Detección de personas basada en HOG y SVM

El presente proyecto se centra en proteger a los peatones de las vías urbanas o de una fábrica donde conviven con robots móviles, pues son los mayores afectados en los acci- dentes producidos en estos entornos. El objetivo es diseñar un algoritmo basado en visión monocular capaz de detectar a los usuarios de forma rápida y precisa de tal forma que se tenga constancia en todo momento de los peatones que se encuentran delante del vehículo.

Efficient HOG human detection

While Histograms of Oriented Gradients (HOG) plus Support Vector Machine (SVM) (HOG+SVM) is the most successful human detection algorithm, it is time-consuming. This paper proposes two ways to deal with this problem. One way is to reuse the features in blocks to construct the HOG features for intersecting detection windows. Another way is to utilize sub-cell based interpolation to efficiently compute the HOG features for each block. The combination of the two ways results in significant increase in detecting humans-more than five times better. To evaluate the proposed method, we have established a top-view human database. Experimental results on the top-view database and the well-known INRIA data set have demonstrated the effectiveness and efficiency of the proposed method. (C) 2010 Elsevier B.V. All rights reserved.

FPGA Soft-core Processors, Compiler and Hardware Optimizations validated using HOG

There is demand for an easily programmable, high performance image processing platform based on FPGAs. In previous work, a novel, high performance processor - IPPro was developed and a Histogram of Orientated Gradients (HOG) algorithm study undertaken on a Xilinx Zynq platform. Here, we identify and explore a number of mapping strategies to improve processing efficiency for soft-cores and a number of options for creation of a division coprocessor. This is demonstrated for the revised high definition HOG implementation on a Zynq platform, resulting in a performance of 328 fps which represents a 146% speed improvement over the original realization and a tenfold reduction in energy.

Note from the Port Dalhousie and Thorold Railway to Mrs. E. Parnell with prices for digging a well, moving a hog pen and repairing a fence

Note from the Port Dalhousie and Thorold Railway to Mrs. E. Parnell with prices for digging a well, moving a hog pen and repairing a fence, n.d.

Benchmark Evaluation of HOG Descriptors as Features for Classification of Traffic Signs

The purpose of this paper is to analyze the performance of the Histograms of Oriented Gradients (HOG) as descriptors for traffic signs recognition. The test dataset consists of speed limit traffic signs because of their high inter-class similarities.   HOG features of speed limit signs, which were extracted from different traffic scenes, were computed and a Gentle AdaBoost classifier was invoked to evaluate the different features. The performance of HOG was tested with a dataset consisting of 1727 Swedish speed signs images. Different numbers of HOG features per descriptor, ranging from 36 features up 396 features, were computed for each traffic sign in the benchmark testing. The results show that HOG features perform high classification rate as the Gentle AdaBoost classification rate was 99.42%, and they are suitable to real time traffic sign recognition. However, it is found that changing the number of orientation bins has insignificant effect on the classification rate. In addition to this, HOG descriptors are not robust with respect to sign orientation.

Hog dreaming

Hog dreaming is representative of the 'notion of rupture/rapture and the contradictory thought that the space we represent ourselves and others in do not always designate the human in us; they can also triangulate and expose that secret and hidden part of us that is animal.'

ChIP-seq reveals a role for CrzA in the Aspergillus fumigatus high-osmolarity glycerol response (HOG) signalling pathway

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Variation of Environmental Regulation Stringency Among the Top Ten U.S. Hog Producing States

The U.S. hog industry, once primarily made up of small owner-operated crop-hog farms, has become dominated by large specialized operations characterized by low costs and improved technologies in livestock management. Such changes have triggered concerns over the dangers large Hog Feeding Operations (HFOs) are likely to pose to the environment. In 2007, the top ten states accounted for more than 85 percent of total U.S. hog production (Iowa (IA), North Carolina (NC), Minnesota (MN), Illinois (IL), Nebraska (NE), Indiana (IN), Missouri (MO), Oklahoma (OK), Ohio (OH), and Kansas (KS)). With such domination on production, these states are often the subject of environmental debate relating to hog production. When farmers are required to incorporate environmental measures in hog production, their costs of production increase. Metcalfe (2001) found that small HFOs have found it difficult to cope with such costs and many have exited the industry, while large operations have not been affected at the same level. Due to the variation of environmental regulations among states, other operations moved to states with lax regulations (e.g. NC prior to the late 1990s). Such regulations appear to have played a major role in shaping the structure of the hog industry.