41,358 research outputs found
Cross-orientation transfer of adaptation for facial identity is asymmetric: A study using contrast-based recognition thresholds
AbstractRecent studies suggest that adaptation effects for face shape and gender transfer from upright to inverted faces more than the reverse. We investigated whether a similar asymmetry occurred for face identity, using a recently developed adaptation method based on contrast-recognition thresholds. When adapting and test stimuli shared the same orientation, aftereffects were similar for upright and inverted faces. When orientation differed, there was significant transfer of aftereffects from upright adapting to inverted test faces, but none from inverted to upright faces. We show that asymmetric cross-orientation transfer of face aftereffects generalize across two distinct face adaptation paradigms: the previously used perceptual-bias methodology and the recently introduced contrast-threshold based adaptation paradigm. These results also represent a generalization from aftereffects for face shape and gender to aftereffects for face identity. While these results are consistent with the dual-mode hypothesis, they can also be accounted for by a single population of units of varying orientation selectivity
Geometric and form feature recognition tools applied to a design for assembly methodology
The paper presents geometric tools for an automated Design for Assembly (DFA) assessment system. For each component in an assembly a two step features search is performed: firstly (using the minimal bounding box) mass, dimensions and symmetries are identified allowing the part to be classified, according to DFA convention, as either rotational or prismatic; secondly form features are extracted allowing an effective method of mechanised orientation to be determined. Together these algorithms support the fuzzy decision support system, of an assembly-orientated CAD system known as FuzzyDFA
A Proximity-Aware Hierarchical Clustering of Faces
In this paper, we propose an unsupervised face clustering algorithm called
"Proximity-Aware Hierarchical Clustering" (PAHC) that exploits the local
structure of deep representations. In the proposed method, a similarity measure
between deep features is computed by evaluating linear SVM margins. SVMs are
trained using nearest neighbors of sample data, and thus do not require any
external training data. Clusters are then formed by thresholding the similarity
scores. We evaluate the clustering performance using three challenging
unconstrained face datasets, including Celebrity in Frontal-Profile (CFP),
IARPA JANUS Benchmark A (IJB-A), and JANUS Challenge Set 3 (JANUS CS3)
datasets. Experimental results demonstrate that the proposed approach can
achieve significant improvements over state-of-the-art methods. Moreover, we
also show that the proposed clustering algorithm can be applied to curate a set
of large-scale and noisy training dataset while maintaining sufficient amount
of images and their variations due to nuisance factors. The face verification
performance on JANUS CS3 improves significantly by finetuning a DCNN model with
the curated MS-Celeb-1M dataset which contains over three million face images
Incremental Training of a Detector Using Online Sparse Eigen-decomposition
The ability to efficiently and accurately detect objects plays a very crucial
role for many computer vision tasks. Recently, offline object detectors have
shown a tremendous success. However, one major drawback of offline techniques
is that a complete set of training data has to be collected beforehand. In
addition, once learned, an offline detector can not make use of newly arriving
data. To alleviate these drawbacks, online learning has been adopted with the
following objectives: (1) the technique should be computationally and storage
efficient; (2) the updated classifier must maintain its high classification
accuracy. In this paper, we propose an effective and efficient framework for
learning an adaptive online greedy sparse linear discriminant analysis (GSLDA)
model. Unlike many existing online boosting detectors, which usually apply
exponential or logistic loss, our online algorithm makes use of LDA's learning
criterion that not only aims to maximize the class-separation criterion but
also incorporates the asymmetrical property of training data distributions. We
provide a better alternative for online boosting algorithms in the context of
training a visual object detector. We demonstrate the robustness and efficiency
of our methods on handwriting digit and face data sets. Our results confirm
that object detection tasks benefit significantly when trained in an online
manner.Comment: 14 page
A Unified Framework for Compositional Fitting of Active Appearance Models
Active Appearance Models (AAMs) are one of the most popular and
well-established techniques for modeling deformable objects in computer vision.
In this paper, we study the problem of fitting AAMs using Compositional
Gradient Descent (CGD) algorithms. We present a unified and complete view of
these algorithms and classify them with respect to three main characteristics:
i) cost function; ii) type of composition; and iii) optimization method.
Furthermore, we extend the previous view by: a) proposing a novel Bayesian cost
function that can be interpreted as a general probabilistic formulation of the
well-known project-out loss; b) introducing two new types of composition,
asymmetric and bidirectional, that combine the gradients of both image and
appearance model to derive better conver- gent and more robust CGD algorithms;
and c) providing new valuable insights into existent CGD algorithms by
reinterpreting them as direct applications of the Schur complement and the
Wiberg method. Finally, in order to encourage open research and facilitate
future comparisons with our work, we make the implementa- tion of the
algorithms studied in this paper publicly available as part of the Menpo
Project.Comment: 39 page
Asymmetric Pruning for Learning Cascade Detectors
Cascade classifiers are one of the most important contributions to real-time
object detection. Nonetheless, there are many challenging problems arising in
training cascade detectors. One common issue is that the node classifier is
trained with a symmetric classifier. Having a low misclassification error rate
does not guarantee an optimal node learning goal in cascade classifiers, i.e.,
an extremely high detection rate with a moderate false positive rate. In this
work, we present a new approach to train an effective node classifier in a
cascade detector. The algorithm is based on two key observations: 1) Redundant
weak classifiers can be safely discarded; 2) The final detector should satisfy
the asymmetric learning objective of the cascade architecture. To achieve this,
we separate the classifier training into two steps: finding a pool of
discriminative weak classifiers/features and training the final classifier by
pruning weak classifiers which contribute little to the asymmetric learning
criterion (asymmetric classifier construction). Our model reduction approach
helps accelerate the learning time while achieving the pre-determined learning
objective. Experimental results on both face and car data sets verify the
effectiveness of the proposed algorithm. On the FDDB face data sets, our
approach achieves the state-of-the-art performance, which demonstrates the
advantage of our approach.Comment: 14 page
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