6,317 research outputs found
DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration
We present DeepICP - a novel end-to-end learning-based 3D point cloud
registration framework that achieves comparable registration accuracy to prior
state-of-the-art geometric methods. Different from other keypoint based methods
where a RANSAC procedure is usually needed, we implement the use of various
deep neural network structures to establish an end-to-end trainable network.
Our keypoint detector is trained through this end-to-end structure and enables
the system to avoid the inference of dynamic objects, leverages the help of
sufficiently salient features on stationary objects, and as a result, achieves
high robustness. Rather than searching the corresponding points among existing
points, the key contribution is that we innovatively generate them based on
learned matching probabilities among a group of candidates, which can boost the
registration accuracy. Our loss function incorporates both the local similarity
and the global geometric constraints to ensure all above network designs can
converge towards the right direction. We comprehensively validate the
effectiveness of our approach using both the KITTI dataset and the
Apollo-SouthBay dataset. Results demonstrate that our method achieves
comparable or better performance than the state-of-the-art geometry-based
methods. Detailed ablation and visualization analysis are included to further
illustrate the behavior and insights of our network. The low registration error
and high robustness of our method makes it attractive for substantial
applications relying on the point cloud registration task.Comment: 10 pages, 6 figures, 3 tables, typos corrected, experimental results
updated, accepted by ICCV 201
Impact of Ground Truth Annotation Quality on Performance of Semantic Image Segmentation of Traffic Conditions
Preparation of high-quality datasets for the urban scene understanding is a
labor-intensive task, especially, for datasets designed for the autonomous
driving applications. The application of the coarse ground truth (GT)
annotations of these datasets without detriment to the accuracy of semantic
image segmentation (by the mean intersection over union - mIoU) could simplify
and speedup the dataset preparation and model fine tuning before its practical
application. Here the results of the comparative analysis for semantic
segmentation accuracy obtained by PSPNet deep learning architecture are
presented for fine and coarse annotated images from Cityscapes dataset. Two
scenarios were investigated: scenario 1 - the fine GT images for training and
prediction, and scenario 2 - the fine GT images for training and the coarse GT
images for prediction. The obtained results demonstrated that for the most
important classes the mean accuracy values of semantic image segmentation for
coarse GT annotations are higher than for the fine GT ones, and the standard
deviation values are vice versa. It means that for some applications some
unimportant classes can be excluded and the model can be tuned further for some
classes and specific regions on the coarse GT dataset without loss of the
accuracy even. Moreover, this opens the perspectives to use deep neural
networks for the preparation of such coarse GT datasets.Comment: 10 pages, 6 figures, 2 tables, The Second International Conference on
Computer Science, Engineering and Education Applications (ICCSEEA2019) 26-27
January 2019, Kiev, Ukrain
Graded requirement for the zygotic terminal gene, tailless, in the brain and tail region of the Drosophila embryo
We have used hypomorphic and null tailless (tll) alleles to carry out a detailed analysis of the effects of the lack of tll gene activity on anterior and posterior regions of the embryo. The arrangement of tll alleles into a continuous series clarifies the relationship between the anterior and posterior functions of the tll gene and indicates that there is a graded sensitivity of anterior and posterior structures to a decrease in tll gene activity. With the deletion of both anterior and posterior pattern domains in tll null embryos, there is a poleward expansion of the remaining pattern. Using anti-horseradish peroxidase staining, we show that the formation of the embryonic brain requires tll. A phenotypic and genetic study of other pattern mutants places the tll gene within the hierarchy of maternal and zygotic genes required for the formation of the normal body pattern. Analysis of mutants doubly deficient in tll and maternal terminal genes is consistent with the idea that these genes act together in a common pathway to establish the domains at opposite ends of the embryo. We propose that tll establishes anterior and posterior subdomains (acron and tail regions, respectively) within the larger pattern regions affected by the maternal terminal genes
Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries
With advanced image journaling tools, one can easily alter the semantic
meaning of an image by exploiting certain manipulation techniques such as
copy-clone, object splicing, and removal, which mislead the viewers. In
contrast, the identification of these manipulations becomes a very challenging
task as manipulated regions are not visually apparent. This paper proposes a
high-confidence manipulation localization architecture which utilizes
resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder
network to segment out manipulated regions from non-manipulated ones.
Resampling features are used to capture artifacts like JPEG quality loss,
upsampling, downsampling, rotation, and shearing. The proposed network exploits
larger receptive fields (spatial maps) and frequency domain correlation to
analyze the discriminative characteristics between manipulated and
non-manipulated regions by incorporating encoder and LSTM network. Finally,
decoder network learns the mapping from low-resolution feature maps to
pixel-wise predictions for image tamper localization. With predicted mask
provided by final layer (softmax) of the proposed architecture, end-to-end
training is performed to learn the network parameters through back-propagation
using ground-truth masks. Furthermore, a large image splicing dataset is
introduced to guide the training process. The proposed method is capable of
localizing image manipulations at pixel level with high precision, which is
demonstrated through rigorous experimentation on three diverse datasets
Joint segmentation of many aCGH profiles using fast group LARS
Array-Based Comparative Genomic Hybridization (aCGH) is a method used to
search for genomic regions with copy numbers variations. For a given aCGH
profile, one challenge is to accurately segment it into regions of constant
copy number. Subjects sharing the same disease status, for example a type of
cancer, often have aCGH profiles with similar copy number variations, due to
duplications and deletions relevant to that particular disease. We introduce a
constrained optimization algorithm that jointly segments aCGH profiles of many
subjects. It simultaneously penalizes the amount of freedom the set of profiles
have to jump from one level of constant copy number to another, at genomic
locations known as breakpoints. We show that breakpoints shared by many
different profiles tend to be found first by the algorithm, even in the
presence of significant amounts of noise. The algorithm can be formulated as a
group LARS problem. We propose an extremely fast way to find the solution path,
i.e., a sequence of shared breakpoints in order of importance. For no extra
cost the algorithm smoothes all of the aCGH profiles into piecewise-constant
regions of equal copy number, giving low-dimensional versions of the original
data. These can be shown for all profiles on a single graph, allowing for
intuitive visual interpretation. Simulations and an implementation of the
algorithm on bladder cancer aCGH profiles are provided
- …