Besma Abidi

Bio: Besma Abidi is an academic researcher from University of Tennessee. The author has contributed to research in topics: Video tracking & Facial recognition system. The author has an hindex of 26, co-authored 93 publications receiving 2985 citations.

Gray-level grouping (GLG): an automatic method for optimized image contrast Enhancement-part I: the basic method

Abstract: This is Part II of the paper, "Gray-Level Grouping (GLG): an Automatic Method for Optimized Image Contrast Enhancement". Part I of this paper introduced a new automatic contrast enhancement technique: gray-level grouping (GLG). GLG is a general and powerful technique, which can be conveniently applied to a broad variety of low-contrast images and outperforms conventional contrast enhancement techniques. However, the basic GLG method still has limitations and cannot enhance certain classes of low-contrast images well, e.g., images with a noisy background. The basic GLG also cannot fulfill certain special application purposes, e.g., enhancing only part of an image which corresponds to a certain segment of the image histogram. In order to break through these limitations, this paper introduces an extension of the basic GLG algorithm, selective gray-level grouping (SGLG), which groups the histogram components in different segments of the grayscale using different criteria and, hence, is able to enhance different parts of the histogram to various extents. This paper also introduces two new preprocessing methods to eliminate background noise in noisy low-contrast images so that such images can be properly enhanced by the (S)GLG technique. The extension of (S)GLG to color images is also discussed in this paper. SGLG and its variations extend the capability of the basic GLG to a larger variety of low-contrast images, and can fulfill special application requirements. SGLG and its variations not only produce results superior to conventional contrast enhancement techniques, but are also fully automatic under most circumstances, and are applicable to a broad variety of images.

Multiscale Fusion of Visible and Thermal IR Images for Illumination-Invariant Face Recognition

Abstract: This paper describes a new software-based registration and fusion of visible and thermal infrared (IR) image data for face recognition in challenging operating environments that involve illumination variations. The combined use of visible and thermal IR imaging sensors offers a viable means for improving the performance of face recognition techniques based on a single imaging modality. Despite successes in indoor access control applications, imaging in the visible spectrum demonstrates difficulties in recognizing the faces in varying illumination conditions. Thermal IR sensors measure energy radiations from the object, which is less sensitive to illumination changes, and are even operable in darkness. However, thermal images do not provide high-resolution data. Data fusion of visible and thermal images can produce face images robust to illumination variations. However, thermal face images with eyeglasses may fail to provide useful information around the eyes since glass blocks a large portion of thermal energy. In this paper, eyeglass regions are detected using an ellipse fitting method, and replaced with eye template patterns to preserve the details useful for face recognition in the fused image. Software registration of images replaces a special-purpose imaging sensor assembly and produces co-registered image pairs at a reasonable cost for large-scale deployment. Face recognition techniques using visible, thermal IR, and data-fused visible-thermal images are compared using a commercial face recognition software (FaceIt®) and two visible-thermal face image databases (the NIST/Equinox and the UTK-IRIS databases). The proposed multiscale data-fusion technique improved the recognition accuracy under a wide range of illumination changes. Experimental results showed that the eyeglass replacement increased the number of correct first match subjects by 85% (NIST/Equinox) and 67% (UTK-IRIS).

Fusion of Visual and Thermal Signatures with Eyeglass Removal for Robust Face Recognition

Abstract: This paper describes a fusion of visual and thermal infrared (IR) images for robust face recognition. Two types of fusion methods are discussed: data fusion and decision fusion. Data fusion produces an illumination-invariant face image by adaptively integrating registered visual and thermal face images. Decision fusion combines matching scores of individual face recognition modules. In the data fusion process, eyeglasses, which block thermal energy, are detected from thermal images and replaced with an eye template. Three fusion-based face recognition techniques are implemented and tested: Data fusion of visual and thermal images (Df), Decision fusion with highest matching score (Fh), and Decision fusion with average matching score (Fa). A commercial face recognition software FaceIt® is used as an individual recognition module. Comparison results show that fusion-based face recognition techniques outperformed individual visual and thermal face recognizers under illumination variations and facial expressions.

An Overview of Color Constancy Algorithms

Abstract: Color constancy is one of the important research areas with a wide range of applications in the elds of color image processing and computer vision. One such application is video tracking. Color is used as one of the salient features and its robustness to illumination variation is essential to the adaptability of video tracking algorithms. Color constancy can be applied to discount the inuence of changing illuminations. In this paper, we present a review of established color constancy approaches. We also investigate whether these approaches in their present form of implementation can be applied to the video tracking problem. The approaches are grouped into two categories, namely, Pre-Calibrated and Data-driven approaches. The paper also talks about the ill-posedness of the color constancy problem, implementation assumptions of color constancy approaches, and problem statement for tracking. Publications on video tracking algorithms involving color correction or color compensation techniques are not included in this review.

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

Abstract: 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.

Image Processing

Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

When is nearest neighbor meaningful

Abstract: We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.