A touch panel is an input device for human computer interaction. It consists of a network of sensors, a sampling circuit and a micro controller for detecting and locating a touch input. Touch input can come from either finger or stylus depending upon the type of touch technology. These touch panels provide an intuitive and collaborative workspace so that people can perform various tasks with the use of their fingers instead of traditional input devices like keyboard and mouse. Touch sensing technology is not new. At the time of this writing, various technologies are available in the market and this paper reviews the most common ones. We review traditional designs and sensing algorithms for touch technology. We also observe that due to its various strengths, capacitive touch will dominate the large-scale touch panel industry in years to come. In the end, we discuss the motivation for doing academic research on large-scale panels.
Compressive sensing is a technique used in signal processing applications to reduce sampling time. This paper talks about an efficient sampling framework based on compressive sensing for capacitive touch technology. We aim to minimize the number of measurements required during capacitance touch sensing process and in order to achieve this, we use structured matrices which can be used as a driving sensing framework for a touch controller. The novel contribution of this research is that we have modelled our recovery algorithm according to the structure of our sampling matrix, thus making it extremely efficient and simple to implement in a practical application. In this paper, we exploit the structure of the sensing matrix and conduct experiments to test the robustness of our proposed algorithm. Calculations of the floating point multiplication operations for the reconstruction algorithm and sensing matrix have also been looked into detail.
Computer vision researchers have recently developed automated methods for rating the aesthetic appeal of a photograph. Machine learning techniques, applied to large databases of photos, mimic with reasonably good accuracy the mean ratings of online viewers. However, owing to the many factors underlying aesthetics, it is likely that such techniques for rating photos do not generalize well beyond the data on which they are trained. This paper reviews recent attempts to compare human ratings, obtained in a controlled setting, to ratings provided by machine learning techniques. We review methods to obtain meaningful ratings both from selected groups of judges and also from crowd sourcing. We find that state-of-the-art techniques for automatic aesthetic evaluation are only weakly correlated with human ratings. This shows the importance of obtaining data used for training automated systems under carefully controlled conditions.
This paper introduces the concept of three dimensional (3D) barcodes. A 3D barcode is composed of an array
of 3D cells, called modules, and each can be either filled or empty, corresponding to two possible values of a bit.
These barcodes have great theoretical promise thanks to their very large information capacity, which grows as
the cube of the linear size of the barcode, and in addition are becoming practically manufacturable thanks to
the ubiquitous use of 3D printers.
In order to make these 3D barcodes practical for consumers, it is important to keep the decoding simple using
commonly available means like smartphones. We therefore limit ourselves to decoding mechanisms based only
on three projections of the barcode, which imply specific constraints on the barcode itself. The three projections
produce the marginal sums of the 3D cube, which are the counts of filled-in modules along each Cartesian axis.
In this paper we present some of the theoretical aspects of the 2D and 3D cases, and describe the resulting
complexity of the 3D case. We then describe a method to reduce these complexities into a practical application.
The method features an asymmetric coding scheme, where the decoder is much simpler than the encoder. We
close by demonstrating 3D barcodes we created and their usability.
Fourier descriptors have long been used to describe the underling continuous contours of two-dimensional shapes. Approximations of shapes by polygons is a natural step for efficient algorithms in computer graphics and computer vision. This paper derives mathematical relationships between the Fourier descriptors of the continuous contour, and the corresponding descriptors of a polygon obtained by connecting samples on the contour. We show that the polygon's descriptors may be obtained analytically in two ways: first, by summing subsets of the contour's descriptors; and second, from the discrete Fourier transform (DFT) of the polygon's vertices. We also analyze, in the Fourier domain, shape approximation using interpolators. Our results show that polygonal approximation, with its potential benefits for efficient analysis of shape, is achievable in the Fourier descriptor domain.
In this study, our primary aim is to determine empirically the role that skill plays in determining image aesthetics, and
whether it can be deciphered from the ratings given by a diverse group of judges. To this end, we have collected and
analyzed data from a large number of subjects (total 168) on a set of 221 of images taken by 33 photographers having
different photographic skill and experience. We also experimented with the rating scales used by previous studies in this
domain by introducing a binary rating system for collecting judges’ opinions. The study also demonstrates the use of
Amazon Mechanical Turk as a crowd-sourcing platform in collecting scientific data and evaluating the skill of the judges
participating in the experiment. We use a variety of performance and correlation metrics to evaluate the consistency of
ratings across different rating scales and compare our findings. A novel feature of our study is an attempt to define a
threshold based on the consistency of ratings when judges rate duplicate images. Our conclusion deviates from earlier
findings and our own expectations, with ratings not being able to determine skill levels of photographers to a statistically
Matching shapes accurately is an important requirement in various applications; the most notable of which is object recognition. Precisely matching shapes is a difficult task and is an active area of research in the computer vision community. Most shape matching techniques rely on the contour of the object to provide the object's shape properties. However, we show that using the contour alone cannot help in matching all kinds of shapes. Many objects are recognised because of their overall visual similarity, rather than just their contour properties. In this paper, we assert that modelling the interior properties of the shape can help in extracting this overall visual similarity. We propose a simple way to extract the shape's interior properties. This is done by densely sampling points from within the shape and using it to describe the shape's features. We show that using such an approach provides an effective way to perform matching of shapes that are visually similar to each other, but have vastly different contour properties.
In this paper, we identify some of the existing problems in shape context matching. We first identify the need for reflection
invariance in shape context matching algorithms and propose a method to achieve the same. With the use of these reflection
invariance techniques, we bring all the objects, in a database, to their canonical form, which halves the time required to
match two shapes using their contexts. We then show how we can build better shape descriptors by the use of geodesic
information from the shapes and hence improve upon the well-known Inner Distance Shape Context (IDSC). The IDSC is
used by many pre- and post-processing algorithms as the baseline shape-matching algorithm. Our improvements to IDSC
will remain compatible for use with those algorithms. Finally, we introduce new comparison metrics that can be used for
the comparison of two or more algorithms. We have tested our proposals on the MPEG-7 database and show that our
methods significantly outperform the IDSC.
One of the main tools in shape matching and pattern recognition are invariants. For three-dimensional data, rotation invariants
comprise of two main kinds: moments and spherical harmonic magnitudes. Both are well examined and both suffer
from certain limitations. In search for better performance, a new kind of spherical-harmonic invariants have been proposed
recently, called bispectral invariants. They are well-established from theoretical point of view. They posses numerous beneficial
properties and advantages over other invariants, include the ability to distinguish rotation from reflection, and the
sensitivity to phase. However, insufficient research has been conducted to check their behavior in practice. In this paper,
results are presented pertaining to the discriminative power of bispectral invariants. Objects from Princeton Shape Benchmark
database are used for evaluation. It is shown that the bispectral invariants outperform power spectral invariants, but
perform worse than other descriptors proposed in the literature such as SHELLS and SHD. The difference in performance
is attributable to the implicit filtering used to compute the invariants.
In this paper, we describe a method to fuse multiple images taken with varying exposure times in the JPEG domain.
The proposed algorithm finds its application in HDR image acquisition and image stabilization for hand-held devices like
mobile phones, music players with cameras, digital cameras etc. Image acquisition at low light typically results in blurry
and noisy images for hand-held camera's. Altering camera settings like ISO sensitivity, exposure times and aperture for low
light image capture results in noise amplification, motion blur and reduction of depth-of-field respectively. The purpose
of fusing multiple exposures is to combine the sharp details of the shorter exposure images with high signal-to-noise-ratio
(SNR) of the longer exposure images.
The algorithm requires only a single pass over all images, making it efficient. It comprises of - sigmoidal boosting of
shorter exposed images, image fusion, artifact removal and saturation detection. Algorithm does not need more memory
than a single JPEG macro block to be kept in memory making it feasible to be implemented as the part of a digital cameras
hardware image processing engine. The Artifact removal step reuses the JPEGs built-in frequency analysis and hence
benefits from the considerable optimization and design experience that is available for JPEG.
Capacitive touch sensors have been in use for many years, and recently gained center stage with the ubiquitous
use in smart-phones. In this work we will analyze the most common method of projected capacitive sensing,
that of absolute capacitive sensing, together with the most common sensing pattern, that of diamond-shaped
sensors. After a brief introduction to the problem, and the reasons behind its popularity, we will formulate the
problem as a reconstruction from projections. We derive analytic solutions for two simple cases: circular finger
on a wire grid, and square finger on a square grid. The solutions give insight into the ambiguities of finding finger
location from sensor readings. The main contribution of our paper is the discussion of interpolation algorithms
including simple linear interpolation , curve fitting (parabolic and Gaussian), filtering, general look-up-table,
and combinations thereof. We conclude with observations on the limits of the present algorithmic methods, and
point to possible future research.
As the demand for reduction in the thickness of cameras rises, so too does the interest in thinner lens designs.
One such radical approach toward developing a thin lens is obtained from nature's superposition principle as used in the
eyes of many insects. But generally the images obtained from those lenses are fuzzy, and require reconstruction
algorithms to complete the imaging process. A hurdle to developing such algorithms is that the existing literature does
not provide realistic test images, aside from using commercial ray-tracing software which is costly. A solution for that
problem is presented in this paper. Here a Gabor Super Lens (GSL), which is based on the superposition principle, is
simulated using the public-domain ray-tracing software POV-Ray. The image obtained is of a grating surface as viewed
through an actual GSL, which can be used to test reconstruction algorithms. The large computational time in rendering
such images requires further optimization, and methods to do so are discussed.
Many optical inspection systems today can capture surface slope information directly or indirectly. For these systems, it is possible to perform a 3-D surface reconstruction which converts surface slopes to surface heights. Since the slope information obtained in such systems tend to be noisy and sometimes heavily quantized, a noise-tolerant reconstruction method is needed. We used a simple bayes reconstruction method to improve noise tolerance, and multi-resolution processing to improve the speed of calculations. For each resolution level, the surface slopes between pixels are first calculated from the original surface slopes. Then the height reconstruction for this resolution level is calculated by solving the linear equations that relate relative heights of each point and its related surface slopes. This is done through a Bayesian method which makes it easier to incorporate prior knowledge about height ranges and noise levels. The reconstructions are done for a small window of pixels at a time for each resolution level to make the linear equations manageable. The relative height solutions from all resolution levels are then combined to generate the final height map.
This method has been used in optical inspection applications where slope data are quite noisy.
In many digital color-image systems, most notably digital cameras, raw data from the sensor is processed to produce a pleasing image. One of the main steps in this process is demosaicing, which is the process of interpolating the raw data into a full color image. The resulting image is in turn compressed to enable compact storage. Each of these two steps, namely the demosaicing and compression, creates its own artifacts on the final image. In this work we consider the two stages together, and design a demosaicing algorithm which takes into account the fact that the final image is to be compressed. Examples are given to demonstrate the above ideas.