Recent studies have confirmed that the multichannel Gabor decomposition represents an excellent tool for image segmentation and boundary detection. Unfortunately, this approach when used for unsupervised image analysis tasks imposes excessive storage requirements due to the nonorthogonality of the basis functions and is computationally highly demanding. In this correspondence, we propose a novel method for efficient image analysis that uses tuned matched Gabor filters. The algorithmic determination of the parameters of the Gabor filters is based on the analysis of spectral feature contrasts obtained from iterative computation of pyramidal Gabor transforms with progressive dyadic decrease of elementary cell sizes. The method requires no a priori knowledge of the analyzed image so that the analysis is unsupervised. Computer simulations applied to different classes of textures illustrate the matching property of the tuned Gabor filters derived using our determination algorithm. Also, their capability to extract significant image information and thus enable an easy and efficient low-level image analysis will be demonstrated.

No abstract

No abstract

In this paper we present a programmable intraocular pressure sensor system implant integrated on a single CMOS chip. It contains on-chip micromechanical pressure sensor array, a temperature sensor, readout and calibration electronics, a µC-based digital control unit, and an RF-transponder. The transponder enables wireless data transmission and wireless power reception, thus making batteryless operation feasible. The chip has been fabricated in a 1.2 µm n-well CMOS process complemented by additional processing steps