Theory of fuzzy integrals and its applications

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Remote Sensing And Image Interpretation

We present an efficient method for detecting anomalies in videos. Recent applications of convolutional neural networks have shown promises of convolutional layers for object detection and recognition, especially in images. However, convolutional neural networks are supervised and require labels as learning signals. We propose a spatiotemporal architecture for anomaly detection in videos including crowded scenes. Our architecture includes two main components, one for spatial feature representation, and one for learning the temporal evolution of the spatial features. Experimental results on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of our method is comparable to state-of-the-art methods at a considerable speed of up to 140 fps.

Abnormal Event Detection in Videos using Spatiotemporal Autoencoder

Skin detection from images, typically used as a preprocessing step, has a wide range of applications such as dermatology diagnostics, human computer interaction designs, and etc. It is a challenging problem due to many factors such as variation in pigment melanin, uneven illumination, and differences in ethnicity geographics. Besides, age and gender introduce additional difficulties to the detection process. It is hard to determine whether a single pixel is skin or nonskin without considering the context. An efficient traditional hand-engineered skin color detection algorithm requires extensive work by domain experts. Recently, deep learning algorithms, especially convolutional neural networks (CNNs), have achieved great success in pixel-wise labeling tasks. However, CNN-based architectures are not sufficient for modeling the relationship between pixels and their neighbors. In this letter, we integrate recurrent neural networks (RNNs) layers into the fully convolutional neural networks (FCNs), and develop an end-to-end network for human skin detection. In particular, FCN layers capture generic local features, while RNN layers model the semantic contextual dependencies in images. Experimental results on the COMPAQ and ECU skin datasets validate the effectiveness of the proposed approach, where RNN layers enhance the discriminative power of skin detection in complex background situations.

Combining Convolutional and Recurrent Neural Networks for Human Skin Detection

Automatic segmentation of dermoscopy images using saliency combined with Otsu threshold.

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.

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

Despite its associated challenges, the development of mechanisms for storing, indexing, transmitting and visualizing multimedia content is crucial in order to efficiently deal with the large amount of data generated by several different sources. Digital video technology has advanced rapidly, such that temporal video segmentation methods for automatically detecting transitions in video sequences play an important role in the content analysis tasks. In this work, we propose and evaluate a video shot boundary detection approach based on the Weber local descriptor. Experiments conducted on different datasets demonstrate the effectiveness of our method, whose results are compared against other approaches of the literature.

Shot boundary detection for video temporal segmentation based on the weber local descriptor

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.

Human Skin Segmentation Improved by Saliency Detection

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.

Adaptive video shot detection improved by fusion of dissimilarity measures

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.

Anderson Carlos Sousa e Santos

Papers

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

Shot boundary detection for video temporal segmentation based on the weber local descriptor

Human Skin Segmentation Improved by Saliency Detection

Adaptive video shot detection improved by fusion of dissimilarity measures

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