Alberto Signoroni

State-of-the-Art and Trends in Scalable Video Compression With Wavelet-Based Approaches

Scalable video coding (SVC) differs form traditional single point approaches mainly because it allows to encode in a unique bit stream several working points corresponding to different quality, picture size and frame rate. This work describes the current state-of-the-art in SVC, focusing on wavelet based motion-compensated approaches (WSVC). It reviews individual components that have been designed to address the problem over the years and how such components are typically combined to achieve meaningful WSVC architectures. Coding schemes which mainly differ from the space-time order in which the wavelet transforms operate are here compared, discussing strengths and weaknesses of the resulting implementations. An evaluation of the achievable coding performances is provided considering the reference architectures studied and developed by ISO/MPEG in its exploration on WSVC. The paper also attempts to draw a list of major differences between wavelet based solutions and the SVC standard jointly targeted by ITU and ISO/MPEG. A major emphasis is devoted to a promising WSVC solution, named STP-tool, which presents architectural similarities with respect to the SVC standard. The paper ends drawing some evolution trends for WSVC systems and giving insights on video coding applications which could benefit by a wavelet based approach.

A Critical Analysis of a Hand Orthosis Reverse Engineering and 3D Printing Process.

The possibility to realize highly customized orthoses is receiving boost thanks to the widespread diffusion of low-cost 3D printing technologies. However, rapid prototyping (RP) with 3D printers is only the final stage of patient personalized orthotics processes. A reverse engineering (RE) process is in fact essential before RP, to digitize the 3D anatomy of interest and to process the obtained surface with suitable modeling software, in order to produce the virtual solid model of the orthosis to be printed. In this paper, we focus on the specific and demanding case of the customized production of hand orthosis. We design and test the essential steps of the entire production process with particular emphasis on the accurate acquisition of the forearm geometry and on the subsequent production of a printable model of the orthosis. The choice of the various hardware and software tools (3D scanner, modeling software, and FDM printer) is aimed at the mitigation of the design and production costs while guaranteeing suitable levels of data accuracy, process efficiency, and design versatility. Eventually, the proposed method is critically analyzed so that the residual issues and critical aspects are highlighted in order to discuss possible alternative approaches and to derive insightful observations that could guide future research activities.

High-performance embedded morphological wavelet coding

In this letter, an efficient morphological wavelet coder is proposed. The clustering trend of significant coefficients is captured by a new kind of multiresolution binary dilation operator. The layered and adaptive nature of the subband dilation makes it possible for the coding technique to produce an embedded bit-stream with a modest computational cost and state-of-the-art rate-distortion performance. Morphological wavelet coding appears promising because the localized analysis of wavelet coefficient clusters is adequate to capture intrinsic patterns of the source, which can have substantial benefits for reducing further the data redundancy.

Bacterial colony counting with Convolutional Neural Networks in Digital Microbiology Imaging

Abstract Counting bacterial colonies on microbiological culture plates is a time-consuming, error-prone, nevertheless essential quantitative task in Clinical Microbiology Laboratories. With this work we explore the possibility to find effective solutions to the above issue by designing and testing two different machine learning approaches. The first one is based on the extraction of a complete set of handcrafted morphometric and radiometric features used within a Support Vector Machines solution. The second one is based on the design and configuration of a Convolutional Neural Networks deep learning architecture. To validate, in a real and challenging clinical scenario, the proposed bacterial load estimation techniques, we built and publicly released a fully labeled large and representative database of both single and aggregated bacterial colonies extracted from routine clinical laboratory culture plates. Dataset enhancement approaches have also been experimentally tested for performance optimization. The adopted deep learning approach outperformed the handcrafted feature based one, and also a conventional reference technique, by a large margin, becoming a preferable solution for the addressed Digital Microbiology Imaging quantification task, especially in the emerging context of Full Laboratory Automation systems.

Exploitation and extension of the region-of-interest coding functionalities in JPEG2000

The purpose of this paper is to present a technique to extend the functionality and the application fields of a spatially selective coding within a JPEG2000 framework. The image quality drop between the regions of interest (ROI) and the background (BG) is considered. From the conventional point of view, the reconstructed image quality steeply drops along the ROI boundary; however this effect can be considered is perceived to be objectionable in some situations. Here we propose a simple quality decay management, which makes use of the concentric ROI with different scaling factors. This allows the technique to be perfectly consistent with the JPEG2000 part 2 ROI definitions and description. The proposed techniques may have a significant impact on applications where both coding rate minimization and coded image quality are important and/or become critical factors. Experiments and examples demonstrate the benefits of the presented approach.

A fully scalable video coder with inter-scale wavelet prediction and morphological coding

In this paper a new fully scalable - wavelet based - video coding architecture is proposed, where motion compensated temporal filtered subbands of spatially scaled versions of a video sequence can be used as base layer for inter-scale predictions. These predictions take place between data at the same resolution level without the need of interpolation. The prediction residuals are further transformed by spatial wavelet decompositions. The resulting multi-scale spatiotemporal wavelet subbands are coded thanks to an embedded morphological dilation technique and context based arithmetic coding. Dyadic spatio-temporal scalability and progressive SNR scalability are achieved. Multiple adaptation decoding can be easily implemented without the need of knowing a predefined set of operating points. The proposed coding system allows to compensate some of the typical drawbacks of current wavelet based scalable video coding architectures and shows interesting visual results even when compared with the single operating point video coding standard AVC/H.264.

Deformable registration using patch-wise shape matching

We present a novel approach for non-rigid registration of partially overlapping surfaces acquired from a deforming object. To allow for large and general deformations our method employs a nonlinear physics-inspired deformation model, which has been designed with a particular focus on robustness and performance. We discretize the surface into a set of overlapping patches, for each of which an optimal rigid motion is found and interpolated faithfully using dual quaternion blending. Using this discretization we can formulate the two components of our objective function-a fitting and a regularization term-as a combined global shape matching problem, which can be solved through a very robust numerical approach. Interleaving the optimization with successive patch refinement results in an efficient hierarchical coarse-to-fine optimization. Compared to other approaches our as-rigid-as-possible deformation model is faster, causes less distortion, and gives more accurate fitting results.

Multistage classification for bacterial colonies recognition on solid agar images

The advent of laboratory automation in clinical microbiology is entailing a revolution in the way most common bacteriological clinical exams are accomplished. As an essential part of these systems, digital recording and processing of cultured bacteria images is expected to improve plate reading, with a key role of image analysis tools in guaranteeing cost-effectiveness, accuracy, flexibility and reliability of the clinical tasks. In this work, we propose an image analysis system capable to address the complex problem of different bacteria species identification on cultured agar plates. Our solution is based on a modular segmentation/classification pipeline where a chain of supervised classification stages provides solutions to a series of nested task issues, from foreground separation toward isolated colony detection and classification. Performance assessment, based on an experimental dataset obtained in standardized laboratory conditions, clearly demonstrates the feasibility and the potentiality of the proposed solution and favorably opens to generalizations as well as to clinical validation studies.

Bacterial colony counting by Convolutional Neural Networks

Counting bacterial colonies on microbiological culture plates is a time-consuming, error-prone, nevertheless fundamental task in microbiology. Computer vision based approaches can increase the efficiency and the reliability of the process, but accurate counting is challenging, due to the high degree of variability of agglomerated colonies. In this paper, we propose a solution which adopts Convolutional Neural Networks (CNN) for counting the number of colonies contained in confluent agglomerates, that scored an overall accuracy of the 92.8% on a large challenging dataset. The proposed CNN-based technique for estimating the cardinality of colony aggregates outperforms traditional image processing approaches, becoming a promising approach to many related applications.

A unified framework for content-aware view selection and planning through view importance

Massimo Mauro1 m.mauro001@unibs.it Hayko Riemenschneider2 http://www.vision.ee.ethz.ch/~rhayko/ Alberto Signoroni1 http://www.ing.unibs.it/~signoron/ Riccardo Leonardi1 http://www.ing.unibs.it/~leon/ Luc Van Gool2 http://www.vision.ee.ethz.ch/~vangool/ 1 Department of Information Engineering University of Brescia Brescia, Italia 2 Computer Vision Lab Swiss Federal Institute of Technology Zurich, Switzerland