33,413 research outputs found
Design of Novel Algorithm and Architecture for Gaussian Based Color Image Enhancement System for Real Time Applications
This paper presents the development of a new algorithm for Gaussian based
color image enhancement system. The algorithm has been designed into
architecture suitable for FPGA/ASIC implementation. The color image enhancement
is achieved by first convolving an original image with a Gaussian kernel since
Gaussian distribution is a point spread function which smoothen the image.
Further, logarithm-domain processing and gain/offset corrections are employed
in order to enhance and translate pixels into the display range of 0 to 255.
The proposed algorithm not only provides better dynamic range compression and
color rendition effect but also achieves color constancy in an image. The
design exploits high degrees of pipelining and parallel processing to achieve
real time performance. The design has been realized by RTL compliant Verilog
coding and fits into a single FPGA with a gate count utilization of 321,804.
The proposed method is implemented using Xilinx Virtex-II Pro XC2VP40-7FF1148
FPGA device and is capable of processing high resolution color motion pictures
of sizes of up to 1600x1200 pixels at the real time video rate of 116 frames
per second. This shows that the proposed design would work for not only still
images but also for high resolution video sequences.Comment: 15 pages, 15 figure
Image enhancement using fuzzy intensity measure and adaptive clipping histogram equalization
Image enhancement aims at processing an input
image so that the visual content of the output image is more
pleasing or more useful for certain applications. Although
histogram equalization is widely used in image enhancement due
to its simplicity and effectiveness, it changes the mean brightness
of the enhanced image and introduces a high level of noise and
distortion. To address these problems, this paper proposes
image enhancement using fuzzy intensity measure and adaptive
clipping histogram equalization (FIMHE). FIMHE uses fuzzy
intensity measure to first segment the histogram of the original
image, and then clip the histogram adaptively in order to
prevent excessive image enhancement. Experiments on the
Berkeley database and CVF-UGR-Image database show that
FIMHE outperforms state-of-the-art histogram equalization
based methods
Method and apparatus for predicting the direction of movement in machine vision
A computer-simulated cortical network is presented. The network is capable of computing the visibility of shifts in the direction of movement. Additionally, the network can compute the following: (1) the magnitude of the position difference between the test and background patterns; (2) localized contrast differences at different spatial scales analyzed by computing temporal gradients of the difference and sum of the outputs of paired even- and odd-symmetric bandpass filters convolved with the input pattern; and (3) the direction of a test pattern moved relative to a textured background. The direction of movement of an object in the field of view of a robotic vision system is detected in accordance with nonlinear Gabor function algorithms. The movement of objects relative to their background is used to infer the 3-dimensional structure and motion of object surfaces
Reflectance Transformation Imaging (RTI) System for Ancient Documentary Artefacts
This tutorial summarises our uses of reflectance transformation imaging in archaeological contexts. It introduces the UK AHRC funded project reflectance Transformation Imaging for Anciant Documentary Artefacts and demonstrates imaging methodologies
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
This work addresses the problem of semantic scene understanding under dense
fog. Although considerable progress has been made in semantic scene
understanding, it is mainly related to clear-weather scenes. Extending
recognition methods to adverse weather conditions such as fog is crucial for
outdoor applications. In this paper, we propose a novel method, named
Curriculum Model Adaptation (CMAda), which gradually adapts a semantic
segmentation model from light synthetic fog to dense real fog in multiple
steps, using both synthetic and real foggy data. In addition, we present three
other main stand-alone contributions: 1) a novel method to add synthetic fog to
real, clear-weather scenes using semantic input; 2) a new fog density
estimator; 3) the Foggy Zurich dataset comprising real foggy images,
with pixel-level semantic annotations for images with dense fog. Our
experiments show that 1) our fog simulation slightly outperforms a
state-of-the-art competing simulation with respect to the task of semantic
foggy scene understanding (SFSU); 2) CMAda improves the performance of
state-of-the-art models for SFSU significantly by leveraging unlabeled real
foggy data. The datasets and code are publicly available.Comment: final version, ECCV 201
- …