Quantization is a process that maps a continous or discrete set of values into approximations that belong to a smaller set. Quantization is a lossy: some information about the original data is lost in the process. The key to a successful quantization is therefore the selection of an error criterion – such as entropy and signal-to-noise ratio – and the development of optimal quantizers for this criterion.

Quantization

We explore a new approach to shape recognition based on a virtually infinite family of binary features (queries) of the image data, designed to accommodate prior information about shape invariance and regularity. Each query corresponds to a spatial arrangement of several local topographic codes (or tags), which are in themselves too primitive and common to be informative about shape. All the discriminating power derives from relative angles and distances among the tags. The important attributes of the queries are a natural partial ordering corresponding to increasing structure and complexity; semi-invariance, meaning that most shapes of a given class will answer the same way to two queries that are successive in the ordering; and stability, since the queries are not based on distinguished points and substructures. No classifier based on the full feature set can be evaluated, and it is impossible to determine a priori which arrangements are informative. Our approach is to select informative features and build tree classifiers at the same time by inductive learning. In effect, each tree provides an approximation to the full posterior where the features chosen depend on the branch that is traversed. Due to the number and nature of the queries, standard decision tree construction based on a fixed-length feature vector is not feasible. Instead we entertain only a small random sample of queries at each node, constrain their complexity to increase with tree depth, and grow multiple trees. The terminal nodes are labeled by estimates of the corresponding posterior distribution over shape classes. An image is classified by sending it down every tree and aggregating the resulting distributions. The method is applied to classifying handwritten digits and synthetic linear and nonlinear deformations of three hundred L AT E X symbols. Stateof-the-art error rates are achieved on the National Institute of Standards and Technology database of digits. The principal goal of the experiments on L AT E X symbols is to analyze invariance, generalization error and related issues, and a comparison with artificial neural networks methods is presented in this context.

/pdf/shape-quantization-and-recognition-with-randomized-trees-ebdg8vh6xj.pdf

Shape quantization and recognition with randomized trees

Computational imaging is becoming widely adopted in consumer products to reconstruct high quality pictures from raw pixel data provided by special optics and image sensors. This forum will provide details of such systems. We will commence with an overview of computational photograpy and imaging. This is followed by object recognition and tracking techniques including interesting point techniques and face detection algorithms optimized for cameras. Camera array techniques, multiple shot techniques and coded aperture techniques for improved resolution and extended depth of field will be presented. The popular compressed sensing technique is also covered with the aim to reduce data rate without severely impacting image quality. Lastly, existing implementation on parallel processing architecture will be introduced.

http://ieeexplore.ieee.org/iel5/6171933/6176863/06177116.pdf

Computational imaging

This paper presents an automatic system for the measurement of carotid intima-media thickness (IMT) that is based on the digital processing of ultrasound images. The measurement technique is described in detail, highlighting the advantages compared to other methods and, reporting some experimental results. Finally, an analytical approach is used to estimate the intrinsic accuracy of the system.

An automatic measurement system for the evaluation of carotid intima-media thickness

In this paper an automatic measurement system for the on-line evaluation of carotid Intima-Media Thickness (IMT) by ultrasound images is presented. The hardware and the software are described in detail highlighting the advantages with respect to actual techniques. Metrological characterization of the realized system allows its accuracy to be estimated.

The field of image and multidimensional signal processing began as a field of strong theoretical framework based on mathematics, statistics, and physics. Later, with advances in computing, memory, and image-sensing technology, techniques developed for image enhancement, still and moving image compression, image understanding gave this field a solid base of practical applications. Furthermore, the exploding growth of the Internet and the ubiquity of images and video, the field of image and multidimensional signal processing is becoming more and more exciting. Topics covered in the article include: multidimensional signal-processing theory, image acquisition, image transforms, image modeling, image enhancement and restoration, image and video analysis, processing, coding, hardware and software implementation issues, and computed imaging.

The past, present, and future of image and multidimensional signal processing

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The past, present, and future of image and multidimensional signal processing