Computer vision and pattern recognition technologies make robots' eyes possible, which has rapidly promoted the development of robotic sciences. Barcode, which is simple but contains much information, has been used in navigation, manufacture and other situations by robots. Reading a barcode based on image analysis is an essential technique for such a robot system. Unfortunately, few existing image analysis methods about it are capable of identifying the codes under uncertain conditions, such as varying illumination, different sizes or orientations. This paper presents a method which can locate barcode region at an uncertain 3-D background through hierarchically extracting the distinct features of it with a high precision even for the broken, aberrant, partly blotted out stripes and other poor qualities of images. The method begins with a barcode quality assessment, in which a set of omnidirectional scan lines (active set lines) and a set of description of the whole image (resembling barcode measure), are defined. According to RBM, a series of corresponding methods are chosen to have a pre-processing process of the image and decode the codes respectively. The experiment results prove that our proposed method has two main characteristics. One is fast to locate barcode (get ROI of barcode). The other is to read barcode under unpredicted conditions robustly.

A Robust Barcode Reading Method Based on Image Analysis of a Hierarchical Feature Classification

The surface color of the mature tomato has little difference with the background color or the entire fruit surface color has inconsistent under the nature growth condition. This fruit image segmentation is very difficulty with the color threshold or the segmentation result is the serious error of the goal outline. According to the near-infrared spectrum and the obvious spectrum respectively effective biology information, the revision and the complement of the harvesting goal outline can been obtained by using the multi-sensor image fusion technology. Taken harvesting tomato into account as an example, based on mutual information best threshold value iteration automatic optimization segmentation (MI-OPT) and the morphology restructuring watershed segmentation are applied to the multi-sensor fusion image of the same target. The shape feature (circle shape, concavity, area ratio) and texture feature (uniformity) are extracted respectively on the basis of morphological process of binary image. And flow of tomato recognition and its growth states' distinguish are determined according to selected feature quantities. Keywords: Image segmentation; multi-spectral images; image fusion; mutual information; target identification.

Target Identification and Growth State Distinguish of Tomato

In order to make the morphological edge detection method have strong anti-noise and accurate edge extraction ability, an improved edge detection method based on composite morphology is proposed. Firstly, two different sets of structural elements are constructed. Then an improved morphology gradient operator is used in edge detection. Finally, the final edge is obtained according to the weights which are determined by the extended spatial frequency. The simulation results show that the method can extract more complete and coherent image edge, and it also has a strong noise-restrain ability.

An Improved Edge Detection Method Based on Composite Morphology

An aim to solve the problem of missing details of image and discontinuous edge. A new method of image edge detection based on the least mean square filter and mathematical morphology is presented. Experiment results show that using this method to detect edge we can effectively get rid of noise and correctly detect the continuous edge of image.

Edge Detection Based on Least Mean Square Filter and Mathematical Morphology

A novel method for optimal morphological filtering parameters, namely the genetic training algorithm for morphological filters (GTAMF), which adopts new crossover and mutation operators called the curved cylinder crossover and master-slave mutation, is presented in this paper Experimental results show that this method is practical, easy to extend, and improves the performances of morphological filters The operation of a morphological filer can be divided into two basic problems that include morphological operation and structuring element (SE) selection The rules for morphological operations are predefined so the filter's properties depend merely on the selection of SE By means of adaptive optimizing training, structuring elements possess the shape and structural characteristics of image targets, namely some information can be obtained by SE Morphological filters formed in this way become intelligent and can provide good filtering results and robust adaptability to image targets with clutter background

Qu En

Papers

Morphological filter based on genetic learning