A New Texture Based Segmentation Method to Extract Object from Background

Extraction of object regions from complex background is a hard task and it is an essential part of image segmentation and recognition. Image segmentation denotes a process of dividing an image into different regions. Several segmentation approaches for images have been developed. Image segmentation plays a vital role in image analysis. According to several authors, segmentation terminates when the observer2019;s goal is satisfied. The very first problem of segmentation is that a unique general method still does not exist: depending on the application, algorithm performances vary. This paper studies the insect segmentation in complex background. The segmentation methodology on insect images consists of five steps. Firstly, the original image of RGB space is converted into Lab color space. In the second step 2018;a2019; component of Lab color space is extracted. Then segmentation by two-dimension OTSU of automatic threshold in 2018;a-channel2019; is performed. Based on the color segmentation result, and the texture differences between the background image and the required object, the object is extracted by the gray level co-occurrence matrix for texture segmentation. The algorithm was tested on dreamstime image database and the results prove to be satisfactory

Sign Language Tutoring Tool

In this project, we have developed a sign language tutor that lets users learn isolated signs by watching recorded videos and by trying the same signs. The system records the user's video and analyses it. If the sign is recognized, both verbal and animated feedback is given to the user. The system is able to recognize complex signs that involve both hand gestures and head movements and expressions. Our performance tests yield a 99% recognition rate on signs involving only manual gestures and 85% recognition rate on signs that involve both manual and non manual components, such as head movement and facial expressions.Comment: eNTERFACE'06. Summer Workshop. on Multimodal Interfaces, Dubrovnik : Croatie (2007

Effect of Colorspace Transformation, the Illuminance Component, and Color Modeling on Skin Detection

motion analysis. It is commonly performed in three steps: transforming the pixel color to a non-RGB colorspace, dropping the illuminance component of skin color, and classifying by modeling the skin color distribution. In this paper, we evaluate the effect of these three steps on the skin detection performance. The importance of this study is a new comprehensive colorspace and color modeling testing methodology that would allow for making the best choices for skin detection. Combinations of nine colorspaces, the presence of the absence of the illuminance component, and the two color modeling approaches are compared. The performance is measured by using a receiver operating characteristic (ROC) curve on a large dataset of 805 images with manual ground truth. The results reveal that (1) colorspace transformations can improve performance in certain instances, (2) the absence of the illuminance component decreases performance, and (3) skin color modeling has a greater impact than colorspace transformation. We found that the best performance was obtained by transforming the pixel color to the SCT or HSI colorspaces, keeping the illuminance component, and modeling the color with the histogram approach

Segmentação de pele em imagens digitais para a detecção automática de conteúdo ofensivo

This work presents a study of suitable approaches for automatic detection of offensive content (pornography) in digital images. Extensive experiments were conducted for skin pixel segmentation, colour spaces and content descriptors. This work focus its efforts on skin pixel segmentation, since this segmentation is the pre-processing stage for almost every content-based offensive image classification methods in the literature. Four skin skin segmentation methods were tested in six colour spaces. Also, a structured image database was built to help improve studies in skin segmentation, with the possibility of adding meta-information to the images in the database, such as illumination conditions and camera standards. With the help of meta information from the image database, experimets involving illumination conditions and skin colour segmentation were also done. Finally, some feature extraction algorithms were implemented in order to apply content-based image retrieval (CBIR) algorithms to classify offensive images.CAPES; UOLO presente trabalho tem como objetivo estudar meios de efetuar a detecção automática de conteúdo ofensivo (pornografia) em imagens digitais. Para tal estudou-se largamente segmentação de pixels de pele, espaços de cor e descritores de conteúdo. Esse trabalho tem um foco maior na segmentação de pele, pois é a etapa primordial nos trabalhos envolvendo detecção de conteúdo ofensivo. Testou-se quatro métodos de segmentação de pixels de pele e foi construído um banco de dados estruturado para o estudo de segmentação de pele, com meios de anotação de imagens para auxiliar na estruturação e no controle das características das imagens do banco. Com o auxílio das metainformações do banco de imagens, foram conduzidos estudos envolvendo as condições de iluminação e a segmentação de pele. Por fim, foi implementado um algoritmo de extração de características em sistemas de classificação pelo conteúdo de imagens (CBIR) para detecção de conteúdo ofensivo