Titlebook: Computer Vision -- ECCV 2014; 13th European Confer David Fleet,Tomas Pajdla,Tinne Tuytelaars Conference proceedings 2014 Springer Internati

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Human Detection Using Learned Part Alphabet and Pose Dictionarylow for robust, efficient matching. A pose dictionary is constructed from training examples, which is used to verify hypotheses at runtime. Experiments on standard benchmarks demonstrate that the proposed algorithm achieves state-of-the-art or competitive performance.

Landlocked

Detecting Snap Points in Egocentric Video with a Web Photo Priorf egocentric video from both human and mobile robot camera wearers, we show that the approach accurately isolates those frames that human judges would believe to be intentionally snapped photos. In addition, we demonstrate the utility of snap point detection for improving object detection and keyframe selection in egocentric video.

自由职业者

Spectral Edge Image Fusion: Theory and Applicationsient algorithm. Our approach is generic in that it can map any .-D image data to any .-D output, and can be used in a variety of applications using the same basic algorithm. In this paper we focus on the problem of mapping .-D inputs to 3-D colour outputs. We present results in three applications: h

ROOF

Spatio-chromatic Opponent Featuresspatial processing of standard opponent colour spaces, and these are the first chromatic descriptors that appear to achieve such performance levels individually. For all stages, parametrisations are suggested that allow successful optimisation using categorization performance as an objective. Classi

BOLT

Modeling Perceptual Color Differences by Local Metric Learningquisition conditions and computed only from rendered image colors. Using the theoretical framework of uniform stability, we provide consistency guarantees on the learned model. Moreover, our experimental evaluation shows its great ability (i) to generalize to new colors and devices and (ii) to deal

NIL

Online Graph-Based Trackingstanding performance with respect to the state-of-the-art trackers. We illustrate quantitative and qualitative performance of our algorithm in all the sequences in tracking benchmark and other challenging videos.

Grievance

Fast Visual Tracking via Dense Spatio-temporal Context Learningns. Implemented in MATLAB without code optimization, the proposed tracker runs at 350 frames per second on an i7 machine. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy and robustness.

ARM