Anderson Carlos Sousa e Santos

Adaptive video shot detection improved by fusion of dissimilarity measures

Due to the large amount of videos generated through several data sources, the development of efficient mechanisms for storing, indexing, retrieving and visualizing their content is a challenging task. Temporal video segmentation is the automatic process of detecting transitions in video sequences, which is a fundamental step in the analysis of video content. This work proposes and evaluates an improved shot detection method based on the fusion of multiple frame dissimilarity measures and an adaptive threshold strategy. Experimental results demonstrate that the combination of different temporal features associated with an adequate threshold estimation can substantially improve the performance of individual methods.

A Self-adaptation Method for Human Skin Segmentation based on Seed Growing

Human skin segmentation has several applications in image and video processing fields, whose main purpose is to distinguish image portions between skin and non-skin regions. Despite the large number of methods available in the literature, accurate skin segmentation is still a challenging task. Many methods rely on color information, which does not completely discriminate the image regions due to variations in lighting conditions and ambiguity between skin and background color. Therefore, there is still need to adapt the segmentation to particular conditions of the images. In contrast to the methods that rely on faces, hands or any other body content detector, we describe a self-contained method for adaptive skin segmentation that makes use of spatial analysis to produce regions from which the overall skin can be estimated. A comparison with state-of-the-art methods using a well known challenging data set shows that our method provides significant improvement on the skin segmentation.

Human Skin Segmentation Improved by Saliency Detection

Several applications demand the segmentation of images in skin and non-skin regions, such as face recognition, hand gesture detection, nudity recognition, among others. Human skin detection is still a challenging task since it depends on inumerous factors, for instance, illumination conditions, ethnicity variation and image resolution. This work proposes and analyzes a skin segmentation method improved by saliency detection. Experimental results on public data sets demonstrate significant improvement of the proposed skin segmentation method over state-of-the-art approaches.

Adaptive Video Transition Detection Based on Multiscale Structural Dissimilarity

The fast growth in the acquisition and dissemination of videos has driven the development of diverse multimedia applications, such as interactive broadcasting, entertainment, surveillance, telemedicine, among others. Due to the massive amount of generated data, a challenging task is to store, browse and retrieve video content efficiently. This work describes and analyzes a novel automatic video transition method based on multiscale inter-frame dissimilarity vectors. The shot frames are identified by means of an adaptive local threshold mechanism. Experimental results demonstrate that the proposed approach is capable of achieving high accuracy rates when applied to several video sequences.

A combination of k-means clustering and entropy filtering for band selection and classification in hyperspectral images

ABSTRACT Hyperspectral images usually have large volumes of data comprising hundreds of spectral bands. Removal of redundant bands can both reduce computational time and improve classification performance. This work proposes and analyses a band-selection method based on the k-means clustering strategy combined with a classification approach using entropy filtering. Experimental results in different terrain images show that our method can significantly reduce the number of bands while maintaining an accurate classification.

Video Temporal Segmentation Based on Color Histograms and Cross-Correlation

Several fields of knowledge generate and consume massive volumes of videos, such as entertainment, telemedicine, surveillance and security. The rapid growth in the demand for multimedia content has driven the development of fast and scalable mechanisms for storing, retrieving and transmitting video sequences. The automatic temporal segmentation is a fundamental process in the analysis of video content. This work proposes and evaluates an adaptive video shot detection based on color histograms and normalized cross-correlation. Experiments conducted on several video sequences demonstrate that the combination of these two features achieve high accuracy rates.

Human Skin Segmentation Improved by Texture Energy Under Superpixels

Several applications demand the segmentation of images in skin and non-skin regions, such as face recognition, hand gesture detection, nudity recognition, among others. Human skin detection is still a challenging task and, although color attribute is a very important clue, it usually generates high rate of false positives. This work proposes and analyzes a skin segmentation method improved by texture energy. Experimental results on a challenging public data set demonstrate significant improvement of the proposed skin segmentation method over color-based state-of-the-art approaches.

Image Thresholding Based on Fuzzy Particle Swarm Optimization

Segmentation is a crucial stage in the image analysis process, whose main purpose is to partition an image into meaningful regions of interest. Thresholding is the simplest image segmentation method, where a global or local threshold value is selected for segmenting pixels into background and foreground regions. However, the determination of a proper threshold value is typically dependent on subjective assumptions or empirical rules. In this work, we propose and analyze an image thresholding technique based on a fuzzy particle swarm optimization. Several images are used in our experiments to show the effectiveness of the developed approach.

Exploring Image Bit Planes for Video Shot Boundary Detection

The wide availability of digital content and the advances in multimedia technology have leveraged the development of efficient mechanisms for storing, indexing, transmitting, retrieving and visualizing video data. A challenging task is to automatically construct a compact representation of video sequences to help users comprehend the most relevant information present in their content. In this work, we develop and evaluate a novel method for detecting abrupt transitions based on bit planes extracted from the video frames. Experiments are conducted on two public datasets to demonstrate the effectiveness of the proposed method. Results are compared against other approaches of the literature.

Improved Human Skin Segmentation Using Fuzzy Fusion Based on Optimized Thresholds by Genetic Algorithms

Human skin segmentation has several applications in computer vision beyond its main purpose of distinguishing between skin and nonskin regions. Despite the large number of methods available in the literature, accurate skin segmentation is still a challenging task. Many methods rely only on color information, which does not completely discriminate the image regions due to variations in lighting conditions and ambiguity between skin and background color. This chapter extends upon a self-contained method for skin segmentation that outlines regions from which the overall skin color can be estimated and such that the color model is adjusted to a particular image. This process is based on thresholds that were empirically defined in a first approach. The proposed method has three main contributions over the previous one. First, genetic algorithm (GA) is applied to search for better thresholds that will be used to extract appropriate seeds from the general probability and texture maps. Next, the GA is also applied to define thresholds for edge detectors aiming to improve edge connections. Finally, a fuzzy method for fusion is included where its parameters are optimized by GA during a learning phase. The improvements added to the skin segmentation method are evaluated on a set of hand gesture images. A statistical analysis is conducted over the computational results achieved by each evaluated method, indicating a superior performance of our novel skin segmentation method.