14 research outputs found
Joint Learning of Intrinsic Images and Semantic Segmentation
Semantic segmentation of outdoor scenes is problematic when there are
variations in imaging conditions. It is known that albedo (reflectance) is
invariant to all kinds of illumination effects. Thus, using reflectance images
for semantic segmentation task can be favorable. Additionally, not only
segmentation may benefit from reflectance, but also segmentation may be useful
for reflectance computation. Therefore, in this paper, the tasks of semantic
segmentation and intrinsic image decomposition are considered as a combined
process by exploring their mutual relationship in a joint fashion. To that end,
we propose a supervised end-to-end CNN architecture to jointly learn intrinsic
image decomposition and semantic segmentation. We analyze the gains of
addressing those two problems jointly. Moreover, new cascade CNN architectures
for intrinsic-for-segmentation and segmentation-for-intrinsic are proposed as
single tasks. Furthermore, a dataset of 35K synthetic images of natural
environments is created with corresponding albedo and shading (intrinsics), as
well as semantic labels (segmentation) assigned to each object/scene. The
experiments show that joint learning of intrinsic image decomposition and
semantic segmentation is beneficial for both tasks for natural scenes. Dataset
and models are available at: https://ivi.fnwi.uva.nl/cv/intrinsegComment: ECCV 201
SEMANTIC SEGMENTATION VIA SPARSE CODING OVER HIERARCHICAL REGIONS
International audienceThe purpose of this paper is segmenting objects in an image and assigning a predefined semantic label to each object. There are two areas of novelty in this paper. On one hand, hierarchical regions are used to guide semantic segmenta-tion instead of using single-level regions or multi-scale regions generated by multiple segmentations. On the other hand, sparse coding is introduced as high level description of the regions, which contributes to less quantization error than traditional bag-of-visual-words method. Experiments on the challenging Microsoft Research Cambridge dataset (MSRC 21) show that our algorithm achieves state-of-the-art performance
SEMANTIC SEGMENTATION VIA SPARSE CODING OVER HIERARCHICAL REGIONS
International audienceThe purpose of this paper is segmenting objects in an image and assigning a predefined semantic label to each object. There are two areas of novelty in this paper. On one hand, hierarchical regions are used to guide semantic segmenta-tion instead of using single-level regions or multi-scale regions generated by multiple segmentations. On the other hand, sparse coding is introduced as high level description of the regions, which contributes to less quantization error than traditional bag-of-visual-words method. Experiments on the challenging Microsoft Research Cambridge dataset (MSRC 21) show that our algorithm achieves state-of-the-art performance
Semantic Image Segmentation Using Region Bank
International audienceSemantic image segmentation assigns a predefined class label to each pixel. This paper proposes a unified framework by using region bank to solve this task. Images are hierarchically segmented leading to region banks. Local features and high-level descriptors are extracted on each region of the banks. Discriminative classifiers are learned based the histograms of features descriptors computed from training region bank (TRB). Optimally merging predicted regions of query region bank (QRB) results in semantic labeling. This paper details each algorithmic module used in our system, however, any algorithm fits corresponding modules can be plugged into the proposed framework. Experiments on the challenging Microsoft Research Cambridge (MSRC 21) dataset show that the proposed approach achieves the state-of-the-art performance
Three for one and one for three: Flow, Segmentation, and Surface Normals
Optical flow, semantic segmentation, and surface normals represent different
information modalities, yet together they bring better cues for scene
understanding problems. In this paper, we study the influence between the three
modalities: how one impacts on the others and their efficiency in combination.
We employ a modular approach using a convolutional refinement network which is
trained supervised but isolated from RGB images to enforce joint modality
features. To assist the training process, we create a large-scale synthetic
outdoor dataset that supports dense annotation of semantic segmentation,
optical flow, and surface normals. The experimental results show positive
influence among the three modalities, especially for objects' boundaries,
region consistency, and scene structures.Comment: BMVC 201
Image segmentation for automated taxiing of unmanned aircraft
This paper details a method of detecting collision risks for Unmanned Aircraft during taxiing. Using images captured from an on-board camera, semantic segmentation can be used to identify surface types and detect potential collisions. A review of classifier lead segmentation concludes that texture feature descriptors lack the pixel level accuracy required for collision avoidance. Instead, segmentation prior to classification is suggested as a better method for accurate region border extraction. This is achieved through an initial over-segmentation using the established SLIC superpixel technique with further untrained clustering using DBSCAN algorithm. Known classes are used to train a classifier through construction of a texton dictionary and models of texton content typical to each class. The paper demonstrates the application of said system to real world images, and shows good automated segment identification. Remaining issues are identified and contextual information is suggested as a method of resolving them going forward