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PubMed2006

Geodesic matching of triangulated surfaces.

Recognition of images and shapes has long been the central theme of computer vision. Its importance is increasing rapidly in the field of computer graphics and multimedia communication because it is difficult to process information efficiently without its recognition. In this paper, we propose a new approach for object matching based on a global geodesic measure. The key idea behind our methodology is to represent an object by a probabilistic shape descriptor that measures the global geodesic distance between two arbitrary points on the surface of an object. In contrast to the Euclidean distance which is more suitable for linear spaces, the geodesic distance has the advantage to be able to capture the intrinsic geometric structure of the data. The matching task therefore becomes a one-dimensional comparison problem between probability distributions which is clearly much simpler than comparing three-dimensional structures. Object matching can then be carried out by an information-theoretic dissimilarity measure calculations between geodesic shape distributions, and is additionally computationally efficient and inexpensive.

Algorithms↗
PubMed1999

A space-time delay neural network for motion recognition and its application to lipreading.

Motion recognition has received increasing attention in recent years owing to heightened demand for computer vision in many domains, including the surveillance system, multimodal human computer interface, and traffic control system. Most conventional approaches classify the motion recognition task into partial feature extraction and time-domain recognition subtasks. However, the information of motion resides in the space-time domain instead of the time domain or space domain independently, implying that fusing the feature extraction and classification in the space and time domains into a single framework is preferred. Based on this notion, this work presents a novel Space-Time Delay Neural Network (STDNN) capable of handling the space-time dynamic information for motion recognition. The STDNN is unified structure, in which the low-level spatiotemporal feature extraction and high-level space-time-domain recognition are fused. The proposed network possesses the spatiotemporal shift-invariant recognition ability that is inherited from the time delay neural network (TDNN) and space displacement neural network (SDNN), where TDNN and SDNN are good at temporal and spatial shift-invariant recognition, respectively. In contrast to multilayer perceptron (MLP), TDNN, and SDNN, STDNN is constructed by vector-type nodes and matrix-type links such that the spatiotemporal information can be accurately represented in a neural network. Also evaluated herein is the performance of the proposed STDNN via two experiments. The moving Arabic numerals (MAN) experiment simulates the object's free movement in the space-time domain on image sequences. According to these results, STDNN possesses a good generalization ability with respect to the spatiotemporal shift-invariant recognition. In the lipreading experiment, STDNN recognizes the lip motions based on the inputs of real image sequences. This observation confirms that STDNN yields a better performance than the existing TDNN-based system, particularly in terms of the generalization ability. In addition to the lipreading application, the STDNN can be applied to other problems since no domain-dependent knowledge is used in the experiment.

Algorithms↗
PubMed1998

Computer assisted orthopaedic surgery. Image guided and robotic assistive technologies.

Technologies are emerging that will influence the way in which orthopaedic surgery is planned, simulated, and performed. Recent advances in the fields of medical imaging, computer vision, and robotics have provided the enabling technologies to permit computer aided surgery to become an established area which can address clinical needs. Although these technologies have been applied in industry for more than 20 years, the field of computer assisted orthopaedic surgery is still in its infancy. Image guided and surgical navigation systems, robotic assistive devices, and surgical simulators have begun to emerge from the laboratory and hold the potential to improve current surgical practice and patients' outcomes. The goals of these new clinically focused technologies are to develop interactive, patient specific preoperative planners to optimize the performance of surgery and the postoperative biologic response, and develop more precise and less invasive interactive smart tools and sensors to assist in the accurate and precise performance of surgery. The medical community is beginning to see the benefit of these enabling technologies which can be realized only through the collaboration and combined expertise of engineers, roboticists, computer scientists, and surgeons.

Biomedical Engineering↗
PubMed1988

Integration of depth modules: stereo and shading.

We studied the integration of image disparities, edge information, and shading in the three-dimensional perception of complex yet well-controlled images generated with a computer-graphics system. The images showed end-on views of flat- and smooth-shaded ellipsoids, i.e., images with and without intensity discontinuities (edges). A map of perceived depth was measured by adjusting a small stereo depth probe interactively to the perceived surface. Our data show that disparate shading (even in the absence of disparate edges) yields a vivid stereoscopic depth perception. The perceived depth is significantly reduced if the disparities are completely removed (shape-from-shading). If edge information is available, it overrides both shape-from-shading and disparate shading. Degradations of depth perception corresponded to a reduced depth rather than to an increased scatter in the depth measurement. The results are compared with computer-vision algorithms for both single cues and their integration for three-dimensional vision.

Computer Graphics↗
PubMed2002

Comparison of tests of accommodation for computer users.

BACKGROUND: With the increased use of computers in the workplace and at home, optometrists are finding more patients presenting with symptoms of Computer Vision Syndrome. Among these symptomatic individuals, research supports that accommodative disorders are the most common vision finding. METHODS: A prepresbyopic group (N= 30) and a presbyopic group (N = 30) were selected from a private practice. Assignment to a group was determined by age, accommodative amplitude, and near visual acuity with their distance prescription. Each subject was given a thorough vision and ocular health examination, then administered several nearpoint tests of accommodation at a computer working distance. RESULTS: All the tests produced similar results in the presbyopic group. For the prepresbyopic group, the tests yielded very different results. CONCLUSION: To effectively treat symptomatic VDT users, optometrists must assess the accommodative system along with the binocular and refractive status. For presbyopic patients, all nearpoint tests studied will yield virtually the same result. However, the method of testing accommodation, as well as the test stimulus presented, will yield significantly different responses for prepresbyopic patients. Previous research indicates that a majority of patients prefer the higher plus prescription yielded by the Gaussian image test.

Accommodation, Ocular↗
PubMed2001

The use of optical flow to characterize muscle contraction.

Muscle contraction is usually measured and characterized with force and displacement transducers. The contraction of muscle fibers, however, evokes in the tissue a two and even three-dimensional displacement field, which is not properly quantified by these transducers because they provide just a single scalar quantity. This problem can be circumvented by using optical measurements and standard tools of computer vision, developed for the analysis of time varying image sequences. By computing the so called optical flow, i.e. the apparent motion of points in a time varying image sequence, it is possible to recover a two-dimensional motion field, describing rather precisely the displacement caused by muscle contraction in a flattened piece of skin. The obtained two-dimensional optical flow can be further analyzed by computing its elementary deformation components, providing a novel and accurate characterization of the contraction induced by different motoneurons. This technique is demonstrated analyzing the displacement caused by muscle contraction in the skin of the leech, Hirudo medicinalis. The proposed technique can be applied to monitor and characterize all contractions in almost flat tissues with enough visual texture.

Action Potentials↗
PubMed1988

Computational studies of the extraction of visual spatial information from binocular and motion cues.

This paper reviews some of the contributions that work in computational vision has made to the study of biological vision systems. We concentrate on two areas where there has been strong interaction between computational and experimental studies: the use of binocular stereo to recover the distances to surfaces in space, and the recovery of the three-dimensional shape of objects from relative motion in the image. With regard to stereo, we consider models proposed for solving the stereo correspondence problem, focussing on the way in which physical properties of the world constrain possible methods of solution. We also show how critical observations regarding human stereo vision have helped to shape these models. With regard to the recovery of structure from motion, we focus on how the constraint of object rigidity has been used in computational models of this process.

Humans↗
PubMed1992

Artificial vision approach to the understanding of heart motion.

To overcome the major drawbacks of conventional descriptive methods, we have developed a computer vision approach to aid understanding of heart motion from a series of sequential X-ray images. The computation is addressed of local descriptors of the heart pumping function from ventricular contours. Physical constraints are exploited such as spatial smoothness of the displacement field and shape correspondence between ventricular boundaries during the beat. A computational method is proposed for the estimation of the displacement field of the left ventricular boundary. Moreover, the spatial arrangement of the estimated motion field is rendered explicit so that it may be utilized in the medical clinic (or for high-level symbolic processing). This is achieved using a grouping criterion which allows the clustering of contiguous points of the left ventricular outline into curve segments which have homogeneous motion properties.

Algorithms↗
PubMed2004

From FNS to HEIV: a link between two vision parameter estimation methods.

Problems requiring accurate determination of parameters from image-based quantities arise often in computer vision. Two recent, independently developed frameworks for estimating such parameters are the FNS and HEIV schemes. Here, it is shown that FNS and a core version of HEIV are essentially equivalent, solving a common underlying equation via different means. The analysis is driven by the search for a nondegenerate form of a certain generalized eigenvalue problem and effectively leads to a new derivation of the relevant case of the HEIV algorithm. This work may be seen as an extension of previous efforts to rationalize and interrelate a spectrum of estimators, including the renormalization method of Kanatani and the normalized eight-point method of Hartley.

Algorithms↗
PubMed1991

Reliability of cephalometric analysis using manual and interactive computer methods.

This study compares the results of cephalometric analyses using manual and interactive computer graphics methods. Results are statistically in favour of the interactive computer system. This study provides a basis for ongoing research into alternative methods of cephalometric analyses, such as digitization and automatic landmark identification using sophisticated computer vision systems.

Cephalometry↗
PubMed2000

Computer-aided diagnosis of small pulmonary nodules.

Computer-aided methods are now being developed for the detection and characterization of pulmonary nodules found in CT images, based on techniques from computer vision, image processing, and pattern classification. With the increasing resolution of modern CT scanners, computer methods provide continually improving accuracy, reproducibility, and utility in analyzing the larger numbers of images acquired in a lung screening exam or diagnostic study. This article describes the fundamental tools and issues involved in computer-aided nodule detection and characterization, as we move from two-dimensional toward three-dimensional automated methods. In particular, we focus on the new domain of "small" pulmonary nodules.

Artificial Intelligence↗
PubMed2006

A systolic algorithm for Euclidean distance transform.

The Euclidean distance transform is one of the fundamental operations in image processing. It has been widely used in computer vision, pattern recognition, morphological filtering, and robotics. This paper proposes a systolic algorithm that computes the Euclidean distance map of an N x N binary image in 3N clocks on 2N(2) processing cells. The algorithm is designed so that the hardware resources are reduced; especially no mulitipliers are used and, thus, it facilitates VLSI implementation.

Algorithms↗
PubMed1999

Interaction of ON and OFF pathways for visual contrast measurement.

We propose a novel model of visual contrast measurement based on segregated On and Off pathways. Two driving forces have shaped our investigation: (1) establishing a mechanism selective for sharp local transitions in the luminance distribution; (2) generating a robust scheme of oriented contrast detection. Our starting point was the architecture of early stages in the mammalian visual system. We show that the circuit behaves as a soft AND-gate and analyze the scale-space selectivity properties of the model in detail. The theoretical analysis is supplemented by computer simulations in which we selectively investigate key functionalities of the proposed contrast detection scheme. We demonstrate that the model is capable of successfully processing synthetic as well as natural images, thus illustrating the potential of the method for computer vision applications.

Computer Simulation↗
PubMed1991

[Assessment of vision using a computer].

1. The authors elaborated and tested a computer method for assessment of vision. This method is compatible with the basic procedure for assessment of vision and its variability. 2. The authors recommend to supplement values of vision by the parameter of line steepness. This parameter is independent on the value of vision and gives the number of normalized lines on which the number of correctly assessed optotypes declines from 95 to 5%.

Adult↗
PubMed2004

Specifying colours for colour vision testing using computer graphics.

AIMS: This paper describes a novel test of colour vision using a standard personal computer, which is simple and reliable to perform. METHODS: Twenty healthy individuals with normal colour vision and 10 healthy individuals with a red/green colour defect were tested binocularly at 13 selected points in the CIE (Commission International d'Eclairage, 1931) chromaticity triangle, representing the gamut of a computer monitor, where the x, y coordinates of the primary colour phosphors were known. RESULTS: The mean results from individuals with normal colour vision were compared to those with defective colour vision. Of the 13 points tested, five demonstrated consistently high sensitivity in detecting colour defects. CONCLUSION: The test may provide a convenient method for classifying colour vision abnormalities.

Adolescent↗
PubMed2006

Fast IIR isotropic 2-D complex Gabor filters with boundary initialization.

Gabor filters are widely applied in image analysis and computer vision applications. This paper describes a fast algorithm for isotropic complex Gabor filtering that outperforms existing implementations. The main computational improvement arises from the decomposition of Gabor filtering into more efficient Gaussian filtering and sinusoidal modulations. Appropriate filter initial conditions are derived to avoid boundary transients, without requiring explicit image border extension. Our proposal reduces up to 39% the number of required operations with respect to state-of-the-art approaches. A full C++ implementation of the method is publicly available.

Algorithms↗
PubMed1999

Flexible structural comparison allowing hinge-bending, swiveling motions.

We present an efficient method for flexible comparison of protein structures, allowing swiveling motions. In all currently available methodologies developed and applied to the comparisons of protein structures, the molecules are considered to be rigid objects. The method described here extends and generalizes current approaches to searches for structural similarity between molecules by viewing proteins as objects consisting of rigid parts connected by rotary joints. During the matching, the rigid subparts are allowed to be rotated with respect to each other around swiveling points in one of the molecules. This technique straightforwardly detects structural motifs having hinge(s) between their domains. Whereas other existing methods detect hinge-bent motifs by initially finding the matching rigid parts and subsequently merging these together, our method automatically detects recurring substructures, allowing full 3 dimensional rotations about their swiveling points. Yet the method is extremely fast, avoiding the time-consuming full conformational space search. Comparison of two protein structures, without a predefinition of the motif, takes only seconds to one minute on a workstation per hinge. Hence, the molecule can be scanned for many potential hinge sites, allowing practically all C(alpha) atoms to be tried as swiveling points. This algorithm provides a highly efficient, fully automated tool. Its complexity is only O(n2), where n is the number of C(alpha) atoms in the compared molecules. As in our previous methodologies, the matching is independent of the order of the amino acids in the polypeptide chain. Here we illustrate the performance of this highly powerful tool on a large number of proteins exhibiting hinge-bending domain movements. Despite the motions, known hinge-bent domains/motifs which have been assembled and classified, are correctly identified. Additional matches are detected as well. This approach has been motivated by a technique for model based recognition of articulated objects originating in computer vision and robotics.

Algorithms↗