52,561 research outputs found
Development Of Hierarchical Skin-Adaboost-Neural Network (H-Skann) For Multiface Detection In Video Surveillance System
Automatic face detection is mainly the first step for most of the face-based
biometric systems today such as face recognition, facial expression recognition, and
tracking head pose. However, face detection technology has various drawbacks caused
by challenges in indoor and outdoor environment such as uncontrolled lighting and
illumination, features occlusions and pose variation. This thesis proposed a technique
to detect multiface in video surveillance application with strategic architecture
algorithm based on the hierarchical and structural design. This technique consists of
two major blocks which are known as Face Skin Localization (FSL) and Hierarchical
Skin Area (HSA). FSL is formulated to extract valuable skin data to be processed at
the first stage of system detection, which also includes Face Skin Merging (FSM) in
order to correctly merge separated skin areas. HSA is proposed to extend the searching
of face candidates in selected segmentation area based on the hierarchical architecture
strategy, in which each level of the hierarchy employs an integration of Adaboost and
Neural Network Algorithm. Experiments were conducted on eleven types database
which consists of various challenges to human face detection system. Results reveal
that the proposed H-SKANN achieves 98.03% and 97.02% of of averaged accuracy
for benchmark database and surveillance area databases, respectively
Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians
Convolutional neural nets (CNNs) have demonstrated remarkable performance in
recent history. Such approaches tend to work in a unidirectional bottom-up
feed-forward fashion. However, practical experience and biological evidence
tells us that feedback plays a crucial role, particularly for detailed spatial
understanding tasks. This work explores bidirectional architectures that also
reason with top-down feedback: neural units are influenced by both lower and
higher-level units.
We do so by treating units as rectified latent variables in a quadratic
energy function, which can be seen as a hierarchical Rectified Gaussian model
(RGs). We show that RGs can be optimized with a quadratic program (QP), that
can in turn be optimized with a recurrent neural network (with rectified linear
units). This allows RGs to be trained with GPU-optimized gradient descent. From
a theoretical perspective, RGs help establish a connection between CNNs and
hierarchical probabilistic models. From a practical perspective, RGs are well
suited for detailed spatial tasks that can benefit from top-down reasoning. We
illustrate them on the challenging task of keypoint localization under
occlusions, where local bottom-up evidence may be misleading. We demonstrate
state-of-the-art results on challenging benchmarks.Comment: To appear in CVPR 201
Challenges in identifying and interpreting organizational modules in morphology
Form is a rich concept that agglutinates information about the proportions and topological arrangement of body parts. Modularity is readily measurable in both features, the variation of proportions (variational modules) and the organization of topology (organizational modules). The study of variational modularity and of organizational modularity faces similar challenges regarding the identification of meaningful modules and the validation of generative processes; however, most studies in morphology focus solely on variational modularity, while organizational modularity is much less understood. A possible cause for this bias is the successful development in the last twenty years of morphometrics, and specially geometric morphometrics, to study patters of variation. This contrasts with the lack of a similar mathematical framework to deal with patterns of organization. Recently, a new mathematical framework has been proposed to study the organization of gross anatomy using tools from Network Theory, so‐called Anatomical Network Analysis (AnNA). In this essay, I explore the potential use of this new framework—and the challenges it faces in identifying and validating biologically meaningful modules in morphological systems—by providing working examples of a complete analysis of modularity of the human skull and upper limb. Finally, I suggest further directions of research that may bridge the gap between variational and organizational modularity studies, and discuss how alternative modeling strategies of morphological systems using networks can benefit from each other
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