From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

When one captures images in low-light conditions, the images often suffer from low visibility. Besides degrading the visual aesthetics of images, this poor quality may also significantly degenerate the performance of many computer vision and multimedia algorithms that are primarily designed for high-quality inputs. In this paper, we propose a simple yet effective low-light image enhancement (LIME) method. More concretely, the illumination of each pixel is first estimated individually by finding the maximum value in R, G, and B channels. Furthermore, we refine the initial illumination map by imposing a structure prior on it, as the final illumination map. Having the well-constructed illumination map, the enhancement can be achieved accordingly. Experiments on a number of challenging low-light images are present to reveal the efficacy of our LIME and show its superiority over several state-of-the-arts in terms of enhancement quality and efficiency.

https://ieeexplore.ieee.org/ielaam/83/7763897/7782813-aam.pdf

LIME: Low-Light Image Enhancement via Illumination Map Estimation

Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one--after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges.

http://www.cvc.uab.es/~asappa/publications/J__IEEE_PAMI_vol_32_Issue_7_July_2010.pdf

Survey of Pedestrian Detection for Advanced Driver Assistance Systems

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

The Foundations of Statistics By Prof. Leonard J. Savage. (Wiley Publications in Statistics.) Pp. xv + 294. (New York; John Wiley and Sons, Inc.; London: Chapman and Hall, Ltd., 1954.) 48s. net.

Probability and Statistical Inference

In this paper we present a parametric model for automatic color naming where each color category is modeled as a fuzzy set with a parametric membership function. The parameters of the functions are estimated in a fitting process using data derived from psychophysical experiments. The name assignments obtained by the model agree with previous psychophysical experiments, and therefore the high-level color-naming information provided can be useful for different computer vision applications where the use of a parametric model will introduce interesting advantages in terms of implementation costs, data representation, model analysis, and model updating.

/pdf/parametric-fuzzy-sets-for-automatic-color-naming-542tvd2a2m.pdf

Parametric fuzzy sets for automatic color naming

Road segmentation is an essential functionality for supporting advanced driver assistance systems (ADAS) such as road following or vehicle detection and tracking. Significant efforts have been made in order to solve this task using vision-based techniques. One of the major challenges of these techniques is dealing with lighting variations, especially shadows. Many of the approaches presented within this field use ad-hoc mechanisms applied after an initial segmentation to recover shadowed road patches. In this paper, we present an innovative method to obtain a road segmentation algorithm robust to extreme shadow conditions. The novelty of the proposal is the use of a shadowless feature space in combination with a model-based region growing algorithm. The former projects the color images such that the shadow effect is greatly attenuated. The latter uses histogram models to label the pixels as belonging to the road or to the background. These models are constructed on a frame by frame basis independently of the road shape to avoid limitations when addressing unstructured roads. The results presented show the validity of our approach.

http://www.cvc.uab.es/adas/publications/alvarez_2008.pdf

Illuminant-invariant model-based road segmentation

A good visibility of the road ahead is a major issue for safe nighttime driving. However, high beams are sparsely used because drivers are afraid of dazzling others. Thus, the intelligent automatic control of vehicles' headlight is of great relevance. It requires the detection of oncoming and preceding vehicles up to such a distance that only camera based approaches are reliable. At nighttime, detecting vehicles using a camera requires to identify their head or tail lights. The main challenge of this approach is to distinguish these lights from reflections due to infrastructure elements. In this paper we confront such a challenge by using a novel image sensor also suitable for other driver assistance applications. Different appearance features obtained from that sensor are used as input to a novel classifier---based module which, for each detected target, yields a degree of resemblance to a vehicle light. This resemblance is integrated in time using a novel temporal coherence analysis which allows to react in one single frame for targets that are clear vehicle lights, or in only a few frames for those whose type is more difficult to discern.

/pdf/nighttime-vehicle-detection-for-intelligent-headlight-10thbxzue9.pdf

Nighttime Vehicle Detection for Intelligent Headlight Control

In this paper we approach the problem of fast surface grading of flat pieces decorated with random patterns. The proposed method is based on the use of global statistics of color computed in the CIE Lab space. Two other fast methods based on color histograms [1] and Centile-LBP features [8] are introduced for comparison purposes. We used CIE Lab in order to provide accuracy and perceptual approach in color difference computation. Experiments with RGB were also carried out to study CIE Lab reliability. The ground truth was provided through an image database of ceramic tiles. Nevertheless, the approach is suitable to be extended to other random decorated surfaces like marble, granite, wood or textile stuff. The experiments make us to conclude that a simple collection of global statistics of color in the CIE Lab space is powerful enough to well discriminate surface grades. The average success surpasses 95% in most of the tests, improving literature methods and achieving factory compliance.

Fast surface grading using color statistics in the CIE lab space

Although colour naming has been studied from different points of view, the automation of this visual task has not been an active topic until recently. In computer vision, colour naming has been posed as a fuzzy-set problem where each colour category is modelled by a function that assigns a membership value to any given sample. However, the success in the automation of this process relies on having an appropriate psycho-physical data set for this purpose. Up to the present, such a dataset has not been available. In this paper we present a data set obtained from a colour-naming experiment. In this experiment, we have used a scoring method to collect a set of judgements adequate for the fuzzy modelling of the colour-naming task. The data set is composed of 387 colour reflectances, their CIELab and Munsell values, and the corresponding judgements provided by the subjects in the experiment. These judgements are the membership values to the eleven basic colour categories proposed by Berlin and Kay 1 . All these data have been made available online (http://www.cvc.uab.es/color_naming) and, in this paper we provide a wide analysis of it. To prove the suitability of the proposed scoring methodology, we have computed a set of common statistics in colour-naming experiments, such as consensus and consistency, on our dataset. The results make us to conclude the coherence of our data with previous experiments and, thus, its usefulness for the fuzzy modelling of colour naming.

/pdf/a-data-set-for-fuzzy-colour-naming-3jyw7pj7qr.pdf

Ramon Baldrich

Papers

Parametric fuzzy sets for automatic color naming

Illuminant-invariant model-based road segmentation

Nighttime Vehicle Detection for Intelligent Headlight Control

Fast surface grading using color statistics in the CIE lab space

A data set for fuzzy colour naming