Tanghuai Fan

Artificial bee colony algorithm with accelerating convergence

To overcome the drawbacks of Artificial Bee Colony ABC algorithm, which converges slowly in the process of searching and easily suffers from premature, this paper presents an effective approach, called ABC with accelerating convergence AC-ABC. In the process of evolution, first, the employed bees position is regarded as the general centre position, the bees choose a location greedily as the new global optimal position in the original and general centre position; then we put the advantage of global optimal bee into evolution rule; we add the ability of best bees learning into the standard ABC and reduce the value of convergence factor linearly according to the iteration times, which can improve the convergence of the new algorithm effectively. Experiments are conducted on 12 test functions to verify the performance of AC-ABC; the results demonstrate promising performance of our method AC-ABC on convergence velocity, precision, and stability of solution.

New method of image background suppression based on soft morphology and Retinex theory

A new river flow measurement method based on graphic process has been proposed recently, which gets the velocity in optical imaging modality through measuring the continuous displacement of floating debris, then reconstructs a two-dimensional river surface velocity field by using the velocity of floating debris, and computes the section flow at last. However, the surface optical images have not only lights of target information, but also surface optical noise. It is difficult for reliable and stable continuous displacement detection of complex small observation target, which occupies only a small number of pixels comparing to a large field imaging area and has complex optical reflection properties. To solve this problem, this paper presents a background suppression method based on soft morphology and Retinex theory. Soft morphology is firstly used for the opening operation of the image, and then Retinex theory is used for optimal estimation of image incident component to suppress background of image. Finally, the simulations show that our method is superior to gray morphology and soft morphology on the performance of targets enhancement, noise filtering, and background suppression, and it has better background and targets discrimination quality subjective evaluation and higher signal-to-clutter ratio.

Multi-objective firefly algorithm based on compensation factor and elite learning

Abstract Aimed at early maturing and poor accuracy of multi-objective firefly algorithms, we propose a multi-objective firefly algorithm based on compensation factor and elite learning (CFMOFA). Based on iterations by introducing a compensation factor into the firefly learning formula, constraints by population can be overcome and the Pareto optimal solution can be approached in a reduced period. The non-inferior solutions produced in iterations were stored in the external archive and a random external archive particle was employed as the elite particle for population evolution. In this way, the detection range of firefly was extended and diversity and accuracy of non-inferior solution set were enhanced. The conventional algorithms, the improved algorithms and the proposed multi-objective optimization algorithm were tested and compared with each other. The results indicated great advantages of the proposed algorithm in convergence, diversity, and robustness and the proposed algorithm is an effective multi-objective optimization method.

Spectrum Analysis-Based Traffic Video Synopsis

Camera-based monitoring systems have a wide range of applications in traffic management, since they can collect more informative data in contrast to other sensors. An increasing number of traffic camera systems collect a large volume of traffic video data daily, forming the Big Data of traffic video. One of the challenges for traffic video processing is their high cost of resources and time, which seriously block the development of intelligent transportation systems. This paper proposes a spectrum analysis method for traffic video synopsis, including motion detection and tracking. Our method can largely remove the background noises and correctly extract motion information. Spatial and temporal spectrum analysis (Fourier transformation) are jointly used to detect objects and their motions in traffic videos. Further, the detected motions are tracked by the particle filter, generating trajectories of motions. Motion detection and tracking results given by our method can provide a synopsis for Big Data of traffic videos. The outperformance of our method is demonstrated comparing to the state of art video analysis methods.

Object tracking based on support vector dictionary learning

Dictionary learning is widely used to track targets in video sequences. However, a target can be lost during the tracking because of rotation, motion, background clutter, and so on. A dictionary learning method has recently been developed to reduce the chances of missing the target. We developed a new approach using support vector dictionary learning with histograms of sparse codes for a particle filter framework. The representation with support vector can help balance the residual between the candidate and the target. The experiments conducted on challenging sequences demonstrate that the proposed method outperforms seven state-of-the-art algorithms in terms of the overlap rate, center error, and accuracy.

Underwater Object Segmentation Based on Optical Features

Underwater optical environments are seriously affected by various optical inputs, such as artificial light, sky light, and ambient scattered light. The latter two can block underwater object segmentation tasks, since they inhibit the emergence of objects of interest and distort image information, while artificial light can contribute to segmentation. Artificial light often focuses on the object of interest, and, therefore, we can initially identify the region of target objects if the collimation of artificial light is recognized. Based on this concept, we propose an optical feature extraction, calculation, and decision method to identify the collimated region of artificial light as a candidate object region. Then, the second phase employs a level set method to segment the objects of interest within the candidate region. This two-phase structure largely removes background noise and highlights the outline of underwater objects. We test the performance of the method with diverse underwater datasets, demonstrating that it outperforms previous methods.

Color–depth multi-task learning for object detection in haze

Haze environments pose serious challenges for object detection, making existing methods difficult to generate satisfied results. However, there is no escape from haze environments in real-world applications, especially in water and bad weather. Hence, it is necessary to enable object detection methods to conquer the difficulties caused by the haze effect. In spite of the diversity between various conditions, haze environments share a common characteristic that the haze concentration is changed with the scene depth. Hence, this haze concentration feature can be used as a representation of the scene depth. This provides us a novel cue available for object detection in haze that the object-background depth contrast can be identified. In this paper, we propose a multi-task learning-based object detection method by jointly using the color and depth features. A pair of background models is built separately with the color and depth features, forming two streams of our multi-task learning framework. The final object detection results are generated by fusing the results given by color and depth features. In contrast to existing object detection methods, the novelty of our method lies in the combination of the color and depth features under a unified multi-task learning mechanism, which is experimentally demonstrated to be robust against challenging haze environments.

An On-line Automatic Flow Measurement Method for an Open Channel under Complex Flow Conditions

Buildings flow measurement method using the pre-established upstream and downstream water levels and flow to estimate the flow is the common method for open channel flow measurement. However, due to the changes of import and export, flow pattern, and hydraulic boundary conditions, traditional mechanism modeling-based flow measurement methods which establish the relation between the upstream-downstream water levels and flow by historical records and empirical equation models are usually not able to meet the demands of precision and adaptability. The improvement is based no the neural network (data-driving). However, the neural network based method is commonly offline and the model parameters are constant in the application.If the degree of opening of the weir sluice gate changes frequently, it is hard to construct a neural network model of high precision for on-line and real-time measurement. This research designs a real-time on-line automatic measurement system, for the Pi River canal weir gate, that collects upstream and downstream water levels and the degree of opening of the gate. Moreover, it establishes a three layer BP neural network model based on on-line real-time data correction. This model comprised of a Kalman filter with forgetting factor and a three layer BP neural network data fusion center. In contrast to the standard hydrometric propeller based method, the average relative error is lower than 5%, meeting the “River Discharge Measurement Criterion” proposed by Ministry of Water Resources of the Peoples Republic of China. Both the precision and the repeatability can cater for the engineering applications.