Abstract: We as humans use words with accompanying visual and facial cues to communicate effectively. Classifying facial emotion using computer vision methodologies has been an active research area in the computer vision field. In this paper, we propose a simple method for facial expression recognition that enhances accuracy. We tested our method on the FER-2013 dataset that contains static images. Instead of using Histogram equalization to preprocess the dataset, we used Unsharp Mask to emphasize texture and details and sharpened the edges. We also used ImageDataGenerator from Keras library for data augmentation. Then we used Convolutional Neural Networks (CNN) model to classify the images into 7 different facial expressions, yielding an accuracy of 69.46% on the test set. Our results show that using image preprocessing such as the sharpening technique for a CNN model can improve the performance, even when the CNN model is relatively simple.
Abstract: This paper proposes a stroke extraction method for use in off-line signature verification. After giving a brief overview of the current ongoing researches an algorithm is introduced for detecting and following strokes in static images of signatures. Problems like the handling of junctions and variations in line width and line intensity are discussed in detail. Results are validated by both using an existing on-line signature database and by employing image registration methods.
Abstract: This paper presents a general trainable framework
for fast and robust upright human face and non-human object
detection and verification in static images. To enhance the
performance of the detection process, the technique we develop is
based on the combination of fast neural network (FNN) and
classical neural network (CNN). In FNN, a useful correlation is
exploited to sustain high level of detection accuracy between input
image and the weight of the hidden neurons. This is to enable the
use of Fourier transform that significantly speed up the time
detection. The combination of CNN is responsible to verify the
face region. A bootstrap algorithm is used to collect non human
object, which adds the false detection to the training process of the
human and non-human object. Experimental results on test images
with both simple and complex background demonstrate that the
proposed method has obtained high detection rate and low false
positive rate in detecting both human face and non-human object.
Abstract: This paper reports the study results on neural network
training algorithm of numerical optimization techniques multiface
detection in static images. The training algorithms involved are scale
gradient conjugate backpropagation, conjugate gradient
backpropagation with Polak-Riebre updates, conjugate gradient
backpropagation with Fletcher-Reeves updates, one secant
backpropagation and resilent backpropagation. The final result of
each training algorithms for multiface detection application will also
be discussed and compared.
Abstract: In this paper, the computation of the electrical field distribution around AC high-voltage lines is demonstrated. The advantages and disadvantages of two different methods are described to evaluate the electrical field quantity. The first method is a seminumerical method using the laws of electrostatic techniques to simulate the two-dimensional electric field under the high-voltage overhead line. The second method which will be discussed is the finite element method (FEM) using specific boundary conditions to compute the two- dimensional electric field distributions in an efficient way.
Abstract: Distance visualization of large datasets often takes the direction of remote viewing and zooming techniques of stored static images. However, the continuous increase in the size of datasets and visualization operation causes insufficient performance with traditional desktop computers. Additionally, the visualization techniques such as Isosurface depend on the available resources of the running machine and the size of datasets. Moreover, the continuous demand for powerful computing powers and continuous increase in the size of datasets results an urgent need for a grid computing infrastructure. However, some issues arise in current grid such as resources availability at the client machines which are not sufficient enough to process large datasets. On top of that, different output devices and different network bandwidth between the visualization pipeline components often result output suitable for one machine and not suitable for another. In this paper we investigate how the grid services could be used to support remote visualization of large datasets and to break the constraint of physical co-location of the resources by applying the grid computing technologies. We show our grid enabled architecture to visualize large medical datasets (circa 5 million polygons) for remote interactive visualization on modest resources clients.
Abstract: Animation is simply defined as the sequencing of a
series of static images to generate the illusion of movement. Most
people believe that actual drawings or creation of the individual
images is the animation, when in actuality it is the arrangement of
those static images that conveys the motion. To become an animator,
it is often assumed that needed the ability to quickly design
masterpiece after masterpiece. Although some semblance of artistic
skill is a necessity for the job, the real key to becoming a great
animator is in the comprehension of timing. This paper will use a
combination of sprite animation, frame animation, and some other
techniques to cause a group of multi-colored static images to slither
around in the bounded area. In addition to slithering, the images
will also change the color of different parts of their body, much like
the real world creatures that have this amazing ability to change the
colors on their bodies do. This paper was implemented by using
Java 2 Standard Edition (J2SE).
It is both time-consuming and expensive to create animations,
regardless if they are created by hand or by using motion-capture
equipment. If the animators could reuse old animations and even
blend different animations together, a lot of work would be saved in
the process. The main objective of this paper is to examine a method
for blending several animations together in real time. This paper
presents and analyses a solution using Weighted Skeleton
Animation (WSA) resulting in limited CPU time and memory waste
as well as saving time for the animators. The idea presented is
described in detail and implemented. In this paper, text animation,
vertex animation, sprite part animation and whole sprite animation
were tested.
In this research paper, the resolution, smoothness and movement
of animated images will be carried out from the parameters, which
will be obtained from the experimental research of implementing
this paper.