500 research outputs found
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
SIGN LANGUAGE RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS
Sign Language Recognition (SLR) targets on interpreting the sign language into text or speech, so as to facilitate the communication between deaf-mute people and ordinary people. This task has broad social impact, but is still very challenging due to the complexity and large variations in hand actions. Existing methods for SLR use hand-crafted features to describe sign language motion and build classification models based on those features. However, it is difficult to design reliable features to adapt to the large variations of hand gestures. To approach this problem, we propose a novel convolution neural network (CNN) which extracts discriminative spatial-temporal features from raw video stream automatically without any prior knowledge, avoiding designing features. To boost the performance, multi-channels of video streams, including color information, depth clue, and body joint positions, are used as input to the CNN in order to integrate color, depth and trajectory information. We validate the proposed model on a real dataset collected with Microsoft Kinect and demonstrate its effectiveness over the traditional approaches based on hand-crafted features
Automatic recognition of Bangla sign language
This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2012.Cataloged from PDF version of thesis report.Includes bibliographical references (page 39).Sign Language is the mode of communication among the deaf and dumb. However, integrating them into the main stream is very difficult as the majority of the society is unaware of their language. So, to bridge the communication gap between the hearing and speech impaired and the rest in Bangladesh, we conducted a research to recognize Bangla sign language using a computer-vision based approach. To achieve our goals we used Neural Networks to train individual signs. In the future, this research, besides helping as an interpreter, can also open doors to numerous other applications like sign language tutorials or dictionaries and also help the deaf and dumb to search the web or send mails more conveniently.Najeefa Nikhat ChoudhuryGolam KayasB. Computer Science and Engineerin
Applications of Artificial Neural Networks to Synthetic Aperture Radar for Feature Extraction in Noisy Environments
It is often that images generated from Synthetic Aperture Radar (SAR) are noisy, distorted, or incomplete pictures of a target or target region. As the goal for most SAR research pertains to automatic target recognition (ATR), extensive filtering and image processing is required in order to extract the features necessary to carry out ATR. This thesis investigates the use of Artificial Neural Networks (ANNs) in order to improve upon the feature extraction process by laying the foundation for ANN SAR ATR algorithms and programs. The first technique investigated is that of an ANN edge detector designed to be invariant to multiplicative speckle noise. The algorithm designed uses the Back Propagation (BP) algorithm to teach a multi-layer perceptron network to detect edges. In order to do so, several parameters within a Sliding Window (SW), are calculated as the inputs to the ANN. The ANN then outputs an edge map that includes the outer edge features of the target as well as some internal edge features. The next technique that is examined is a pattern recognition and target reconstruction algorithm based off of the associative memory ANN known as the Hopfield Network (HN). For this version of the HN, the network is trained with a collection of varying geometric shapes. The output of the network is a nearest-fit representation of the incomplete image data input. Because of the versatility of this program, it is also able to reconstruct incomplete 3D models determined from SAR data. The final technique investigated is an automatic rotation procedure to detect the change in perspective relative to the platform. This type of detection can prove useful if used for target tracking or 3D modeling where the direction vector or relative angle of the target is a desired piece of information
Pattern Recognition and Event Reconstruction in Particle Physics Experiments
This report reviews methods of pattern recognition and event reconstruction
used in modern high energy physics experiments. After a brief introduction into
general concepts of particle detectors and statistical evaluation, different
approaches in global and local methods of track pattern recognition are
reviewed with their typical strengths and shortcomings. The emphasis is then
moved to methods which estimate the particle properties from the signals which
pattern recognition has associated. Finally, the global reconstruction of the
event is briefly addressed.Comment: 101 pages, 58 figure
Zero-Shot Sign Language Recognition: Can Textual Data Uncover Sign Languages?
We introduce the problem of zero-shot sign language recognition (ZSSLR),
where the goal is to leverage models learned over the seen sign class examples
to recognize the instances of unseen signs. To this end, we propose to utilize
the readily available descriptions in sign language dictionaries as an
intermediate-level semantic representation for knowledge transfer. We introduce
a new benchmark dataset called ASL-Text that consists of 250 sign language
classes and their accompanying textual descriptions. Compared to the ZSL
datasets in other domains (such as object recognition), our dataset consists of
limited number of training examples for a large number of classes, which
imposes a significant challenge. We propose a framework that operates over the
body and hand regions by means of 3D-CNNs, and models longer temporal
relationships via bidirectional LSTMs. By leveraging the descriptive text
embeddings along with these spatio-temporal representations within a zero-shot
learning framework, we show that textual data can indeed be useful in
uncovering sign languages. We anticipate that the introduced approach and the
accompanying dataset will provide a basis for further exploration of this new
zero-shot learning problem.Comment: To appear in British Machine Vision Conference (BMVC) 201
Clustering of Cases from Di erent Subtypes of Breast Cancer Using a Hop eld Network Built from Multi-omic Data
Tesis de Graduación (MaestrÃa en Computación) Instituto Tecnológico de Costa Rica, Escuela de Computación, 2018Despite scienti c advances, breast cancer still constitutes a worldwide major cause of death
among women. Given the great heterogeneity between cases, distinct classi cation schemes
have emerged. The intrinsic molecular subtype classi cation (luminal A, luminal B, HER2-
enriched and basal-like) accounts for the molecular characteristics and prognosis of tumors,
which provides valuable input for taking optimal treatment actions. Also, recent advancements
in molecular biology have provided scientists with high quality and diversity of omiclike
data, opening up the possibility of creating computational models for improving and
validating current subtyping systems. On this study, a Hop eld Network model for breast
cancer subtyping and characterization was created using data from The Cancer Genome
Atlas repository. Novel aspects include the usage of the network as a clustering mechanism
and the integrated use of several molecular types of data (gene mRNA expression, miRNA
expression and copy number variation). The results showed clustering capabilities for the
network, but even so, trying to derive a biological model from a Hop eld Network might
be di cult given the mirror attractor phenomena (every cluster might end up with an opposite).
As a methodological aspect, Hop eld was compared with kmeans and OPTICS
clustering algorithms. The last one, surprisingly, hints at the possibility of creating a high
precision model that di erentiates between luminal, HER2-enriched and basal samples using
only 10 genes. The normalization procedure of dividing gene expression values by their
corresponding gene copy number appears to have contributed to the results. This opens up
the possibility of exploring these kind of prediction models for implementing diagnostic tests
at a lower cost
Sign Language Recognition
This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data set
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