An improved image segmentation algorithm for salient object detection

Semantic object detection is one of the most important and challenging problems in image analysis. Segmentation is an optimal approach to detect salient objects, but often fails to generate meaningful regions due to over-segmentation. This paper presents an improved semantic segmentation approach which is based on JSEG algorithm and utilizes multiple region merging criteria. The experimental results demonstrate that the proposed algorithm is encouraging and effective in salient object detection

Ontology-based image annotation

With regard to the long-standing problem of the semantic gap between low-level image features and high-level human knowledge, the image retrieval community has recently shifted its emphasis from low-level features analysis to high-level image semantics extrac- tion. User studies reveal that users tend to seek information using high-level semantics. Therefore, image semantics extraction is of great importance to content-based image retrieval because it allows the users to freely express what images they want. Semantic content annotation is the basis for semantic content retrieval. The aim of image anno- tation is to automatically obtain keywords that can be used to represent the content of images. The major research challenges in image semantic annotation are: what is the basic unit of semantic representation? how can the semantic unit be linked to high-level image knowledge? how can the contextual information be stored and utilized for image annotation? In this thesis, the Semantic Web technology (i.e. ontology) is introduced to the image semantic annotation problem. Semantic Web, the next generation web, aims at mak- ing the content of whatever type of media not only understandable to humans but also to machines. Due to the large amounts of multimedia data prevalent on the Web, re- searchers and industries are beginning to pay more attention to the Multimedia Semantic Web. The Semantic Web technology provides a new opportunity for multimedia-based applications, but the research in this area is still in its infancy. Whether ontology can be used to improve image annotation and how to best use ontology in semantic repre- sentation and extraction is still a worth-while investigation. This thesis deals with the problem of image semantic annotation using ontology and machine learning techniques in four phases as below. 1) Salient object extraction. A salient object servers as the basic unit in image semantic extraction as it captures the common visual property of the objects. Image segmen- tation is often used as the �rst step for detecting salient objects, but most segmenta- tion algorithms often fail to generate meaningful regions due to over-segmentation and under-segmentation. We develop a new salient object detection algorithm by combining multiple homogeneity criteria in a region merging framework. 2) Ontology construction. Since real-world objects tend to exist in a context within their environment, contextual information has been increasingly used for improving object recognition. In the ontology construction phase, visual-contextual ontologies are built from a large set of fully segmented and annotated images. The ontologies are composed of several types of concepts (i.e. mid-level and high-level concepts), and domain contextual knowledge. The visual-contextual ontologies stand as a user-friendly interface between low-level features and high-level concepts. 3) Image objects annotation. In this phase, each object is labelled with a mid-level concept in ontologies. First, a set of candidate labels are obtained by training Support Vectors Machines with features extracted from salient objects. After that, contextual knowledge contained in ontologies is used to obtain the �nal labels by removing the ambiguity concepts. 4) Scene semantic annotation. The scene semantic extraction phase is to get the scene type by using both mid-level concepts and domain contextual knowledge in ontologies. Domain contextual knowledge is used to create scene con�guration that describes which objects co-exist with which scene type more frequently. The scene con�guration is represented in a probabilistic graph model, and probabilistic inference is employed to calculate the scene type given an annotated image. To evaluate the proposed methods, a series of experiments have been conducted in a large set of fully annotated outdoor scene images. These include a subset of the Corel database, a subset of the LabelMe dataset, the evaluation dataset of localized semantics in images, the spatial context evaluation dataset, and the segmented and annotated IAPR TC-12 benchmark

Discovery of N-arylsulfonyl-3-acylindole benzoyl hydrazone derivatives as anti-HIV-1 agents

The discovery and development of novel inhibitors with activity against variants of human immunodeficiency virus type 1 (HIV-1) is pivotal for overcoming treatment failure. As our ongoing work on research of anti-HIV-1 inhibitors, 32 N-arylsulfonyl-3-acylindole benzoyl hydrazone derivatives were prepared by introduction of the hydrazone fragments on the N-arylsulfonyl-3-acylindolyl skeleton and preliminarily screened in vitro as HIV-1 inhibitors for the first time. Among of all the reported analogues, eight compounds exhibited significant anti-HIV-1 activity, especially N-(3-nitro)phenylsulfonyl-3- acetylindole benzoyl hydrazone (18) and N-(3-nitro)phenylsulfonyl-3-acetyl-6-methylindole benzoyl hydrazone (23) displayed the most potent anti-HIV-1 activity with EC50 values of 0.26 and 0.31 μg/mL, and TI values of >769.23 and >645.16, respectively. It is noteworthy that introduction of R3 as the methyl group and R2 as the hydrogen group could result in more potent compounds. This suggested that introduction of R3 as the methyl group could be taken into account for further preparation of these kinds of compounds as anti-HIV-1 agents

Wpływ ekologicznych działań badawczo-rozwojowych na emisje SO2 w Chinach – dane z panelowego modelu progowego

Previous studies on the effectiveness of improving sustainable development have acknowledged the importance of domestic research and development (R&D) activities. However, these studies remain general and ambiguous because they assume that all R&D activities are related to energy-saving and sustainable development. The corresponding empirical evidence is scabrous and ambiguous. In this paper, we focus on the effect of green innovation R&D activities on SO2 emission which is an important greenhouse gas affect global climate change and eco-civilization. Considering that there is heterogeneity exists in the innovation activities, the R&D activities are divided into three performers with two purposes. The empirical results based on a Chinese inter-provincial dataset of 2000-2016 suggest that the green innovation R&D activities are crucial for the reduction of the SO2 emission. However, the innovation R&D activities of different purposes and performers show statistically differentiated effects on SO2 emission. The major positive effect of green innovation R&D activities on SO2 emissions reduction is mainly from enterprises and utility-type of R&D activities. A further study based on the panel threshold also indicates that effects of green innovation R&D activities on SO2 emissions are nonlinear, depending on the technology absorptive ability.Dotychczasowe badania nad zrównoważonym rozwojem potwierdziły znaczenie krajowych działań badawczo-rozwojowych (B + R). Jednak badania te pozostają ogólne i niejednoznaczne, ponieważ zakładają, że wszystkie działania B + R są związane z energooszczędnością i zrównoważonym rozwojem. Odpowiednie dowody empiryczne są niejednoznaczne. W artykule skupiamy się na wpływie działań badawczo-rozwojowych związanych z zielonymi innowacjami na emisję SO2, który jest ważnym gazem cieplarnianym, wpływającym na globalne zmiany klimatyczne. Biorąc pod uwagę, że istnieje heterogeniczność działań innowacyjnych, działalność B + R wskazano 3 aktorów z 2 celami. Wyniki empiryczne oparte na chińskim międzyprowincjalnym zbiorze danych z lat 2000-2016 sugerują, że działania badawczo-rozwojowe związane z zielonymi innowacjami są kluczowe dla redukcji emisji SO2. Jednak innowacyjne działania o różnych celach i różnych wykonawcach wykazują statystycznie zróżnicowany wpływ na emisję SO2. Główny pozytywny wpływ działań B + R w zakresie zielonych innowacji na redukcję emisji SO2 wynika głównie z działalności przedsiębiorstw i działalności B + R o charakterze użytkowym. Dalsze badanie oparte na panelu wskazuje również, że wpływ działań badawczo-rozwojowych związanych z zielonymi innowacjami na emisje SO2 jest nieliniowy, w zależności od zdolności absorpcyjnej technologii

Initial partial response and stable disease according to RECIST indicate similar survival for chemotherapeutical patients with advanced non-small cell lung cancer

<p>Abstract</p> <p>Background</p> <p>Stable disease (SD) has ambiguous clinical significance for patients according to the dominant Response Evaluation Criteria in Solid Tumours (RECIST). The primary aims of the study were: (1) to clarify the clinical significance of SD by comparing the progression-free survival (PFS) of response and SD patients with advanced non-small cell lung cancer (NSCLC) after the first two courses of the standard first-line platinum-based chemotherapy; (2) to explore the relationship between the percentage change in tumour size and PFS among initial SD patients, in order to provide some guidance for clinicians in deciding continuation/termination of the current treatment at a relative early time.</p> <p>Methods</p> <p>A total of 179 advanced NSCLC patients whose baseline CT image was available for review were included in the study. Another CT image was taken in the initial assessment after chemotherapy. A comparison of PFS between initial partial response (PR) and SD was used to determine whether significant differences exist. The relationship between the early percentage of change in tumour size of initial SD patients and their PFS was investigated. In addition, overall survival (OS), the secondary endpoint in this study, was investigated as well.</p> <p>Results</p> <p>Patients with initial PR are not significantly distinguished from those with initial SD when their PFS is concerned (median PFS 249 days [95% confidence interval, 187-310 days] versus 220 days [95% confidence interval, 191-248 days], p > 0.05). Their median OS was 364 days (95% confidence interval, 275-452 days) for the initial PR patients versus 350 days (95% confidence interval, 293-406 days) for the initial SD patients, which suggests no significant difference as well p > 0.05). In addition, all the initial SD patients enjoyed similar PFS and OS.</p> <p>Conclusions</p> <p>Initial PR and SD enjoy similar PFS and OS for patients with advanced NSCLC. Within the initial SD subgroup, different percentages of tumour shrinkage or increase undergo similar PFS and OS. RECIST remains a reliable norm in assessing the effectiveness of chemotherapy for patients with advanced NSCLC before functional assessment has been integrated into the criteria.</p

Real-time power line extraction from Unmanned Aerial System video images

In this paper a real-time vision based power line extraction solution is investigated for active UAV guidance. The line extraction algorithm starts from ridge points detected by steerable filters. A collinear line segments fitting algorithm is followed up by considering global and local information together with multiple collinear measurements. GPU boosted algorithm implementation is also investigated in the experiment. The experimental result shows that the proposed algorithm outperforms two baseline line detection algorithms and is able to fitting long collinear line segments. The low computational cost of the algorithm make suitable for real-time applications

Fast power line detection and localization using steerable filter for active UAV guidance

In this paper we present a fast power line detection and localisation algorithm as well as propose a high-level guidance architecture for active vision-based Unmanned Aerial Vehicle (UAV) guidance. The detection stage is based on steerable filters for edge ridge detection, followed by a line fitting algorithm to refine candidate power lines in images. The guidance architecture assumes an UAV with an onboard Gimbal camera. We first control the position of the Gimbal such that the power line is in the field of view of the camera. Then its pose is used to generate the appropriate control commands such that the aircraft moves and flies above the lines. We present initial experimental results for the detection stage which shows that the proposed algorithm outperforms two state-of-the-art line detection algorithms for power line detection from aerial imagery