Active Shape Models with Invariant Optimal Features: Application to Facial Analysis

Sukno, Federico M.; Ordas, S.; Butakoff, Constantine; Cruz, Santiago; Frangi, Alejandro F.

doi:10.1109/tpami.2007.1041

Cited by 59 publications

(29 citation statements)

References 27 publications

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“…One of these approaches selects the atlases with the greatest similarity to the input image, aiming to reduce the population average bias [12]. Then, the selected atlases feed segmentation models such as active shape patterns [13], appearance models [14], and probabilistic atlases [15]. These models highly rely on the accuracy supplied by the pairwise image alignment.…”

Section: Related Workmentioning

confidence: 99%

Multi-atlas label fusion by using supervised local weighting for brain image segmentation

Cárdenas-Peña

Fernández

Ferrández-Vicente

et al. 2017

TecnoL.

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The automatic segmentation of interest structures is devoted to the morphological analysis of brain magnetic resonance imaging volumes. It demands significant efforts due to its complicated shapes and since it lacks contrast between tissues and intersubject anatomical variability. One aspect that reduces the accuracy of the multi-atlasbased segmentation is the label fusion assumption of one-to-one correspondences between targets and atlas voxels. To improve the performance of brain image segmentation, label fusion approaches include spatial and intensity information by using voxel-wise weighted voting strategies. Although the weights are assessed for a predefined atlas set, they are not very efficient for labeling intricate structures since most tissue shapes are not uniformly distributed in the images. This paper proposes a methodology of voxel-wise feature extraction based on the linear combination of patch intensities. As far as we are concerned, this is the first attempt to locally learn the features by maximizing the centered kernel alignment function. Our methodology aims to build discriminative representations, deal with complex structures, and reduce the image artifacts. The result is an enhanced patch-based segmentation of brain images. For validation, the proposed brain image segmentation approach is compared against Bayesian-based and patch-wise label fusion on three different brain image datasets. In terms of the determined Dice similarity index, our proposal shows the highest segmentation accuracy (90.3% on average); it presents sufficient artifact robustness, and provides suitable repeatability of the segmentation results. KeywordsBrain image segmentation, label fusion, multi-atlas segmentation. ResumenLa segmentación automática de estructuras de interés en imágenes de resonancia magnética cerebral requiere esfuerzos significantes, debido a las formas complicadas, el bajo contraste y la variabilidad anatómica. Un aspecto que reduce el desempeño de la segmentación basada en múltiples atlas es la suposición de correspondencias uno-a-uno entre los voxeles objetivo y los del atlas. Para mejorar el desempeño de la segmentación, las metodologías de fusión de etiquetas incluyen información espacial y de intensidad a través de estrategias de votación ponderada a nivel de voxel. Aunque los pesos se calculan para un conjunto de atlas predefinido, estos no son muy eficientes en etiquetar estructuras intrincadas, ya que la mayoría de las formas de los tejidos no se distribuyen uniformemente en las imágenes. Este artículo propone una metodología de extracción de características a nivel de voxel basado en la combinación lineal de las intensidades de un parche. Hasta el momento, este es el primer intento de extraer características locales maximizando la función de alineamiento de kernel centralizado, buscando construir representaciones discriminativas, superar la complejidad de las estructuras, y reducir la influencia de los artefactos. Para validar los resultados, la estrategia de segmentación propuesta ...

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Section: Related Workmentioning

confidence: 99%

Multi-atlas label fusion by using supervised local weighting for brain image segmentation

Cárdenas-Peña

Fernández

Ferrández-Vicente

et al. 2017

TecnoL.

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“…The latter is computed on the 3D mesh and aligned back into the texture images, allowing for a direct comparison of both input features. As we aim at highly accurate localization, the Invariant Optimal Features (IOF) variant of ASM [24] is used and our data is acquired from a high resolution scanner, which provides texture maps at 10 Mega-pixels, considerably higher than those generally used in the evaluation of automatic methods, and comparable to data reported in clinical studies.…”

Section: A Related Workmentioning

confidence: 99%

A Quantitative Assessment of 3D Facial Key Point Localization Fitting 2D Shape Models to Curvature Information

Sukno

Chowdhury

Waddington

et al. 2011

2011 Irish Machine Vision and Image Processing Conference

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Abstract-This work addresses the localization of 11 prominent facial landmarks in 3D by fitting state of the art shape models to 2D data. Quantitative results are provided for 34 scans at high resolution (texture maps of 10 M-pixels) in terms of accuracy (with respect to manual measurements) and precision (repeatability on different images from the same individual). We obtain an average accuracy of approximately 3 mm, and median repeatability of inter-landmark distances typically below 2 mm, which are values comparable to current algorithms on automatic localization of facial landmarks. We also show that, in our experiments, the replacement of texture information by curvature features produced little change in performance, which is an important finding as it suggests the applicability of the method to any type of 3D data.

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“…Many variations of the ASM method for FER have been introduced: Optimal Features ASM (OFASM) is high in accuracy but is more computationally expensive [10]. F. Sukno extended OFASM to allow application in more complex geometries [11]. However, the above methods do not consider the wrinkle features, which is quite important in real-world FER.…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition Using Digitalised Facial Features Based on Active Shape Model

Sun¹,

Chen²,

Day³

2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Facial Expression Recognition is a hot topic in recent years. As artificial intelligent technology is growing rapidly, to communicate with machines, facial expression recognition is essential. The recent feature extraction methods for facial expression recognition are similar to face recognition, and those caused heavy load for calculation. In this paper, Digitalized Facial Features based on Active Shape Model method is used to reduce the computational complexity and extract the most useful information from the facial image. The result shows by using this method the computational complexity is dramatically reduced, and very good performance was obtained compared with other extraction methods.

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Active Shape Models with Invariant Optimal Features: Application to Facial Analysis

Cited by 59 publications

References 27 publications

Multi-atlas label fusion by using supervised local weighting for brain image segmentation

Multi-atlas label fusion by using supervised local weighting for brain image segmentation

A Quantitative Assessment of 3D Facial Key Point Localization Fitting 2D Shape Models to Curvature Information

Facial Expression Recognition Using Digitalised Facial Features Based on Active Shape Model

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