Visual features constitute compact yet effective representations of visual content, and are being exploited in a large number of heterogeneous applications, including augmented reality, image registration, content-based retrieval, and classification. Several visual content analysis applications are distributed over a network and require the transmission of visual data, either in the pixel or in the feature domain, to a central unit that performs the task at hand. Furthermore, large-scale applications need to store a database composed of up to billions of features and perform matching with low latency. In this context, several different implementations of feature extraction algorithms have been proposed over the last few years, with the aim of reducing computational complexity and memory footprint, while maintaining an adequate level of accuracy. Besides extraction, a large body of research addressed the problem of ad-hoc feature encoding methods, and a number of networking and transmission protocols enabling distributed visual content analysis have been proposed. In this survey, we present an overview of state-of-the-art methods for the extraction, encoding, and transmission of compact features for visual content analysis, thoroughly addressing each step of the pipeline and highlighting the peculiarities of the proposed methods.

Computer vision tasks such as recognition and classification of objects and structures or image registration and retrieval can provide significant information when applied to microscopy images. Recently developed techniques for the detection and description of local features make the extraction and description of local image features that are invariant to various changes possible. The invariance and robustness of feature detection and description techniques play a key role in the design and implementation of object recognition, image registration, or image mosaicing applications. The scale-invariant feature transform (SIFT) technique is a widely used method for the detection, description, and matching of image features. In this article we present the results of our experiments regarding the repeatability of SIFT features, and to the precision of the SIFT feature matching, under image modifications specific to confocal scanning laser microscopy (CSLM). We have analyzed the behavior of SIFT while changing the pinhole aperture, photomultiplier gain, laser beam power, and electronic zoom. Our experiments, conducted on CSLM images, show that the SIFT technique is able to match detected key points between images acquired at different values of the acquisition parameters with good precision and represents a consistent tool for computer vision applications in CSLM.

This paper describes a new approach for estimating the vehicle motion trajectory in complex urban environments by means of visual odometry. A new strategy for robust feature extraction and data post-processing is developed and tested on-road. Images from scale-invariant feature transform (SIFT) features are used in order to cope with the complexity of urban environments. The obtained results are discussed and compared to previous works. In the prototype system, the ego-motion of the vehicle is computed using a stereo-vision system mounted next to the rear view mirror of the car. Feature points are matched between pairs of frames and linked into 3D trajectories. The distance between estimations is dynamically adapted based on re-projection and estimation errors. Vehicle motion is estimated using the non-linear, photogrametric approach based on RAndom SAmple Consensus (RANSAC). The final goal is to provide on-board driver assistance in navigation tasks, or to provide a means of autonomously navigating a vehicle. The method has been tested in real traffic conditions without using prior knowledge about the scene or the vehicle motion. An example of how to estimate a vehicle's trajectory is provided along with suggestions for possible further improvement of the proposed odometry algorithm.