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Heinz Hügli is professor emeritus at Ecole Polytechnique Federale de Lausanne (EPFL). He was a research director and associate professor at the Institute of Microtechnology, University of Neuchâtel, Switzerland, where he teached and headed the Pattern Recognition Laboratory (PARLAB). From 1988 to 2008, this lab was active in computer vision and pattern recognition with emphasis on early vision processing, industrial inspection, 3D object modeling and recognition.

Heinz Hügli received the Diploma in Electrical Engineering and Ph.D. degree from the Swiss Federal Institute of Technology in Zurich (EPFZ). He spent five year with the Image processing research group at EPFZ and later two years with the Medical Imaging Science Group, University of Southern California. In 1982 he joined the University of Neuchâtel were he first conducted speech recognition activities and then founded the PARLAB. He teached at the University of Neuchâtel, EPFL and University of Bern. Author of more than 150 scientific publications, he has chaired international conferences, was a member of several scientific societies, and participated in several conference program committees.

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Attention

Attention is the concentration of awareness on some phenomenon to the exclusion of other stimuli. It is a process of selectively concentrating on a discrete aspect of information, whether considered

Computer vision

Computer vision tasks include methods for , , and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g.

Time-of-flight camera

A time-of-flight camera (ToF camera), also known as time-of-flight sensor (ToF sensor), is a range imaging camera system for measuring distances between the camera and the subject for each point of

Dynamic Visual Attention on the Sphere

Iva Bogdanova Vandergheynst, Alexandre Bur, Pierre-André Farine, Heinz Hügli

In this paper we present a computational model of dynamic visual attention on the sphere which combines static (intensity,chromaticity, orientation) and motion features in order to detect salient locations in omnidirectional image sequences while working directly in spherical coordinates. We build the motion pyramid on the sphere by applying block matching and varying the block size. The spherical motion conspicuity map is obtained by fusing together the spherical motion magnitude and phase conspicuities. Furthermore, we combine this map with the static spherical saliency map in order to obtain the dynamic saliency map on the sphere. Detection of the spots of attention based on the dynamic saliency map on the sphere is applied on a sequence of real spherical images. The effect of using only the spherical motion magnitude or phase for defining the spots of attention on the sphere is examined as well. Finally, we test the spherical versus Euclidean spots detection on the omnidirectional image sequence.

Elsevier2010

Automated inspection of microlens arrays

Heinz Hügli

Industrial inspection of micro-devices is often a very challenging task, especially when those devices are produced in large quantities using micro-fabrication techniques. In the case of microlenses, millions of lenses are produced on the same substrate, thus forming a dense array. In this article, we investigate a possible automation of the microlens array inspection process. First, two image processing methods are considered and compared: reference subtraction and blob analysis. The criteria chosen to compare them are the reliability of the defect detection, the processing time required per frame, as well as the sensitivity to image acquisition conditions, such as varying illumination and focus. Tests performed on a real-world database of microlens array images led to select the blob analysis method. Based on the selected method, an automated inspection software module was then successfully implemented. Its good performance allows to dramatically reduce the inspection time as well as the human intervention in the inspection process.

2008

Dynamic Visual Attention: Motion Direction versus Motion Magnitude

Alexandre Bur, Heinz Hügli

Defined as an attentive process in the context of visual sequences, dynamic visual attention refers to the selection of the most informative parts of video sequence. This paper investigates the contribution of motion in dynamic visual attention, and specifically compares computer models designed with the motion component expressed either as the speed magnitude or as the speed vector. Several computer models, including static features (color, intensity and orientation) and motion features (magnitude and vector) are considered. Qualitative and quantitative evaluations are performed by comparing the computer model output with human saliency maps obtained experimentally from eye movement recordings. The model suitability is evaluated in various situations (synthetic and real sequences, acquired with fixed and moving camera perspective), showing advantages and inconveniences of each method as well as preferred domain of application.

2008