A novel filter is introduced in this paper to improve the ability of radiometric based method on suppressing impulse noise. Firstly, a new method is introduced to design the impulsive weight by measuring how impulsive a pixel is. Then, the impulsive weight is combined with the radiometric weight to obtain the evaluated values on each pixel in the whole corrupted image. The impulsive weight is mainly designed to suppress the impulse noise, while the radiometric weight is mainly designed to protect the noise-free pixel. Extensive experiments demonstrate that the proposed algorithm can perform much better than other filters in terms of the quantitative and qualitative aspects.

A robust pedestrian detection approach in thermal infrared imageries for an all-day surveillance is proposed. Firstly, the candidate regions which are likely to contain pedestrians are extracted based on a saliency detection method. Then a deep convolutional network with a multi-task loss is constructed to recognize the pedestrians. The experimental results show the superiority of the proposed approach in pedestrian detection.

Blob detection is an important part of computer vision and a special case of region detection with important applications in the image analysis. In this paper, the dilation operator in standard mathematical morphology is firstly extended to the order dilation operator of soft morphology, three soft morphological filters are designed by using the operator, and a novel blob detection algorithm called SMBD is proposed on that basis. SMBD had been proven to have better performance of anti-noise and blob shape detection than similar blob filters based on mathematical morphology like Quoit and N-Quoit in terms of theoretical and experimental aspects. Additionally, SMBD was also compared to LoG and DoH in different classes, which are the most commonly used blob detector, and SMBD also achieved significantly great results.

Our purpose in this study is to develop a scheme to segment the rectus abdominis muscle region in X-ray CT images. We propose a new muscle recognition method based on the shape model. In this method, three steps are included in the segmentation process. The first is to generate a shape model for representing the rectus abdominis muscle. The second is to recognize anatomical feature points corresponding to the origin and insertion of the muscle, and the third is to segment the rectus abdominis muscles using the shape model. We generated the shape model from 20 CT cases and tested the model to recognize the muscle in 10 other CT cases. The average value of the Jaccard similarity coefficient (JSC) between the manually and automatically segmented regions was 0.843. The results suggest the validity of the model-based segmentation for the rectus abdominis muscle.

It is shown that simple preprocessing on the reference signals in multichannel feedforward ANC system can improve the convergence performance of the adaptive ANC algorithm. A fast and efficient blind preprocessing algorithm in frequency domain is proposed to reduce the computational complexity even that the reference sensors are located far from the noise sources. The permutation problem at different frequency bin is also addressed and solved by an independent vector analysis algorithm. The basic principle and performance comparison are given to verify our conclusion.

Web Service Composition (WSC) has been well recognized as a convenient and flexible way of service sharing and integration in service-oriented application fields. WSC aims at selecting and composing a set of initial services with respect to the Quality of Service (QoS) values of their attributes (e.g., price), in order to complete a complex task and meet user requirements. A major research challenge of the QoS-aware WSC problem is to select a proper set of services to maximize the QoS of the composite service meeting several QoS constraints upon various attributes, e.g. total price or runtime. In this article, a fast algorithm based on QoS-aware sampling (FAQS) is proposed, which can efficiently find the near-optimal composition result from sampled services. FAQS consists of five steps as follows. 1) QoS normalization is performed to unify different metrics for QoS attributes. 2) The normalized services are sampled and categorized by guaranteeing similar number of services in each class. 3) The frequencies of the sampled services are calculated to guarantee the composed services are the most frequent ones. This process ensures that the sampled services cover as many as possible initial services. 4) The sampled services are composed by solving a linear programming problem. 5) The initial composition results are further optimized by solving a modified multi-choice multi-dimensional knapsack problem (MMKP). Experimental results indicate that FAQS is much faster than existing algorithms and could obtain stable near-optimal result.

Usually an FIR filter is used to model the physical paths in an active noise control system. However, the order of the filter to be modeled is a key factor for determining the computational load for the adaptive algorithms associated with active noise control (ANC), particularly for multi-channel algorithms. In this letter, the relationships among the filter's order, the plant modeling error and the location of poles for the transfer functions of the physical paths in an ANC system are theoretically examined and numerical examples are given to verify the theoretical results.