Automatically changing the expression and physical features of a face from an input image is a topic that has been traditionally tackled in a 2D domain. In this paper, we bring this problem to 3D and propose a framework that given an input RGB video of a human face under a neutral expression, initially computes his/her 3D shape and then performs a transfer to a new and potentially non-observed expression. For this purpose, we parameterize the rest shape -obtained from standard factorization approaches over the input video- using a triangular mesh which is further clustered into larger macro-segments. The expression transfer problem is then posed as a direct mapping between this shape and a source shape, such as the blend shapes of an off-the-shelf 3D dataset of human facial expressions. The mapping is resolved to be geometrically consistent between 3D models by requiring points in specific regions to map on semantic equivalent regions. We validate the approach on several synthetic and real examples of input faces that largely differ from the source shapes, yielding very realistic expression transfers even in cases with topology changes, such as a synthetic video sequence of a single-eyed cyclops.

Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning. Current vision-based road detection methods are usually based solely on low-level features. Furthermore, they generally assume structured roads, road homogeneity, and uniform lighting conditions, constraining their applicability in real-world scenarios. In this paper, road priors and contextual information are introduced for road detection. First, we propose an algorithm to estimate road priors online using geographical information, providing relevant initial information about the road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and road geometry, are used in addition to low-level cues derived from the appearance of roads. Finally, a generative model is used to combine these cues and priors, leading to a road detection method that is, to a large degree, robust to varying imaging conditions, road types, and scenarios.

This paper presents the combined use of gradient and mutual information for infrared and intensity templates matching. We propose to joint: (i) feature matching in a multiresolution context and (ii) information propagation through scale-space representations. Our method consists in combining mutual information with a shape descriptor based on gradient, and propagate them following a coarse-to-fine strategy. The main contributions of this work are: to offer a theoretical formulation towards a multimodal stereo matching; to show that gradient and mutual information can be reinforced while they are propagated between consecutive levels; and to show that they are valid cost functions in multimodal template matchings. Comparisons are presented showing the improvements and viability of the proposed approach.

Road detection is a vital task for the development of autonomous vehicles. The knowledge of the free road surface ahead of the target vehicle can be used for autonomous driving, road departure warning, as well as to support advanced driver assistance systems like vehicle or pedestrian detection. Using vision to detect the road has several advantages in front of other sensors: richness of features, easy integration, low cost or low power consumption. Common vision-based road detection approaches use low-level features (such as color or texture) as visual cues to group pixels exhibiting similar properties. However, it is difficult to foresee a perfect clustering algorithm since roads are in outdoor scenarios being imaged from a mobile platform. In this paper, we propose a novel high-level approach to vision-based road detection based on geographical information. The key idea of the algorithm is exploiting geographical information to provide a rough detection of the road. Then, this segmentation is refined at low-level using color information to provide the final result. The results presented show the validity of our approach.

Photometric stereo aims at finding the surface normal and reflectance at every point of an object from a set of images obtained under different lighting conditions. The obtained intensity image data are stacked into a matrix that can be approximated by a low-dimensional linear subspace, under the Lambertian model. The current paper proposes to use an adaptation of the Alternation technique to tackle this problem when the images contain missing data, which correspond to pixels in shadow and saturated regions. Experimental results considering both synthetic and real images show the good performance of the proposed Alternation-based strategy.

This paper describes the ICAR system, an application for automatic reading of identity cards and passports. The system acquires the image of the document by a flatbed scanner and recognizes the type of the document among a set of predefined models using color information. Textual fields are located in the image by a connected component analysis and identified in terms of their structural arrangement. A set of complementary statistical and structural OCR techniques are combined by a voting strategy to read each text image region. For unknown input documents, lines compliant with machine readable ICAO 9303 format are located and recognized. Although the system has been initially designed for Spanish documents, it allows the integration of new formats by a supervised learning procedure. The system is currently installed as a check-in application in a real environment.

This paper proposes an improvement on a well-known colour normalisation by the introduction of some knowledge on background. Comprehensive normalisation gives an invariant representation of the image colour. This invariant representation can be considered a canonical representation whenever image content is preserved and changes are only due to illuminant conditions. One of the steps of the normalisation is based on the grey-world normalisation that removes colour changes on each channel. Because a diagonal model is assumed, the independence of chromatic variations is also achieved if the channel normalisation is applied only with background mean in spite of image mean. This will allow one to remove illuminant effects meanwhile no influence from the foreground is introduced on the normalised coordinates. It will provide an almost canonical colour space without an explicit estimation of the scene illuminant.

A real person description application needs, as a first step, a robust process of people location. To locate people it is required to find out the subject's position and extent. This paper presents a robust architecture for solving this problem in static images. The work presented combines in a probabilistic manner, information from background, skin and shape models. This method has been tested in a large set of images for a real application.

Ridges and valleys are useful geometric features for image analysis. Different characterizations have been proposed to formalize the intuitive notion of ridge/valley. In this paper, we review their principal characterizations and propose a new one. Subsequently, we evaluate these characterizations with respect to a list of desirable properties and their purpose in the context of representative image analysis tasks.