2,936 research outputs found
The role of object instance re-identification in 3D object localization and semantic 3D reconstruction.
For an autonomous system to completely understand a particular scene, a 3D reconstruction of the world is required which has both the geometric information such as camera pose and semantic information such as the label associated with an object (tree, chair, dog, etc.) mapped within the 3D reconstruction.
In this thesis, we will study the problem of an object-centric 3D reconstruction of a scene in contrast with most of the previous work in the literature which focuses on building a 3D point cloud that has only the structure but lacking any semantic information. We will study how crucial 3D object localization is for this problem and will discuss the limitations faced by the previous related methods. We will present an approach for 3D object localization using only 2D detections observed in multiple views by including 3D object shape priors.
Since our first approach relies on associating 2D detections in multiple views, we will also study an approach to re-identify multiple object instances of an object in rigid scenes and will propose a novel method of joint learning of the foreground and background of an object instance using a triplet-based network in order to identify multiple instances of the same object in multiple views. We will also propose an Augmented Reality-based application using Google's Tango by integrating both the proposed approaches. Finally, we will conclude with some open problems that might benefit from the suggested future work
Benchmarking and Error Diagnosis in Multi-Instance Pose Estimation
We propose a new method to analyze the impact of errors in algorithms for
multi-instance pose estimation and a principled benchmark that can be used to
compare them. We define and characterize three classes of errors -
localization, scoring, and background - study how they are influenced by
instance attributes and their impact on an algorithm's performance. Our
technique is applied to compare the two leading methods for human pose
estimation on the COCO Dataset, measure the sensitivity of pose estimation with
respect to instance size, type and number of visible keypoints, clutter due to
multiple instances, and the relative score of instances. The performance of
algorithms, and the types of error they make, are highly dependent on all these
variables, but mostly on the number of keypoints and the clutter. The analysis
and software tools we propose offer a novel and insightful approach for
understanding the behavior of pose estimation algorithms and an effective
method for measuring their strengths and weaknesses.Comment: Project page available at
http://www.vision.caltech.edu/~mronchi/projects/PoseErrorDiagnosis/; Code
available at https://github.com/matteorr/coco-analyze; published at ICCV 1
Backtracking Spatial Pyramid Pooling (SPP)-based Image Classifier for Weakly Supervised Top-down Salient Object Detection
Top-down saliency models produce a probability map that peaks at target
locations specified by a task/goal such as object detection. They are usually
trained in a fully supervised setting involving pixel-level annotations of
objects. We propose a weakly supervised top-down saliency framework using only
binary labels that indicate the presence/absence of an object in an image.
First, the probabilistic contribution of each image region to the confidence of
a CNN-based image classifier is computed through a backtracking strategy to
produce top-down saliency. From a set of saliency maps of an image produced by
fast bottom-up saliency approaches, we select the best saliency map suitable
for the top-down task. The selected bottom-up saliency map is combined with the
top-down saliency map. Features having high combined saliency are used to train
a linear SVM classifier to estimate feature saliency. This is integrated with
combined saliency and further refined through a multi-scale
superpixel-averaging of saliency map. We evaluate the performance of the
proposed weakly supervised topdown saliency and achieve comparable performance
with fully supervised approaches. Experiments are carried out on seven
challenging datasets and quantitative results are compared with 40 closely
related approaches across 4 different applications.Comment: 14 pages, 7 figure
Text Localization in Video Using Multiscale Weber's Local Descriptor
In this paper, we propose a novel approach for detecting the text present in
videos and scene images based on the Multiscale Weber's Local Descriptor
(MWLD). Given an input video, the shots are identified and the key frames are
extracted based on their spatio-temporal relationship. From each key frame, we
detect the local region information using WLD with different radius and
neighborhood relationship of pixel values and hence obtained intensity enhanced
key frames at multiple scales. These multiscale WLD key frames are merged
together and then the horizontal gradients are computed using morphological
operations. The obtained results are then binarized and the false positives are
eliminated based on geometrical properties. Finally, we employ connected
component analysis and morphological dilation operation to determine the text
regions that aids in text localization. The experimental results obtained on
publicly available standard Hua, Horizontal-1 and Horizontal-2 video dataset
illustrate that the proposed method can accurately detect and localize texts of
various sizes, fonts and colors in videos.Comment: IEEE SPICES, 201
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