Automatic detection and classification of football players

Color segmentation of images usually requires a manual selection and classification of samples to train the system. This paper presents an automatic system that performs these tasks without the need of a long training, providing a useful tool to detect and identify figures. In real situations, it is necessary to repeat the training process if light conditions change, or if, in the same scenario, the colors of the figures and the background may have changed, being useful a fast training method. A direct application of this method is the detection and identification of football players.

Semantic feature-based visual attention model for pedestrian detection

Objective
Pedestrian detection under video surveillance systems has always been a hot topic in computer vision research. These systems are widely used in train stations, airports, large commercial plazas, and other public places. However, pedestrian detection remains difficult because of complex backgrounds. Given its development in recent years, the visual attention mechanism has attracted increasing attention in object detection and tracking research, and previous studies have achieved substantial progress and breakthroughs. We propose a novel pedestrian detection method based on the semantic features under the visual attention mechanism.
Method
The proposed semantic feature-based visual attention model is a spatial-temporal model that consists of two parts: the static visual attention model and the motion visual attention model. The static visual attention model in the spatial domain is constructed by combining bottom-up with top-down attention guidance. Based on the characteristics of pedestrians, the bottom-up visual attention model of Itti is improved by intensifying the orientation vectors of elementary visual features to make the visual saliency map suitable for pedestrian detection. In terms of pedestrian attributes, skin color is selected as a semantic feature for pedestrian detection. The regional and Gaussian models are adopted to construct the skin color model. Skin feature-based visual attention guidance is then proposed to complete the top-down process. The bottom-up and top-down visual attentions are linearly combined using the proper weights obtained from experiments to construct the static visual attention model in the spatial domain. The spatial-temporal visual attention model is then constructed via the motion features in the temporal domain. Based on the static visual attention model in the spatial domain, the frame difference method is combined with optical flowing to detect motion vectors. Filtering is applied to process the field of motion vectors. The saliency of motion vectors can be evaluated via motion entropy to make the selected motion feature more suitable for the spatial-temporal visual attention model.
Result
Standard datasets and practical videos are selected for the experiments. The experiments are performed on a MATLAB R2012a platform. The experimental results show that our spatial-temporal visual attention model demonstrates favorable robustness under various scenes, including indoor train station surveillance videos and outdoor scenes with swaying leaves. Our proposed model outperforms the visual attention model of Itti, the graph-based visual saliency model, the phase spectrum of quaternion Fourier transform model, and the motion channel model of Liu in terms of pedestrian detection. The proposed model achieves a 93% accuracy rate on the test video.
Conclusion
This paper proposes a novel pedestrian method based on the visual attention mechanism. A spatial-temporal visual attention model that uses low-level and semantic features is proposed to calculate the saliency map. Based on this model, the pedestrian targets can be detected through focus of attention shifts. The experimental results verify the effectiveness of the proposed attention model for detecting pedestrians.

Real-time GPU color-based segmentation of football players

In this paper, we propose a multi-camera application capable of processing high resolution images and extracting features based on colors patterns over graphic processing units (GPU). The goal is to work in real time under the uncontrolled environment of a sport event like a football match. Since football players are composed for diverse and complex color patterns, a Gaussian Mixture Models (GMM) is applied as segmentation paradigm, in order to analyze sport live images and video. Optimization techniques have also been applied over the C++ implementation using profiling tools focused on high performance. Time consuming tasks were implemented over NVIDIA's CUDA platform, and later restructured and enhanced, speeding up the whole process significantly. Our resulting code is around 4-11 times faster on a low cost GPU than a highly optimized C++ version on a central processing unit (CPU) over the same data. Real time has been obtained processing until 64 frames per second. An important conclusion derived from our study is the scalability of the application to the number of cores on the GPU. © 2011 Springer-Verlag.

Using entropy based mean shift filter and modified watershed transform for grain segmentation

Life science research aims to continuously improve the quality and standard of human life. One of the major challenges in this area is to maintain food safety and security. A number of image processing techniques have been used to investigate the quality of food products. In this paper,we propose a new algorithm to effectively segment connected grains so that each of them can be inspected in a later processing stage. One family of the existing segmentation methods is based on the idea of watersheding, and it has shown promising results in practice.However,due to the over-segmentation issue,this technique has experienced poor performance in various applications,such as inhomogeneous background and connected targets. To solve this problem,we present a combination of two classical techniques to handle this issue.In the first step,a mean shift filter is used to eliminate the inhomogeneous background, where entropy is used to be a converging criterion. Secondly,a color gradient algorithm is used in order to detect the most significant edges, and a marked watershed transform is applied to segment cluttered objects out of the previous processing stages. The proposed framework is capable of compromising among execution time, usability, efficiency and segmentation outcome in analyzing ring die pellets. The experimental results demonstrate that the proposed approach is effectiveness and robust.

Peptide leucine arginine, a potent immunomodulatory peptide isolated and structurally characterized from the skin of the Northern Leopard frog, Rana pipiens

On the basis of histamine release from rat peritoneal mast cells, an octadecapeptide was isolated from the skin extract of the Northern Leopard frog (Rana pipiens), This peptide was purified to homogeneity using reversed-phase high performance liquid chromatography and found to have the following primary structure by Edman degradation and pyridylethylation: LVRGCWTKSYPPKPCFVR, in which Cys(5) and Cys(15) are disulfide bridged. The peptide was named peptide leucine-arginine (pLR), reflecting the N- and C-terminal residues. Molecular modeling predicted that pLR possessed a rigid tertiary loop structure with flexible end regions, pLR was synthesized and elicited rapid, noncytolytic histamine release that had a a-fold greater potency when compared with one of the most active histamine-liberating peptides, namely melittin, pLR was able to permeabilize negatively charged unilamellar lipid vesicles but not neutral vesicles, a finding that was consistent with its nonhemolytic action, pLR inhibited the early development of granulocyte macrophage colonies from bone marrow stem cells but did not induce apoptosis of the end stage granulocytes, i,e. mature neutrophils, pLR therefore displays biological activity with both granulopoietic progenitor cells and mast cells and thus represents a novel bioactive peptide from frog skin.

Granular gland transcriptomes in stimulated amphibian skin secretions.

Amphibian defensive skin secretions are complex, species-specific cocktails of biologically active molecules, including many uncharacterized peptides. The study of such secretions for novel peptide discovery is time-limited, as amphibians are in rapid global decline. While secretion proteome analysis is non-lethal, transcriptome analysis has until now required killing of specimens prior to skin dissection for cDNA library construction. Here we present the discovery that polyadenylated mRNAs encoding dermal granular gland peptides are present in defensive skin secretions, stabilized by endogenous nucleic acid-binding amphipathic peptides. Thus parallel secretory proteome and transcriptome analyses can be performed without killing the specimen in this model amphibian system--a finding that has important implications in conservation of biodiversity within this threatened vertebrate taxon and whose mechanistics may have broader implications in biomolecular science.