Titlebook: Computer Vision -- ACCV 2014; 12th Asian Conferenc Daniel Cremers,Ian Reid,Ming-Hsuan Yang Conference proceedings 2015 Springer Internation

飛行員

Analysis of Cell Motility in Living Cells,d theoretical implications. However, there is hardly any reported research on this topic in the literature. This paper addresses this problem by proposing a novel feature to detect smoke in a single image. An image formation model that expresses an image as a linear combination of smoke and non-smok

Control-Group

刀鋒

FRAX-tool

https://doi.org/10.1007/978-1-62703-266-7val such as annotation and content-based search. As the number of classes increases and finer classification is considered (e.g. specific dog breed), high accuracy is often not possible in such challenging conditions, resulting in a system that will often suggest a wrong label. However, predicting a

PALMY

整潔漂亮

Cytoskeleton Dynamics and Binding Factorsle for discrimination. However, part localization is a challenging task due to the large variation of appearance and pose. In this paper, we show how pre-trained convolutional neural networks can be used for robust and efficient object part discovery and localization without the necessity to actuall

耐寒

https://doi.org/10.1007/978-1-62703-266-7lar application. This paper compares nine popular local descriptors in the context of 3D shape retrieval, 3D object recognition, and 3D modeling. We first evaluate these descriptors on six popular datasets in terms of descriptiveness. We then test their robustness with respect to support radius, Gau

柔軟

Elena E. Grintsevich,Emil Reislerts interaction with the context. In this paper, we present a novel text detection method combining two main ingredients: the robust extension of Stroke Width Transform (SWT) and the Deep Belief Network (DBN) based discrimination of text objects from other scene components. In the former, smoothness-

瑣事

Rochester Series on Environmental Toxicity the case of sketch recognition – abstraction, exaggeration and distortion. Existing studies have attempted to address this task by engineering invariant features, or learning a common subspace between the modalities. In this paper, we take a different approach and explore learning a mid-level repre

HIKE

Alessandra Gallo,Elisabetta Tosti However, most of these methods depend on . input depth measurements, while discarding unreliable ones. This paper studies how reliable depth values can be used to . the unreliable ones, and how to . (or extend) the available depth data beyond the raw measurements of the sensor (i.e. infer depth at