We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space-if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed "Fisherface" method has error rates that are lower than those of the eigenface technique for tests on the Harvard and Yale face databases.

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Eigenfaces vs. Fisherfaces: recognition using class specific linear projection

As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.

Face recognition: A literature survey

We present a generative appearance-based method for recognizing human faces under variation in lighting and viewpoint. Our method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the space of images. Using a small number of training images of each face taken with different lighting directions, the shape and albedo of the face can be reconstructed. In turn, this reconstruction serves as a generative model that can be used to render (or synthesize) images of the face under novel poses and illumination conditions. The pose space is then sampled and, for each pose, the corresponding illumination cone is approximated by a low-dimensional linear subspace whose basis vectors are estimated using the generative model. Our recognition algorithm assigns to a test image the identity of the closest approximated illumination cone. Test results show that the method performs almost without error, except on the most extreme lighting directions.

From few to many: illumination cone models for face recognition under variable lighting and pose

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

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Computer Vision: Algorithms and Applications

Images containing faces are essential to intelligent vision-based human-computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face, regardless of its 3D position, orientation and lighting conditions. Such a problem is challenging because faces are non-rigid and have a high degree of variability in size, shape, color and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.

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Detecting faces in images: a survey

Ein System (100) und Verfahren (200) zum Wahlen eines Computeralgorithmus zum Verarbeiten eines medizinischen Bilds fur einen klinischen Zweck wird beigefugt. Zu dem Verfahren gehoren die Schritte: Zugreifen auf Bilddaten (210), Zugreifen auf klinische Daten (210) und Zugreifen auf eine strukturierte Informationsdatenbank (210). Ein optimaler Computeralgorithmus wird aus einer Anzahl Computeralgorithmen mit zugeordneten optimalen Betriebsparametern ausgewahlt (220). Der optimale Computeralgorithmus kann basierend auf den Bilddaten, den klinischen Daten und den Daten einer strukturierten Informationsdatenbank ausgewahlt werden. Die Bilddaten konnen mittels des optimalen Computeralgorithmus verarbeitet werden (230). Die strukturierte Informationsdatenbank kann einen endlichen Satz von Algorithmen aufweisen, der die moglichen Algorithmen fur den klinischen Zweck umfasst. Die Bilddaten konnen Metadaten und anatomische Daten beinhalten. Die Metadaten konnen Modalitatsdaten und Bildakquisitionsdaten beinhalten. Die Computeralgorithmen konnen Computeralgorithmen zum Ausfuhren von computergestutzter Detektion (670) beinhalten. Die Computeralgorithmen konnen ferner Computeralgorithmen zum Ausfuhren von volumenrechnergestutztem Auslesen (630) beinhatlen.

Verfahren und Vorrichtung zum Auswählen computergestützter Algorithmen, basierend auf dem Protokoll und/oder Parametern eines Akquisitionssystems

Methods and systems for quantification of a selected attribute of an image volume are provided. The system is configured to receive an image dataset for a volume of interest, process the dataset for a selected attribute based at least on one of shape and texture to obtain a plurality of responses, and compute an index of an aggregate of a plurality of obtained responses.

Systems for monitoring tumor burden

We propose a method that aids in the evaluation of residual strain in blood vessels. Strain measurement is important in stress analysis of cardiovascular tissues. Since its introduction in 1983, arterial residual strain is evaluated manually which is a lengthy process. This involved photography, film development, printing of large photographs, and finally digitization of the edges of the vascular rings before and after cut using a flatbed digitizing tablet. These processes are highly labor intensive and time consuming, in the order of days. In this paper we introduce an image analysis and processing technique in which arterial residual strain can be evaluated efficiently and reliably. To insure portability, a JAVA-based application is developed which is designed for a semi-automatic operation in evaluating residual strain values. The use of the image-based evaluation technique is validated by comparison of arterial residual strain evaluated using photographs and digitizing tablet.

Image-based evaluation of vascular residual strain

An imaging system includes an imaging unit, a display unit, and at least one processor. The at least one processor is configured to acquire a first type of diagnostic imaging information of the patient; reconstruct a first image using the first type of diagnostic imaging information; if a first stop criterion for terminating imaging is not satisfied, acquire a second type of diagnostic imaging information having an increased level of acquisitional burden; reconstruct a second image; if a second stop criterion for terminating imaging is not satisfied, acquire a third type of diagnostic imaging information having an increased level of acquisitional burden, wherein the patient is maintained on a table of the imaging unit during the acquisition of the second type of diagnostic imaging information, reconstruction of the second image, and acquisition of the third type of diagnostic imaging information; reconstruct a third image; and display the third image.

Systems and methods for progressive imaging

According to the invention image data is selectively processed, which are decomposed into a plurality of resolution levels. The image data may be retrieved in the order of progressively higher levels of resolution for display in a display field, which can have a viewport resolution that is lower than a desired one of levels of resolution or higher. At the beginning of the image data in the display field to a correspondingly lower level of the plurality of resolution levels can be displayed. When a user wishes higher resolution display of all or a part of the image, one or more of the progressively higher levels of resolution can be retrieved and scaled to fit within the viewport. Accordingly, the invention processes the data more effectively by accessing the image data in blocks corresponding to the plurality of resolution levels, instead of access to the entire image data. It can also be used a scaling threshold value for signaling to the requirement of the next higher stage of the plurality of resolution levels.

Saad Ahmed Sirohey

Papers

Verfahren und Vorrichtung zum Auswählen computergestützter Algorithmen, basierend auf dem Protokoll und/oder Parametern eines Akquisitionssystems

Systems for monitoring tumor burden

Image-based evaluation of vascular residual strain

Systems and methods for progressive imaging

Method and apparatus for transmitting and displaying a compressed digitized image