Incremental concept learning with few training examples and hierarchical classification

Object recognition and localization are important to automatically interpret video and allow better querying on its content. We propose a method for object localization that learns incrementally and addresses four key aspects. Firstly, we show that for certain applications, recognition is feasible with only a few training samples. Secondly, we show that novel objects can be added incrementally without retraining existing objects, which is important for fast interaction. Thirdly, we show that an unbalanced number of positive training samples leads to biased classi er scores that can be corrected by modifying weights. Fourthly, we show that the detector performance can deteriorate due to hard-negative mining for similar or closely related classes (e.g., for Barbie and dress, because the doll is wearing a dress). This can be solved by our hierarchical classi cation. We introduce a new dataset, which we call TOSO, and use it to demonstrate the e ectiveness of the proposed method for the localization and recognition of multiple objects in images.This research was performed in the GOOSE project, which is jointly funded by the enabling technology program Adaptive Multi Sensor Networks (AMSN) and the MIST research program of the Dutch Ministry of Defense. This publication was supported by the research program Making Sense of Big Data (MSoBD).peer-reviewe

From white elephant to Nobel Prize: Dennis Gabor’s wavefront reconstruction

Dennis Gabor devised a new concept for optical imaging in 1947 that went by a variety of names over the following decade: holoscopy, wavefront reconstruction, interference microscopy, diffraction microscopy and Gaboroscopy. A well-connected and creative research engineer, Gabor worked actively to publicize and exploit his concept, but the scheme failed to capture the interest of many researchers. Gabor’s theory was repeatedly deemed unintuitive and baffling; the technique was appraised by his contemporaries to be of dubious practicality and, at best, constrained to a narrow branch of science. By the late 1950s, Gabor’s subject had been assessed by its handful of practitioners to be a white elephant. Nevertheless, the concept was later rehabilitated by the research of Emmett Leith and Juris Upatnieks at the University of Michigan, and Yury Denisyuk at the Vavilov Institute in Leningrad. What had been judged a failure was recast as a success: evaluations of Gabor’s work were transformed during the 1960s, when it was represented as the foundation on which to construct the new and distinctly different subject of holography, a re-evaluation that gained the Nobel Prize for Physics for Gabor alone in 1971. This paper focuses on the difficulties experienced in constructing a meaningful subject, a practical application and a viable technical community from Gabor’s ideas during the decade 1947-1957

Retrieving groundwater depth in the lower reaches of Tarim River by NDVI

The changes of the coverage of vegetation and groundwater depth during the period of ecological construction and environmental protection are the most important two indicators of the level of success in ecological water transportation project in lower reaches of Tarim River.In this study, a new way to predict the groundwater depth in the arid regions has been presented. The spatial and temporal change of vegetation states in lower reaches of Tarim River under the ecological water transpiration have been discussed by using NDVI data derived from SPOT VEGETATION (VGT) NDVI S10 time sequence image data for the year 1999, 2003 and 2006. It is found that the groundwater depth played a dominant role in determining vegetation growth status in the lower reaches of the Tarim River. After the ecological water transportation, the vegetation has been restored in both sides of the watercourse stretching to Taitema Lake, which extend to 3 km in Akedun section, but decline along the stream flow as 1km in Kaogan section. However the area, which is 3km to 15km away from watercourse, has not been influenced obviously. And the area far away (excess 15km) has no influence. Statistic analysis shows that the groundwater depth has negative relationship with NDVI. And the groundwater depth in lower reaches of Tarim River has been successfully inversed through the statistic method; the simulation precision is 75%. © 2008 SPIE. (29 refs.