Color histogram has been used as one of the most important image descriptor in a wide range of content-based image retrieval (CBIR) projects for color image indexing. It captures the global chromatic distribution of an image. Traditionally, there are two major approaches to quantize the color space: (1) quantize each dimension of a color coordinate system uniformly to generate a fixed number of bins; and (2) quantize a color coordinate system arbitrarily. The first approach works best on cubical color coordinate systems, such as RGB. For other non-cubical color coordinate system, such as CIELAB and CIELUV, some bins may fall out of the gamut (transformed from the RGB cube) of the color space. As a result, it reduces the effectiveness of the color histogram and hence reduces the retrieval performance. The second approach uses arbitrarily quantization. The volume of the bins is not necessary uniform. As a result, it affects the effectiveness of the histogram significantly. In this paper, we propose to develop the color histogram by tessellating the non-cubical color gamut transformed from RGB cube using a vector quantization (VQ) method, the General Loyld Algorithm (GLA) . Using such approach, the problem of empty bins due to the gamut of the color coordinate system can be avoided. Besides, all bins quantized by GLA will occupy the same volume. It guarantees that uniformity of each quantized bins in the histogram. An experiment has been conducted to evaluate the quantitative performance of our approach. The image collection from UC Berkeley's digital library project is used as the test bed. The indexing effectiveness of a histogram space  is used as the measurement of the performance. The experimental result shows that using the GLA quantization approach significantly increase the indexing effectiveness.
In the context of product search in information intermediary or infomediary, text- and nevigation-based searching mechanisms such as keyword search are usually adopted . Google , WebSeer , and Alta Vista Photo Finder  are some prominent examples. However, such search mechanisms are not efficient for feature-based products and the major problem is that the feature-based products are difficult to be described with textual expression. A potential candidate for the search of feature-based products is query-by-example (QBE). However, our study reveals that QBE is not an ideal searching method for feature-based products. This paper proposes an image browsing technique for the search of feature-based products in infomediary. The image browsing technique allows the users to access feature-based products through a two-dimensional map constructed with self organizing map (SOM) technique. The technique overcomes the problem of describing feature-based products. Simple view and pick operations can drive the user to the desired group of products. A task-based user evaluation was conducted to examine the usability of the proposed technique and the experimental results show that the proposed browsing technique is more practical and efficient compared with QBE.
A common approach for color image processing is to first transform the RGB image to a new color space (such as LHS and YIQ), do the processing, and then transform back to RGB space. in many applications, only the luminance component is processed, and the hue and saturation are preserved. However, when the processed values in the new color system are transformed back to RGB, the transformed values may lie outside the RGB cube. Some researchers clip the processed luminance values before transformation from the processing color space to RGB space. This can lead to a loss of luminance contrast because many pixels may be clipped to approximately the same luminance value. Instead of clipping the luminance, this paper investigates clipping the saturation component. The gamuts of two color coordinate systems, LHS and YIQ, are studied. The procedures for saturation clipping are presented for both systems. Experimental results of clipping saturation and luminance in histogram equalization are presented in the paper. The results show that the contrast of the processed image is much better when saturation clipping is used. The luminance of the image can utilize the full dynamic range, thereby resulting in improved contrast enhancement.
Spatial quantization error and displacement error are inherent in automated visual inspection. This kind of error introduces significant distortion and dimensional uncertainty in the inspection of a part. For example, centroid, area, perimeter, length, and orientation of parts are inspected by the vision inspection system. This paper discusses the effect of the spatial quantization error and the displacement error on the precision dimensional measurement of an edge segment of a 3D model. Probabilistic analysis in terms of the resolution of the image is developed for one dimensional and two dimensional quantization error. The mean and variance of these errors are derived. The position and orientation errors (displacement error) of the active vision sensor are assumed to be normally distributed. The probabilistic analysis utilizes these errors and the angle of the line projected on the image. Using this analysis, one can determine whether a given set of sensor setting parameters in an active system is suitable to obtain a desired accuracy for specific line segment dimensional measurements. In addition, based on this approach, one can determine sensor positions and viewing direction which meet the necessary range for tolerance and accuracy of inspection. These mechanisms are helpful for achieving effective, economic, and accurate active inspection.