18,672 research outputs found
Acceleration of stereo-matching on multi-core CPU and GPU
This paper presents an accelerated version of a
dense stereo-correspondence algorithm for two different parallelism
enabled architectures, multi-core CPU and GPU. The
algorithm is part of the vision system developed for a binocular
robot-head in the context of the CloPeMa 1 research project.
This research project focuses on the conception of a new clothes
folding robot with real-time and high resolution requirements
for the vision system. The performance analysis shows that
the parallelised stereo-matching algorithm has been significantly
accelerated, maintaining 12x and 176x speed-up respectively
for multi-core CPU and GPU, compared with non-SIMD singlethread
CPU. To analyse the origin of the speed-up and gain
deeper understanding about the choice of the optimal hardware,
the algorithm was broken into key sub-tasks and the performance
was tested for four different hardware architectures
Demystifying Neural Style Transfer
Neural Style Transfer has recently demonstrated very exciting results which
catches eyes in both academia and industry. Despite the amazing results, the
principle of neural style transfer, especially why the Gram matrices could
represent style remains unclear. In this paper, we propose a novel
interpretation of neural style transfer by treating it as a domain adaptation
problem. Specifically, we theoretically show that matching the Gram matrices of
feature maps is equivalent to minimize the Maximum Mean Discrepancy (MMD) with
the second order polynomial kernel. Thus, we argue that the essence of neural
style transfer is to match the feature distributions between the style images
and the generated images. To further support our standpoint, we experiment with
several other distribution alignment methods, and achieve appealing results. We
believe this novel interpretation connects these two important research fields,
and could enlighten future researches.Comment: Accepted by IJCAI 201
High dynamic range imaging for archaeological recording
This paper notes the adoption of digital photography as a primary recording means within archaeology, and reviews some issues and problems that this presents. Particular attention is given to the problems of recording high-contrast scenes in archaeology and High Dynamic Range imaging using multiple exposures is suggested as a means of providing an archive of high-contrast scenes that can later be tone-mapped to provide a variety of visualisations. Exposure fusion is also considered, although it is noted that this has some disadvantages. Three case studies are then presented (1) a very high contrast photograph taken from within a rock-cut tomb at Cala Morell, Menorca (2) an archaeological test pitting exercise requiring rapid acquisition of photographic records in challenging circumstances and (3) legacy material consisting of three differently exposed colour positive (slide) photographs of the same scene. In each case, HDR methods are shown to significantly aid the generation of a high quality illustrative record photograph, and it is concluded that HDR imaging could serve an effective role in archaeological photographic recording, although there remain problems of archiving and distributing HDR radiance map data
Compact Bilinear Pooling
Bilinear models has been shown to achieve impressive performance on a wide
range of visual tasks, such as semantic segmentation, fine grained recognition
and face recognition. However, bilinear features are high dimensional,
typically on the order of hundreds of thousands to a few million, which makes
them impractical for subsequent analysis. We propose two compact bilinear
representations with the same discriminative power as the full bilinear
representation but with only a few thousand dimensions. Our compact
representations allow back-propagation of classification errors enabling an
end-to-end optimization of the visual recognition system. The compact bilinear
representations are derived through a novel kernelized analysis of bilinear
pooling which provide insights into the discriminative power of bilinear
pooling, and a platform for further research in compact pooling methods.
Experimentation illustrate the utility of the proposed representations for
image classification and few-shot learning across several datasets.Comment: Camera ready version for CVP
A Novel Framework for Highlight Reflectance Transformation Imaging
We propose a novel pipeline and related software tools for processing the multi-light image collections (MLICs) acquired in different application contexts to obtain shape and appearance information of captured surfaces, as well as to derive compact relightable representations of them. Our pipeline extends the popular Highlight Reflectance Transformation Imaging (H-RTI) framework, which is widely used in the Cultural Heritage domain. We support, in particular, perspective camera modeling, per-pixel interpolated light direction estimation, as well as light normalization correcting vignetting and uneven non-directional illumination. Furthermore, we propose two novel easy-to-use software tools to simplify all processing steps. The tools, in addition to support easy processing and encoding of pixel data, implement a variety of visualizations, as well as multiple reflectance-model-fitting options. Experimental tests on synthetic and real-world MLICs demonstrate the usefulness of the novel algorithmic framework and the potential benefits of the proposed tools for end-user applications.Terms: "European Union (EU)" & "Horizon 2020" / Action: H2020-EU.3.6.3. - Reflective societies - cultural heritage and European identity / Acronym: Scan4Reco / Grant number: 665091DSURF project (PRIN 2015) funded by the Italian Ministry of University and ResearchSardinian Regional Authorities under projects VIGEC and Vis&VideoLa
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