Electrical Impedance Tomography (EIT) is a functional imaging method that is being developed for bedside use in critical care medicine. Aiming at improving the chest anatomical resolution of EIT images we developed a fuzzy model based on EIT high temporal resolution and the functional information contained in the pulmonary perfusion and ventilation signals. EIT data from an experimental animal model were collected during normal ventilation and apnea while an injection of hypertonic saline was used as a reference . The fuzzy model was elaborated in three parts: a modeling of the heart, a pulmonary map from ventilation images and, a pulmonary map from perfusion images. Image segmentation was performed using a threshold method and a ventilation/perfusion map was generated. EIT images treated by the fuzzy model were compared with the hypertonic saline injection method and CT-scan images, presenting good results in both qualitative (the image obtained by the model was very similar to that of the CT-scan) and quantitative (the ROC curve provided an area equal to 0.93) point of view. Undoubtedly, these results represent an important step in the EIT images area, since they open the possibility of developing EIT-based bedside clinical methods, which are not available nowadays. These achievements could serve as the base to develop EIT diagnosis system for some life-threatening diseases commonly found in critical care medicine.

In this paper, we address the inverse problem of reconstructing a scene as well as the camera motion from the image sequence taken by an omni-directional camera. Our structure from motion results give sharp conditions under which the reconstruction is unique. For example, if there are three points in general position and three omni-directional cameras in general position, a unique reconstruction is possible up to a similarity. We then look at the reconstruction problem with m cameras and n points, where n and m can be large and the over-determined system is solved by least square methods. The reconstruction is robust and generalizes to the case of a dynamic environment where landmarks can move during the movie capture. Possible applications of the result are computer assisted scene reconstruction, 3D scanning, autonomous robot navigation, medical tomography and city reconstructions.

We live in the Information Age, and information has become a critically important component of our life. The success of the Internet made huge amounts of it easily available and accessible to everyone. To keep the flow of this information manageable, means for its faultless circulation and effective handling have become urgently required. Considerable research efforts are dedicated today to address this necessity, but they are seriously hampered by the lack of a common agreement about "What is information?" In particular, what is "visual information" - human's primary input from the surrounding world. The problem is further aggravated by a long-lasting stance borrowed from the biological vision research that assumes human-like information processing as an enigmatic mix of perceptual and cognitive vision faculties. I am trying to find a remedy for this bizarre situation. Relying on a new definition of "information", which can be derived from Kolmogorov's compexity theory and Chaitin's notion of algorithmic information, I propose a unifying framework for visual information processing, which explicitly accounts for the perceptual and cognitive image processing peculiarities. I believe that this framework will be useful to overcome the difficulties that are impeding our attempts to develop the right model of human-like intelligent image processing.

We have used a simple camera phone to significantly improve an `exploration system' for astrobiology and geology. This camera phone will make it much easier to develop and test computer-vision algorithms for future planetary exploration. We envision that the `Astrobiology Phone-cam' exploration system can be fruitfully used in other problem domains as well.

Biological sensory systems are faced with the problem of encoding a high-fidelity sensory signal with a population of noisy, low-fidelity neurons. This problem can be expressed in information theoretic terms as coding and transmitting a multidimensional, analog signal over a set of noisy channels. Previously, we have shown that robust, overcomplete codes can be learned by minimizing the reconstruction error with a constraint on the channel capacity. Here, we present a theoretical analysis that characterizes the optimal linear coder and decoder for one-and twodimensional data. The analysis allows for an arbitrary number of coding units, thus including both under-and over-complete representations, and provides a number of important insights into optimal coding strategies. In particular, we show how the form of the code adapts to the number of coding units and to different data and noise conditions to achieve robustness. We also report numerical solutions for robust coding of highdimensional image data and show that these codes are substantially more robust compared against other image codes such as ICA and wavelets.

An analog focal-plane processor having a 128 128 photodiode array has been developed for directional edge filtering. It can perform 4 4-pixel kernel convolution for entire pixels only with 256 steps of simple analog processing. Newly developed cyclic line access and row-parallel processing scheme in conjunction with the "only-nearest-neighbor interconnects" architecture has enabled a very simple implementation. A proof-of-conceptchip was fabricated in a 0.35-m 2-poly 3-metal CMOS technology and the edge filtering at a rate of 200 frames/sec.

Images represent an important and abundant source of data. Understanding theirstatistical structure has important applications such as image compression and restoration. In this paper we propose a particular kind of probabilistic model, dubbed the "products of edge-perts model" to describe thestructure of wavelet transformed images. We develop a practical denoising algorithm based on a single edge-pert and show state-ofthe-art denoisingperformance on benchmark images.

Multiple visual cues are used by the visual system to analyze a scene; achromatic cues include luminance, texture, contrast and motion. Singlecell recordingshave shown that the mammalian visual cortex contains neurons that respond similarly to scene structure (e.g., orientation of a boundary), regardless of the cue type conveying this information. This paper shows that cue-invariant response properties of simple-and complex-type cells can be learned from natural image data in an unsupervised manner.In order to do this, we also extend a previous conceptual model of cue invariance so that it can be applied to model simple-and complex-cell responses. Our results relate cue-invariant response properties tonatural image statistics, thereby showing how the statistical modeling approachcan be used to model processing beyond the elemental response properties visual neurons. This work also demonstrates how to learn, from natural image data, more sophisticated feature detectors than those based on changes in mean luminance, thereby paving the way for new data-driven approaches to image processing and computer vision.

An analog focal-plane processor having a 128 128 photodiode array has been developed for directional edge filtering. It can perform 4 4-pixel kernel convolution for entire pixels only with 256 steps of simple analog processing.Newly developed cyclic line access and row-parallel processing scheme in conjunction with the "only-nearest-neighbor interconnects" architecturehas enabled a very simple implementation. A proof-of-conceptchip was fabricated in a 0.35-m 2-poly 3-metal CMOS technology and the edge filtering at a rate of 200 frames/sec.

The correction of bias in magnetic resonance images is an important problem in medical image processing. Most previous approaches have used a maximum likelihood method to increase the likelihood of the pixels in a single image by adaptively estimating a correction to the unknown image bias field. The pixel likelihoods are defined either in terms of a preexisting tissue model, or non-parametrically in terms of the image's own pixel values. In both cases, the specific location of a pixel in the image is not used to calculate the likelihoods. We suggest a new approach in which we simultaneously eliminate the bias from a set of images of the same anatomy, but from different patients. We use the statistics from the same location across different images, rather than within an image, to eliminate bias fields from all of the images simultaneously. The method builds a "multi-resolution" nonparametric tissue model conditioned on image location while eliminating the bias fields associated with the original image set.