On quantization errors in computer vision

The author considers the error resulting in the computation of multivariable functions h(X₁, X, . . ., X_n), where all the X_is are only available in the quantized form. In image processing and computer vision problems, the variables are typically a mixture of the spatial coordinates and the intensity levels of objects in an image. A method is introduced using a first-order Taylor series expansion together with a periodic extension of the resulting expression and its Fourier series representation so that the moments and the probability distribution function of the error can be computed in closed form. This method only requires that the joint probability density function of X_i s be known and makes no assumption on the behavior on the quantization errors of the variables. Examples are also given where these results are applied     

On the use of error propagation for statistical validation of computer vision software

Computer vision software is complex, involving many tens of thousands of lines of code. Coding mistakes are not uncommon. When the vision algorithms are run on controlled data which meet all the algorithm assumptions, the results are often statistically predictable. This renders it possible to statistically validate the computer vision software and its associated theoretical derivations. In this paper, we review the general theory for some relevant kinds of statistical testing and then illustrate this experimental methodology to validate our building parameter estimation software. This software estimates the 3D positions of buildings vertices based on the input data obtained from multi-image photogrammetric resection calculations and 3D geometric information relating some of the points, lines and planes of the buildings to each other.     

Stochastic analysis of stereo quantization error

The probability density function of the range estimation error and the expected value of the range error magnitude are derived in terms of the various design parameters of a stereo imaging system. In addition, the relative range error is proposed as a better way of quantifying the range resolution of a stereo imaging system than the percent range error when the depths in the scene lie within a narrow range     

The role of computer vision in prosthetic vision

The cost of vision loss worldwide has been estimated at nearly $3 trillion (http://www.amdalliance.org/cost-of-blindness.html). Non-preventable diseases cause a significant proportion of blindness in developed nations and will become more prevalent as people live longer. Prosthetic vision technologies including retinal implants will play an important therapeutic role. Retinal implants convert an input image stream to visual percepts via stimulation of the retina. This paper highlights some barriers to restoring functional human vision for current generation visual prosthetic devices that computer vision can help overcome. Such computer vision is interactive, aiming to restore function including visuo-motor tasks and recognition.     

基于计算机视觉技术的鲜蛋等级评定系统

建立了一个基于计算机视觉技术检测鸡蛋的内外品质特性,采用LM神经网络进行分类识别的系统.该系统通过提取鸡蛋的蛋型指数、裂纹及污垢、蛋形尺寸等特征,实现利用神经网络的最优模型完成鸡蛋的分级检测,正确识别率达到87%.     

CAGD-based computer vision

The authors explore the connection between CAGD (computer-aided geometric design) and computer vision. A method for the automatic generation of recognition strategies based on the 3-D geometric properties of shape has been devised and implemented. It uses a novel technique to quantify the following properties of features which compose models used in computer vision: robustness, completeness, consistency, cost, and uniqueness. By utilizing this information, the automatic synthesis of a specialized recognition scheme, called a strategy tree, is accomplished. Strategy trees describe, in a systematic and robust manner, the search process used for recognition and localization of particular objects in the given scene. The consist of selected 3-D features which satisfy system constraints and corroborating evidence subtrees which are used in the formation of hypotheses. Verification techniques, used to substantiate or refute these hypotheses are explored. Experiments utilizing 3-D data are presented     

Similarity measures in computer vision

A similarity measure is needed in many Computer Vision problems. Although Euclidean distance has traditionally been used, median distance was recently proposed as an alternative, mostly due to its robustness properties. In this paper, a parametric class of distances is presented which allow to introduce a notion of similarity depending on the problem being considered.     

Mathematical statistics and computer vision

In this discussion paper, I present my views on the role on mathematical statistics for solving computer vision problems.     

Parallel programming for computer vision

Two Unix environments developed for programming parallel computers to handle image-processing and vision applications are described. Visx is a portable environment for the development of vision applications that has been used for many years on serial computers in research. Visx was adapted to run on a multiprocessor with modest parallelism by using functional decomposition and standard operating-system capabilities to exploit the parallel hardware. Paragon is a high-level environment for multiprocessor systems that has facilities for both functional decomposition and data partitioning. It provides primitives that will work efficiently on several parallel-processing systems. Paragon's primitives can be used to build special image-processing operations, allowing one's own programming environment to be grown naturally     

Polymorphic-torus architecture for computer vision

A massively parallel fine-grained SIMD (single-instruction multi-data-stream) computer for machine vision computations is described. The architecture features a polymorphic-torus network which inserts an individually controllable switch into every node of the two-dimensional torus such that the network is dynamically reconfigurable to match the algorithm. Reconfiguration is accomplished by circuit switching and is achieved at fine-grained level. Using both the processor coordinate in the torus and the data for reconfiguration, the polymorphic-torus achieves solution time that is superior or equivalent to that of popular vision architectures such as mesh, tree, pyramid and hypercube for many vision algorithms discussed. Implementation of the architecture is given to illustrate its VLSI efficiency     

Geometric reasoning for computer vision

Some general principles are formulated about geometric reasoning in the context of model-based computer vision. Such reasoning tries to draw inferences about the spatial relationships between objects in a scene based on the fragmentary and uncertain geometric evidence provided by an image. The paper discusses the tasks the reasoner is to perform for the vision program, the basic competences it requires and the various methods of implementation. In the section on basic competences, some specifications of the data types and operations needed in any geometric reasoner are given.     

Robot guidance using computer vision

This paper describes a procedure for locating an autonomous robot vehicle in a three-dimensional space. The method is an extension of a two-dimensional technique described by Fukui. The method is simple, requires little computation and provides the unique three-dimensional position of the robot relative to a standard mark.     

Closed-loop form quantization algorithm with bounded accumulative error

This paper proposes a closed-loop form quantization algorithm that guarantees the boundness of accumulative error. The algorithm is particularly useful for mobile robot navigation that is usually implemented on embedded systems. If the wheel command of the mobile robot is given by velocity or positional increments at every control instant that are quantized due to the finite word length of the controller's CPU, the quantization error accumulates to produce large position error. Such an error is critical for wheeled mobile robots or autonomous vehicles with non-holonomic constraints. To solve this problem, a non-error accumulative quantization algorithm with a closedloop form is presented. We can extend it to a generalized form corresponding to the nth-order accumulation. The boundness of the accumulative quantization error is proven via mathematic processes and verified by a series of computer simulations. The proposed method is effective to accurately control the autonomous mobile robot, particularly with embedded systems.     

Robust clustering with applications in computer vision

A clustering algorithm based on the minimum volume ellipsoid (MVE) robust estimator is proposed. The MVE estimator identifies the least volume region containing h percent of the data points. The clustering algorithm iteratively partitions the space into clusters without prior information about their number. At each iteration, the MVE estimator is applied several times with values of h decreasing from 0.5. A cluster is hypothesized for each ellipsoid. The shapes of these clusters are compared with shapes corresponding to a known unimodal distribution by the Kolmogorov-Smirnov test. The best fitting cluster is then removed from the space, and a new iteration starts. Constrained random sampling keeps the computation low. The clustering algorithm was successfully applied to several computer vision problems formulated in the feature space paradigm: multithresholding of gray level images, analysis of the Hough space, and range image segmentation     

Structural matching in computer vision using probabilisticrelaxation

In this paper, we develop the theory of probabilistic relaxation for matching features extracted from 2D images, derive as limiting cases the various heuristic formulae used by researchers in matching problems, and state the conditions under which they apply, We successfully apply our theory to the problem of matching and recognizing aerial road network images based on road network models and to the problem of edge matching in a stereo pair. For this purpose, each line network is represented by an attributed relational graph where each node is a straight line segment characterized by certain attributes and related with every other node via a set of binary relations     

Advances in matrix manifolds for computer vision

The attention paid to matrix manifolds has grown considerably in the computer vision community in recent years. There are a wide range of important applications including face recognition, action recognition, clustering, visual tracking, and motion grouping and segmentation. The increased popularity of matrix manifolds is due partly to the need to characterize image features in non-Euclidean spaces. Matrix manifolds provide rigorous formulations allowing patterns to be naturally expressed and classified in a particular parameter space. This paper gives an overview of common matrix manifolds employed in computer vision and presents a summary of related applications. Researchers in computer vision should find this survey beneficial due to the overview of matrix manifolds, the discussion as well as the collective references.