4,198 research outputs found
Hierarchical quantum classifiers
Quantum circuits with hierarchical structure have been used to perform binary
classification of classical data encoded in a quantum state. We demonstrate
that more expressive circuits in the same family achieve better accuracy and
can be used to classify highly entangled quantum states, for which there is no
known efficient classical method. We compare performance for several different
parameterizations on two classical machine learning datasets, Iris and MNIST,
and on a synthetic dataset of quantum states. Finally, we demonstrate that
performance is robust to noise and deploy an Iris dataset classifier on the
ibmqx4 quantum computer
An Architecture for High-throughput and Improved-quality Stereo Vision Processor
This paper presents the VLSI architecture to achieve high-throughput and
improved-quality stereo vision for real applications. The stereo vision processor
generates gray-scale output images with depth information from input images taken by
two CMOS Image Sensors (CIS). The depth estimator using the sum of absolute
differences (SAD) algorithm as stereo matching technique is implemented on hardware
by exploiting pipelining and parallelism. To produce depth maps with improved-quality
at real-time, pre- and post-processing units are adopted, and to enhance the adaptability
of the system to real environments, special function registers (SFRs) are assigned to
vision parameters. The design using 0.18um standard CMOS technology can operate at
120MHz clock, achieving over 140 frames/sec depth maps with 320 by 240 image size
and 64 disparity levels. Experimental results based on images taken in real world and
the Middlebury data set will be presented. Comparison data with existing hardware
systems and hardware specifications of the proposed processor will be given
Current-Mode Techniques for the Implementation of Continuous- and Discrete-Time Cellular Neural Networks
This paper presents a unified, comprehensive approach
to the design of continuous-time (CT) and discrete-time
(DT) cellular neural networks (CNN) using CMOS current-mode
analog techniques. The net input signals are currents instead
of voltages as presented in previous approaches, thus avoiding
the need for current-to-voltage dedicated interfaces in image
processing tasks with photosensor devices. Outputs may be either
currents or voltages. Cell design relies on exploitation of current
mirror properties for the efficient implementation of both linear
and nonlinear analog operators. These cells are simpler and
easier to design than those found in previously reported CT
and DT-CNN devices. Basic design issues are covered, together
with discussions on the influence of nonidealities and advanced
circuit design issues as well as design for manufacturability
considerations associated with statistical analysis. Three prototypes
have been designed for l.6-pm n-well CMOS technologies.
One is discrete-time and can be reconfigured via local logic for
noise removal, feature extraction (borders and edges), shadow
detection, hole filling, and connected component detection (CCD)
on a rectangular grid with unity neighborhood radius. The other
two prototypes are continuous-time and fixed template: one for
CCD and other for noise removal. Experimental results are given
illustrating performance of these prototypes
Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets
In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pix- els. We have proposed a novel image enhancement scheme based on intuitionistic hesi- tant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the pho- tographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.Web of Science500866
Color Image Analysis by Quaternion-Type Moments
International audienceIn this paper, by using the quaternion algebra, the conventional complex-type moments (CTMs) for gray-scale images are generalized to color images as quaternion-type moments (QTMs) in a holistic manner. We first provide a general formula of QTMs from which we derive a set of quaternion-valued QTM invariants (QTMIs) to image rotation, scale and translation transformations by eliminating the influence of transformation parameters. An efficient computation algorithm is also proposed so as to reduce computational complexity. The performance of the proposed QTMs and QTMIs are evaluated considering several application frameworks ranging from color image reconstruction, face recognition to image registration. We show they achieve better performance than CTMs and CTM invariants (CTMIs). We also discuss the choice of the unit pure quaternion influence with the help of experiments. appears to be an optimal choice
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