8367818
Segment Hough Transform -- a Novel Hough-based Algorithm for Curve Detection
A new Hough-based algorithm termed segment Hough transform (SHT) is proposed to detect the curve in the binary image. The segment-transform, which is the main idea of the novel approach, makes the curve detection very efficient. Thanks to the chain code termed angle chain code (ACC) as well as the modified direction measurement, the segmentation operation is also considerably fast and precise. Compared with the conventional Hough transform (CHT), our algorithm is dramatically faster (by over 20 times) And when the background is complex, the SHT is about four times faster than the random Hough transform (RHT), Furthermore, the proposed algorithm inherits the high robustness from CHT. In some situations, it is even more accurate. We implement the novel algorithm in an embedded system to estimate passengers flow on the bus. The detection rate is over 95% which indicates it is quite suitable for real-time application.
31735857
Research on Vehicle Image Classifier Based on Concentration Regulating of Immune Clonal Selection
For time-variation and nonlinearity of vehicle image classifier design in complicated environment, a novel classifier based on Immune Clone Concentration Clustering Algorithm (ICCCA) was proposed, inspired by the clone selection theory in the natural immune system. It overcame the defects of traditional classification algorithms as multi-restraint and trap of local optimum. It also avoided the high complexity and regulating dissimilation caused by the cross and variation rules of exiting immune clone clustering algorithms. Directing towards two-class image pattern recognition problem, we defined the antibody and antigen of vehicle image, established class divisibility standard, and designed the affine mathematical model based on the antibody and antigen's Euclidean distance and their concentration. We proposed a method to create immune clone memory cell by the antibodies concentration as long as concentration regulating. The algorithm can guarantee the antibody diversity, and obtain the global optimal solution quickly and precisely. The experimental results verified that the classification accuracy of the algorithm is superior to other classification algorithm.
6194383
Deep Web Navigation by Example
An arc furnace for quickly and continuously melting directly reduced iron ore so as to make steel, wherein the furnace wall above the hearth line and the furnace roof have partly spherical inner surfaces, the spherical surface defining these inner surfaces having the center located adjacent to the point equidistantly spaced apart from the lower ends of electrodes, and the electrodes are inclined at an angle to the vertical so that the distance between the lower ends thereof may be reduced to a minimum.
14617194
Effects of Suffix Repetition Rates of a String on the Performance of String Matching Algorithms
The highly efficient Boyer-Moore’s string matching algorithm utilizes information on multi-occurrences of string suffixes in a pattern string to avoid backtracks in searching the pattern string. One hypothesis is that Boyer-Moore’s algorithm even benefits more from highly self-repetitive patterns. In this paper, the author studies how multi-occurrences of string suffixes affect the performance of the Boyer-Moore’s algorithm as well as some other well known string search algorithms. The paper introduces a new concept of suffix repetition rate (SRR) to measure how frequently the suffixes of a string occur inside of the string. Using this measurement, experiments with several thousands patterns over the entire range of SRRs have been carried out, and the results show that increasing of SRR on pattern strings does not improve the performance of a searching algorithm in terms of efficiency.
20945105
Real-Time Moving Vehicle Detection With Cast Shadow Removal in Video Based on Conditional Random Field
This paper presents an approach of moving vehicle detection and cast shadow removal for video based traffic monitoring. Based on conditional random field, spatial and temporal dependencies in traffic scenes are formulated under a probabilistic discriminative framework, where contextual constraints during the detection process can be adaptively adjusted in terms of data-dependent neighborhood interaction. Computationally efficient algorithm has been developed to discriminate moving cast shadows and handle nonstationary background processes for real-time vehicle detection in video streams. Experimental results show that the proposed approach effectively fuses contextual dependencies and robustly detects moving vehicles under heavy shadows even in grayscale video.
16089092
Initial experiments of a needle-holding robot for microwave ablation therapy
A needle-holding robot for microwave ablation therapy is developed to free two hands of the surgeon, so that more effort can be concentrated on monitoring the ablation procedure, which in turn guarantees good tumor ablation effect. The overall architecture of the robot is introduced first with the explanation of the mechanism of 3D motion tracking and how to minimize damping force for the robotic hand. Two experiments were conducted, one to quantify the force required for motion tracking, the other to assess the performance of motion tracking. The results of force-measuring experiment show that the force required for rotation and radial translation motion tracking are less than 15 grams, which indicates the high sensitivity of the holding robot. The results of in-vivo animal test performed on a swine show that the holding robot is accurate enough in tracking the 3D motion of the ablation needle, which allows for stable and precise holding of the needle compared to that of freehand holding.
2919977
High-Order Pattern Discovery from Discrete-Valued Data
To uncover qualitative and quantitative patterns in a data set is a challenging task for research in the area of machine learning and data analysis. Due to the complexity of real-world data, high-order (polythetic) patterns or event associations, in addition to first-order class-dependent relationships, have to be acquired. Once the patterns of different orders are found, they should be represented in a form appropriate for further analysis and interpretation. The authors propose a novel method to discover qualitative and quantitative patterns (or event associations) inherent in a data set. It uses the adjusted residual analysis in statistics to test the significance of the occurrence of a pattern candidate against its expectation. To avoid exhaustive search of all possible combinations of primary events, techniques of eliminating the impossible pattern candidates are developed. The detected patterns of different orders are then represented in an attributed hypergraph which is lucid for pattern interpretation and analysis. Test results on artificial and real-world data are discussed toward the end of the paper.