Asynchrony in image analysis: using the luminance-to-response-latency relationship to improve segmentation

We deal with the probiem of segmenting static images, a procedure known to be difficult in the case of very noisy patterns, The proposed approach rests on the transformation of a static image into a data flow in which the first image points to be processed are the brighter ones. This solution, inspired by human perception, in which strong luminances elicit reactions from the visual system before weaker ones, has led to the notion of asynchronous processing. The asynchronous processing of image points has required the design of a specific architecture that exploits time differences in the processing of information. The results otained when very noisy images are segmented demonstrate the strengths of this architecture; they also suggest extensions of the approach to other computer vision problem

Distance-based discriminant analysis method and its applications

This paper proposes a method of finding a discriminative linear transformation that enhances the data's degree of conformance to the compactness hypothesis and its inverse. The problem formulation relies on inter-observation distances only, which is shown to improve non-parametric and non-linear classifier performance on benchmark and real-world data sets. The proposed approach is suitable for both binary and multiple-category classification problems, and can be applied as a dimensionality reduction technique. In the latter case, the number of necessary discriminative dimensions can be determined exactly. Also considered is a kernel-based extension of the proposed discriminant analysis method which overcomes the linearity assumption of the sought discriminative transformation imposed by the initial formulation. This enhancement allows the proposed method to be applied to non-linear classification problems and has an additional benefit of being able to accommodate indefinite kernel

Information-theoretic temporal segmentation of video and applications: multiscale keyframes selection and shot boundaries detection

The first step in the analysis of video content is the partitioning of a long video sequence into short homogeneous temporal segments. The homogeneity property ensures that the segments are taken by a single camera and represent a continuous action in time and space. These segments can then be used as atomic temporal components for higher level analysis like browsing, classification, indexing and retrieval. The novelty of our approach is to use color information to partition the video into segments dynamically homogeneous using a criterion inspired by compact coding theory. We perform an information-based segmentation using a Minimum Message Length (MML) criterion and minimization by a Dynamic Programming Algorithm (DPA). We show that our method is efficient and robust to detect all types of transitions in a generic manner. A specific detector for each type of transition of interest therefore becomes unnecessary. We illustrate our technique by two applications: a multiscale keyframe selection and a generic shot boundaries detectio

Towards geometrically robust data-hiding with structured codebooks

In this paper we analyze performance of practical robust data-hiding in channels with geometrical transformations. By applying information-theoretic argument we show that performance of a system designed based on both random coding and random binning principles is bounded by the same maximal achievable rate for the cases when communication channel includes geometrical transformations or not. Targeting to provide theoretic performance limits of practical robust data-hiding we model it using a multiple access channel (MAC) with side information (SI) available at one of encoders and present the bounds on achievable rates of reliable communications to such a protocol. Finally, considering template-based and redundant-based design of geometrically robust data-hiding systems, we perform security analysis of their performance and present results in terms of number of trial efforts the attacker needs to completely remove hidden informatio

Walking Behavior Change Detector for a “Smart” Walker

AbstractThis study investigates the design of a novel real-time system to detect walking behavior changes using an accelerometer on a rollator. No sensor is required on the user. We propose a new non-invasive approach to detect walking behavior based on the motion transfer by the user on the walker. Our method has two main steps; the first is to extract a gait feature vector by analyzing the three-axis accelerometer data in terms of magnitude, gait cycle and frequency. The second is to classify gait with the use of a decision tree of multilayer perceptrons. To assess the performance of our technique, we evaluated different sampling window lengths of 1, 3 an 5seconds and four different Neural Network architectures. The results revealed that the algorithm can distinguish walking behavior such as normal, slow and fast with an accuracy of about 86%. This research study is part of a project aiming at providing a simple and non-invasive walking behavior detector for elderly who use rollators

Digital Image Processing of Electron Micrographs: The PIC System II

The PIC system, an integrated package of Fortran programs and subroutines designed to run on the Digital Equipment Corporation VAX family of computers, has been developed for analysis of electron micrographs with emphasis on the particular requirements for structural analysis of biological macromolecules. The substantially improved VAX version of PIC reported here has been developed from an earlier PDP-11 version which was, in turn, developed from a set of IBM 370 programs called MDPP. PIC now encompasses over 150 commands or processing operations that afford a comprehensive range of image processing operations including image restoration, enhancement, Fourier analysis, correlation averaging, and multivariate statistical analysis including clustering and classification. In particular, we describe our software for correction of imperfect lattices, as well as programs for correlation alignment and averaging of single particle images

Labolmage: a workstation environment for research in image processing and analysis

Numerous images are produced daily in biomedical research. In order to extract relevant and useful results, various processing and analysis steps are mandatory. The present paper describes a new, powerful and user-friendly image analysis system: Labolmage. In addition to standard image processing modules, Labolmage also contains various specialized tools. These multiple processing modules and tools are first introduced. A one-dimensional gel analysis method is then described. The new concept of ‘normalized virtual one-dimensional gel' is introduced, making comparisons between gels particularly easy. This normalized gel is obtained by compensating for the bending of the lanes automatically; no information loss is incurred in the process. Finally, the model of interaction in a multi-window environment is discussed. Labolmage is designed to run in two ways: interactively, using menus and panels; and in batch mode by means of user-defined macros. Examples are given to illustrate the potentialities of the softwar

Two-dimensional gel electrophoresis in proteomics: A tutorial

Two-dimensional electrophoresis of proteins has preceded, and accompanied, the birth of proteomics. Although it is no longer the only experimental scheme used in modern proteomics, it still has distinct features and advantages. The purpose of this tutorial paper is to guide the reader through the history of the field, then through the main steps of the process, from sample preparation to in-gel detection of proteins, commenting the constraints and caveats of the technique. Then the limitations and positive features of two-dimensional electrophoresis are discussed (e.g. its unique ability to separate complete proteins and its easy interfacing with immunoblotting techniques), so that the optimal type of applications of this technique in current and future proteomics can be perceived. This is illustrated by a detailed example taken from the literature and commented in detail. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 2)

Rank acquisition in rhesus macaque yearlings following permanent maternal separation: The importance of the social and physical environment

Rank acquisition is a developmental milestone for young primates, but the processes by which primate yearlings attain social rank in the absence of the mother remain unclear. We studied 18 maternally reared yearling rhesus macaques (Macaca mulatta) that differed in their social and physical rearing environments. We found that early social experience and maternal rank, but not individual traits (weight, sex, age), predicted dominance acquisition in the new peer‐only social group. Yearlings also used coalitions to reinforce the hierarchy, and social affiliation (play and grooming) was likely a product, rather than a determinant, of rank acquisition. Following relocation to a familiar environment, significant rank changes occurred indicating that familiarity with a physical environment was salient in rank acquisition. Our results add to the growing body of literature emphasizing the role of the social and physical environment on behavioral development, namely social asymmetries among peers