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Active contour model (ACM) has widely used for segmenting two-phase images. However, its performance may not be satisfactory for some color texture images when their features cannot be effectively extracted. To alleviate this problem, in this paper, a novel neutrosophic set transformation matrix factorization-based active contour (NSTMF-AC) approach is proposed for color texture segmentation. The proposed NSTMF-AC is an effective and robust color texture segmentation method. Particularly, to effectively capture a wide range of texture information, the proposed method extracts the features from the triple domains, including spatial, wavelet, and spectral domains, and then uses neutrosophic set (NS) transform and the corresponding operations to reduce the indeterminacy contained in the image. Furthermore, the method obtains the resulting NS transformation matrix and utilizes a factorization-based ACM to perform image segmentation. Finally, the proposed method is compared with the state-of-the-art segmentation algorithms on a variety of natural images. The experimental results demonstrate that the proposed NSTMF-AC method is more robust for two-phase image segmentation than other methods.

Recent years have witnessed great progress of salient object detection methods. However, due to the emerging complex scenes, two problems should be solved urgently: one is on the fast locating of the foreground while preserving the precision, and the other is about reducing the noise near the foreground boundary in saliency maps. In this paper, a hybrid method is proposed to ameliorate the above two issues. At first, to reduce the essential runtime of integrating the prior knowledge, a novel Prior Knowledge Learning based Region Classification (PKL-RC) method is proposed for classifying image regions and preliminarily locating foreground; furthermore, to generate more accurate saliency, a Locality-constrained Linear self-Coding based Region Clustering (LLsC-RC) model is proposed to improve the adjacency structure of the similarity graph for Manifold Ranking (MR). Experimental results demonstrate the effectiveness and superiority of the proposed method in both higher precision and better smoothness.

The local binary pattern (LBP) model is a simple and effective method of texture classification, but it is sensitive to rotational and noisy images. Although many variants of LBP are proposed by scholars, there are still several urgent problems, such as poor noise and rotation immunity. In this paper, we propose a robust texture descriptor, jumping and refined local pattern (JRLP) for texture classification. In particular, we first extract jumping local difference count pattern (JLDCP) consisting of second-order difference count pattern and diagonal difference count pattern to represent the jumping information in a local domain. To capture the detail information left by JLDCP, we extract a refined completed LBP (RCLBP). By concatenating the JLDCP and RCLBP, we build a JRLP-based robust texture descriptor for classification. Experimental results on four representative texture databases (Brodatz, CUReT, UIUC, and VisTex) reveal that our proposed texture classification method is effective and robust for noise, rotation, scale, and illumination variants and outperforms six representative methods.

In complex scenes, foreground saliency can hardly be detected completely, which may further result in the ambiguous cues of objects for other computer vision tasks. In this letter, an extended locality-constrained linear self-coding (eLLsC) scheme is proposed to assist to solve the saliency detection problem under the complex scenes. The locality of both spatial relation and feature distance is preserved in eLLsC, thus making the transformed code involved in the manifold ranking to prompt the generation of the saliency map with more complete foreground and clearer boundary. Experimental results on three saliency detection benchmarks demonstrate the effectiveness of the proposed hybrid method.

Multi-scale analysis has been widely used for constructing texture descriptors by modeling the coefficients in transformed domains. However, the resulting descriptors are not robust to the rotated textures when performing texture classification. To alleviate this problem, we in this paper propose a multi-scale counting and difference representation (CDR) of image textures for texture classification. Particularly, we first extract a single-scale CDR feature consisting of the local counting vector (LCV) and the differential excitation vector (DEV). The LCV is established to capture different types of textural structures using the discrete local counting projection, while the DEV is used to describe the difference information of textures in accordance with the differential excitation projection. Finally, the multi-scale CDR feature of a texture image is constructed by combining CDRs at different scales. Experimental results on Brodatz, VisTex, and Outex databases demonstrate that our proposed multi-scale CDR-based texture classification method outperforms five representative texture classification methods.

The widely used energy features extracted from the wavelet domain can effectively represent the common image textures. However, they are not robust to the rotated textures. In this letter, we propose a multiscale rotation-invariant representation (MRIR) of textures by using multiscale sampling. Particularly, a multiscale wavelet transform is used to decompose the magnitude pattern (MP) mapping of a texture. Furthermore, the sign pattern (SP) mapping of a texture is used as a step function, which is further sampled and used to fit the wavelet subbands of the MP mapping for computing the sampled directional mean vectors (SDMVs) of the subbands. Moreover, we construct frequency vectors (FVs) of those SP mappings for capturing the structural information of textures. Finally, we can obtain the MRIR vector of an image texture by concatenating those SDMVs and FVs for texture classification. The comprehensive experimental results demonstrate that our proposed approach outperforms six representative texture classification methods.

In complex scenes, multiple objects are often concealed in cluttered backgrounds. Their saliency is difficult to be detected by using conventional methods, mainly because single color contrast can not shoulder the mission of saliency measure; other image features should be involved in saliency detection to obtain more accurate results. Using Dempster-Shafer (D-S) evidence theory based region classification, a novel method is presented in this paper. In the proposed framework, depth feature information extracted from a coarse map is employed to generate initial feature evidences which indicate the probabilities of regions belonging to foreground or background. Based on the D-S evidence theory, both uncertainty and imprecision are modeled, and the conflicts between different feature evidences are properly resolved. Moreover, the method can automatically determine the mass functions of the two-stage evidence fusion for region classification. According to the classification result and region relevance, a more precise saliency map can then be generated by manifold ranking. To further improve the detection results, a guided filter is utilized to optimize the saliency map. Both qualitative and quantitative evaluations on three publicly challenging benchmark datasets demonstrate that the proposed method outperforms the contrast state-of-the-art methods, especially for detection in complex scenes.

High environmental pressure exerts an external stress on the survival of microorganisms that are commonly found under normal pressure. In response, many growth traits alter, including cell morphology and physiology, cellular structure, metabolism, physical and chemical properties, the reproductive process, and defense mechanisms. The high-pressure technology (HP) has been industrially utilized in pressurized sterilization, synthesis of stress-induced products, and microbial/enzymatic transformation of chemicals. This article reviews current research on pressure-induced production of metabolites in normal-pressure microbes and their enzymatic reactions. Factors that affect the production of such metabolites are summarized, as well as the effect of pressure on the performance of microbial fermentation and the yield of flavoring compounds, different categories of induced enzymatic reactions and their characteristics in the supercritical carbon dioxide fluid, effects on enzyme activity, and the selection of desirable bacterial strains. Technological challenges are discussed, and future research directions are proposed. Information presented here will benefit the research, development, and application of the HP technology to improve microbial fermentation and enzymatic production of biologically active substances, thereby help to meet their increasing demand from the ever-expanding market.

We introduce a group of virtual sources for generating Lommel–Gauss beams based on beam superposition to analyze nonparaxial propagation. We typically derive the paraxial approximation and integral representations of the nth-order Lommel–Gauss beams. The first three orders of the nonparaxial corrections for the on-axis field of the Lommel–Gauss beams are analytically obtained. The on-axis intensity distribution of the corresponding nonparaxial corrections is also provided.