Helmut E. Bez

A survey of cast shadow detection algorithms

Cast shadows need careful consideration in the development of robust dynamic scene analysis systems. Cast shadow detection is critical for accurate object detection in video streams, and their misclassification can cause errors in segmentation and tracking. Many algorithms for shadow detection have been proposed in the literature; however a complete, comparative evaluation of existing approaches is lacking. This paper presents a comprehensive survey of shadow detection methods, organised in a novel taxonomy based on object/environment dependency and implementation domain. In addition a comparative evaluation of representative algorithms, based on quantitative and qualitative metrics is presented to evaluate the algorithms on a benchmark suite of indoor and outdoor video sequences.

A novel algorithm for initial lesion detection in ultrasound breast images

This paper proposes a novel approach to initial lesion detection in ultrasound breast images. The objective is to automate the manual process of region of interest (ROI) labeling in computer‐aided diagnosis (CAD). We propose the use of hybrid filtering, multifractal processing, and thresholding segmentation in initial lesion detection and automated ROI labeling. We used 360 ultrasound breast images to evaluate the performance of the proposed approach. Images were preprocessed using histogram equalization before hybrid filtering and multifractal analysis were conducted. Subsequently, thresholding segmentation was applied on the image. Finally, the initial lesions are detected using a rule‐based approach. The accuracy of the automated ROI labeling was measured as an overlap of 0.4 with the lesion outline as compared with lesions labeled by an expert radiologist. We compared the performance of the proposed method with that of three state‐of‐the‐art methods, namely, the radial gradient index filtering technique, the local mean technique, and the fractal dimension technique. We conclude that the proposed method is more accurate and performs more effectively than do the benchmark algorithms considered. PACS numbers: 87.57.Nk

Two-dimensional statistical linear discriminant analysis for real-time robust vehicle-type recognition

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic License Plate Recognition (ALPR) systems. Several car MMR systems have been proposed in literature. However these approaches are based on feature detection algorithms that can perform sub-optimally under adverse lighting and/or occlusion conditions. In this paper we propose a real time, appearance based, car MMR approach using Two Dimensional Linear Discriminant Analysis that is capable of addressing this limitation. We provide experimental results to analyse the proposed algorithms robustness under varying illumination and occlusions conditions. We have shown that the best performance with the proposed 2D-LDA based car MMR approach is obtained when the eigenvectors of lower significance are ignored. For the given database of 200 car images of 25 different make-model classifications, a best accuracy of 91% was obtained with the 2D-LDA approach. We use a direct Principle Component Analysis (PCA) based approach as a benchmark to compare and contrast the performance of the proposed 2D-LDA approach to car MMR. We conclude that in general the 2D-LDA based algorithm supersedes the performance of the PCA based approach.

A simple finite element model for cloth drape simulation

Uses Newmark’s method to carry out a time‐stepping finite element analysis to predict the behaviour of a cloth garment as it falls from an initial horizontal position to a final position draped around a human body form. Bases the finite element model on a simple beam element, in order to minimize the computational time. Accounts for large displacement behaviour by including the element geometric stiffness. Bases the body form on anthropomorphic data produced by a shadow scanner. Enlists a novel scheme to model the contact between the cloth and the underlying body form. Uses the finite element model to provide data for an animated display and finds that it produces sufficiently realistic results for the garment designer’s purposes.

Processed images in human perception: A case study in ultrasound breast imaging

Two main research efforts in early detection of breast cancer include the development of software tools to assist radiologists in identifying abnormalities and the development of training tools to enhance their skills. Medical image analysis systems, widely known as Computer-Aided Diagnosis (CADx) systems, play an important role in this respect. Often it is important to determine whether there is a benefit in including computer-processed images in the development of such software tools. In this paper, we investigate the effects of computer-processed images in improving human performance in ultrasound breast cancer detection (a perceptual task) and classification (a cognitive task). A survey was conducted on a group of expert radiologists and a group of non-radiologists. In our experiments, random test images from a large database of ultrasound images were presented to subjects. In order to gather appropriate formal feedback, questionnaires were prepared to comment on random selections of original images only, and on image pairs consisting of original images displayed alongside computer-processed images. We critically compare and contrast the performance of the two groups according to perceptual and cognitive tasks. From a Receiver Operating Curve (ROC) analysis, we conclude that the provision of computer-processed images alongside the original ultrasound images, significantly improve the perceptual tasks of non-radiologists but only marginal improvements are shown in the perceptual and cognitive tasks of the group of expert radiologists.

An extended H.264 CODEC for stereoscopic video coding

We propose an extension to the H.264 video coding standard, which is capable of efficiently coding stereoscopic video sequences. In contrast to previous techniques, the proposed Stereoscopic Video CODEC uses a single modified H.264 encoder and a single modified H.264 decoder in its design. The left (reference) and right (predicted) sequences are fed alternatively to the encoder. The modified H.264 encoder uses a Decoded Picture Buffer Store (DPBS) in addition to the regular DPB of the original H.264 encoder. An effective buffer management strategy between DPBS and DPB is used so that the left sequence frames are coded only based on its previously coded frames while the right frames are coded based on previously coded frames from both left and right sequences. We show that the proposed CODEC has the capability of exploiting worldline correlation present in stereo video sequences, in addition to the exploitation of joint spatial-temporal-binocular correlation. Further we show that the coded bit stream fully conforms to a standard H.264 bit-stream and a standard H.264 decoder will be able to effectively decode the left video stream ignoring the right. We provide experimental results on two popular test stereoscopic video sequences to prove the efficiency of the proposed CODEC

A garment design system using constrained Bézier curves

A CAD tool for the garment industry is described. The tool generates garment patterns using Bezier curves and is currently embedded within an intelligent knitwear design support system. The curves fulfil the design constraints imposed by the domain, are adaptable to individual styles and enable intuitive manipulation by the user. The system described is designed primarily to provide a means of improved communication between designers and technicians but it has the potential to become a key component in a bespoke design system.

Fully Automatic Lesion Boundary Detection in Ultrasound Breast Images

We propose a novel approach to fully automatic lesion boundary detection in ultrasound breast images. The novelty of the proposed work lies in the complete automation of the manual process of initial Region-of-Interest (ROI) labeling and in the procedure adopted for the subsequent lesion boundary detection. Histogram equalization is initially used to pre-process the images followed by hybrid filtering and multifractal analysis stages. Subsequently, a single valued thresholding segmentation stage and a rule-based approach is used for the identification of the lesion ROI and the point of interest that is used as the seed-point. Next, starting from this point an Isotropic Gaussian function is applied on the inverted, original ultrasound image. The lesion area is then separated from the background by a thresholding segmentation stage and the initial boundary is detected via edge detection. Finally to further improve and refine the initial boundary, we make use of a state-of-the-art active contour method (i.e. gradient vector flow (GVF) snake model). We provide results that include judgments from expert radiologists on 360 ultrasound images proving that the final boundary detected by the proposed method is highly accurate. We compare the proposed method with two existing state-of- the-art methods, namely the radial gradient index filtering (RGI) technique of Drukker et. al. and the local mean technique proposed by Yap et. al., in proving the proposed methods robustness and accuracy.

Object Boundary Detection in Ultrasound Images

This paper presents a novel approach to boundary detection of regions-of-interest (ROI) in ultrasound images, more specifically applied to ultrasound breast images. In the proposed method, histogram equalization is used to preprocess the ultrasound images followed by a hybrid filtering stage that consists of a combination of a nonlinear diffusion filter and a linear filter. Subsequently the multifractal dimension is used to analyse the visually distinct areas of the ultrasound image. Finally, using different threshold values, region growing segmentation is used to the partition the image. The partition with the highest Radial Gradient Index (RGI) is selected as the lesion. A total of 200 images have been used in the analysis of the presented results. We compare the performance of our algorithm with two well known methods proposed by Kupinski et al. and Joo et al. We show that the proposed method performs better in solving the boundary detection problem in ultrasound images.

Baseline JPEG-like DWT CODEC for disparity compensated residual coding of stereo images

We propose a novel stereo image coding technique, which uses an architecture similar to that of a discrete cosine transform (DCT) based baseline JPEG-CODEC (Pennebaker and Mitchell, 1993), but effectively replaces the DCT technology by the more recently popularized discrete wavelet transform (DWT) technology. We show that as a result of this hybrid design, which combines the advantage of two popular technologies, the proposed CODEC has improved rate distortion and subjective image quality performance as compared to DCT based stereo image compression techniques (Perkins, 1992). In particular, at very low bit rates (0.15 bpp), we report peak-signal-to-noise-ratio (PSNR) gains of up to 3.66 dB, whereas at higher bit rates we report gains in the order of 1 dB.