Automatic inspection of manufactured products with natural looking textures is a challenging task. Products
such as tiles, textile, leather, and lumber project image textures that cannot be modeled as periodic or otherwise
regular; therefore, a stochastic modeling of local intensity distribution is required. An inspection system to
replace human inspectors should be flexible in detecting flaws such as scratches, cracks, and stains occurring
in various shapes and sizes that have never been seen before. A computer vision algorithm is proposed in this
paper that extracts local statistical features from grey-level texture images decomposed with wavelet frames into
subbands of various orientations and scales. The local features extracted are second order statistics derived from
grey-level co-occurrence matrices. Subsequently, a support vector machine (SVM) classifier is trained to learn
a general description of normal texture from defect-free samples. This algorithm is implemented in LabVIEW
and is capable of processing natural texture images in real-time.
Image binarization under non-uniform lighting conditions is required in many industrial machine vision applications. Many local adaptive thresholding algorithms have been proposed in the literature for this purpose. However, existing local adaptive thresholding algorithms are either not robust enough or too expensive for real-time implementation due to very high computation costs. This paper presents a new algorithm for local adaptive thresholding based on a multi-stage framework. In the first stage, a mean filtering algorithm, with kernel-size independent computation cost, is proposed for background modeling to eliminate the non-uniform lighting effect. In the second stage, a background-corrected image is generated based on the background color. In the final stage, a global thresholding algorithm is applied to the background-corrected image. The kernel-size independent computation algorithm reduces the order of computation cost of background modeling from NML2 to ML+NL+6NM for an N x M image with an L x L kernel, which enables the real-time processing of objects of arbitrary size. Experiments show that the proposed algorithm performs better than other local thresholding algorithms, such as the Niblack algorithm, in terms of both speed and segmentation results for many machine vision applications under non-uniform lighting conditions.
Color representation and comparison based on the histogram has proved to be very efficient for image indexing in content-based image retrieval and machine vision applications. However, the issues of color constancy and accurate color similarity measures remain unsolved. This paper presents a new algorithm for intensity- insensitive color characterization for image retrieval and machine vision applications. The color characterization algorithm divides the HSI (hue, saturation and intensity) color space into a given number of bins in such a way that the color characterization represents all the colors in the hue/saturation plane as well as black, white and gray colors. The color distribution in these bins of the HSI space is represented in the form of a one-dimensional vector called Color Spectrum Vector (CSV). The color information that is stored in the CSV is insensitive to changes in the luminance. A weighted version of CSV called WCSV is introduced to take the similarity of the neighboring bins into account. A Fuzzy Color Spectrum Vector (FCSV) color representation vector that takes into account the human uncertainty in color classification process is also introduced here. The accuracy and speed of the algorithm is demonstrated in this paper through a series of experiments on image indexing and machine vision applications.
In a two-dimensional pattern matching problem, a known template image has be located in another image, irrespective of the template's position, orientation and size in the image. One way to accomplish invariance to the changes in the template is by forming a set of feature vectors that encompass all the variations in the template. Matching is then performed by finding the best similarity between the feature vector extracted from the image to the feature vectors in the template set. In this paper we introduce a new concept of a generalized Fourier transform. The generalized Fourier transform offers a relatively robust and extremely fast solution to the described matching problem. The application of the generalized Fourier transform to scale invariant pattern matching is shown here.
KEYWORDS: Image processing, Medical imaging, Monte Carlo methods, Statistical analysis, Reconstruction algorithms, Databases, Fractal analysis, Ray tracing, Algorithms, Machine vision
It is well known that high-dimensional integral can be solved with Monte Carlo algorithms. Recently, it was discovered that there is a relationship between low discrepancy sets and the efficient evaluation of higher-dimensional integral. Theory suggests that for midsize dimensional problems, algorithms based on low discrepancy sets should outperform all other existing methods by an order of magnitude in terms of the number of sample points used to evaluate the integral. We show that the field of image processing can potentially take advantage of specific properties of low discrepancy sets. To illustrate this, we applied the theory of low discrepancy sequences to some relatively simple image processing and computer vision related operations such as the estimation of gray level image statistics, fast location of objects in a binary image and the reconstruction of images from a sparse set of points. Our experiments show that compared to standard methods, the proposed new algorithms are faster and statistically more robust. Classical low discrepancy sets based on the Halton and Sobol' sequences were investigated thoroughly and showed promising results. The use of low discrepancy sequences in image processing for image characterization, understanding and object recognition is a novel and promising area for further investigation.
Conference Committee Involvement (8)
Image Processing: Machine Vision Applications VI
5 February 2013 | Burlingame, California, United States
Image Processing: Machine Vision Applications V
25 January 2012 | Burlingame, California, United States
Image Processing: Machine Vision Applications IV
25 January 2011 | San Francisco Airport, California, United States
Image Processing: Machine Vision Applications III
20 January 2010 | San Jose, California, United States
Image Processing: Machine Vision Applications II
22 January 2009 | San Jose, California, United States
Image Processing: Machine Vision Applications
29 January 2008 | San Jose, California, United States
Machine Vision Applications in Industrial Inspection XV
29 January 2007 | San Jose, CA, United States
Machine Vision Applications in Industrial Inspection XIV
16 January 2006 | San Jose, California, United States
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