A system for 3D texture-based probabilistic object recognition and its applications

This article presents a system for texture-based probabilistic classification and localisation of three-dimensional objects in two-dimensional digital images and discusses selected applications. In contrast to shape-based approaches, our texture-based method does not rely on object features extracted using image segmentation techniques. Rather, the objects are described by local feature vectors computed directly from image pixel values using the wavelet transform. Both gray level and colour images can be processed. In the training phase, object features are statistically modelled as normal density functions. In the recognition phase, the system classifies and localises objects in scenes with real heterogeneous backgrounds. Feature vectors are calculated and a maximisation algorithm compares the learned density functions with the extracted feature vectors and yields the classes and poses of objects found in the scene. Experiments carried out on a real dataset of over 40,000 images demonstrate the robustness of the system in terms of classification and localisation accuracy. Finally, two important real application scenarios are discussed, namely recognising museum exhibits from visitors’ own photographs and classification of metallography images.     

Hand-held 3D scanning based on coarse and fine registration of multiple range images

A hand-held 3D scanning technique is proposed to reconstruct 3D models of real objects. A sequence of range images captured from a hand-held stereo camera is automatically registered to a reference coordinate system. The automated scanning process consists of two states, coarse and fine registration. At the beginning, scanning process starts at the fine registration state. A fast and accurate registration refinement technique is used to align range images in a pair-wise manner. If the refinement technique fails, the process changes to the coarse registration state. A feature-based coarse registration technique is proposed to find correspondences between the last successful frame and the current frame. If the coarse registration successes, the process returns to the fine registration state again. A fast point-to-plane refinement technique is employed to do shape-based registration. After the shape-based alignment, a texture-based refinement technique matches texture features to enhance visual appearance of the reconstructed models. Through a graphic and video display, a human operator adjusts the pose of the camera to change the view of the next acquisition. Experimental results show that 3D models of real objects are reconstructed from sequences of range images.     

The operating core of an organization: A constrained optimization model

This paper suggests a constrained optimization model for the operating core of an organization. Structurally, the core consists of the basic technological module and several modules of support facilities. The production function of the operating core is represented through the superposition of Leontief production functions corresponding to each of the modules. We reduce the optimization problem to a linear programming problem with a parameter describing the operational structure of the core. In addition, we develop a certain algorithm for automatic construction of the basic equations for each value of the parameter. Finally, the optimization problem is solved numerically for a wide range of the model variables and parameters. The proposed model may serve for designing generalized control mechanisms for an organizational system on a large horizon.     

BLISS/S: a new method for two-level structural optimization

The paper describes a two-level method for structural optimization for a minimum weight under the local strength and displacement constraints. The method divides the optimization task into separate optimizations of the individual substructures (in the extreme, the individual components) coordinated by the assembled structure optimization. The substructure optimizations use local cross-sections as design variables and satisfy the highly nonlinear local constraints of strength and buckling. The design variables in the assembled structure optimization govern the structure overall shape and handle the displacement constraints. The assembled structure objective function is the objective in each of the above optimizations. The substructure optimizations are linked to the assembled structure optimization by the sensitivity derivatives. The method was derived from a previously reported two-level optimization method for engineering systems, e.g. aerospace vehicles, that comprise interacting modules to be optimized independently, coordination provided by a system-level optimization. This scheme was adapted to structural optimization by treating each substructure as a module in a system, and using the standard finite element analysis as the system analysis. A numerical example, a hub structure framework, is provided to show the new method agreement with a standard, no-decomposition optimization. The new method advantage lies primarily in the autonomy of the individual substructure optimization that enables concurrency of execution to compress the overall task elapsed time. The advantage increases with the magnitude of that task. Received December 5, 1999?Revised mansucript received April 26, 2000     

A simple model-free butterfly shape-based detection (BSD) method integrated with deep learning CNN for valve stiction detection and quantification

Control valve stiction is a long-standing problem within process industries. In most methods for shape-based stiction detection, they rely heavily on the traditional controller output (OP) and process variable (PV) plot (i.e. PV-OP plot) that tends to produce an “elliptical” shape which is the widely acknowledged pattern indication for the presence of stiction. However, many of the methods suffered from unsatisfactory generalization capability when subjected to different loop dynamics. In this paper, a “butterfly” shape derived from the manipulation of the standard PV and OP data, which is more robust towards different loop dynamics, is developed for stiction detection. This simple model-free butterfly shape-based detection (BSD) method uses Stenman's one parameter stiction model, which results in a distinctive ‘butterfly’ pattern in the presence of stiction. The proposed BSD is tested on simulated data, as well as 26 benchmark industrial case studies and has shown a relatively higher generalization capability with relatively higher successful detection rate on stiction loops and on non-stiction loops. A simple quantification algorithm based on BSD-convolutional neural network (BSD-CNN) framework is then developed to quantify the stiction severity. Based on the 15 benchmark industrial loops with stiction, the proposed BSD-CNN quantification algorithm has shown reasonable accuracy when compared to other published quantification methods in literature.     

Appearance-Cloning: Photo-Consistent Scene Recovery from Multi-View Images

This paper introduces the novel volumetric methodology “appearance-cloning” as a viable solution for achieving a more improved photo-consistent scene recovery, including a greatly enhanced geometric recovery performance, from a set of photographs taken at arbitrarily distributed multiple camera viewpoints. We do so while solving many of the problems associated with previous stereo-based and volumetric methodologies. We redesign the photo-consistency decision problem of individual voxel in volumetric space as the photo-consistent shape search problem in image space, by generalizing the concept of the point correspondence search between two images in stereo-based approach, within a volumetric framework. In detail, we introduce a self-constrained greedy-style optimization methodology, which iteratively searches a more photo-consistent shape based on the probabilistic shape photo-consistency measure, by using the probabilistic competition between candidate shapes. Our new measure is designed to bring back the probabilistic photo-consistency of a shape by comparing the appearances captured from multiple cameras with those rendered from that shape using the per-pixel Maxwell model in image space. Through various scene recoveries experiments including specular and dynamic scenes, we demonstrate that if sufficient appearances are given enough to reflect scene characteristics, our appearance-cloning approach can successfully recover both the geometry and photometry information of a scene without any kind of scene-dependent algorithm tuning.     

故障检测中核参数优化方法性能评估

近年来,基于核主元分析与核偏最小二乘的方法经常被应用于过程监控与故障检测领域以克服工业过程的非线性.研究发现此类方法的检测性能很大程度上受核参数的影响,而目前学术界对该参数的优化方法研究较少.因此,本文以最常用的高斯核方法为例,首先总结了3类常用的核参数优化方法:二分法、基于BP神经网络的重构法和基于样本分类的重构法;其次重点分析每个方法的特点和它们之间的联系,并评估它们的性能;最后将上述方法设计成一个核参数优化系统应用于热连轧过程的故障检测中.应用结果表明,优化后的核参数能显著提高故障检测性能.     

Robust object recognition with cortex-like mechanisms

We introduce a new general framework for the recognition of complex visual scenes, which is motivated by biology: We describe a hierarchical system that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation by alternating between a template matching and a maximum pooling operation. We demonstrate the strength of the approach on a range of recognition tasks: From invariant single object recognition in clutter to multiclass categorization problems and complex scene understanding tasks that rely on the recognition of both shape-based as well as texture-based objects. Given the biological constraints that the system had to satisfy, the approach performs surprisingly well: It has the capability of learning from only a few training examples and competes with state-of-the-art systems. We also discuss the existence of a universal, redundant dictionary of features that could handle the recognition of most object categories. In addition to its relevance for computer vision, the success of this approach suggests a plausibility proof for a class of feedforward models of object recognition in cortex     

Texture feature benchmarking and evaluation for historical document image analysis

The use of different texture-based methods is pervasive in different subfields and tasks of document image analysis (DIA) and particularly in historical DIA (HDIA). Nevertheless, faced with a large diversity of texture-based methods used for HDIA, few questions arise. Which texture methods are firstly well suited for segmenting graphical contents from textual ones, discriminating various text fonts and scales, and separating different types of graphics? Then, which texture-based method represents a constructive compromise between the performance and the computational cost? Thus, in this article a benchmarking of the most classical and widely used texture-based feature sets has been conducted using a classical texture-based pixel-labeling scheme on a large corpus of historical documents to have satisfactory and clear answers to the above questions. We focus on determining the performance of each texture-based feature set according to the document content. The results reported in this study provide firstly a qualitative measure of which texture-based feature sets are the most appropriate and secondly a useful benchmark in terms of performance and computational cost for current and future research efforts in HDIA.