at times of emergency. The present research, proposes a new state-of-the-art method for automatic damage assessment of buildings using high resolution satellite imagery. In this method, at the first step a set of pre-processing algorithms are performed on the images. Then, extracting a candidate building from both pre- and post-event images, the intact roof part after an earthquake is found. Afterwards, by considering the shape and other structural properties of this roof part with its pre-event condition in a fuzzy inference system, the rate of damage for each candidate building is estimated. The results obtained from evaluation of this algorithm using QuickBird images of the December 2003 Bam, Iran, earthquake prove the ability of this method for post-earthquake damage assessment of buildings

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“…However, most of the existing methods for automatic object extraction and recognition from data are just based on the range information and employ parametric methods while object's vagueness behaviour is basically neglected [1].…”

Section: Related Workmentioning

confidence: 99%

Semi-Automatic Objects Recognition in Urban Areas Based on Fuzzy Logic

Prandi¹,

Brumana²,

Fassi

2010

JGIS

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Three dimensional object extraction and recognition (OER) from geographic data has been definitely one of more important topic in photogrammetry for quite a long time. Today, the capability of rapid generating high-density DSM increases the supply of geographic information but the discrete nature of the measuring makes more difficult to recognize correctly and to extract 3D objects from these surface. The proposed methodology wants to semi-automate some geographic objects clustering operations, in order to perform the recognition process. The clustering is a subjective process; the same set of data items often needs to be partitioned differently based on the application. Fuzzy logic gives the possibility to use in a mathematical process the uncertain information typical of human reasoning. The concept at the base of our proposal is to use the information contained in Image Matching or LiDAR DSM, and typically understood by the human operator, in a fuzzy recognition process able to combine the different input in order to perform the classification. So the object recognition approach proposed in our workflow integrates 3D structural descriptive components of objects, extracted from DSM, into a fuzzy reasoning process in order to exploit more fully all available information, which can contribute to the extraction and recognition process and, to handling the object's vagueness. The recognition algorithm has been tested with to different data set and different objectives. An important issue is to apply the typical human process which allows to recognize objects in a range image in a fuzzy reasoning process. The investigations presented here have given a first demonstration of the capability of this approach.

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Automatic 3D object recognition and reconstruction based on neuro-fuzzy modelling

Cited by 30 publications

References 14 publications

A Multiple-Objects Recognition Method Based on Region Similarity Measures: Application to Roof Extraction from Orthophotoplans

A Multiple-Objects Recognition Method Based on Region Similarity Measures: Application to Roof Extraction from Orthophotoplans

A fuzzy decision making system for building damage map creation using high resolution satellite imagery

Semi-Automatic Objects Recognition in Urban Areas Based on Fuzzy Logic

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