The binary descriptor based on Learned Arrangements of Three Patch Codes (LATCH) lacks of scale invariance and its rotation invariance depends upon feature detector, so a new feature detection and description algorithm was proposed based on Oriented fast and Rotated Binary robust independent elementary feature (ORB) and LATCH. Firstly, the Features from Accelerated Segment Test (FAST) was adopted to detect corner feature on the scale space of image pyramid. Then, the intensity centroid method of ORB was used to obtain orientation compensation. Finally, the LATCH was used to describe the feature. The experimental results indicate that, the proposed algorithm has the characteristics of low computational complexity, high real-time performance, rotation invariance and scale invariance. Under the same accuracy, the recall rate of the proposed algorithm is better than ORB and HARRIS-LATCH algorithm, the matching inner rate of the proposed algorithm is higher than ORB algorithm by 4.2 percentage points. In conclusion, the proposed algorithm can reduce the performance gap with histogram based algorithms such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) while maintaining the real-time property, and it can deal with image sequence in real-time quickly and exactly.

In order to reduce the time consumption in solving the problem of large-scale nonlinear 0-1 programming, an intelligent algorithm acceleration strategy based on the improved Markov neighborhood was presented by analyzing the characteristics of nonlinear 0-1 programming and the Markov process of intelligent algorithm. First, a rewritten model of nonlinear 0-1 programming problem was given. Next, an improved Markov neighborhood was constructed based on the rewritten model, and the reachable probability between two random statuses with its conditions under the improved Markov neighborhood was derived and proven. With a further analysis of the structure of nonlinear 0-1 programming together with the improved Markov neighborhood, a recursive updating strategy of the constraint and objective function was designed to accelerate the intelligent algorithms. The experimental results illustrate that the proposed strategy improves the operating efficiency of intelligent algorithms while keeping a correspondence with the original algorithms in search results.