2,060 research outputs found
Stratified decision forests for accurate anatomical landmark localization in cardiac images
Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifier and/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D highresolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-theart landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in a multi-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy
Occlusion Coherence: Detecting and Localizing Occluded Faces
The presence of occluders significantly impacts object recognition accuracy.
However, occlusion is typically treated as an unstructured source of noise and
explicit models for occluders have lagged behind those for object appearance
and shape. In this paper we describe a hierarchical deformable part model for
face detection and landmark localization that explicitly models part occlusion.
The proposed model structure makes it possible to augment positive training
data with large numbers of synthetically occluded instances. This allows us to
easily incorporate the statistics of occlusion patterns in a discriminatively
trained model. We test the model on several benchmarks for landmark
localization and detection including challenging new data sets featuring
significant occlusion. We find that the addition of an explicit occlusion model
yields a detection system that outperforms existing approaches for occluded
instances while maintaining competitive accuracy in detection and landmark
localization for unoccluded instances
Discriminatively Trained Latent Ordinal Model for Video Classification
We study the problem of video classification for facial analysis and human
action recognition. We propose a novel weakly supervised learning method that
models the video as a sequence of automatically mined, discriminative
sub-events (eg. onset and offset phase for "smile", running and jumping for
"highjump"). The proposed model is inspired by the recent works on Multiple
Instance Learning and latent SVM/HCRF -- it extends such frameworks to model
the ordinal aspect in the videos, approximately. We obtain consistent
improvements over relevant competitive baselines on four challenging and
publicly available video based facial analysis datasets for prediction of
expression, clinical pain and intent in dyadic conversations and on three
challenging human action datasets. We also validate the method with qualitative
results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text
overlap with arXiv:1604.0150
Face Centered Image Analysis Using Saliency and Deep Learning Based Techniques
Image analysis starts with the purpose of configuring vision machines that can perceive like human to intelligently infer general principles and sense the surrounding situations from imagery. This dissertation studies the face centered image analysis as the core problem in high level computer vision research and addresses the problem by tackling three challenging subjects: Are there anything interesting in the image? If there is, what is/are that/they? If there is a person presenting, who is he/she? What kind of expression he/she is performing? Can we know his/her age? Answering these problems results in the saliency-based object detection, deep learning structured objects categorization and recognition, human facial landmark detection and multitask biometrics.
To implement object detection, a three-level saliency detection based on the self-similarity technique (SMAP) is firstly proposed in the work. The first level of SMAP accommodates statistical methods to generate proto-background patches, followed by the second level that implements local contrast computation based on image self-similarity characteristics. At last, the spatial color distribution constraint is considered to realize the saliency detection. The outcome of the algorithm is a full resolution image with highlighted saliency objects and well-defined edges.
In object recognition, the Adaptive Deconvolution Network (ADN) is implemented to categorize the objects extracted from saliency detection. To improve the system performance, L1/2 norm regularized ADN has been proposed and tested in different applications. The results demonstrate the efficiency and significance of the new structure.
To fully understand the facial biometrics related activity contained in the image, the low rank matrix decomposition is introduced to help locate the landmark points on the face images. The natural extension of this work is beneficial in human facial expression recognition and facial feature parsing research.
To facilitate the understanding of the detected facial image, the automatic facial image analysis becomes essential. We present a novel deeply learnt tree-structured face representation to uniformly model the human face with different semantic meanings. We show that the proposed feature yields unified representation in multi-task facial biometrics and the multi-task learning framework is applicable to many other computer vision tasks
Face Image Retrieval with Landmark Detection and Semantic Concepts Extraction
This thesis proposes various novel approaches for improving the performances of automatic facial landmarks detection system based on the concept of pictorial tree structure model. Furthermore, a robust glasses landmark detection system is also proposed as glasses are commonly used. These proposed approaches are employed to develop an automatic semantic based face images retrieval system. The experiment results demonstrate significant improvements of all the proposed approaches towards accuracy and efficiency
Unsupervised Learning Facial Parameter Regressor for Action Unit Intensity Estimation via Differentiable Renderer
Facial action unit (AU) intensity is an index to describe all visually
discernible facial movements. Most existing methods learn intensity estimator
with limited AU data, while they lack generalization ability out of the
dataset. In this paper, we present a framework to predict the facial parameters
(including identity parameters and AU parameters) based on a bone-driven face
model (BDFM) under different views. The proposed framework consists of a
feature extractor, a generator, and a facial parameter regressor. The regressor
can fit the physical meaning parameters of the BDFM from a single face image
with the help of the generator, which maps the facial parameters to the
game-face images as a differentiable renderer. Besides, identity loss, loopback
loss, and adversarial loss can improve the regressive results. Quantitative
evaluations are performed on two public databases BP4D and DISFA, which
demonstrates that the proposed method can achieve comparable or better
performance than the state-of-the-art methods. What's more, the qualitative
results also demonstrate the validity of our method in the wild
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