A graph-theoretic approach to 3D shape classification

Hamza, A. Ben

doi:10.1016/j.neucom.2015.12.130

Cited by 12 publications

(4 citation statements)

References 33 publications

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“…Characteristic immune phenotypes, especially of clonally expanded T cells, suggest reactivity with antigens associated with persistent viral infections such as Epstein–Barr virus (EBV) or cytomegalovirus. Indeed, expanded T‐cell clones in tumor environments and autoimmune diseases have been determined virus reactive [3–6]; however, there is no straightforward approach to systematically study the virus reactivity of a given set of T‐cell clones.…”

Section: Figurementioning

confidence: 99%

Oligonucleotide library screening for identification of virus‐specific T‐cell receptors

Welters,

Stadler,

Anastasopoulou

et al. 2024

Eur J Immunol

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Section: Figurementioning

confidence: 99%

Oligonucleotide library screening for identification of virus‐specific T‐cell receptors

Welters,

Stadler,

Anastasopoulou

et al. 2024

Eur J Immunol

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“…Generally, there are two main approaches to design shape descriptors: general purpose shape descriptors and specific shape estimators. The first one is based on different transforms such as Fourier transform [2], Hough transform [3], Radon transform [4], dominant points [5], similarity map [6], graph-based representation [7], and image moments [8] to describe shapes. More different methods have been addressed in this survey [9].…”

Section: Introductionmentioning

confidence: 99%

A Projection-Based Method for Shape Measurement

et al. 2020

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This work addresses two main contributions for shape measurement. First, a new circularity measure for planar shapes is introduced based on their geometrical properties in the projection space of Radon transform. Second, a general-purpose evaluation criterion, Power Of Discrimination (POD), for assessing the efficiency of a shape measure is proposed. The new measure ranges over the interval [0,1], and produces the value 1 if and only if the measured shape is a perfect circle. The proposed measure is invariant with respect to translation, rotation and scaling transformations. Moreover, it is also robust against border distortion of shapes. It is theoretically well founded and can be extended to other problems of shape measurement. Our approach can deal with complex shapes composed of connected components that cannot be handled by classical contour-based methods. Several experiments show its good behavior and demonstrate the efficiency and applicability of our proposed measure. Finally, we also consider our proposed evaluation criterion for assessing different circularity measures.

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“…La clasificación de formas es un problema intrigante y desafiante que se encuentra en el cruce de la visión por computadora, el procesamiento de la geometría y el aprendizaje automático [1]. La forma es una característica intrínseca para la comprensión de la imagen, que es estable a la iluminación y las variaciones en el color y la textura del objeto.…”

Section: Introductionunclassified

Clasificación de formas por códigos de cadena mediante un algoritmo de búsqueda

Sandoval¹,

Ramírez-Cruz²,

Islas³

et al. 2018

RCS

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La clasificación de formas es un proceso utilizado en diversas áreas, para realizar una clasificación a partir de la manipulación de contornos de formas es necesario la extracción de ciertas características comunes para ello se utilizan diferentes técnicas, una de estas es el uso de algoritmos de búsqueda los cuales se encargan de buscar un elemento con ciertas propiedades dentro de una estructura de datos. En este trabajo se presenta una clasificación de formas usando el algoritmo de búsqueda A* y cadenas de código de Freeman, el algoritmo de búsqueda A* busca los pixeles que contienen la información que representa el borde de la forma, esta información es procesada para convertirse en códigos de cadena de Freeman que representan eficientemente los bordes de las formas geométricas, estas técnicas son probadas con diversas formas para comparar las cadenas obtenidas en cada una de ellas y comprobar que se trata de la misma forma con la que se trabajó inicialmente antes de aplicarle las técnicas propuestas.Palabras clave: algoritmo de búsqueda, códigos de cadena, clasificación.

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A graph-theoretic approach to 3D shape classification

Cited by 12 publications

References 33 publications

Oligonucleotide library screening for identification of virus‐specific T‐cell receptors

Oligonucleotide library screening for identification of virus‐specific T‐cell receptors

A Projection-Based Method for Shape Measurement

Clasificación de formas por códigos de cadena mediante un algoritmo de búsqueda

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