Wyniki wyszukiwania - BazTech

1

Local binary pattern defect recognition approach for the friction stir welded AA 1200 and AA 6061-T6 aluminum alloy

Mishra Akshansh

Journal of Mechanical and Energy Engineering

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2020

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Vol. 4, No 1

27--32

EN

The research reported in this paper focuses on the application of local binary patterns (LBPs) for surface defects detection. The surface defection detection algorithm for friction stir welded aluminum plates is the key part of the entire surface defect recognition system. Two different grades i.e AA 1200 and AA 6061 plates were similarly joined with the help of Friction Stir Welding process. Python codes for the proposed algorithm were executed on Google Colaboratory platform. The results obtained prove that the local binary patterns method can be used for real-time surface defects detection in friction stir welded joints.

2

Emotion recognition from facial images using binary face relevance maps

Herud T., Kawulok M., Smolka B.

Studia Informatica

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2015

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Vol. 36, nr 4

29--41

EN

This paper is focused on automatic emotion recognition from static grayscale images. Here, we propose a new approach to this problem, which combines a few other methods. The facial region is divided into small subregions, which are selected for processing based on a face relevance map. From these regions, local directional pattern histograms are extracted and concatenated into a single feature histogram, which is classified into one of seven defined emotional states using support vector machines. In our case, we distinguish: anger, disgust, fear, happiness, neutrality, sadness and surprise. In our experimental study we demonstrate that the expression recognition accuracy for Japanese Female Facial Expression database is one of the best compared with the results reported in the literature.

PL

W artykule tym przedstawiono zagadnienie rozpoznawania emocji na podstawie obrazów w skali szarości. Prezentujemy w nim nowe podejście, stanowiące połączenie kilku istniejących metod. Obszar twarzy jest dzielony na mniejsze regiony, które są wybierane do dalszego przetwarzania, z uwzględnieniem binarnych map istotności. Z każdego regionu ekstrahowany jest histogram lokalnych wzorców binarnych, a następnie histogramy są składane do wektora cech i klasyfikowane za pomocą maszyny wektorów podpierających. W naszym przypadku rozróżniamy takie emocje, jak: gniew, wstręt, strach, szczęście, neutralność, smutek i zaskoczenie. Podczas naszych eksperymentów pokazaliśmy, że nasze podejście umożliwia poprawę skuteczności rozpoznawania emocji dla bazy Japanese Female Facial Expression względem innych istniejących metod.

3

Optimal classification method for smiling vs neutral facial display recognition

Nurzyńska K., Smołka B.

Journal of Medical Informatics & Technologies

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2014

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Vol. 23

87--94

EN

Human face depicts what happens in the soul, therefore correct recognition of emotion on the basis of facial display is of high importance. This work concentrates on the problem of optimal classification technique selection for solving the issue of smiling versus neutral face recognition. There are compared most frequently applied classification techniques: k-nearest neighbourhood, support vector machines, and template matching. Their performance is evaluated on facial images from several image datasets, but with similar image description methods based on local binary patterns. According to the experiments results the linear support vector machine gives the most satisfactory outcomes for all conditions.

4

Human emotion recognition based on facial texture information

Piątkowska E., Martyna J.

Studia Informatica

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2012

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Vol. 33, nr 2B

49-59

EN

The paper presents the system for automatic emotion recognition. Firstly, face detection algorithm [5] is performed on input image to create face representation. Then, face texture is encoded with Local Binary Patterns [11] and used as a feature set in emotion recognition. The Support Vector Machine [15] is used as a classifier. The proposed system was tested with spontaneous emotions.

PL

W niniejszym artykule opisany został system do automatycznego rozpoznawania emocji. Pierwszym etapem systemu są detekcja i lokalizacja twarzy [5] na obrazie wejściowym. Następnie tekstura twarzy kodowana jest przy użyciu Local Binary Patterns [11] i zastosowana jako zbiór cech opisujących emocję. Maszyna wektorów wpierających [15] pełni rolę klasyfikatora w rozpoznawaniu emocji. Skuteczność przedstawionego systemu została zbadana dla zadania rozpoznawania emocji spontanicznych.

5

Color and texture image segmentation using uniform local binary patterns

Arasteh S., Hung C. C.

Machine Graphics and Vision

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2006

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Vol. 15, No. 3/4

265-274

EN

The paper describes a new algorithm for image segmentation based on the color and texture features. The Uniform Local Binary Pattern (ULBP) method is used to extract texture features. Color features are defined based on the pixels' color bands. Image segmentation is carried out using the K-means algorithm on feature vectors, including color and texture features. The distance measure is defined as a function of the color and texture feature vector distances from the K-means defined centers. The weighting parameter is used to adjust the relative contribution of the color and texture features. The proposed algorithm is applied to color images in the RGB, HSV and IHLS color spaces. Experimental results show that the proposed algorithm yields good performance in combining color and texture features to distinguish different texture patterns. In particular, for textures with high color contrast, the results are prominent. The main advantage of the method is its speed and simplicity, which are inherited from the K-means algorithm.