Noninformation-preserving shape features at multiple resolution

Pernuš, Franjo; Leonardis, A.; Kovačič, Stanislav

doi:10.1109/icpr.1992.201746

Cited by 2 publications

(2 citation statements)

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“…Un método común de reconocimiento de patrones es la identificación de las propias minutiaes, que son las líneas que según su grosor, separación y formas configuran estos patrones. Su distancia y sus intersecciones o falta de ellas permite definir de un modo más fino estos puntos de comparación entre huellas, lo cual suele inducir resultados bastante satisfactorios [10]. Ahora bien, este método difiere un poco del reconocimiendo de patrones en sí mismo siendo más próximo a lo que hace una red neuronal convolucional (CNN, del inglés Convolutional Neural Network).…”

Section: Estado Del Arteunclassified

Entrenamiento optimizado de redes neuronales para reconocimiento biométrico

Miranda¹,

García²,

Álvarez-Aparicio³

et al. 2021

Colección Jornadas Y Congresos

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Section: Estado Del Arteunclassified

Entrenamiento optimizado de redes neuronales para reconocimiento biométrico

Miranda¹,

García²,

Álvarez-Aparicio³

et al. 2021

Colección Jornadas Y Congresos

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“…There are many techniques of shape description and recognition. These methods can be categorized as information preserving or unambiguous, and non-information preserving or ambiguous, depending on whether or not it is possible to reconstruct a reasonable approximation of the object from the shape descriptor [90,91]. The information preserving methods such as [64,65] are mostly used in data compression application.…”

Section: Shape Feature Analysismentioning

confidence: 99%

Gland segmentation of microscopic image for prostate cancer

Han¹

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Computer-aided image processing has attracted much research interest in recent years. In particular, there has been growing interest in processing and analyzing biomedical images by computer. Traditionally, biomedical images used for diagnosis are examined visually by experienced pathologists. However, the subjective nature of visual examination frequently results in variant conclusions by different pathologists. Hence, there's an increasing demand for automated method to help improve the efficiency and accuracy of biomedical images diagnosis by incorporating the power of digital image processing techniques into pathological image analysis. The research work of this thesis focuses on the image segmentation of prostate microscopic images. Prostate cancer is reported as the second leading cause of cancer death in men and the leading cause of cancer death in males over 85. It can only be confirmed by a biopsy, which makes computer-aided image processing an ideal auxiliary tool to help the pathologists make objective diagnosis. This thesis aims to develop a systematic computer-aided method to obtain precise segmentation of abnormal glands from given prostate biopsy images. This research is important and necessary since it constitutes the basis for further computer-aided processing and analyzing of prostate microscopic images. It is easy for human eyes to distinguish the gland from the image due to the color difference between the gland and stroma.

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Noninformation-preserving shape features at multiple resolution

Cited by 2 publications

References 5 publications

Entrenamiento optimizado de redes neuronales para reconocimiento biométrico

Entrenamiento optimizado de redes neuronales para reconocimiento biométrico

Gland segmentation of microscopic image for prostate cancer

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