A comparison of key-point descriptors for the stereo matching algorithm

Satnik, Andrej; Hudec, Róbert; Kamencay, Patrik; Hlubik, Jan; Benčo, Miroslav

doi:10.1109/radioelek.2016.7477419

Cited by 11 publications

(3 citation statements)

References 8 publications

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“…The feature points of two imageries are matched after being described by high dimensional descriptors, then the sea ice motion vectors are calculated based on the offset value of matched points. Different feature point operators generate different results of sea ice motion field in terms of accuracy and vector density [9]. It finds that A-KAZE performs best on sea ice motion deriving than other features in terms of both calculation efficiency and coverage by testing Scale Invariant Feature Transform (SIFT), Oriented FAST and Rotated BRIEF (ORB), and A-KAZE to Sentinel-1 imagery [10].…”

Section: Introductionmentioning

confidence: 99%

Matching Vector Filtering Methods For Sea Ice Motion Detection Using SAR Imagery Feature Tracking

Chen

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Applying feature tracking techniques to SAR imagery generates high resolution sea ice motion fields. However, the bad matching vectors still exist after the NNDR (Nearest Neighbor Distance Ratio) test and contaminate the derived motion fields, which need to be identified and filtered out. In this research, we propose two algorithms to eliminate such wrong matching vectors. The first employs the matching results derived by the maximum cross-correlation (MCC) method as the reference motion fields to evaluate such wrong matches. The second method employs the local spatial consistency presumption of sea ice motion fields. A Voronoi diagram is applied to slice the overlapping area of two SAR images into many fractions, and each fraction extends its size 50% outward to calculate the regional mean sea ice flow vector and standard deviation. Any vector within the fraction that exceeds 3 times the regional standard deviation will be recognized as an outlier and filtered out. Two methods are tested to two cases with strong rotation or irregular sea ice motion fields derived from Sentinel-1 imagery. The overall accuracy of our two methods is 93.9% and 98.7%, and they sacrifice 6.12% /1.22% of the correct vectors to filter out 100.0% / 94.12% of the wrong vectors for the MCC referenced filter and Voronoi fragmented filter, respectively.

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Section: Introductionmentioning

confidence: 99%

Matching Vector Filtering Methods For Sea Ice Motion Detection Using SAR Imagery Feature Tracking

Chen

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…No estado da arte para comparação de Pontos de Interesse [Satnik et al 2016] [Pusztai and Hajder 2016] [Bureš and Müller 2016] [Cowan et al 2016] [Wu et al 2017 faz-se uso de parâmetros padrões utilizados pelas bibliotecas, como do OpenCV [Bradski and Kaehler 2008], entretanto dependendo da imagem, se não configurar os parâmetros, o resultado retornado pode não ser esperado, com uso de método de otimização pretende-se obter resultados de comparações mais justos, pois os parâmetros serão alterados individualmente para cada imagem de teste, buscando uma solução subótima dos parâmetros para obter um erro menor. Essa comparação faz parte de um trabalho em andamento, onde esses parâmetros serão configurados automaticamente de acordo com uma meta-heurística para um sistema de posicionamento da aeronave em tempo real.…”

Section: Introductionunclassified

Parameters configuration for interest points in images obtained by Drone

Rebouças¹,

Shiguemori²,

Guimarães³

2018

Anais Do XV Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2018)

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Drone use has grown with the use of image processing and computer vision techniques, such as autonomous image navigation, mosaic generation, elevation modeling, 3D reconstruction, and object recognition. In all techniques, an important step is an extraction of features, such as methods of interest points. This work addresses the modes of application of interest points, such as BRISK, ORB, FREAK, AKAZE and LATCH with the parameters configured automatically using the optimization method for images with different textures. This process is one of the pieces of final software that selects the use of a meta heuristic the best parameters automatically according to an input image.

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“…Apesar de todo o avanço tecnológica, em alguns casos não é possível obter uma imagem fiel e precisa de uma região de interesse de uma determinada cena. Como exemplo, em imagens de satélite e sensoriamento remoto (PATEL; PATEL; HOLIA, 2015; NAZNEEN; SHAFIQ; HAMEED, 2016; SEDAGHAT; EBADI, 2015; LIU et al, 2016), imagens médicas (GALANDE;PATIL, 2013;PHAN et al, 2016), restauração de imagens (BRITO et al, 2016;SATNIK et al, 2016), perícia de documentos para indicar autenticidade (ARDIZZONE; BRUNO; MAZZOLA, 2015), reconhecimento de padrões e recuperação por conteúdo (GURUPRASAD; GURUPRA-SAD, 2015; TAKÁCS et al, 2015; CHOI; HAN, 2014), entre outras (LIQIAN;YUEHUI, 2010;TONG et al, 2012). Nestes casos, a integração/fusão de imagens pode ser aplicada.…”

Section: Int Roduçãounclassified

Algoritmos de casamento de imagens com filtragem adaptativa de outliers

Ramos¹

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Image matching plays a major role in many applications, such as pattern recognition and microscopic imaging. It encompasses three steps: 1) interest point selection; 2) feature extraction from each point; 3) feature point matching. For steps 1 and 2, traditional interest point detectors/extractors have worked well. However, for step 3 even a few points incorrectly matched (outliers), might lead to an undesirable result. State-of-the-art consensus algorithms present a high time cost as the number of outlier increases. Aiming at overcoming this problem, we present FOMP, a preprocessing approach, that reduces the number of outliers in the initial set of matched points. FOMP filters out the vertices that present a higher difference among their edges in a complete graph representation of the points. To validate the proposed method, experiments were performed with four image database: (a) variations of rotation or camera zoom; (b) repetitive patterns, which leads to duplicity of features vectors; (c) deformable objects, such as plastics, clothes or papers; (d) affine transformations (different viewpoint). The experimental results showed that FOMP removes more than 65% of the outliers, while keeping over 98% of the inliers. Moreover, the precision of traditional methods is kept, while reducing the processing time of graph based approaches by half. K ey-wor ds: Image matching, Feature point matching; Outlier removal, Outlier filtering.

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A comparison of key-point descriptors for the stereo matching algorithm

Cited by 11 publications

References 8 publications

Matching Vector Filtering Methods For Sea Ice Motion Detection Using SAR Imagery Feature Tracking

Matching Vector Filtering Methods For Sea Ice Motion Detection Using SAR Imagery Feature Tracking

Parameters configuration for interest points in images obtained by Drone

Algoritmos de casamento de imagens com filtragem adaptativa de outliers

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